research articles on sampling

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings
My Bibliography
Collections
Citation manager

Save citation to file

Email citation, add to collections.

Create a new collection
Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

Search in PubMed
Search in NLM Catalog
Add to Search

Qualitative Sampling Methods

Affiliation.

1 14742 School of Nursing, University of Texas Health Science Center, San Antonio, TX, USA.
PMID: 32813616
DOI: 10.1177/0890334420949218

Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros and cons of each. Sample size and data saturation are discussed.

Keywords: breastfeeding; qualitative methods; sampling; sampling methods.

PubMed Disclaimer

LinkOut - more resources

Full text sources.

Ovid Technologies, Inc.

Miscellaneous

NCI CPTAC Assay Portal

Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Sampling Techniques for Qualitative Research

First Online: 27 October 2022

Cite this chapter

Heather Douglas 4

3629 Accesses

5 Citations

This chapter explains how to design suitable sampling strategies for qualitative research. The focus of this chapter is purposive (or theoretical) sampling to produce credible and trustworthy explanations of a phenomenon (a specific aspect of society). A specific research question (RQ) guides the methodology (the study design or approach ). It defines the participants, location, and actions to be used to answer the question. Qualitative studies use specific tools and techniques ( methods ) to sample people, organizations, or whatever is to be examined. The methodology guides the selection of tools and techniques for sampling, data analysis, quality assurance, etc. These all vary according to the purpose and design of the study and the RQ. In this chapter, a fake example is used to demonstrate how to apply your sampling strategy in a developing country.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

Get 10 units per month
Download Article/Chapter or eBook
1 Unit = 1 Article or 1 Chapter
Cancel anytime
Available as PDF
Read on any device
Instant download
Own it forever
Available as EPUB and PDF
Compact, lightweight edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info
Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preparation of Qualitative Research

Douglas, H. (2010). Divergent orientations in social entrepreneurship organisations. In K. Hockerts, J. Robinson, & J. Mair (Eds.), Values and opportunities in social entrepreneurship (pp. 71–95). Palgrave Macmillan.

Chapter Google Scholar

Douglas, H., Eti-Tofinga, B., & Singh, G. (2018a). Contextualising social enterprise in Fiji. Social Enterprise Journal, 14 (2), 208–224. https://doi.org/10.1108/SEJ-05-2017-0032

Article Google Scholar

Douglas, H., Eti-Tofinga, B., & Singh, G. (2018b). Hybrid organisations contributing to wellbeing in small Pacific island countries. Sustainability Accounting, Management and Policy Journal, 9 (4), 490–514. https://doi.org/10.1108/SAMPJ-08-2017-0081

Douglas, H., & Borbasi, S. (2009). Parental perspectives on disability: The story of Sam, Anna, and Marcus. Disabilities: Insights from across fields and around the world, 2 , 201–217.

Google Scholar

Douglas, H. (1999). Community transport in rural Queensland: Using community resources effectively in small communities. Paper presented at the 5th National Rural Health Conference, Adelaide, South Australia, pp. 14–17th March.

Douglas, H. (2006). Action, blastoff, chaos: ABC of successful youth participation. Child, Youth and Environments, 16 (1). Retrieved from http://www.colorado.edu/journals/cye

Douglas, H. (2007). Methodological sampling issues for researching new nonprofit organisations. Paper presented at the 52nd International Council for Small Business (ICSB) 13–15 June, Turku, Finland.

Draper, H., Wilson, S., Flanagan, S., & Ives, J. (2009). Offering payments, reimbursement and incentives to patients and family doctors to encourage participation in research. Family Practice, 26 (3), 231–238. https://doi.org/10.1093/fampra/cmp011

Puamua, P. Q. (1999). Understanding Fijian under-achievement: An integrated perspective. Directions, 21 (2), 100–112.

Download references

Author information

Authors and affiliations.

The University of Queensland, The Royal Society of Queensland, Activation Australia, Brisbane, Australia

Heather Douglas

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heather Douglas .

Editor information

Editors and affiliations.

Centre for Family and Child Studies, Research Institute of Humanities and Social Sciences, University of Sharjah, Sharjah, United Arab Emirates

M. Rezaul Islam

Department of Development Studies, University of Dhaka, Dhaka, Bangladesh

Niaz Ahmed Khan

Department of Social Work, School of Humanities, University of Johannesburg, Johannesburg, South Africa

Rajendra Baikady

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

Douglas, H. (2022). Sampling Techniques for Qualitative Research. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_29

Download citation

DOI : https://doi.org/10.1007/978-981-19-5441-2_29

Published : 27 October 2022

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-5219-7

Online ISBN : 978-981-19-5441-2

eBook Packages : Social Sciences Social Sciences (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

Get new issue alerts Get alerts
Submit a Manuscript

Secondary Logo

Journal logo.

Colleague's E-mail is Invalid

Your message has been successfully sent to your colleague.

Save my selection

Types of Sampling in Research

Bhardwaj, Pooja

Department of Cardiology, AIIMS, New Delhi, India

Address for correspondence: Dr. Pooja Bhardwaj, AIIMS, New Delhi, India. E-mail: [email protected]

Received November 01, 2019

Received in revised form November 20, 2019

Accepted November 28, 2019

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

Sampling is one of the most important factors which determines the accuracy of a study. This article review the sampling techniques used in research including Probability sampling techniques, which include simple random sampling, systematic random sampling and stratified random sampling and Non-probability sampling, which include quota sampling, self-selection sampling, convenience sampling, snowball sampling and purposive sampling.

INTRODUCTION TO RESEARCH

Research in common language means to search for knowledge.

Research is made up of two words – Re + cerchier derived from old French recherchier meaning to search.

Definition of research

D. Slesinger and M. Stephenson in the Encyclopaedia of the Social Sciences define research as “the manipulation of things, concepts or symbols for the purpose of generalising to extend, correct or verify knowledge, whether that knowledge aids in construction of theory or in the practice of an art.”

According to Clifford Woody, research comprises defining and redefining problems; formulating hypothesis or suggested solutions; collecting, organizing, and evaluating the data; making deductions and reaching conclusions; and at last carefully testing the conclusions to determine whether they fit the formulating hypothesis.

Research can be taken as the contribution to the existing bundle of knowledge, making it more advanced.

The main objective of research is to know or to find out the answers to questions in a scientific way.

Some of the general objectives of research are as follows:

To know about a subject or to find out something new in that – exploratory or formulative research
To know about the subject in depth, for example, the characteristics, nature of a particular group, or individual-descriptive research
To correlate the association of some particulars with something else – diagnostic research.

There are different types of research; some of them are listed below:

Descriptive and analytical
Applied and fundamental
Quantitative and qualitative
Conceptual and empirical
Other types include clinical, historical, and conclusion oriented.

There are different steps which provide a useful procedural guideline regarding the research process, some of the steps are as follows:

Formulating the research problem
Extensive literature survey
Hypothesis developing
Preparing research design
Determining the sample size
Collecting the data
Execution of the project
Analysis of data
Hypothesis testing
Generalization and interpretation
Preparation of report or presentation of the results.

According to the above steps, we have to prepare the research design and determine the sample size to carry out a complete research. Hence, we will discuss in detail about the different types of sampling or the sample designs.

WHAT IS SAMPLING

Sampling is defined as a procedure to select a sample from individual or from a large group of population for certain kind of research purpose. There are different advantages and disadvantages of sampling. We would be thinking sometimes that – Why there is a need of sampling? the answer is as it is too expensive and too time consuming to survey a whole population in a research study, we use sampling [ Figure 1 ].[ 1 , 2 , 3 , 4 , 5 ]

Advantages and disadvantages of sampling

Saves time and money and gives faster results as the sample size is smaller than the whole population
Sampling gives more accurate results as it is performed by trained and experienced investigators
When there is large population, sampling is the best way
Sampling enables to estimate the sampling errors. Hence, it assists in getting information concerning to some characteristics of the population
Study of samples requires less space and equipment as they are small in size
When there is limited resources, sampling is best.

The main disadvantage of the sampling is chances of bias. But, seeing so many of advantages, sampling is the best way to proceed in a research.

Types of sampling

Before we discuss about the different kinds of sampling, let us discuss about what the word sample mean.

In research term, a sample is a group of people, objects, or items that are taken from a large population for measurement. So, to get the accurate results, sampling is done.[ 6 , 7 , 8 , 9 , 10 ]

For example, if we have to check all the chips in a factory made are good or not, it is very difficult to check each chip, so to check, we will be taking a random chip and check for its accurate taste, shape, and size.

Hence, sampling is an important tool in research, when the population size is large. Based on this, we have divided it into two types: (1) probability (2) and nonprobability [ Figure 2 ].

These two types of sampling are further divided into the following subtypes:

Probability sampling

In this type of sampling, there is a known probability of each member of the population of being selected in the sample. When population is highly homogenous, there are high chances of each member of being selected in a sample. For example, in a bag full of rice, if we want to pick some rice, there are high chances of each rice grain of being selected in a sample. Hence, the sample collected will be a representative of the whole rice bag.

For such a study, the population serves as relatively a homogenous group as every member of the population is the target respondent of the research [ Figure 3 ].

Simple random sampling

In this type of sampling, the members of the sample are selected randomly and purely by chance. Hence, the quality of the sample is not affected as every member has an equal chance of being selected in the sample.

This type of sampling is best for population which is highly homogenous.

There are two different ways in which this type of sampling is carried out:

Lottery method/envelope method

In this method, we assign unique numbers to each member or element of the population, say in a population of 100 members, we give number from 1 to 100 to the members on a paper and keep it in a box. Then, we will take out any chit, and the number on that chit is a random sample.

However, in this method, when the population size is larger, it is difficult to write the name of every number on the chits. Hence, another method is used, i.e., random number table (which will be discussed later).

Another example given is an envelope method, say we want to select dilated cardiomyopathy patients DCM patients for yoga in a research project. The details of the 100 patients will be in each envelope and any one will be selected randomly. Hence, here, the chances of all patients to be selected as a sample are equal.

Random number table method

There are different number of random tables available, for example, Fisher's and yates tables and Tippets random number.

Here also, first we assign numbers to the population. If we have population of 20 and we have to choose five samples from this, we have to choose five random numbers from the table. For example, we choose – 12, 19, 01, 08, and 15. Hence, members of these numbers will be selected as the sample.

Types of simple random sampling

In the above section, we have discussed about the methods of doing simple random sampling. In this section, we will be discussing about the types of simple random sampling.

There are two types of simple random sampling:

Simple random sampling with replacement (SRSWR)
Simple random sampling without replacement (SRSWOR).

Simple random sampling with replacement

Selecting “ n ” number of units out of “ N ” units one by one in such a way that at each stage of selection, the sample each unit has equal chance of being selected, i.e., 1/ N .

Simple random sampling without replacement

Selecting “ n ” number of units out of “ N ” one by one at any stage of selecting a sample in such a way that anyone of the left units have the probability of being selected as a sample, i.e., 1/ N .

For example, if we want to know the number of turtles in a pond of a village, so if we are catching turtles from water, measure them, and return them to water, there are high chances that we choose the same turtle, this is SRSWR. However, if we take out the turtle from the water and don't return it without taking the next, it becomes SRSWOR.

Stratified random sampling

In this type, the population is first divided into subgroups called strata on the basis of similarities and then from each group or strata, the members are selected randomly.

Here, the purpose is to address the issue of less homogeneity of the population and to make a true representative sample.

For example, in a school of 1000 students, if we want to know how many of them will choose medical as their career, asking each student is difficult. Hence, as inquiring the whole class is difficult, we will ask few grades and from them, we will choose samples.[ 6 , 7 , 8 , 9 , 10 ]

For example, consider the following number of students in the class:

Grade No. of students “n”

Grade–6 – 50
Grade–7 – 50
Grade–8 – 100
Grade–9 – 100
Grade–10 – 200
Grade–11 – 200
Grade–12 – 300

Now calculate the sample of each grade using the following formula:

Stratified sample: n 6 = 100/1000 × 50 = 5, n 7 = 100/1000 × 50 = 5…. and so on.

So, in this, from each grade, five samples will be selected, and these will be selected according to the simple random method.

This type of sample is also called random quota sampling.

There should be classification on the basis of age, socioeconomics, nationality, religion, and other such classifications.

Detailed steps to select stratified random sample:

First, we will target the audience
Then, we will recognize the stratification variables which should match with the research objective and then will figure out the number of strata to be used
After gathering the information of stratification variables, we will create a frame on this basis for all elements in target audience
The whole population is then divided into different strata which will be unique and different from each but should cover each and every element/member of population. But, each member should be in one strata only
Now, we will assign random, unique number to each element
Then divide the number of samples to be taken with the total number of population into number of people in that group
The number now what we got is the number of samples to be selected for that particular strata. Here, we will use the simple random technique.

Types of stratified sampling

There are two types – (a)-Proportionate stratified random sampling – in this type, the sample size is directly proportional to the entire population of strata, i.e., each strata sample has the same sampling fraction. (b) When the sample size is not proportional.

Examples – in a medical college of 1000 students doing postgraduation (PG), there are five different branches of doing PG and we want to study the reading pattern of all the students. Hence, it is highly difficult for us to go and ask every PG student. So, here, we will divide the class according to the subjects and then according to the formulation, we will count each number of samples to be taken from each stratum.

In another example – if in a study a researcher wants to study which sex, male or female, is predominantly affected by heart failure and what are the causes behind that. He/she will divide the given population into two groups – one male and then female. According to the stratified formula, the number of males and females to be selected from each strata will be counted and then the members in sample with simple random method will be selected.

From 1000 people, 700 males and 300 females, according to which if we want to choose 100 people, then 70 males should be selected and 30 females should be selected, and this selection will be random.

Importance of this sampling

The main advantage of this sampling is that it gives better accuracy in results as compared to other sampling methods
It is very easy to teach and easy to grasp by the trainees
Even smaller sample sizes can also give good results using strata
We can divide the large population into different subgroups/strata according to our need.

When to use stratified random sampling

When we want to focus on a particular strata from the given population data
When we want to establish relationship between two strata
When it is difficult to contact/access the sample population, this method is best as samples are easily involved in research with this method
As the elements of samples are chosen from some specific strata, the accuracy of statistical results is higher than that of simple random sampling.

Systematic sampling

Systematic sampling is an advanced form of simple random sampling, in which we need complete data about the population.

In this, a member is selected after a fixed interval. The member thus selected will be known as the K th element.

Steps to form/select the sample using systematic sampling:

First develop a well-defined structural population to start on sampling aspect
Figure out the ideal size of sample
After deciding the sample size, assign number to every member of sample
Then, the interval of the sample is decided.

For example, we want to select a total of ten patients from a group of forty, then the K th element will be selected by dividing 40/10 = 4, so every 4 th patient will be taken for sampling – 4, 8, 12, 16, 20, 24, 28, 32, 36, and 40.

Types of systematic sampling

Linear systematic sampling

A list is made in a sequential manner of the whole population list. Decide the sample size and find the sampling interval by formula: K = N / n , where K is the K th element, N is the whole population, and n = number of samples. Now, choose random number between 1 and K and then to the number what we got add K to that to get the next sample.

Circular systematic sampling

In this, first, we will determine sample interval and then select number nearest to N / n . For example, if N = 17 and n = 4, then k is taken as 4 not 5 and then start selecting randomly between 1 to N , skip K units each time when we select the next unit until we get n units. In this type, there will be N number of samples unlike K samples in linear systematic sampling method.

It is very easy to create, conduct, and analyze the sample
Risk factor is very minimal
As there is even distribution of members to form a sample, systematic sampling is beneficial when there are diverse members of population.

Cluster sampling

In cluster sampling, various segments of a population are treated as cluster, and members from each cluster are selected randomly.

Cluster sampling and stratified sampling are different from each other.

In stratified sampling, the researcher is dividing the population into subgroups on the basis of age, sex, profession, etc., but in cluster sampling, we are selecting randomly from already-existing or naturally occurring groups/cluster, for example, towns within a district and families within a society.

For example, in a city, if we want to know the list of individuals affected by HBsAg, here it is difficult to find, but if we search area wise, we may get better results. Here, the area acts as a cluster and the individuals will be treated as sampling unit.

In this method, first, we make clusters according to our need and then we select sample according to simple random sampling/systematic sampling.

Multistage sampling

As the name suggests, it contains many stages and hence called multistage sampling.

In this, each cluster of samples is further divided into smaller clusters and the members are selected from each smaller cluster randomly. It is a complex form of cluster sampling

Naturally, groups in a population selected as cluster

Each cluster is divided into smaller cluster

Then, from each smaller cluster, members are selected randomly.

Nonprobability sampling

Nonprobability sampling is a type of sampling where each member of the population does not have known probability of being selected in the sample. For example, to study the impact of child labor on children, the researcher will search and interview only the children who are subjected to child labor.

It is of the following types:

Purposive sampling

In this type of sampling, according to the purpose of the study, the members for a sample are selected. It is also called deliberate sampling. It is also called judgmental sampling.

For example, to study the impact of yoga on DCM patients, only the DCM patients can be the best respondents for this study; every member of heart disease is not the best respondent for this study. Hence, the researcher deliberately selects only the DCM patients as respondents for this study.

When to use/execute judgmental sampling:

When the number of people is less in the population and the researcher knows that the target population fulfill his/her demands, in that case, the judgmental sampling is the best sample
When there is a need to filter the samples chosen by other sample methods, this sampling method is best as it depends on the researcher's knowledge and experience.

Another example of this type of sampling is if a researcher wants to know how many patients of depression are doing particular yoga postures and meditation, he/she will select those patients who he/she thinks will give 100% feedback.

Advantage of judgmental sampling

As selection of the sampling is done by experienced researcher, there will be no hurdles and thus selecting the sample becomes convenient
As the samples selected will be good respondents for that particular study, almost we will get the real-time results, as members will have appropriate knowledge and they understand the subject well
A researcher can produce desired results as he/she can directly communicate with the target audience.

Convenience sampling

Selecting the members of a sample on the basis of their convenient accessibility is called convenience sampling. In this, only those members are selected who are easily accessible to the researcher.

In this sampling, the available data are used without any further additional requirements.

This is used in pilot testing more commonly.

The participants/samples are selected which are easier to recruit for the study.

Some of the examples for this type of sampling are:

Different challenges/games at the shopping malls on different festivals
In a study, a researcher wants to know how many people in a particular area know about dengue, so the researcher will ask questionnaire to the people present and who knows something about dengue will participate in it.

Even the researcher can use the different social networking sites by putting his/her questions on them and interested people will join.

Advantage of convenience sampling

Very easy to implement and inexpensive to create samples
Useful for pilot studies and for hypothesis generations
In a very short duration of time, we can collect data.

Disadvantages

Chances of high sampling error.

Snow-ball sampling

Also known as chain sampling or sequential sampling, it is used where one respondent identifies other respondents (from his/her friends or relatives or known-to). This kind of sampling is adopted in situ ations where it is difficult to identify the members in a sample.

For example, a researcher wants to study problems faced by the migrants in an area. So, he/she will start from one and that migrant will give him/her the information about the other migrant and so it makes a chain and in this way, sample goes on growing like a snowball and the researcher continues this method until the required sample size is achieved.

When to use snowball sampling:

Snowball sampling totally depends on referrals. In this, the population is unknown and rare, due to which it is highly difficult to find the samples/participants.

Just as snowball increases on adding more snow, samples increase in this technique until we collect enough data to analyze. Hence, it is named snowball sampling.

Types of snowball sampling

There are three types:

Linear snowball sampling: In this, the collection of samples starts from collecting data from one and then that individual tells about the other and so in this way, a chain is formed and it continues till we get enough number of individuals to analyze.
Exponential nondiscrimination snowball sampling: In this, one individual will be giving information about more than one individual and those individuals in turn will be giving information about the others and in this way, with more and more referrals, the chain is formed and we collect data.

For example, to collect data regarding Diabetic mellitus from an area, we find an individual who is suffering from Diabetic mellitus. So from him, there are high chances that we will get some information about other people he may know suffering from Diabetic mellitus.

Exponential discrimination snowball sampling: In this type of snowball sampling, one patient gives multiple referrals, but the recruitment will be done only for one patient on the basis of the nature and type of the research study.

In the following areas, snowball sampling can be applied:

Medical records: There are many rare diseases which are yet to be researched and there could be restricted number of individuals suffering from such rare disease. Some of the examples of such disease are mad cow disease, Alice in Wonderland, water allergy, laughing death, pica, and Moebius syndrome. Hence, with this kind of sampling, the people affected with such disease can be traced and research could be done
Social research: In this, we take as many participants as much possible
Cases of discord: In cases of disputes and act of terrorism, rights violation, we will choose people who are witness for that or people who are affected by that.

Advantage of snowball sampling

Can collect samples very quickly
It is cost-effective.

Disadvantages of snowball sampling

High chances of sampling bias and margin of error
If no one cooperates, it is difficult to find the samples.

Quota sampling

In this kind of sampling, members are selected on the basis of some specific characteristics chosen by the researcher. These specific characteristics serve as a quota for selection of the members of the sample.

In this type of sampling, we gather representative data from a group. It is similar to stratified random sampling which is a type of probability sampling. The only difference between both is that in stratified random sampling, the elements of sample are chosen randomly, but in quota sampling, it is not so.

The number of participants is taken in specific category in well-planned manner; for example, 100 males and 100 females.

It is of two types – controlled quota sampling in which there are limitations to the choice of the researcher. The other type is uncontrolled quota sampling in which there are no limitations, and samples are selected according to the convenience of the researcher.

Consecutive sampling

In this type of nonprobability sampling, the researcher will select the samples according to his/her ease/convenience. This is also similar to convenience sampling with little change.

In this, the researcher first picks up a group of people for research, does it for some time period, collects samples, gives results, and once the research completes, he/she will move on to the next group of people. Hence, in this way, a researcher will fine tune his/her research work with the help of this sampling, and he/she gets chance to work with multiple sampling.

In many of the researches, the techniques used, the data analyzed, and conclusion given by researcher will either come under null hypothesis or disapproving it and accepting the alternative hypothesis.

Null hypothesis is denoted by H0, and there is no significant difference in the variables, whereas alternative hypothesis is denoted by H1, which is opposite to null hypothesis where there is some relationship between the two variables.

However, consecutively, the 3 rd option is available, that is, here the researcher, will either come under null hypothesis or if he disapproves it, he accepts the alternative hypothesis.

For example, for advertising the hospital, we distribute leaflets telling about the hospital and its facilities, once the camp organized for checking blood sugar and blood pressure (BP) as free, people will come and do their checkups. Many of the people will just see the leaflet and will move, but some of them will come and check for GRBS and BP. In this case, some might be only checking and going, and there will be another group of people who will check and want to show results to doctor and consult them. Hence, this group of people will provide conclusive results for showing the reports to doctor.

In this, there are different options to sample size and sampling schedule
Sampling schedule depends on the nature of research, if we are not able to get conclusive results with one sample, then we will go to next
This is not time-consuming and also very little effort is required.

The samples obtained cannot be randomized, and we cannot represent the whole population by this.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Cited Here |
View Full Text | PubMed | CrossRef
PubMed | CrossRef

Sampling; statistics; methods; cluster; snowball

+ Favorites
View in Gallery

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

Knowledge Base

Methodology

Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

The population is the entire group that you want to draw conclusions about.
The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

Academic style
Vague sentences
Style consistency

See an example

Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, June 22). Sampling Methods | Types, Techniques & Examples. Scribbr. Retrieved August 14, 2024, from https://www.scribbr.com/methodology/sampling-methods/

Is this article helpful?

Shona McCombes

Other students also liked, population vs. sample | definitions, differences & examples, simple random sampling | definition, steps & examples, sampling bias and how to avoid it | types & examples, what is your plagiarism score.

Educational resources and simple solutions for your research journey

What are Sampling Methods? Techniques, Types, and Examples

Every type of research includes samples from which inferences are drawn. The sample could be biological specimens or a subset of a specific group or population selected for analysis. The goal is often to conclude the entire population based on the characteristics observed in the sample. Now, the question comes to mind: how does one collect the samples? Answer: Using sampling methods. Various sampling strategies are available to researchers to define and collect samples that will form the basis of their research study.

In a study focusing on individuals experiencing anxiety, gathering data from the entire population is practically impossible due to the widespread prevalence of anxiety. Consequently, a sample is carefully selected—a subset of individuals meant to represent (or not in some cases accurately) the demographics of those experiencing anxiety. The study’s outcomes hinge significantly on the chosen sample, emphasizing the critical importance of a thoughtful and precise selection process. The conclusions drawn about the broader population rely heavily on the selected sample’s characteristics and diversity.

Table of Contents

What is sampling?

Sampling involves the strategic selection of individuals or a subset from a population, aiming to derive statistical inferences and predict the characteristics of the entire population. It offers a pragmatic and practical approach to examining the features of the whole population, which would otherwise be difficult to achieve because studying the total population is expensive, time-consuming, and often impossible. Market researchers use various sampling methods to collect samples from a large population to acquire relevant insights. The best sampling strategy for research is determined by criteria such as the purpose of the study, available resources (time and money), and research hypothesis.

For example, if a pet food manufacturer wants to investigate the positive impact of a new cat food on feline growth, studying all the cats in the country is impractical. In such cases, employing an appropriate sampling technique from the extensive dataset allows the researcher to focus on a manageable subset. This enables the researcher to study the growth-promoting effects of the new pet food. This article will delve into the standard sampling methods and explore the situations in which each is most appropriately applied.

What are sampling methods or sampling techniques?

Sampling methods or sampling techniques in research are statistical methods for selecting a sample representative of the whole population to study the population’s characteristics. Sampling methods serve as invaluable tools for researchers, enabling the collection of meaningful data and facilitating analysis to identify distinctive features of the people. Different sampling strategies can be used based on the characteristics of the population, the study purpose, and the available resources. Now that we understand why sampling methods are essential in research, we review the various sample methods in the following sections.

Types of sampling methods

Before we go into the specifics of each sampling method, it’s vital to understand terms like sample, sample frame, and sample space. In probability theory, the sample space comprises all possible outcomes of a random experiment, while the sample frame is the list or source guiding sample selection in statistical research. The sample represents the group of individuals participating in the study, forming the basis for the research findings. Selecting the correct sample is critical to ensuring the validity and reliability of any research; the sample should be representative of the population.

There are two most common sampling methods:

Probability sampling: A sampling method in which each unit or element in the population has an equal chance of being selected in the final sample. This is called random sampling, emphasizing the random and non-zero probability nature of selecting samples. Such a sampling technique ensures a more representative and unbiased sample, enabling robust inferences about the entire population.
Non-probability sampling: Another sampling method is non-probability sampling, which involves collecting data conveniently through a non-random selection based on predefined criteria. This offers a straightforward way to gather data, although the resulting sample may or may not accurately represent the entire population.

Irrespective of the research method you opt for, it is essential to explicitly state the chosen sampling technique in the methodology section of your research article. Now, we will explore the different characteristics of both sampling methods, along with various subtypes falling under these categories.

What is probability sampling?

The probability sampling method is based on the probability theory, which means that the sample selection criteria involve some random selection. The probability sampling method provides an equal opportunity for all elements or units within the entire sample space to be chosen. While it can be labor-intensive and expensive, the advantage lies in its ability to offer a more accurate representation of the population, thereby enhancing confidence in the inferences drawn in the research.

Types of probability sampling

Various probability sampling methods exist, such as simple random sampling, systematic sampling, stratified sampling, and clustered sampling. Here, we provide detailed discussions and illustrative examples for each of these sampling methods:

Simple random sampling: In simple random sampling, each individual has an equal probability of being chosen, and each selection is independent of the others. Because the choice is entirely based on chance, this is also known as the method of chance selection. In the simple random sampling method, the sample frame comprises the entire population.

For example, A fitness sports brand is launching a new protein drink and aims to select 20 individuals from a 200-person fitness center to try it. Employing a simple random sampling approach, each of the 200 people is assigned a unique identifier. Of these, 20 individuals are then chosen by generating random numbers between 1 and 200, either manually or through a computer program. Matching these numbers with the individuals creates a randomly selected group of 20 people. This method minimizes sampling bias and ensures a representative subset of the entire population under study.

Systematic sampling: The systematic sampling approach involves selecting units or elements at regular intervals from an ordered list of the population. Because the starting point of this sampling method is chosen at random, it is more convenient than essential random sampling. For a better understanding, consider the following example.

For example, considering the previous model, individuals at the fitness facility are arranged alphabetically. The manufacturer then initiates the process by randomly selecting a starting point from the first ten positions, let’s say 8. Starting from the 8th position, every tenth person on the list is then chosen (e.g., 8, 18, 28, 38, and so forth) until a sample of 20 individuals is obtained.

Stratified sampling: Stratified sampling divides the population into subgroups (strata), and random samples are drawn from each stratum in proportion to its size in the population. Stratified sampling provides improved representation because each subgroup that differs in significant ways is included in the final sample.

For example, Expanding on the previous simple random sampling example, suppose the manufacturer aims for a more comprehensive representation of genders in a sample of 200 people, consisting of 90 males, 80 females, and 30 others. The manufacturer categorizes the population into three gender strata (Male, Female, and Others). Within each group, random sampling is employed to select nine males, eight females, and three individuals from the others category, resulting in a well-rounded and representative sample of 200 individuals.

Clustered sampling: In this sampling method, the population is divided into clusters, and then a random sample of clusters is included in the final sample. Clustered sampling, distinct from stratified sampling, involves subgroups (clusters) that exhibit characteristics similar to the whole sample. In the case of small clusters, all members can be included in the final sample, whereas for larger clusters, individuals within each cluster may be sampled using the sampling above methods. This approach is referred to as multistage sampling. This sampling method is well-suited for large and widely distributed populations; however, there is a potential risk of sample error because ensuring that the sampled clusters truly represent the entire population can be challenging.

For example, Researchers conducting a nationwide health study can select specific geographic clusters, like cities or regions, instead of trying to survey the entire population individually. Within each chosen cluster, they sample individuals, providing a representative subset without the logistical challenges of attempting a nationwide survey.

Use s of probability sampling

Probability sampling methods find widespread use across diverse research disciplines because of their ability to yield representative and unbiased samples. The advantages of employing probability sampling include the following:

Representativeness

Probability sampling assures that every element in the population has a non-zero chance of being included in the sample, ensuring representativeness of the entire population and decreasing research bias to minimal to non-existent levels. The researcher can acquire higher-quality data via probability sampling, increasing confidence in the conclusions.

Statistical inference

Statistical methods, like confidence intervals and hypothesis testing, depend on probability sampling to generalize findings from a sample to the broader population. Probability sampling methods ensure unbiased representation, allowing inferences about the population based on the characteristics of the sample.

Precision and reliability

The use of probability sampling improves the precision and reliability of study results. Because the probability of selecting any single element/individual is known, the chance variations that may occur in non-probability sampling methods are reduced, resulting in more dependable and precise estimations.

Generalizability

Probability sampling enables the researcher to generalize study findings to the entire population from which they were derived. The results produced through probability sampling methods are more likely to be applicable to the larger population, laying the foundation for making broad predictions or recommendations.

Minimization of Selection Bias

By ensuring that each member of the population has an equal chance of being selected in the sample, probability sampling lowers the possibility of selection bias. This reduces the impact of systematic errors that may occur in non-probability sampling methods, where data may be skewed toward a specific demographic due to inadequate representation of each segment of the population.

What is non-probability sampling?

Non-probability sampling methods involve selecting individuals based on non-random criteria, often relying on the researcher’s judgment or predefined criteria. While it is easier and more economical, it tends to introduce sampling bias, resulting in weaker inferences compared to probability sampling techniques in research.

Types of Non-probability Sampling

Non-probability sampling methods are further classified as convenience sampling, consecutive sampling, quota sampling, purposive or judgmental sampling, and snowball sampling. Let’s explore these types of sampling methods in detail.

Convenience sampling: In convenience sampling, individuals are recruited directly from the population based on the accessibility and proximity to the researcher. It is a simple, inexpensive, and practical method of sample selection, yet convenience sampling suffers from both sampling and selection bias due to a lack of appropriate population representation.

For example, imagine you’re a researcher investigating smartphone usage patterns in your city. The most convenient way to select participants is by approaching people in a shopping mall on a weekday afternoon. However, this convenience sampling method may not be an accurate representation of the city’s overall smartphone usage patterns as the sample is limited to individuals present at the mall during weekdays, excluding those who visit on other days or never visit the mall.

Consecutive sampling: Participants in consecutive sampling (or sequential sampling) are chosen based on their availability and desire to participate in the study as they become available. This strategy entails sequentially recruiting individuals who fulfill the researcher’s requirements.

For example, In researching the prevalence of stroke in a hospital, instead of randomly selecting patients from the entire population, the researcher can opt to include all eligible patients admitted over three months. Participants are then consecutively recruited upon admission during that timeframe, forming the study sample.

Quota sampling: The selection of individuals in quota sampling is based on non-random selection criteria in which only participants with certain traits or proportions that are representative of the population are included. Quota sampling involves setting predetermined quotas for specific subgroups based on key demographics or other relevant characteristics. This sampling method employs dividing the population into mutually exclusive subgroups and then selecting sample units until the set quota is reached.

For example, In a survey on a college campus to assess student interest in a new policy, the researcher should establish quotas aligned with the distribution of student majors, ensuring representation from various academic disciplines. If the campus has 20% biology majors, 30% engineering majors, 20% business majors, and 30% liberal arts majors, participants should be recruited to mirror these proportions.

Purposive or judgmental sampling: In purposive sampling, the researcher leverages expertise to select a sample relevant to the study’s specific questions. This sampling method is commonly applied in qualitative research, mainly when aiming to understand a particular phenomenon, and is suitable for smaller population sizes.

For example, imagine a researcher who wants to study public policy issues for a focus group. The researcher might purposely select participants with expertise in economics, law, and public administration to take advantage of their knowledge and ensure a depth of understanding.

Snowball sampling: This sampling method is used when accessing the population is challenging. It involves collecting the sample through a chain-referral process, where each recruited candidate aids in finding others. These candidates share common traits, representing the targeted population. This method is often used in qualitative research, particularly when studying phenomena related to stigmatized or hidden populations.

For example, In a study focusing on understanding the experiences and challenges of individuals in hidden or stigmatized communities (e.g., LGBTQ+ individuals in specific cultural contexts), the snowball sampling technique can be employed. The researcher initiates contact with one community member, who then assists in identifying additional candidates until the desired sample size is achieved.

Uses of non-probability sampling

Non-probability sampling approaches are employed in qualitative or exploratory research where the goal is to investigate underlying population traits rather than generalizability. Non-probability sampling methods are also helpful for the following purposes:

Generating a hypothesis

In the initial stages of exploratory research, non-probability methods such as purposive or convenience allow researchers to quickly gather information and generate hypothesis that helps build a future research plan.

Qualitative research

Qualitative research is usually focused on understanding the depth and complexity of human experiences, behaviors, and perspectives. Non-probability methods like purposive or snowball sampling are commonly used to select participants with specific traits that are relevant to the research question.

Convenience and pragmatism

Non-probability sampling methods are valuable when resource and time are limited or when preliminary data is required to test the pilot study. For example, conducting a survey at a local shopping mall to gather opinions on a consumer product due to the ease of access to potential participants.

Probability vs Non-probability Sampling Methods


Selection of participants	Random selection of participants from the population using randomization methods	Non-random selection of participants from the population based on convenience or criteria
Representativeness	Likely to yield a representative sample of the whole population allowing for generalizations	May not yield a representative sample of the whole population; poor generalizability
Precision and accuracy	Provides more precise and accurate estimates of population characteristics	May have less precision and accuracy due to non-random selection
Bias	Minimizes selection bias	May introduce selection bias if criteria are subjective and not well-defined
Statistical inference	Suited for statistical inference and hypothesis testing and for making generalization to the population	Less suited for statistical inference and hypothesis testing on the population
Application	Useful for quantitative research where generalizability is crucial	Commonly used in qualitative and exploratory research where in-depth insights are the goal

Frequently asked questions

What is multistage sampling ? Multistage sampling is a form of probability sampling approach that involves the progressive selection of samples in stages, going from larger clusters to a small number of participants, making it suited for large-scale research with enormous population lists.
What are the methods of probability sampling? Probability sampling methods are simple random sampling, stratified random sampling, systematic sampling, cluster sampling, and multistage sampling.
How to decide which type of sampling method to use? Choose a sampling method based on the goals, population, and resources. Probability for statistics and non-probability for efficiency or qualitative insights can be considered . Also, consider the population characteristics, size, and alignment with study objectives.
What are the methods of non-probability sampling? Non-probability sampling methods are convenience sampling, consecutive sampling, purposive sampling, snowball sampling, and quota sampling.
Why are sampling methods used in research? Sampling methods in research are employed to efficiently gather representative data from a subset of a larger population, enabling valid conclusions and generalizations while minimizing costs and time.

R Discovery is a literature search and research reading platform that accelerates your research discovery journey by keeping you updated on the latest, most relevant scholarly content. With 250M+ research articles sourced from trusted aggregators like CrossRef, Unpaywall, PubMed, PubMed Central, Open Alex and top publishing houses like Springer Nature, JAMA, IOP, Taylor & Francis, NEJM, BMJ, Karger, SAGE, Emerald Publishing and more, R Discovery puts a world of research at your fingertips.

Try R Discovery Prime FREE for 1 week or upgrade at just US$72 a year to access premium features that let you listen to research on the go, read in your language, collaborate with peers, auto sync with reference managers, and much more. Choose a simpler, smarter way to find and read research – Download the app and start your free 7-day trial today !

What are the Best Research Funding Sources

What are Experimental Groups in Research

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Archaeology in space: The Sampling Quadrangle Assemblages Research Experiment (SQuARE) on the International Space Station. Report 1: Squares 03 and 05

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Art, Chapman University, Orange, CA, United States of America, Space Engineering Research Center, University of Southern California, Marina del Rey, CA, United States of America

Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of History, Carleton University, Ottawa, ON, United States of America

Roles Conceptualization, Data curation, Methodology, Project administration, Supervision, Writing – review & editing

Affiliation College of Humanities, Arts and Social Sciences, Flinders University, Adelaide, Australia

Roles Software, Writing – original draft

Roles Investigation, Writing – original draft

Affiliation Archaeology Research Center, University of Southern California, Los Angeles, CA, United States of America

Justin St. P. Walsh,
Shawn Graham,
Alice C. Gorman,
Chantal Brousseau,
Salma Abdullah

Published: August 7, 2024
https://doi.org/10.1371/journal.pone.0304229
Reader Comments

Between January and March 2022, crew aboard the International Space Station (ISS) performed the first archaeological fieldwork in space, the Sampling Quadrangle Assemblages Research Experiment (SQuARE). The experiment aimed to: (1) develop a new understanding of how humans adapt to life in an environmental context for which we are not evolutionarily adapted, using evidence from the observation of material culture; (2) identify disjunctions between planned and actual usage of facilities on a space station; (3) develop and test techniques that enable archaeological research at a distance; and (4) demonstrate the relevance of social science methods and perspectives for improving life in space. In this article, we describe our methodology, which involves a creative re-imagining of a long-standing sampling practice for the characterization of a site, the shovel test pit. The ISS crew marked out six sample locations (“squares”) around the ISS and documented them through daily photography over a 60-day period. Here we present the results from two of the six squares: an equipment maintenance area, and an area near exercise equipment and the latrine. Using the photographs and an innovative webtool, we identified 5,438 instances of items, labeling them by type and function. We then performed chronological analyses to determine how the documented areas were actually used. Our results show differences between intended and actual use, with storage the most common function of the maintenance area, and personal hygiene activities most common in an undesignated area near locations for exercise and waste.

Citation: Walsh JSP, Graham S, Gorman AC, Brousseau C, Abdullah S (2024) Archaeology in space: The Sampling Quadrangle Assemblages Research Experiment (SQuARE) on the International Space Station. Report 1: Squares 03 and 05. PLoS ONE 19(8): e0304229. https://doi.org/10.1371/journal.pone.0304229

Editor: Peter F. Biehl, University of California Santa Cruz, UNITED STATES OF AMERICA

Received: March 9, 2024; Accepted: May 7, 2024; Published: August 7, 2024

Copyright: © 2024 Walsh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: JW was the recipient of funding from Chapman University’s Office of Research and Sponsored Programs to support the activities of Axiom Space as implementation partner for the research presented in this article. There are no associated grant numbers for this financial support. Axiom Space served in the role of a contractor hired by Chapman University for the purpose of overseeing logistics relating to our research. In-kind support in the form of ISS crew time and access to the space station’s facilities, also awarded to JW from the ISS National Laboratory, resulted from an unsolicited proposal, and therefore there is no opportunity title or number associated with our work. No salary was received by any of the investigators as a result of the grant support. No additional external funding was received for this study.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The International Space Station Archaeological Project (ISSAP) aims to fill a gap in social science investigation into the human experience of long-duration spaceflight [ 1 – 3 ]. As the largest, most intensively inhabited space station to date, with over 270 visitors from 23 countries during more than 23 years of continuous habitation, the International Space Station (ISS) is the ideal example of a new kind of spacefaring community—“a microsociety in a miniworld” [ 4 ]. While it is possible to interview crew members about their experiences, the value of an approach focused on material culture is that it allows identification of longer-term patterns of behaviors and associations that interlocutors are unable or even unwilling to articulate. In this respect, we are inspired by previous examples of contemporary archaeology such as the Tucson Garbage Project and the Undocumented Migration Project [ 5 – 7 ]. We also follow previous discussions of material culture in space contexts that highlight the social and cultural features of space technology [ 8 , 9 ].

Our primary goal is to identify how humans adapt to life in a new environment for which our species has not evolved, one characterized by isolation, confinement, and especially microgravity. Microgravity introduces opportunities, such as the ability to move and work in 360 degrees, and to carry out experiments impossible in full Earth gravity, but also limitations, as unrestrained objects float away. The most routine activities carried out on Earth become the focus of intense planning and technological intervention in microgravity. By extension, our project also seeks to develop archaeological techniques that permit the study of other habitats in remote, extreme, or dangerous environments [ 10 , 11 ]. Since it is too costly and difficult to visit our archaeological site in person, we have to creatively re-imagine traditional archaeological methods to answer key questions. To date, our team has studied crew-created visual displays [ 12 , 13 ], meanings and processes associated with items returned to Earth [ 14 ], distribution of different population groups around the various modules [ 15 ], and the development of machine learning (ML) computational techniques to extract data about people and places, all from historic photographs of life on the ISS [ 16 ].

From January to March 2022, we developed a new dataset through the first archaeological work conducted off-Earth. We documented material culture in six locations around the ISS habitat, using daily photography taken by the crew which we then annotated and studied as evidence for changes in archaeological assemblages of material culture over time. This was the first time such data had been captured in a way that allowed statistical analysis. Here, we present the data and results from Squares 03 and 05, the first two sample locations to be completed.

Materials and methods

Square concept and planning.

Gorman proposed the concept behind the investigation, deriving it from one of the most traditional terrestrial archaeological techniques, the shovel test pit. This method is used to understand the overall characteristics of a site quickly through sampling. A site is mapped with a grid of one-meter squares. Some of the squares are selected for initial excavation to understand the likely spatial and chronological distribution of features across the entire site. In effect, the technique is a way to sample a known percentage of the entire site systematically. In the ISS application of this method, we documented a notional stratigraphy through daily photography, rather than excavation.

Historic photography is a key dataset for the International Space Station Archaeological Project. Tens of thousands of images have been made available to us, either through publication [ 17 ], or through an arrangement with the ISS Research Integration Office, which supplied previously unpublished images from the first eight years of the station’s habitation. These photographs are informative about the relationships between people, places, and objects over time in the ISS. However, they were taken randomly (from an archaeological perspective) and released only according to NASA’s priorities and rules. Most significantly, they were not made with the purpose of answering archaeological questions. By contrast, the photographs taken during the present investigation were systematic, representative of a defined proportion of the habitat’s area, and targeted towards capturing archaeology’s primary evidence: material culture. We were interested in how objects move around individual spaces and the station, what these movements revealed about crew adherence to terrestrial planning, and the creative use of material culture to make the laboratory-like interior of the ISS more habitable.

Access to the field site was gained through approval of a proposal submitted to the Center for the Advancement of Science in Space (also known as the ISS National Laboratory [ISS NL]). Upon acceptance, Axiom Space was assigned as the Implementation Partner for carriage of the experiment according to standard procedure. No other permits were required for this work.

Experiment design

Since our work envisioned one-meter sample squares, and recognizing the use of acronyms as a persistent element of spacefaring culture, we named our payload the Sampling Quadrangle Assemblages Research Experiment (SQuARE). Permission from the ISS NL to conduct SQuARE was contingent on using equipment that was already on board the space station. SQuARE required only five items: a camera, a wide-angle lens, adhesive tape (for marking the boundaries of the sample locations), a ruler (for scale), and a color calibration card (for post-processing of the images). All of these were already present on the ISS.

Walsh performed tests on the walls of a terrestrial art gallery to assess the feasibility of creating perfect one-meter squares in microgravity. He worked on a vertical surface, using the Pythagorean theorem to determine where the corners should be located. The only additional items used for these tests were two metric measuring tapes and a pencil for marking the wall (these were also already on the ISS). While it was possible to make a square this way, it also became clear that at least two people were needed to manage holding the tape measures in position while marking the points for the corners. This was not possible in the ISS context.

Walsh and Gorman identified seven locations for the placement of squares. Five of these were in the US Orbital Segment (USOS, consisting of American, European, and Japanese modules) and two in the Russian Orbital Segment. Unfortunately, tense relations between the US and Russian governments meant we could only document areas in the USOS. The five locations were (with their SQuARE designations):

01—an experimental rack on the forward wall, starboard end, of the Japanese Experiment Module
02—an experimental rack on the forward wall, port end, of the European laboratory module Columbus
03—the starboard Maintenance Work Area (workstation) in the US Node 2 module
04—the wall area “above” (according to typical crew body orientation) the galley table in the US Node 1 module
05—the aft wall, center location, of the US Node 3 module

Our square selection encompassed different modules and activities, including work and leisure. We also asked the crew to select a sixth sample location based on their understanding of the experiment and what they thought would be interesting to document. They chose a workstation on the port wall of the US laboratory module, at the aft end, which they described in a debriefing following their return to Earth in June 2022 as “our central command post, like our shared office situation in the lab.” Results from the four squares not included here will appear in future publications.

Walsh worked with NASA staff to determine payload procedures, including precise locations for the placement of the tape that would mark the square boundaries. The squares could not obstruct other facilities or experiments, so (unlike in terrestrial excavations, where string is typically used to demarcate trench boundaries) only the corners of each square were marked, not the entire perimeter. We used Kapton tape due to its bright yellow-orange color, which aided visibility for the crew taking photographs and for us when cropping the images. In practice, due to space constraints, the procedures that could actually be performed by crew in the ISS context, and the need to avoid interfering with other ongoing experiments, none of the locations actually measured one square meter or had precise 90° corners like a trench on Earth.

On January 14, 2022, NASA astronaut Kayla Barron set up the sample locations, marking the beginning of archaeological work in space ( S1 Movie ). For 30 days, starting on January 21, a crew member took photos of the sample locations at approximately the same time each day; the process was repeated at a random time each day for a second 30-day period to eliminate biases. Photography ended on March 21, 2022. The crew were instructed not to move any items prior to taking the photographs. Walsh led image management, including color and barrel distortion correction, fixing the alignment of each image, and cropping them to the boundaries of the taped corners.

Data processing—Item tagging, statistics, visualizations

We refer to each day’s photo as a “context” by analogy with chronologically-linked assemblages of artifacts and installations at terrestrial archaeological sites ( S1 and S2 Datasets). As previously noted, each context represented a moment roughly 24 hours distant from the previous one, showing evidence of changes in that time. ISS mission planners attempted to schedule the activity at the same time in the first month, but there were inevitable changes due to contingencies. Remarkably, the average time between contexts in Phase 1 was an almost-perfect 24h 0m 13s. Most of the Phase 1 photos were taken between 1200 and 1300 GMT (the time zone in which life on the ISS is organized). In Phase 2, the times were much more variable, but the average time between contexts during this period was still 23h 31m 45s. The earliest Phase 2 photo was taken at 0815 GMT, and the latest at 2101. We did not identify any meaningful differences between results from the two phases.

Since the “test pits” were formed of images rather than soil matrices, we needed a tool to capture information about the identity, nature, and location of every object. An open-source image annotator platform [ 18 ] mostly suited our needs. Brousseau rebuilt the platform to work within the constraints of our access to the imagery (turning it into a desktop tool with secure access to our private server), to permit a greater range of metadata to be added to each item or be imported, to autosave, and to export the resulting annotations. The tool also had to respect privacy and security limitations required by NASA.

The platform Brousseau developed and iterated was rechristened “Rocket-Anno” ( S1 File ). For each context photograph, the user draws an outline around every object, creating a polygon; each polygon is assigned a unique ID and the user provides the relevant descriptive information, using a controlled vocabulary developed for ISS material culture by Walsh and Gorman. Walsh and Abdullah used Rocket-Anno to tag the items in each context for Squares 03 and 05. Once all the objects were outlined for every context’s photograph, the tool exported a JSON file with all of the metadata for both the images themselves and all of the annotations, including the coordinate points for every polygon ( S3 Dataset ). We then developed Python code using Jupyter “notebooks” (an interactive development environment) that ingests the JSON file and generates dataframes for various facets of the data. Graham created a “core” notebook that exports summary statistics, calculates Brainerd-Robinson coefficients of similarity, and visualizes the changing use of the square over time by indicating use-areas based on artifact types and subtypes ( S2 File ). Walsh and Abdullah also wrote detailed square notes with context-by-context discussions and interpretations of features and patterns.

We asked NASA for access to the ISS Crew Planner, a computer system that shows each astronaut’s tasks in five-minute increments, to aid with our interpretation of contexts, but were denied. As a proxy, we use another, less detailed source: the ISS Daily Summary Reports (DSRs), published on a semi-regular basis by NASA on its website [ 19 ]. Any activities mentioned in the DSRs often must be connected with a context by inference. Therefore, our conclusions are likely less precise than if we had seen the Crew Planner, but they also more clearly represent the result of simply observing and interpreting the material culture record.

The crew during our sample period formed ISS Expedition 66 (October 2021-March 2022). They were responsible for the movement of objects in the sample squares as they carried out their daily tasks. The group consisted of two Russians affiliated with Roscosmos (the Russian space agency, 26%), one German belonging to the European Space Agency (ESA, 14%), and four Americans employed by NASA (57%). There were six men (86%) and one woman (14%), approximately equivalent to the historic proportions in the ISS population (84% and 16%, respectively). The Russian crew had their sleeping quarters at the aft end of the station, in the Zvezda module. The ESA astronaut slept in the European Columbus laboratory module. The four NASA crew slept in the US Node 2 module (see below). These arrangements emphasize the national character of discrete spaces around the ISS, also evident in our previous study of population distributions [ 15 ]. Both of the sample areas in this study were located in US modules.

Square 03 was placed in the starboard Maintenance Work Area (MWA, Fig 1 ), one of a pair of workstations located opposite one another in the center of the Node 2 module, with four crew berths towards the aft and a series of five ports for the docking of visiting crew/cargo vehicles and two modules on the forward end ( Fig 2 ). Node 2 (sometimes called “Harmony”) is a connector that links the US, Japanese, and European lab modules. According to prevailing design standards when the workstation was developed, an MWA “shall serve as the primary location for servicing and repair of maximum sized replacement unit/system components” [ 20 ]. Historic images published by NASA showing its use suggested that its primary function was maintenance of equipment and also scientific work that did not require a specific facility such as a centrifuge or furnace.

PPT PowerPoint slide
PNG larger image
TIFF original image

An open crew berth is visible at right. The yellow dotted line indicates the boundaries of the sample area. Credit: NASA/ISSAP.

https://doi.org/10.1371/journal.pone.0304229.g001

Credit: Tor Finseth, by permission, modified by Justin Walsh.

https://doi.org/10.1371/journal.pone.0304229.g002

Square 03 measured 90.3 cm (top) x 87.8 (left) x 89.4 (bottom) x 87.6 (right), for an area of approximately 0.79 m 2 . Its primary feature was a blue metal panel with 40 square loop-type Velcro patches arranged in four rows of ten. During daily photography, many items were attached to the Velcro patches (or held by a clip or in a resealable bag which had its own hook-type Velcro). Above and below the blue panel were additional Velcro patches placed directly on the white plastic wall surface. These patches were white, in different sizes and shapes and irregularly arranged, indicating that they had been placed on the wall in response to different needs. Some were dirty, indicating long use. The patches below the blue panel were rarely used during the sample period, but the patches above were used frequently to hold packages of wet wipes, as well as resealable bags with electrostatic dispersion kits and other items. Outside the sample area, the primary features were a crew berth to the right, and a blue metal table attached to the wall below. This table, the primary component of the MWA, “provides a rigid surface on which to perform maintenance tasks,” according to NASA [ 21 ]. It is modular and can be oriented in several configurations, from flat against the wall to horizontal ( i . e ., perpendicular to the wall). A laptop to the left of the square occasionally showed information about work happening in the area.

In the 60 context photos of Square 03, we recorded 3,608 instances of items, an average of 60.1 (median = 60.5) per context. The lowest count was 24 in context 2 (where most of the wall was hidden from view behind an opaque storage bag), and the highest was 75 in both contexts 20 and 21. For comparison between squares, we can also calculate the item densities per m 2 . The average count was 76.1/m 2 (minimum = 30, maximum = 95). The count per context ( Fig 3(A)) began much lower than average in the first three contexts because of a portable glovebag and a stowage bag that obscured much of the sample square. It rose to an above-average level which was sustained (with the exception of contexts 11 and 12, which involved the appearance of another portable glovebag) until about context 43, when the count dipped again and the area seemed to show less use. Contexts 42–59 showed below-average numbers, as much as 20% lower than previously.

(a) Count of artifacts in Square 03 over time. (b) Proportions of artifacts by function in Square 03. Credit: Rao Hamza Ali.

https://doi.org/10.1371/journal.pone.0304229.g003

74 types of items appeared at least once here, belonging to six categories: equipment (41%), office supplies (31%), electronic (17%), stowage (9%), media (1%), and food (<1%). To better understand the significance of various items in the archaeological record, we assigned them to functional categories ( Table 1 , Fig 3(B)) . 35% of artifacts were restraints, or items used for holding other things in place; 12% for tools; 9% for containers; 9% for writing items; 6% for audiovisual items; 6% for experimental items; 4% for lights; 4% for safety items; 4% for body maintenance; 4% for power items; 3% for computing items; 1% for labels; and less than 1% drinks. We could not identify a function for two percent of the items.

https://doi.org/10.1371/journal.pone.0304229.t001

One of the project goals is understanding cultural adaptations to the microgravity environment. We placed special attention on “gravity surrogates,” pieces of (often simple) technology that are used in space to replicate the terrestrial experience of things staying where they are placed. Gravity surrogates include restraints and containers. It is quite noticeable that gravity surrogates comprise close to half of all items (44%) in Square 03, while the tools category, which might have been expected to be most prominent in an area designated for maintenance, is less than one-third as large (12%). Adding other groups associated with work, such as “experiment” and “light,” only brings the total to 22%.

Square 05 (Figs 2 and 4 ) was placed in a central location on the aft wall of the multipurpose Node 3 (“Tranquility”) module. This module does not include any specific science facilities. Instead, there are two large pieces of exercise equipment, the TVIS (Treadmill with Vibration Isolation Stabilization System, on the forward wall at the starboard end), and the ARED (Advanced Resistive Exercise Device, on the overhead wall at the port end). Use of the machines forms a significant part of crew activities, as they are required to exercise for two hours each day to counteract loss of muscle mass and bone density, and enable readjustment to terrestrial gravity on their return. The Waste and Hygiene Compartment (WHC), which includes the USOS latrine, is also here, on the forward wall in the center of the module, opposite Square 05. Finally, three modules are docked at Node 3’s port end. Most notable is the Cupola, a kind of miniature module on the nadir side with a panoramic window looking at Earth. This is the most popular leisure space for the crew, who often describe the hours they spend there. The Permanent Multipurpose Module (PMM) is docked on the forward side, storing equipment, food, and trash. In previous expeditions, some crew described installing a curtain in the PMM to create a private space for changing clothes and performing body maintenance activities such as cleaning oneself [ 22 , 23 ], but it was unclear whether that continued to be its function during the expedition we observed. One crew member during our sample period posted a video on Instagram showing the PMM interior and their efforts to re-stow equipment in a bag [ 24 ]. The last space attached to Node 3 is an experimental inflatable module docked on the aft side, called the Bigelow Expandable Activity Module (BEAM), which is used for storage of equipment.

The yellow dotted line indicates the boundaries of the sample area. The ARED machine is at the far upper right, on the overhead wall. The TVIS treadmill is outside this image to the left, on the forward wall. The WHC is directly behind the photographer. Credit: NASA/ISSAP.

https://doi.org/10.1371/journal.pone.0304229.g004

Square 05 was on a mostly featureless wall, with a vertical handrail in the middle. Handrails are metal bars located throughout the ISS that are used by the crew to hold themselves in place or provide a point from which to propel oneself to another location. NASA’s most recent design standards acknowledge that “[t]hey also serve as convenient locations for temporary mounting, affixing, or restraint of loose equipment and as attachment points for equipment” [ 25 ]. The handrail in Square 05 was used as an impromptu object restraint when a resealable bag filled with other bags was squeezed between the handrail and the wall.

The Brine Processing Assembly (BPA), a white plastic box which separates water from other components of urine for treatment and re-introduction to the station’s drinkable water supply [ 26 ], was fixed to the wall outside the square boundaries at lower left. A bungee cord was attached to both sides of the box; the one on the right was connected at its other end to the handrail attachment bracket. Numerous items were attached to or wedged into this bungee cord during the survey, bringing “gravity” into being. A red plastic duct ran through the square from top center into the BPA. This duct led from the latrine via the overhead wall. About halfway through the survey period, in context 32, the duct was wrapped in Kapton tape. According to the DSR for that day, “the crew used duct tape [ sic ] to make a seal around the BPA exhaust to prevent odor permeation in the cabin” [ 27 ], revealing an aspect of the crew’s experience of this area that is captured only indirectly in the context photograph. Permanently attached to the wall were approximately 20 loop-type Velcro patches in many shapes and sizes, placed in a seemingly random pattern that likely indicates that they were put there at different times and for different reasons.

Other common items in Square 05 were a mirror, a laptop computer, and an experimental item belonging to the German space agency DLR called the Touch Array Assembly [ 28 ]. The laptop moved just three times, and only by a few centimeters each time, during the sample period. The Touch Array was a black frame enclosing three metal surfaces which were being tested for their bacterial resistance; members of the crew touched the surfaces at various moments during the sample period. Finally, and most prominent due to its size, frequency of appearance, and use (judged by its movement between context photos) was an unidentified crew member’s toiletry kit.

By contrast with Square 03, 05 was the most irregular sample location, roughly twice as wide as it was tall. Its dimensions were 111 cm (top) x 61.9 (left) x 111.4 (bottom) x 64.6 (right), for an area of approximately 0.7 m 2 , about 89% of Square 03. We identified 1,830 instances of items in the 60 contexts, an average of 30.5 (median = 32) per context. The minimum was 18 items in context 5, and the maximum was 39 in contexts 24, 51, and 52. The average item density was 43.6/m 2 (minimum = 26, maximum = 56), 57% of Square 03.

The number of items trended upward throughout the sample period ( Fig 5(A)) . The largest spike occurred in context 6 with the appearance of the toiletry kit, which stored (and revealed) a number of related items. The kit can also be linked to one of the largest dips in item count, seen from contexts 52 to 53, when it was closed (but remained in the square). Other major changes can often be attributed to the addition and removal of bungee cords, which had other items such as carabiners and brackets attached. For example, the dip seen in context 25 correlates with the removal of a bungee cord with four carabiners.

(a) Count of artifacts and average count in Square 05 over time. (b) Proportions of artifacts by function in Square 05. Credit: Rao Hamza Ali.

https://doi.org/10.1371/journal.pone.0304229.g005

41 different item types were found in Square 05, about 55% as many as in Square 03. These belonged to five different categories: equipment (63%), electronic (17%), stowage (10%), office supplies (5%), and food (2%). The distribution of function proportions was quite different in this sample location ( Table 2 and Fig 5(B)) . Even though restraints were still most prominent, making up 32% of all items, body maintenance was almost as high (30%), indicating how strongly this area was associated with the activity of cleaning and caring for oneself. Computing (8%, represented by the laptop, which seems not to have been used), power (8%, from various cables), container (7%, resealable bags and Cargo Transfer Bags), and hygiene (6%, primarily the BPA duct) were the next most common items. Experiment was the function of 4% of the items, mostly the Touch Array, which appeared in every context, followed by drink (2%) and life support (1%). Safety, audiovisual, food, and light each made up less than 1% of the functional categories.

https://doi.org/10.1371/journal.pone.0304229.t002

Tracking changes over time is critical to understanding the activity happening in each area. We now explore how the assemblages change by calculating the Brainerd-Robinson Coefficient of Similarity [ 29 , 30 ] as operationalized by Peeples [ 31 , 32 ]. This metric is used in archaeology for comparing all pairs of the contexts by the proportions of categorical artifact data, here functional type. Applying the coefficient to the SQuARE contexts enables identification of time periods for distinct activities using artifact function and frequency alone, independent of documentary or oral evidence.

Multiple phases of activities took place in the square. Moments of connected activity are visible as red clusters in contexts 0–2, 11–12, 28–32, and 41 ( Fig 6(A)) . Combining this visualization with close observation of the photos themselves, we argue that there are actually eight distinct chronological periods.

Contexts 0–2: Period 1 (S1 Fig in S3 File ) is a three-day period of work involving a portable glovebag (contexts 0–1) and a large blue stowage bag (context 2). It is difficult to describe trends in functional types because the glovebag and stowage bag obstruct the view of many objects. Items which appear at the top of the sample area, such as audiovisual and body maintenance items, are overemphasized in the data as a result. It appears that some kind of science is happening here, perhaps medical sample collection due to the presence of several small resealable bags visible in the glovebag. The work appears particularly intense in context 1, with the positioning of the video camera and light to point into the glovebag. These items indicate observation and oversight of crew activities by ground control. A white cargo transfer bag for storage and the stowage bag for holding packing materials in the context 2 photo likely relate to the packing of a Cargo Dragon vehicle that was docked to Node 2. The Dragon departed from the ISS for Earth, full of scientific samples, equipment, and crew personal items, a little more than three hours after the context 2 photo was taken [ 33 ].
Contexts 3–10: Period 2 (S2 Fig in S3 File ) was a “stable” eight-day period in the sample, when little activity is apparent, few objects were moved or transferred in or out the square, and the primary function of the area seems to be storage rather than work. In context 6, a large Post-It notepad appeared in the center of the metal panel with a phone number written on it. This number belonged to another astronaut, presumably indicating that someone on the ISS had been told to call that colleague on the ground (for reasons of privacy, and in accordance with NASA rules for disseminating imagery, we have blurred the number in the relevant images). In context 8, the same notepad sheet had new writing appear on it, this time reading “COL A1 L1,” the location of an experimental rack in the European lab module.
Contexts 11–12: Period 3 (S3 Fig in S3 File ) involves a second appearance of a portable glovebag (a different one from that used in contexts 0–1, according to its serial number), this time for a known activity, a concrete hardening experiment belonging to the European Space Agency [ 34 , 35 ]. This two-day phase indicates how the MWA space can be shared with non-US agencies when required. It also demonstrates the utility of this flexible area for work beyond biology/medicine, such as material science. Oversight of the crew’s activities by ground staff is evident from the positioning of the video camera and LED light pointing into the glovebag.
Contexts 13–27: Period 4 (S4 Fig in S3 File ) is another stable fifteen-day period, similar to Period 2. Many items continued to be stored on the aluminum panel. The LED light’s presence is a trace of the activity in Period 3 that persists throughout this phase. Only in context 25 can a movement of the lamp potentially be connected to an activity relating to one of the stored items on the wall: at least one nitrile glove was removed from a resealable bag behind the lamp. In general, the primary identifiable activity during Period 4 is storage.
Contexts 28–32: Period 5 (S5 Fig in S3 File ), by contrast, represents a short period of five days of relatively high and diverse activity. In context 28, a Microsoft Hololens augmented reality headset appeared. According to the DSR for the previous day, a training activity called Sidekick was carried out using the headset [ 36 ]. The following day, a Saturday, showed no change in the quantity or type of objects, but many were moved around and grouped by function—adhesive tape rolls were placed together, tools were moved from Velcro patches into pouches or straightened, and writing implements were placed in a vertical orientation when previously they were tilted. Context 29 represents a cleaning and re-organization of the sample area, which is a common activity for the crew on Saturdays [ 37 ]. Finally, in context 32, an optical coherence tomography scanner—a large piece of equipment for medical research involving crew members’ eyes—appeared [ 38 ]. This device was used previously during the sample period, but on the same day as the ESA concrete experiment, so that earlier work seems to have happened elsewhere [ 39 ].
Contexts 33–40: Period 6 (S6 Fig in S3 File ) is the third stable period, in which almost no changes are visible over eight days. The only sign of activity is a digital timer which was started six hours before the context 39 image was made and continued to run at least through context 42.
Context 41: Period 7 (S7 Fig in S3 File ) is a single context in which medical sample collection may have occurred. Resealable bags (some holding others) appeared in the center of the image and at lower right. One of the bags at lower right had a printed label reading “Reservoir Containers.” We were not able to discern which type of reservoir containers the label refers to, although the DSR for the day mentions “[Human Research Facility] Generic Saliva Collection,” without stating the location for this work [ 40 ]. Evidence from photos of other squares shows that labeled bags could be re-used for other purposes, so our interpretation of medical activity for this context is not conclusive.
Contexts 42–60: Period 8 (S8 Fig in S3 File ) is the last and longest period of stability and low activity—eighteen days in which no specific activity other than the storage of items can be detected. The most notable change is the appearance for the first time of a foil water pouch in the central part of the blue panel.

Visualization of Brainerd-Robinson similarity, compared context-by-context by item function, for (a) Square 03 and (b) Square 05. The more alike a pair of contexts is, the higher the coefficient value, with a context compared against itself where a value of 200 equals perfect similarity. The resulting matrix of coefficients is visualized on a scale from blue to red where blue is lowest and red is highest similarity. The dark red diagonal line indicates complete similarity, where each context is compared to itself. Dark blue represents a complete difference. Credit: Shawn Graham.

https://doi.org/10.1371/journal.pone.0304229.g006

In the standards used at the time of installation, “stowage space” was the sixth design requirement listed for the MWA after accessibility; equipment size capability; scratch-resistant surfaces; capabilities for electrical, mechanical, vacuum, and fluid support during maintenance; and the accommodation of diagnostic equipment [ 20 ]. Only capabilities for fabrication were listed lower than stowage. Yet 50 of the 60 contexts (83%) fell within stable periods where little or no activity is identifiable in Square 03. According to the sample results, therefore, this area seems to exist not for “maintenance,” but primarily for the storage and arrangement of items. The most recent update of the design standards does not mention the MWA, but states, “Stowage location of tool kits should be optimized for accessibility to workstations and/or maintenance workbenches” [ 25 ]. Our observation confirms the importance of this suggestion.

The MWA was also a flexible location for certain science work, like the concrete study or crew health monitoring. Actual maintenance of equipment was hardly in evidence in the sample (possibly contexts 25, 39, and 44), and may not even have happened at all in this location. Some training did happen here, such as review of procedures for the Electromagnetic Levitator camera (instructions for changing settings on a high-speed camera appeared on the laptop screen; the day’s DSR shows that this camera is part of the Electromagnetic Levitator facility, located in the Columbus module [ 41 ]. The training required the use of the Hololens system (context 28 DSR, cited above).

Although many item types were represented in Square 03, it became clear during data capture how many things were basically static, unmoving and therefore unused, especially certain tools, writing implements, and body maintenance items. The MWA was seen as an appropriate place to store these items. It may be the case that their presence here also indicates that their function was seen as an appropriate one for this space, but the function(s) may not be carried out—or perhaps not in this location. Actualization of object function was only visible to us when the state of the item changed—it appeared, it moved, it changed orientation, it disappeared, or, in the case of artifacts that were grouped in collections rather than found as singletons, its shape changed or it became visibly smaller/lesser. We therefore have the opportunity to explore not only actuality of object use, but also potentiality of use or function, and the meaning of that quality for archaeological interpretation [ 42 , 43 ]. This possibility is particularly intriguing in light of the archaeological turn towards recognizing the agency of objects to impact human activity [ 44 , 45 ]. We will explore these implications in a future publication.

We performed the same chronological analysis for Square 05. Fig 6(B) represents the analysis for both item types and for item functions. We identified three major phases of activity, corresponding to contexts 0–5, 6–52, and 53–59 (S9-S11 Figs in S3 File ). The primary characteristics of these phases relate to an early period of unclear associations (0–5) marked by the presence of rolls of adhesive tape and a few body maintenance items (toothpaste and toothbrush, wet wipes); the appearance of a toiletry kit on the right side of the sample area, fully open with clear views of many of the items contained within (6–52); and finally, the closure of the toiletry kit so that its contents can no longer be seen (53–59). We interpret the phases as follows:

Contexts 0–5: In Period 1 (six days, S9 Fig in S3 File ), while items such as a mirror, dental floss picks, wet wipes, and a toothbrush held in the end of a toothpaste tube were visible, the presence of various other kinds of items confounds easy interpretation. Two rolls of duct tape were stored on the handrail in the center of the sample area, and the Touch Array and laptop appeared in the center. Little movement can be identified, apart from a blue nitrile glove that appeared in context 1 and moved left across the area until it was wedged into the bungee cord for contexts 3 and 4. The tape rolls were removed prior to context 5. A collection of resealable bags was wedged behind the handrail in context 3, remaining there until context 9. Overall, this appears to be a period characterized by eclectic associations, showing an area without a clear designated function.
Contexts 6–52: Period 2 (S10 Fig in S3 File ) is clearly the most significant one for this location due to its duration (47 days). It was dominated by the number of body maintenance items located in and around the toiletry kit, especially a white hand towel (on which a brown stain was visible from context 11, allowing us to confirm that the same towel was present until context 46). A second towel appeared alongside the toiletry kit in context 47, and the first one was fixed at the same time to the handrail, where it remained through the end of the sample period. A third towel appeared in context 52, attached to the handrail together with the first one by a bungee cord, continuing to the end of the sample period. Individual body maintenance items moved frequently from one context to the next, showing the importance of this type of activity for this part of Node 3. For reasons that are unclear, the mirror shifted orientation from vertical to diagonal in context 22, and then was put back in a vertical orientation in context 31 (a Monday, a day which is not traditionally associated with cleaning and organization). Collections of resealable bags appeared at various times, including a large one labeled “KYNAR BAG OF ZIPLOCKS” in green marker at the upper left part of the sample area beginning of context 12 (Kynar is a non-flammable plastic material that NASA prefers for resealable bags to the generic commercial off-the-shelf variety because it is non-flammable; however, its resistance to heat makes it less desirable for creating custom sizes, so bags made from traditional but flammable low-density polyethylene still dominate on the ISS [ 14 ]). The Kynar bag contained varying numbers of bags within it over time; occasionally, it appeared to be empty. The Touch Array changed orientation on seven of 47 days in period 2, or 15% of the time (12% of all days in the survey), showing activity associated with scientific research in this area. In context 49, a life-support item, the Airborne Particulate Monitor (APM) was installed [ 46 ]. This device, which measures “real-time particulate data” to assess hazards to crew health [ 47 ], persisted through the end of the sample period.
Contexts 53–59: Period 3 (S11 Fig in S3 File ) appears as a seven-day phase marked by low activity. Visually, the most notable feature is the closure of the toiletry kit, which led to much lower counts of body maintenance items. Hardly any of the items on the wall moved at all during this period.

While body maintenance in the form of cleaning and caring for oneself could be an expected function for an area with exercise and excretion facilities, it is worth noting that the ISS provides, at most, minimal accommodation for this activity. A description of the WHC stated, “To provide privacy…an enclosure was added to the front of the rack. This enclosure, referred to as the Cabin, is approximately the size of a typical bathroom stall and provides room for system consumables and hygiene item stowage. Space is available to also support limited hygiene functions such as hand and body washing” [ 48 ]. A diagram of the WHC in the same publication shows the Cabin without a scale but suggests that it measures roughly 2 m (h) x .75 (w) x .75 (d), a volume of approximately 1.125 m 3 . NASA’s current design standards state that the body volume of a 95th percentile male astronaut is 0.99 m 3 [ 20 ], meaning that a person of that size would take up 88% of the space of the Cabin, leaving little room for performing cleaning functions—especially if the Cabin is used as apparently intended, to also hold “system consumables and hygiene item[s]” that would further diminish the usable volume. This situation explains why crews try to adapt other spaces, such as storage areas like the PMM, for these activities instead. According to the crew debriefing statement, only one of them used the WHC for body maintenance purposes; it is not clear whether the toiletry kit belonged to that individual. But the appearance of the toiletry kit in Square 05—outside of the WHC, in a public space where others frequently pass by—may have been a response to the limitations of the WHC Cabin. It suggests a need for designers to re-evaluate affordances for body maintenance practices and storage for related items.

Although Square 03 and 05 were different sizes and shapes, comparing the density of items by function shows evidence of their usage ( Table 3 ). The typical context in Square 03 had twice as many restraints and containers, but less than one-quarter as many body maintenance items as Square 05. 03 also had many tools, lights, audiovisual equipment, and writing implements, while there were none of any of these types in 05. 05 had life support and hygiene items which were missing from 03. It appears that flexibility and multifunctionality were key elements for 03, while in 05 there was emphasis on one primary function (albeit an improvised one, designated by the crew rather than architects or ground control), cleaning and caring for one’s body, with a secondary function of housing static equipment for crew hygiene and life support.

https://doi.org/10.1371/journal.pone.0304229.t003

As this is the first time such an analysis has been performed, it is not yet possible to say how typical or unusual these squares are regarding the types of activities taking place; but they provide a baseline for eventual comparison with the other four squares and future work on ISS or other space habitats.

Some general characteristics are revealed by archaeological analysis of a space station’s material culture. First, even in a small, enclosed site, occupied by only a few people over a relatively short sample period, we can observe divergent patterns for different locations and activity phases. Second, while distinct functions are apparent for these two squares, they are not the functions that we expected prior to this research. As a result, our work fulfills the promise of the archaeological approach to understanding life in a space station by revealing new, previously unrecognized phenomena relating to life and work on the ISS. There is now systematically recorded archaeological data for a space habitat.

Squares 03 and 05 served quite different purposes. The reasons for this fact are their respective affordances and their locations relative to activity areas designated for science and exercise. Their national associations, especially the manifestation of the control wielded by NASA over its modules, also played a role in the use of certain materials, the placement of facilities, and the organization of work. How each area was used was also the result of an interplay between the original plans developed by mission planners and habitat designers (or the lack of such plans), the utility of the equipment and architecture in each location, and the contingent needs of the crew as they lived in the station. This interplay became visible in the station’s material culture, as certain areas were associated with particular behaviors, over time and through tradition—over the long duration across many crews (Node 2, location of Square 03, docked with the ISS in 2007, and Node 3, location of Square 05, docked in 2010), and during the specific period of this survey, from January to March 2022. During the crew debriefing, one astronaut said, “We were a pretty organized crew who was also pretty much on the same page about how to do things…. As time went on…we organized the lab and kind of got on the same page about where we put things and how we’re going to do things.” This statement shows how functional associations can become linked to different areas of the ISS through usage and mutual agreement. At the same time, the station is not frozen in time. Different people have divergent ideas about how and where to do things. It seems from the appearance of just one Russian item—a packet of generic wipes ( salfetky sukhiye ) stored in the toiletry kit throughout the sample period—that the people who used these spaces and carried out their functions did not typically include the ISS’s Russian crew. Enabling greater flexibility to define how spaces can be used could have a significant impact on improving crew autonomy over their lives, such as how and where to work. It could also lead to opening of all spaces within a habitat to the entire crew, which seems likely to improve general well-being.

An apparent disjunction between planned and actual usage appeared in Square 03. It is intended for maintenance as well as other kinds of work. But much of the time, there was nobody working here—a fact that is not captured by historic photos of the area, precisely because nothing is happening. The space has instead become the equivalent of a pegboard mounted on a wall in a home garage or shed, convenient for storage for all kinds of items—not necessarily items being used there—because it has an enormous number of attachment points. Storage has become its primary function. Designers of future workstations in space should consider that they might need to optimize for functions other than work, because most of the time, there might not be any work happening there. They could optimize for quick storage, considering whether to impose a system of organization, or allow users to organize as they want.

We expected from previous (though unsystematic) observation of historic photos and other research, that resealable plastic bags (combined with Velcro patches on the bags and walls) would be the primary means for creating gravity surrogates to control items in microgravity. They only comprise 7% of all items in Square 03 (256 instances). There are more than twice as many clips (572—more than 9 per context) in the sample. There were 193 instances of adhesive tape rolls, and more than 100 cable ties, but these were latent (not holding anything), representing potentiality of restraint rather than actualization. The squares showed different approaches to managing “gravity.” While Square 03 had a pre-existing structured array of Velcro patches, Square 05 showed a more expedient strategy with Velcro added in response to particular activities. Different needs require different affordances; creating “gravity” is a more nuanced endeavor than it initially appears. More work remains to be done to optimize gravity surrogates for future space habitats, because this is evidently one of the most critical adaptations that crews have to make in microgravity (44% of all items in Square 03, 39% in 05).

Square 05 is an empty space, seemingly just one side of a passageway for people going to use the lifting machine or the latrine, to look out of the Cupola, or get something out of deep storage in one of the ISS’s closets. In our survey, this square was a storage place for toiletries, resealable bags, and a computer that never (or almost never) gets used. It was associated with computing and hygiene simply by virtue of its location, rather than due to any particular facilities it possessed. It has no affordances for storage. There are no cabinets or drawers, as would be appropriate for organizing and holding crew personal items. A crew member decided that this was an appropriate place to leave their toiletry kit for almost two months. Whether this choice was appreciated or resented by fellow crew members cannot be discerned based on our evidence, but it seems to have been tolerated, given its long duration. The location of the other four USOS crew members’ toiletry kits during the sample period is unknown. A question raised by our observations is: how might a function be more clearly defined by designers for this area, perhaps by providing lockers for individual crew members to store their toiletries and towels? This would have a benefit not only for reducing clutter, but also for reducing exposure of toiletry kits and the items stored in them to flying sweat from the exercise equipment or other waste particles from the latrine. A larger compartment providing privacy for body maintenance and a greater range of motion would also be desirable.

As the first systematic collection of archaeological data from a space site outside Earth, this analysis of two areas on the ISS as part of the SQuARE payload has shown that novel insights into material culture use can be obtained, such as the use of wall areas as storage or staging posts between activities, the accretion of objects associated with different functions, and the complexity of using material replacements for gravity. These results enable better space station design and raise new questions that will be addressed through analysis of the remaining four squares.

Supporting information

S1 movie. nasa astronaut kayla barron installs the first square for the sampling quadrangle assemblages research experiment in the japanese experiment module (also known as kibo) on the international space station, january 14, 2022..

She places Kapton tape to mark the square’s upper right corner. Credit: NASA.

https://doi.org/10.1371/journal.pone.0304229.s001

S1 Dataset.

https://doi.org/10.1371/journal.pone.0304229.s002

S2 Dataset.

https://doi.org/10.1371/journal.pone.0304229.s003

S3 Dataset. The image annotations are represented according to sample square in json formatted text files.

The data is available in the ‘SQuARE-notebooks’ repository on Github.com in the ‘data’ subfolder at https://github.com/issarchaeologicalproject/SQuARE-notebooks/tree/main ; archived version of the repository is at Zenodo, DOI: 10.5281/zenodo.10654812 .

https://doi.org/10.1371/journal.pone.0304229.s004

S1 File. The ‘Rocket-Anno’ image annotation software is available on Github at https://github.com/issarchaeologicalproject/MRE-RocketAnno .

The archived version of the repository is at Zenodo, DOI: 10.5281/zenodo.10648399 .

https://doi.org/10.1371/journal.pone.0304229.s005

S2 File. The computational notebooks that process the data json files to reshape the data suitable for basic statistics as well as the computation of the Brainerd-Robinson coefficients of similarity are in the.ipynb notebook format.

The code is available in the ‘SQuARE-notebooks’ repository on Github.com in the ‘notebooks’ subfolder at https://github.com/issarchaeologicalproject/SQuARE-notebooks/tree/main ; archived version of the repository is at Zenodo, DOI: 10.5281/zenodo.10654812 . The software can be run online in the Google Colab environment ( https://colab.research.google.com ) or any system running Jupyter Notebooks ( https://jupyter.org/ ).

https://doi.org/10.1371/journal.pone.0304229.s006

https://doi.org/10.1371/journal.pone.0304229.s007

Acknowledgments

We thank Chapman University’s Office of Research and Sponsored Programs, and especially Dr. Thomas Piechota and Dr. Janeen Hill, for funding the Implementation Partner costs associated with the SQuARE payload. Chapman’s Leatherby Libraries’ Supporting Open Access Research and Scholarship (SOARS) program funded the article processing fee for this publication. Ken Savin and Ken Shields at the ISS National Laboratory gave major support by agreeing to sponsor SQuARE and providing access to ISS NL’s allocation of crew time. David Zuniga and Kryn Ambs at Axiom Space were key collaborators in managing payload logistics. NASA staff and contractors were critical to the experiment’s success, especially Kristen Fortson, Jay Weber, Crissy Canerday, Sierra Wolbert, and Jade Conway. We also gratefully acknowledge the help and resources provided by Dr. Erik Linstead, director of the Machine Learning and Affiliated Technology Lab at Chapman University. Aidan St. P. Walsh corrected the color and lens barrel distortion in all of the SQuARE imagery. Rao Hamza Ali produced charts using accessible color combinations for Figs 3 and 5 . And finally, of course, we are extremely appreciative of the efforts of the five USOS members of the Expedition 66 crew on the ISS—Kayla Barron, Raja Chari, Thomas Marshburn, Matthias Maurer, and Mark Vande Hei—who were the first archaeologists in space.

1. Buchli V. Extraterrestrial methods: Towards an ethnography of the ISS. In: Carroll T, Walford A, Walton S, editors. Lineages and advancements in material culture studies: Perspectives from UCL anthropology. London: Routledge; 2021, pp. 17–32.
2. Gorman A, Walsh J. Archaeology in a vacuum: obstacles to and solutions for developing a real space archaeology. In: Barnard H, editor. Archaeology outside the box: investigations at the edge of the discipline. Los Angeles, Cotsen Institute of Archaeology Press; 2023. pp. 131–123.
3. Walsh J. Adapting to space: The International Space Station Archaeological Project. In: Salazar Sutil JF, Gorman A, editors. Routledge handbook of social studies of outer space. London, Routledge; 2023. pp. 400–412. https://doi.org/10.4324/9781003280507-37
View Article
Google Scholar
6. Rathje W, Murphy C. Rubbish! The archaeology of garbage Tucson: University of Arizona Press; 2001.
7. De León J. The land of open graves: living and dying on the migrant trail. Berkeley, University of California Press; 2015.
8. Garrison Darrin A, O’Leary B, editors. Handbook of space engineering, archaeology, and heritage. Boca Raton, CRC Press; 2009.
9. Capelotti PJ. The human archaeology of space: Lunar, planetary, and interstellar relics of exploration. Jefferson, NC, McFarland Press; 2010.
11. Gorman A. Space and time through material culture: An account of space archaeology. In: Salazar Sutil JF, Gorman A, editors. Routledge handbook of social studies of outer space. London, Routledge; 2023. pp. 44–56. https://doi.org/10.4324/9781003280507-5
17. NASA. NASA Johnson. 2008 Aug [cited May 12 2024]. In: Flickr [Internet]. San Francisco. Available from https://www.flickr.com/photos/nasa2explore/
19. NASA. ISS Daily Status Reports. 2012 Mar 1 [Cited May 12 2024]. Available from: https://blogs.nasa.gov/stationreport/
20. NASA. Man-systems integration. STD-3000 Vol. 1. Houston, NASA Johnson; 1995, pp. 9–15, 78
21. NASA. Maintenance Work Area | Glenn Research Center. 2020 Mar 6 [cited May 12 2024]. Available from: https://www1.grc.nasa.gov/space/iss-research/mwa/
22. Cristoforetti S. Diario di un’apprendista astronauta. Milan, Le Polene; 2018. pp. 379.
23. Kelly S. Endurance: A year in space, a lifetime of discovery. New York, Knopf; 2017. pp. 175, 285–86.
24. Barron K. Instagram post, 2022 Feb 12 [cited 2024 May 12]. Available from: https://www.instagram.com/tv/CZ4pW9HJ2Wg/?igsh=ZDE1MWVjZGVmZQ==
25. NASA. NASA space flight human-system standard. STD-3001 Volume 1: Human integration design handbook. Rev. 1 Houston, NASA Johnson; 2014. pp. 814, 829–833.
27. Keeter B. ISS daily summary report– 2/21/2022. 2022 Feb 21 [cited May 12 2024]. In: NASA ISS On-Orbit Status Report blog [Internet]. Houston. Available from: https://blogs.nasa.gov/stationreport/2022/02/page/6/
28. DLR. Fingerprint research to combat harmful bacteria. 2022 Jan 18 [cited May 12 2024]. Available from: https://www.dlr.de/en/latest/news/2022/01/20220118_fingerprint-research-to-combat-harmful-bacteria
31. Peeples MA. R script for calculating the Brainerd-Robinson coefficient of similarity and assessing sampling error. 2011 [cited May 12 2024]. Available from: http://www.mattpeeples.net/br.html .
33. Garcia M. Cargo Dragon Splashes Down Ending SpaceX CRS-24 Mission. 2022 Jan 24 [cited May 12 2024]. NASA Space Station blog [Internet]. Available from: https://blogs.nasa.gov/spacestation/2022/01/24/cargo-dragon-splashes-down-ending-spacex-crs-24-mission/
34. ESA. Concrete Hardening | Cosmic Kiss 360°. 2022 Mar 5 [cited May 12 2024]. Available from: https://www.esa.int/ESA_Multimedia/Videos/2022/05/Concrete_Hardening_Cosmic_Kiss_360
35. Keeter B. ISS daily summary report– 2/01/2022. 2022 Feb 1 [cited May 12 2024]. In: NASA ISS On-Orbit Status Report blog [Internet]. Houston. Available from: https://blogs.nasa.gov/stationreport/2022/02/page/19/
36. Keeter B. ISS daily summary report– 2/17/2022. 2022 Feb 17 [cited May 12 2024]. In: NASA ISS On-Orbit Status Report blog [Internet]. Houston. Available from: https://blogs.nasa.gov/stationreport/2022/02/page/8/
37. T. Pultarova, How Do You Clean a Space Station? Astronaut Thomas Pesquet Shares Orbital Spring Cleaning Tips, Space.com, May 6, 2021. Online at https://www.space.com/space-station-cleaning-tips-astronaut-thomas-pesquet
38. Keeter B. ISS daily summary report– 2/22/2022. 2022 Feb 22 [cited May 12 2024]. In: NASA ISS On-Orbit Status Report blog [Internet]. Houston. Available from: https://blogs.nasa.gov/stationreport/2022/02/page/5/
39. Keeter B. ISS daily summary report– 2/02/2022. 2022 Feb 2 [cited May 12 2024]. NASA ISS On-Orbit Status Report blog [Internet]. Houston. Online at https://blogs.nasa.gov/stationreport/2022/02/page/18/
40. Keeter B. ISS daily summary report– 3/03/2022. 2022 Mar 3 [cited May 12 2024]. In: NASA ISS On-Orbit Status Report blog [Internet]. Houston. Available from: https://blogs.nasa.gov/stationreport/2022/03/page/21/
41. Keeter B. ISS daily summary report– 2/08/2022. 2022 Feb 8 [cited May 12 2024]. NASA ISS On-Orbit Status Report blog [Internet]. Houston. Available from: https://blogs.nasa.gov/stationreport/2022/02/page/15/
42. Aristotle of Stageira. Metaphysics, Volume I: Books 1–9, Tredennick H, translator. Loeb Classical Library 271. Cambridge, MA, Harvard University Press; 1933. pp. 429–473.
44. Hodder I. Entangled: An archaeology of the relationships between humans and things. Hoboken. NJ, Wiley-Blackwell; 2012.
45. Malafouris L., How Things Shape the Mind: A Theory of Material Engagement (MIT Press, 2016).
46. Keeter B. ISS daily summary report– 3/11/2022. 2022 Mar 11 [cited May 12 2024]. NASA ISS On-Orbit Status Report blog [Internet]. Houston. Available from: https://blogs.nasa.gov/stationreport/2022/03/page/15/

Title Page Setup

A title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages.

Student title page

The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.

Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6

Related handouts

Student Title Page Guide (PDF, 263KB)
Student Paper Setup Guide (PDF, 3MB)

Student papers do not include a running head unless requested by the instructor or institution.

Follow the guidelines described next to format each element of the student title page.


Paper title	Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms.
Author names	Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name.	Cecily J. Sinclair and Adam Gonzaga
Author affiliation	For a student paper, the affiliation is the institution where the student attends school. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author name(s).	Department of Psychology, University of Georgia
Course number and name	Provide the course number as shown on instructional materials, followed by a colon and the course name. Center the course number and name on the next double-spaced line after the author affiliation.	PSY 201: Introduction to Psychology
Instructor name	Provide the name of the instructor for the course using the format shown on instructional materials. Center the instructor name on the next double-spaced line after the course number and name.	Dr. Rowan J. Estes
Assignment due date	Provide the due date for the assignment. Center the due date on the next double-spaced line after the instructor name. Use the date format commonly used in your country.	October 18, 2020 18 October 2020
	Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header.	1

Professional title page

The professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example.

Follow the guidelines described next to format each element of the professional title page.


Paper title	Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms.
Author names	Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name.	Francesca Humboldt
Author names	When different authors have different affiliations, use superscript numerals after author names to connect the names to the appropriate affiliation(s). If all authors have the same affiliation, superscript numerals are not used (see Section 2.3 of the for more on how to set up bylines and affiliations).	Tracy Reuter , Arielle Borovsky , and Casey Lew-Williams
Author affiliation	For a professional paper, the affiliation is the institution at which the research was conducted. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author names; when there are multiple affiliations, center each affiliation on its own line.	Department of Nursing, Morrigan University
Author affiliation	When different authors have different affiliations, use superscript numerals before affiliations to connect the affiliations to the appropriate author(s). Do not use superscript numerals if all authors share the same affiliations (see Section 2.3 of the for more).	Department of Psychology, Princeton University Department of Speech, Language, and Hearing Sciences, Purdue University
Author note	Place the author note in the bottom half of the title page. Center and bold the label “Author Note.” Align the paragraphs of the author note to the left. For further information on the contents of the author note, see Section 2.7 of the .	n/a
	The running head appears in all-capital letters in the page header of all pages, including the title page. Align the running head to the left margin. Do not use the label “Running head:” before the running head.	Prediction errors support children’s word learning
	Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header.	1

Research article
Open access
Published: 12 August 2024

Viral shedding and environmental dispersion of two clade 2.3.4.4b H5 high pathogenicity avian influenza viruses in experimentally infected mule ducks: implications for environmental sampling

Fabien Filaire 1 , 2 na1 ,
Kateri Bertran 3 , 4 na1 ,
Nicolas Gaide 1 ,
Rosa Valle 3 , 4 ,
Aurélie Secula 1 ,
Albert Perlas 3 , 4 ,
Charlotte Foret-Lucas 1 ,
Miquel Nofrarías 3 , 4 ,
Guillermo Cantero 3 , 4 ,
Guillaume Croville 1 ,
Natàlia Majó 3 , 5 na2 &
Jean-Luc Guerin ORCID: orcid.org/0000-0001-7770-4012 1 na2

Veterinary Research volume 55 , Article number: 100 ( 2024 ) Cite this article

35 Accesses

1 Altmetric

Metrics details

High pathogenicity avian influenza viruses (HPAIVs) have caused major epizootics in recent years, with devastating consequences for poultry and wildlife worldwide. Domestic and wild ducks can be highly susceptible to HPAIVs, and infection leads to efficient viral replication and massive shedding (i.e., high titres for an extended time), contributing to widespread viral dissemination. Importantly, ducks are known to shed high amounts of virus in the earliest phase of infection, but the dynamics and impact of environmental contamination on the epidemiology of HPAIV outbreaks are poorly understood. In this study, we monitored mule ducks experimentally infected with two H5N8 clade 2.3.4.4b goose/Guangdong HPAIVs sampled in France in 2016–2017 and 2020–2021 epizootics. We investigated viral shedding dynamics in the oropharynx, cloaca, conjunctiva, and feathers; bird-to-bird viral transmission; and the role of the environment in viral spread and as a source of samples for early detection and surveillance. Our findings showed that viral shedding started before the onset of clinical signs, i.e., as early as 1 day post-inoculation (dpi) or post-contact exposure, peaked at 4 dpi, and lasted for up to 14 dpi. The detection of viral RNA in aerosols, dust, and water samples mirrored viral shedding dynamics, and viral isolation from these environmental samples was successful throughout the experiment. Our results confirm that mule ducks can shed high HPAIV titres through the four excretion routes tested (oropharyngeal, cloacal, conjunctival, and feather) while being asymptomatic and that environmental sampling could be a non-invasive tool for early viral RNA detection in HPAIV-infected farms.

Introduction

High pathogenicity avian influenza virus (HPAIV) has become a major threat for the poultry industry and wildlife biodiversity since the worldwide spread of the emerging clade 2.3.4.4b derived from the A/goose/Guangdong/1/1996 (Gs/GD) H5 lineage [ 1 , 2 , 3 ]. These viruses affect a remarkably wide range of bird species, which could explain the massive viral dissemination in wild bird populations globally [ 1 , 4 ]. Among poultry species, waterfowl are more susceptible to clade 2.3.4.4b viruses than are gallinaceous birds [ 5 , 6 , 7 ]. Ducks were found to have lower mean bird infectious and lethal doses than chickens when infected with clade 2.3.4.4b virus but still exhibited high viral shedding during the course of the infection [ 7 ]. These features, which are typically associated with mild to low clinical signs upon infection, play a crucial role in the epidemiology of HPAI in duck farming [ 5 , 6 , 8 , 9 ]. Once infected birds shed the virus, the environment plays a major role in viral persistence and viral spread; both airborne and waterborne viral transmission are thought to drive intra- and inter-flock dissemination [ 10 , 11 , 12 ]. Since the virus is present in the environment, environmental sampling could be an alternative to bird swabbing for HPAIV monitoring since it is non-invasive for birds, less time-consuming, and does not require the intervention of trained staff. Field investigations by our group suggested that the collection of aerosols, or even dust, allows for the detection of high viral loads, even early in the course of infection at the flock level [ 13 ].

Here, we investigated the kinetics of viral shedding and environmental contamination in ducks experimentally infected with clade 2.3.4.4b H5N8 HPAIVs. We used two different strains isolated from French outbreaks in 2017 and 2020 to evaluate potential changes in their biological properties. Our objective was to assess the potential application of environmental sampling and the impact of the persistence and dissemination of virus particles in the environment of poultry houses.

Materials and methods

Two clade 2.3.4.4b Gs/GD lineage H5N8 HPAIV isolates were used as challenge viruses: A/mulard duck/France/171201 g/2017 (H5N8) HPAIV (H5N8/2017) reverse genetics-engineered (accession numbers MK859904 to MK859911) [ 14 ] and A/Mule_duck/France/20353/2020 (H5N8/2020) (accession numbers MZ166297 to MZ166304). H5N8/2020 was obtained from pooled feather samples from an infected duck farm in France during the 2020–2021 epizootic. Both viruses were propagated and titrated by allantoic sac inoculation in 9- to 11-day-old specific-pathogen-free (SPF) embryonated chicken eggs by standard methods [ 15 ].

Animals and housing

Thirty-two 3-week-old mule ducks ( Cairina moschata × Anas platyrhynchos ) were obtained from a commercial producer (courtesy of Manel Vinyes, GALLSA, Tarragona, Spain). Birds were randomly allocated into two rooms in the animal BSL-3 facilities. The birds had ad libitum access to feed and water. Each room was provided with a swimming pool 1.5 m in diameter and 30 cm in depth with an access ramp. Oropharyngeal (OP) and cloacal (CL) swabs and blood samples were collected from all the birds prior to inoculation. Current infection was tested by qRT-PCR in swab samples [ 16 ], and the presence of antibodies from previous exposure was determined by competitive ELISA (AI MultiS-Screen Ab Test, IDEXX) and hemagglutination inhibition (HI) assays [ 15 ].

Experimental design, clinical monitoring, and biological sampling

After 1 week of acclimation, 10 birds per room were intrachoanally inoculated with 10 5 mean embryo infectious doses of either H5N8/2017 or H5N8/2020. To evaluate viral transmission, six non-inoculated ducks were added to each room at 1 day post-inoculation (dpi). Clinical signs were monitored daily for 14 dpi.

The following clinical scoring system was used [ 17 ]: 0 (healthy), 1 (sick with one HPAIV typical clinical sign), 2 (severely sick with two or more HPAIV typical clinical signs), and 3 (dead). Severely sick birds were euthanized by intravenous overdose of sodium pentobarbital (140 mg/kg) under intravenous anaesthesia with ketamine/xylazine (201 mg/kg) and counted as dead the next day. At 14 dpi, the surviving birds were bled and euthanized. To investigate individual viral shedding, oropharyngeal (OP), cloacal (CL), and conjunctival (CJ) swabs, as well as feather pulp (FP) samples, were collected from inoculated and contact birds at 2, 4, 7, 10, and 14 dpi. For the CJ samples, the conjunctival mucosa of the birds was gently swabbed. Immature wing or caudal feathers were sampled for FP extraction.

Sera from all surviving birds collected at 14 dpi were tested by a commercial competitive ELISA (Innovative Diagnostics, Grabel, France) to evaluate seroconversion and by an HI assay to detect antibody levels. The HI assay was performed using standard methods and homologous antigens [ 15 ].

Environmental sampling

To evaluate viral contamination in the environment from experimentally inoculated and contact ducks, aerosol, dust, and water samples were collected. Aerosol sampling was performed using two dry cyclonic air samplers: the Coriolis Compact (Bertin Technologies, Montigny-le-Bretonneux, France) and the National Institute of Occupational Safety and Health (NIOSH) BC 251. Briefly, the Coriolis compact sampler, with a 50 L/min calibrated flow rate, enables the dry collection of all aerosol particles ranging from 500 nm to 10 µm in size. The 2-stage bioaerosol cyclone (BC) NIOSH BC 251 sampler, with a calibrated 3.5 L/min flow rate, enables dry collection and sorting of the aerosol particles into > 4 µm, 1–4 µm, and < 1 µm fractions. Thus, aerosols are separated into a 15 mL tube for the largest fraction, a 1.5 mL tube for the medium fraction, and a 37 mm diameter polytetrafluoroethylene (PTFE) filter with a 1.5 µm pore for the smallest fraction. Both instruments were used simultaneously at every sampling time point—20 min for the Coriolis Compact and 40 min to 1 h for the NIOSH BC 251—at approximately 1 m above ground and 2 m away from each other to avoid air flow interference.

Dust samples were collected using dry gauzes, one on the fences and one on the feeders, which were immediately placed in individual sealable bags. The aim was to collect the maximum amount of dust from these surfaces while avoiding contamination with food and feces, which could inhibit molecular analysis.

Pool water samples and drinking water samples were collected using 50 mL Falcon tubes. While the pool water was only refilled when needed and not renewed at any time point during the experiment, the drinking water was changed daily after the sampling.

All environmental sampling was performed in each group at 1, 2, 3, 4, 5, 7, 10, and 14 dpi. Additionally, aerosol sampling with the Coriolis Compact was performed before challenge as a negative control. All samples were stored at 4 °C for up to 2 days post-sampling and at -80 °C until processing.

An overview of the experimental design is summarized in Figure 1 .

General overview of the experimental design. Blood and biological sampling were performed on all living birds on the day of the sampling. Aerosols were collected using the Coriolis Compact (Bertin Technologies) and the National Institute of Occupational Safety and Health (NIOSH) BC 251. Dust was independently sampled on the walls and feeders using gauzes. Pool water and drinking water were collected using 50 mL Falcon tubes. OP oropharyngeal swabs, CL cloacal swabs, CJ conjunctival swabs, FP feather pulp, dpi days post-inoculation.

Processing methods

Before RNA extraction, all the samples were prepared as follows. The swabs (OP, CL, and CJ) and FPs were individually placed into single 1.5 mL centrifuge tubes filled with 500 µL of 1X phosphate buffered saline (PBS) and vigorously vortexed for 10–15 s.

Aerosols collected with the Coriolis Compact sampler and the two largest particle sizes of the NIOSH BC 251 sampler were resuspended in 1 mL of PBS. The collection tubes were vigorously vortexed for 5–15 s to remove particles from the tube walls and edges. When needed, up-and-down pipetting was additionally performed to detach the particles from the tube’s edges. The NIOSH BC 251 third fraction membrane filter was carefully removed from the cassette and placed into a dry 50 mL collection tube. The filter was dry vortexed for 10–15 s before adding 1.5 mL of PBS and then vortexed again. The aerosol liquid resuspension was aliquoted into a 1.5 mL centrifuge tube. Gauze with dust was processed by adding 20 mL of PBS to sealable bags, hand-massaging the contents for 2–3 min, and collecting and aliquoting the supernatants into 1.5 mL centrifuge tubes. Pool and drinking water samples were aliquoted into 2 mL centrifuge tubes.

All samples were stored at −80 °C at IRTA-CReSA for the duration of the experiment and then shipped to the National Veterinary School of Toulouse BSL-3 for analysis.

Viral RNA detection

Total RNA from all samples (bird samples and environmental samples individually) was extracted using the magnetic bead-based ID Gene Mag Fast Extraction Kit (Innovative Diagnostics, Grabel, France) associated with the KingFisher 96 automated magnetic extraction robot (Thermo Fisher, Carlsbad, CA, USA) following the manufacturer’s instructions. The presence of AIV RNA from the H5 subtype was detected by performing 1-step real-time reverse transcription quantitative PCR (rRT-qPCR) using an influenza H5/H7 Triplex kit (Innovative Diagnostics, Grabel, France) (Additional file 1 ). The rRT-qPCR amplification procedure consisted of 40 cycles. Unamplified vRNA was considered negative.

Virus isolation

To assess the extent and duration of HPAIV environmental spread, the presence of infectious particles was determined in RNA-positive aerosol, water, and dust samples. Both drinking water and pool water were used for viral isolation. For aerosols and dust, samples with higher RNA yields were selected. Therefore, Coriolis aerosols and wall dust samples were selected for viral isolation from aerosols and dust, respectively.

Virus isolation was conducted in 9- to 11-day-old SPF embryonated chicken eggs. Briefly, 200 µL of each sample was mixed with 400 µL of penicillin (1000 U/mL) and streptomycin (1 mg/mL). Three eggs per sample were inoculated with 150 µL of the solution and kept in a humidity chamber at 37 °C for 48 h before being placed at 4 °C for 12 h. Allantoic fluid was collected from each egg and directly analysed via RT-qPCR targeting the H5 subtype (Influenza H5/H7 Triplex kit, Innovative Diagnostics, France) and a hemagglutination assay [ 15 ]. Up to three successive passages per sample were performed.

Data analysis

All analyses were performed using R Statistical Software version 4.1.1 [ 18 ]. Statistical analyses were performed using the nlme package [ 19 ]. Figures were made using the ggplot2 package [ 20 ].

Clinical signs and mortality

All the acclimated birds were confirmed to be AIV PCR negative and AIV serologically negative by both HI and ELISA and were clinically healthy before challenge. Clinical signs started at 3 dpi and 4 dpi for H5/2017- and H5/2020-inoculated birds, respectively. Clinical signs included non-specific depression to prostration and inability to stand upright, neurological signs (ataxia, head tremor, head tilt), and unresponsiveness to visual stimuli. For inoculated birds, mortality started at 5 and 6 dpi for the H5N8/2017- and H5N8/2020-inoculated groups, respectively, and lasted for 4 and 2 days, respectively (Figures 2 and 3 ). Overall, the mortality rates of inoculated birds were 30% (H5N8/2017) and 20% (H5N8/2020), with mean death times (MDTs) of 7 days (H5N8/2017) and 6.5 days (H5N8/2020), respectively (Figure 2 ). The survival rate, analysed by the chi-square test, was not significantly different between the groups ( p > 0.05). For contact birds, mortality started at 7 dpi (i.e., 6 days post-contact exposure) with H5N8/2017 and 8 dpi (i.e., 7 days post-contact exposure) with H5N8/2020, and lasted for 5 days. The survival rate of contact birds was significantly greater in the H5N8/2020 group ( p < 0.05). Overall, the mortality rates of contact birds were 100% (H5N8/2017) and 33% (H5N8/2020), with MDTs of 8.2 days (H5N8/2017) and 10 days (H5N8/2020), respectively (Figure 2 ). All inoculated and contact surviving birds seroconverted following challenge, as confirmed by HI and ELISA. The antibody titres included log 2 8.4 geometrical mean titres (GMTs) (H5N8/2017 inoculated ducks), log 2 8.6 GMTs (H5N8/2020 inoculated ducks), and log 2 7.5 GMTs (H5N8/2020 contact ducks) (Additional file 2 ).

Evolution of clinical scoring over time for ducks experimentally infected with H5N8/2017 and H5N8/2020 HPAIVs . The H5N8/2017 and H5N8/2020 HPAIVs correspond to A/mulard duck/France/171201 g/2017 (H5N8) and A/Mule_duck/France/20353/2020, respectively. Each row represents a single bird. Birds were grouped based on the virus strain and exposure route (inoculated vs contact). 0: healthy (green), 1: sick with one HPAIV typical clinical sign (yellow), 2: severely sick with two or more HPAIV typical clinical signs (orange), 3: dead bird (found death or euthanasia) (red). dpi: days post-inoculation. *: severely sick birds euthanized for ethical reasons, **: birds that were found dead.

Evolution of the survival rate of ducks experimentally infected with H5N8/2017 and H5N8/2020 HPAIVs . The H5N8/2017 and H5N8/2020 HPAIVs correspond to A/mulard duck/France/171201 g/2017 (H5N8) and A/Mule_duck/France/20353/2020, respectively. A Percentage of survival in inoculated ducks. B Percentage of survival in contact ducks. dpi days post-inoculation.

Viral excretion in ducks

The overall profiles of excretion were similar regardless of the virus and infection route (inoculation vs contact) (Figure 4 ). In particular, viral RNA (vRNA) was first detected at 2 dpi, and viral shedding peaked at 4 dpi and gradually decreased until 10 dpi, when it stabilized until 14 dpi (Figures 4 and 5 ).

Viral shedding in oropharyngeal, cloacal, and conjunctival swabs and feather pulp samples with animal status information . Graph: dots and whiskers represent the mean amount of viral RNA detected and the standard deviation, respectively. Animal status: Each dot represents one bird. The health status of each bird is described in each panel. + vRNA positive detection, −: vRNA negative detection, dpi days post-inoculation, H5N8/2017_I H5N8/2017 inoculated birds, H5N8/2017_C H5N8/2017 contact birds, H5N8/2020_I H5N8/2020 inoculated birds, H5N8/2020_C H5N8/2020 contact birds.

Viral shedding in biological samples at each sampling time point. OP oropharyngeal swabs, CL cloacal swabs, CJ conjunctival swabs, FP feather pulp, dpi days post-inoculation, H5N8/2017_I H5N8/2017 inoculated birds, H5N8/2017_C H5N8/2017 contact birds, H5N8/2020_I H5N8/2020 inoculated birds, H5N8/2020_C H5N8/2020 contact birds, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. Statistical analysis: linear mixed ANOVA.

Viral RNA of both viruses was already detectable in OP swabs, CL swabs, CJ swabs, and FP samples at 2 dpi, in inoculated birds and in contact birds except for the H5N8/2017 FP samples (Figures 1 , 4 , and 5 ). At 2 dpi, the vRNA mean load was greater in the H5N8/2020 samples than in the H5N8/2017 samples (Figures 4 and 5 A) for all sample types except for the FP samples, with significant differences in the OP swabs (Figure 5 A). However, from 4 dpi onwards, the H5N8/2017 vRNA load was greater than that of H5N8/2020 in most samples and in both inoculated and contact birds (Figures 4 and 5 ). The H5N8/2020 vRNA load in FP and CL swabs was greater in contact birds than in inoculated birds from 4 to 14 dpi. This was also true for OP swabs, but only at 4 dpi and 7 dpi. For the H5N8/2017 samples, the vRNA load in OP and CJ swabs from contact birds was greater than that in inoculated birds at 4 and 7 dpi. From 10 dpi to the end of the experiment, FP, OP, and CJ samples enabled the detection of vRNA in more birds than CL swabs. The differences in Ct values between viruses may vary greatly, up to 10 Ct for feather pulp samples at 7 dpi.

Viral RNA detection in the environment

To investigate bird-to-bird transmission, viral shedding, and the role of the environment in the spread of the virus, dust, water (drinking and pool), and aerosol samples were collected at different time points and analysed by rRT-qPCR targeting the H5 subtype. The results are presented in Figure 6 .

Viral shedding monitoring in environmental samples. A Dust samples collected using gauzes on walls and feeders separately. B Water samples collected from the drinking tank and the pool. C aerosols collected using the Coriolis Compact (Bertin Technologies, Montigny-le-Bretonneux, France). D Aerosols collected using the NIOSH BC 251. The NIOSH BC 251 model separates aerosols based on their size: NIOSH 1 < 4 µm, NIOSH 2 1-to-4 µm, and NIOSH 3 < 1 µm. dpi: days postinoculation.

Viral RNA was first detected in environmental samples as early as 1 dpi in the H5N8/2020 Coriolis aerosol sample (C t : 35.05) and drinking water sample (C t : 31.37) and at 2 dpi in the H5N8/2017 samples. Overall, the kinetics for all the environmental samples were similar regardless of the virus. In particular, the vRNA load gradually increased and peaked at 5 dpi before decreasing until 10 dpi. From 10 to 14 dpi, the environmental vRNA load remained roughly stable. Dust samples from walls yielded higher vRNA loads than dust from feeders for both viruses (Figure 6 A). This kinetic trend was different for the pool water samples; for both viruses, the vRNA load peaked at 3 dpi (H5N8/2017 average C t : 26.9, sd: 0.65; H5N8/2020 average C t : 26.81, sd: 0.67) and remained stable until the end of the experiment (Figure 6 B, Additional files 3 and 4 ). Viral RNA detection in aerosol NIOSH samples had similar kinetics to that in Coriolis samples, even though the first positive samples were not detected before 3 dpi. Interestingly, for both viruses, the vRNA load decreased with increasing fraction size.

Virus isolation from environmental samples

To investigate the presence of infectious particles in the environment and their association with viral shedding and clinical signs, virus isolation from SPF chicken embryonated eggs was performed from a selection of PCR-positive samples and time points, i.e., aerosols (Coriolis), dust (walls), pool water, and drinking water (Table 1 ).

For the H5N8/2017 virus, very few PCR-positive samples yielded infectious viruses (Figure 6 , Table 1 and Additional file 3 ). The earliest recovery of the infectious virus from the drinking water occurred at 2 dpi. At 5 dpi, infectious particles could be isolated from dust, pool water, and drinking water. Coriolis aerosol samples only enabled virus isolation at 14 dpi, the only positive sample at this time point (Table 1 ).

In contrast to H5N8/2017 samples, H5N8/2020 environmental samples were successfully isolated from drinking water at 1 dpi and from all sample types at 2 dpi and 5 dpi. Infectious particles were also isolated from wall dust and drinking water at 14 dpi (Table 1 ).

Domestic and wild waterfowl play a crucial role in the worldwide spread of HPAIVs of the Gs/GD lineage due to their high susceptibility and efficient viral shedding [ 5 , 6 , 8 , 9 , 21 , 22 ]. Wild and domestic ducks are key players at the wild-domestic interface [ 10 , 22 , 23 ] and in maintaining these viruses in the environment [ 5 , 6 , 24 ]. By monitoring H5N8/2017 and H5N8/2020 clade 2.3.4.4b Gs/GD HPAIV-infected mule ducks in an experimental setting, we investigated viral shedding dynamics, bird-to-bird transmission, the role of the environment as transmission vehicle, and the reliability of environmental sampling for viral detection.

Clinical signs were first observed at 3 dpi and 4 dpi in H5N8/2017- and H5N8/2020-inoculated ducks, respectively, and at 5 dpi and 6 dpi in H5N8/2017- and H5N8/2020-contact ducks, respectively. These observations indicate a 3-to-5-day presymptomatic period. This presymptomatic period is in agreement with previous experimental infections using different HPAIVs in ducks [ 25 , 26 , 27 ] and with mathematical modelling approaches performed on field data from 2016–2017 and 2020–2021 clade 2.3.4.4b HPAIV outbreaks [ 28 , 29 ]. Lambert et al. calculated that during the H5N8 HPAIV 2020–2021 epizootic in France, the interval between the onset of clinical signs on two close farms was, on average, 4.78 days [ 28 ], suggesting that farm-to-farm transmission occurs during the presymptomatic period.

The mortality rates obtained here (30%, H5N8/2017 inoculated; 20%, H5N8/2020 inoculated; 100%, H5N8/2017 contact; and 33%, H5N8/2020 contact) confirmed previous results using clade 2.3.4.4a and clade 2.3.4.4b viruses in experimentally infected domestic birds [ 21 ]. However, numerous studies performed on different duck species have not shown any mortality in either inoculated or contact birds infected with recent clade 2.3.4.4a or clade 2.3.4.4b viruses [ 12 , 23 , 27 ]. Differences in mortality rates among different studies could be explained by the viral strain, the age of the birds, the inoculum titre, and the inoculation route. Here, the higher mortality rates and earlier onset of clinical signs in the H5N8/2017 inoculated and contact groups than in the H5N8/2020 inoculated and contact groups suggest greater virulence and/or adaptation of H5N8/2017 in mule ducks. The difference in mortality rates between H5N8/2017-inoculated ducks and contact ducks could be attributed to differences in the infectious dose they received. In fact, following inoculation, all inoculated ducks started to shed viruses at a high rate, which likely contaminated the contact ducks with a higher infectious dose, leading to more severe infection and, consequently, a higher mortality rate.

Here, viral shedding was monitored for 14 days using officially recognized OP and CL swab samples [ 17 ] and additional nonofficial samples such as FP and CJ swabs. On the one hand, FP sampling was performed because early and high levels of vRNA are often detected in H5Nx HPAIV-infected ducks [ 30 , 31 , 32 ]. On the other hand, CJ swabs were included because HPAIV can replicate in the ocular system, including the conjunctival mucosa, cornea, and Harderian glands, of ducks, turkeys and chickens but not exclusively [ 27 , 33 , 34 , 35 , 36 , 37 ]. Additionally, CJ swabs proved to be a reliable sample for Gs/GD clade 2.3.2.1 [ 38 , 39 ] and clade 2.3.4.4 [ 40 ] HPAIV detection. In our study, all samples were already positive at 2 dpi, and in contrast to the onset of clinical signs, no difference in viral shedding between the two viral strains was detected, allowing up to two more days of H5N8/2020 presymptomatic viral shedding. Importantly, vRNA detection in contact bird samples suggested that bird-to-bird transmission begins during the first 24 h after the first viral exposure. Our findings confirmed previous observations of efficient presymptomatic viral shedding in ducks infected with Gs/GD HPAIVs, which differs from findings in other bird species, such as chickens, turkeys, ostriches, sparrows, crows or pheasants [ 11 , 12 , 21 , 25 , 27 , 41 , 42 , 43 ]. Specifically, HPAIV-infected chickens typically have a short presymptomatic viral shedding period, and the onset of clinical signs is closely associated with rapid death, usually < 3 days [ 11 , 12 , 21 , 25 , 44 ]. Beerens et al. showed that chickens inoculated with different clade 2.3.4.4a and clade 2.3.4.4b H5Nx HPAIVs had much shorter OP shedding durations than did Pekin ducks (mean duration of 1.1–1.7 days in chickens vs 6.3–12.0 days in ducks) [ 12 ].

Analysis of unconventional CJ swabs and FP samples provided interesting results. Both sampling methods, such as OP and CL swabs, yielded early vRNA detection and high vRNA quantities. Interestingly, towards the end of the infection (10 and 14 dpi), both CJ swabs and FP samples showed high detection performance compared to CL swabs. Therefore, the use of CJ and FP samples could be a useful sampling strategy for viral detection in the field; although CJ swabbing requires trained personnel, similar to OP and CL swabbing, FP sampling is easy to perform, even by non-trained staff, and could constitute a valid alternative to CL and OP swabs.

To investigate the role of the environment in HPAIV transmission and to confirm previous findings regarding the potential of dust sampling for early detection and surveillance of HPAIV in farms [ 13 ], aerosols, dust, and water samples were collected throughout the experiment. The detection of vRNA in all environmental samples was in accordance with the viral shedding results. All environmental samples were positive for vRNA during the presymptomatic period (from 2 to 3 dpi in H5N8/2017 and from 2 to 5 dpi in H5N8/2020). Interestingly, H5N8/2020 drinking water and aerosols yielded positive results as early as 1 dpi, in line with the greater viral shedding observed in the H5N8/2020 group for the first 2 days than in the H5N8/2017 group, suggesting that these samples could be used for early viral detection in the field. Successful isolation of H5N8/2020 from drinking water at 1 dpi suggested high virus titres due to early host replication. Virus isolation was successful in all four types of environmental samples (drinking water, pool water, aerosols, and dust), but differences based on viral strain and sampling day were observed. In general, virus isolation was more successful on H5N8/2020 samples and around peak viral shedding days. Successful H5N8/2020 vRNA detection and viral isolation from both biological and environmental samples at 1 and 2 dpi confirmed that despite a longer incubation period, H5N8/2020-infected ducks shed more virus than H5N8/2017-infected ducks during the earliest days of infection. Importantly, virus isolation from environmental samples is difficult because all types of samples are subjected to a wide variety of stresses (e.g., chemical or physical) and contaminants that affect the successful isolation rate. The stresses induced in environmental samples and the presence of contaminants are not always easy to determine or quantify, hampering any correlation between RNA load and viral isolation. Quality control and standard analytical methods could play a major role in using environmental sampling for virus surveillance.

Our study compared a “stagnant water” model (pool water, not changed throughout the experiment) with a “renewed water” model (drinking water, daily changed). The stability of vRNA in pool water samples from 2–3 dpi up to 14 dpi could be explained by physical and chemical water parameters not limited to temperature, pH, or salinity [ 45 , 46 ]. In contrast to those in pool water, vRNA in drinking water was not stable over time but rather mirrored the overall viral shedding kinetics observed in biological samples and other environmental samples. The role of water in HPAIV transmission has been widely investigated in recent years [ 10 , 12 , 43 , 47 , 48 , 49 , 50 , 51 ], and waterborne infection in different bird species has been experimentally proven [ 10 , 12 , 49 , 51 ]. Importantly, the transmission role of water among waterfowl could also be enhanced by animal behaviour, specifically by preening activities [ 52 ]. This finding offers new possibilities for the surveillance and early vRNA detection of HPAIV.

In contrast to drinking water, which is more likely to enable bird-to-bird transmission within commercial flocks, dust and aerosols can spread the virus at a larger scale. Our results confirm that airborne transmission of the infectious virus may lead to infection in contact birds; vRNA detection and virus isolation results for dust and aerosol samples are in agreement with the findings of viral shedding in contact birds. Airborne transmission of H5Nx HPAIVs, including biologically generated aerosols and aerosolized dust infection, has been largely documented in recent years [ 11 , 13 , 53 , 54 , 55 , 56 , 57 ]. Ample evidence has confirmed bird-to-bird transmission due to airborne particles [ 53 , 54 ], but farm-to-farm spread has been more difficult to investigate due to sampling challenges mostly associated with weather and sampling device sensitivity [ 11 , 51 , 55 ]. To the best of our knowledge, direct evidence of farm-to-farm airborne transmission has not been demonstrated and has only been suggested by modelling approaches [ 56 , 58 ].

The diversity of environmental samples that tested positive for HPAIV early in the course of infection raises numerous questions and challenges regarding the control of future HPAIV epizootics. In addition to respiratory and digestive shedding routes, growing feathers of domestic ducks have been established as an alternative mechanism for viral diffusion via epithelial infection through viremia, active viral replication in the feather epithelium, and subsequent release of contaminated debris [ 59 ]. The high infectivity of these viruses, their potential resistance in the environment [ 60 ], and their ability to contaminate different environmental matrix types can drastically impact current biosecurity measures, not only during the productive life of birds but also during their movement and culling operations. Overall, our results show that clade 2.3.4.4b H5N8 HPAIVs are spread not only by living animals but also by the environment in which infected animals live, such as water or dust, which can be aerosolized and lead to long-range dissemination [ 57 ]. Therefore, culling operations, as well as cleaning and disinfection, could pose a risk for further viral dissemination if not performed properly.

Efficient viral shedding during the presymptomatic period in H5N8 clade 2.3.4.4b HPAIV-experimentally infected mule ducks suggests that a passive detection strategy based on overt clinical signs is not optimal for containing viral spread. Viral RNA detection in environmental samples in the absence of clinical signs would allow for a quicker response, limiting the number of infected birds and the number of infectious particles shed. Environmental sampling, particularly drinking water and dust sampling, could be a valuable, easy-to-perform, fast, non-invasive, cheap, and accurate strategy for active HPAIV detection and surveillance activities on farms.

Availability of data and materials

The datasets supporting the conclusions of this article are included within the article and its additional files.

Lewis NS, Banyard AC, Whittard E, Karibayev T, Al Kafagi T, Chvala I, Byrne A, Meruyert Akberovna S, King J, Harder T, Grund C, Essen S, Reid SM, Brouwer A, Zinyakov NG, Tegzhanov A, Irza V, Pohlmann A, Beer M, Fouchier RAM, Akhmetzhan Akievich S, Brown IH (2021) Emergence and spread of novel H5N8, H5N5 and H5N1 clade 2.3.4.4 highly pathogenic avian influenza in 2020. Emerg Microbes Infect 10:148–151

Article PubMed PubMed Central Google Scholar

Schreuder J, de Knegt HJ, Velkers FC, Elbers ARW, Stahl J, Slaterus R, Stegeman JA, de Boer WF (2022) Wild bird densities and landscape variables predict spatial patterns in HPAI outbreak risk across The Netherlands. Pathogens 11:549

Adlhoch C, Baldinelli F (2023) Avian influenza, new aspects of an old threat. Euro Surveill 28:2300227

Swayne DE, Suarez DL (2000) Highly pathogenic avian influenza. Rev Sci Tech 19:463–482

Article CAS PubMed Google Scholar

Kwon J, Bahl J, Swayne DE, Lee Y, Lee Y, Song C, Lee D (2020) Domestic ducks play a major role in the maintenance and spread of H5N8 highly pathogenic avian influenza viruses in South Korea. Transbound Emerg Dis 67:844–851

Article PubMed Google Scholar

Hulse-Post DJ, Sturm-Ramirez KM, Humberd J, Seiler P, Govorkova EA, Krauss S, Scholtissek C, Puthavathana P, Buranathai C, Nguyen TD, Long HT, Naipospos TSP, Chen H, Ellis TM, Guan Y, Peiris JSM, Webster RG (2005) Role of domestic ducks in the propagation and biological evolution of highly pathogenic H5N1 influenza viruses in Asia. Proc Natl Acad Sci U S A 102:10682–10687

Article CAS PubMed PubMed Central Google Scholar

Leyson C, Youk S, Smith D, Dimitrov K, Lee D-H, Larsen LE, Swayne DE, Pantin-Jackwood MJ (2019) Pathogenicity and genomic changes of a 2016 European H5N8 highly pathogenic avian influenza virus (clade 2.3.4.4) in experimentally infected mallards and chickens. Virology 537:172–185

Hill SC, Lee Y-J, Song B-M, Kang H-M, Lee E-K, Hanna A, Gilbert M, Brown IH, Pybus OG (2015) Wild waterfowl migration and domestic duck density shape the epidemiology of highly pathogenic H5N8 influenza in the Republic of Korea. Infect Genet Evol 34:267–277

Keawcharoen J, van Riel D, van Amerongen G, Bestebroer T, Beyer WE, van Lavieren R, Osterhaus ADME, Fouchier RAM, Kuiken T (2008) Wild ducks as long-distance vectors of highly pathogenic avian influenza virus (H5N1). Emerg Infect Dis 14:600–607

Ahrens AK, Selinka HC, Mettenleiter TC, Beer M, Harder TC (2022) Exploring surface water as a transmission medium of avian influenza viruses–systematic infection studies in mallards. Emerg Microbes Infect 11:1250–1261

James J, Warren CJ, De Silva D, Lewis T, Grace K, Reid SM, Falchieri M, Brown IH, Banyard AC (2023) The role of airborne particles in the epidemiology of clade 2.3.4.4b H5N1 high pathogenicity avian influenza virus in commercial poultry production units. Viruses 15:1002

Beerens N, Germeraad EA, Venema S, Verheij E, Pritz-Verschuren SBE, Gonzales JL (2021) Comparative pathogenicity and environmental transmission of recent highly pathogenic avian influenza H5 viruses. Emerg Microbes Infect 10:97–108

Filaire F, Lebre L, Foret-Lucas C, Vergne T, Daniel P, Lelièvre A, de Barros A, Jbenyeni A, Bolon P, Paul M, Croville G, Guérin J-L (2022) Highly pathogenic avian influenza A(H5N8) clade 2.3.4.4b virus in dust samples from poultry farms, France, 2021. Emerg Microbes Infect 28:1446–1450

Article CAS Google Scholar

Bessière P, Figueroa T, Coggon A, Foret-Lucas C, Houffschmitt A, Fusade-Boyer M, Dupré G, Guérin J-L, Delverdier M, Volmer R (2022) Opposite outcomes of the within-host competition between high- and low-pathogenic H5N8 avian influenza viruses in chickens compared to ducks. J Virol 96:e01366-e1421

Brown JD, Stallknecht DE (2008) Avian influenza virus. In: Spackman E (ed) Methods in molecular biology. Humana Press, Totowa, NJ

Google Scholar

Spackman E, Senne DA, Myers TJ, Bulaga LL, Garber LP, Perdue ML, Lohman K, Daum LT, Suarez DL (2002) Development of a real-time reverse transcriptase PCR assay for type A influenza virus and the avian H5 and H7 hemagglutinin subtypes. J Clin Microbiol 40:3256–3260

World Organisation for Animal Health (WOAH). Avian influenza (including infection with high pathogenicity avian influenza viruses). OIE Terrestrial Manual 2021 (2021)

R Core Team (2017) A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Vienna, Austria

Pinheiro José, Bates Douglas, R Core Team (2023) nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–164 https://CRAN.R-project.org/package=nlme

Wickham H (2016) ggplot2: elegant graphics for data analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org

Grund C, Hoffmann D, Ulrich R, Naguib M, Schinköthe J, Hoffmann B, Harder T, Saenger S, Zscheppang K, Tönnies M, Hippenstiel S, Hocke A, Wolff T, Beer M (2018) A novel European H5N8 influenza A virus has increased virulence in ducks but low zoonotic potential. Emerg Microbes Infect 7:132

Verhagen JH, Fouchier RAMM, Lewis N (2021) Highly pathogenic avian influenza viruses at the wild–domestic bird interface in Europe: future directions for research and surveillance. Viruses 13:212

Seekings AH, Warren CJ, Thomas SS, Mahmood S, James J, Byrne AMP, Watson S, Bianco C, Nunez A, Brown IH, Brookes SM, Slomka MJ (2021) Highly pathogenic avian influenza virus H5N6 (clade 2.3.4.4b) has a preferable host tropism for waterfowl reflected in its inefficient transmission to terrestrial poultry. Virology 559:74–85

Chen H, Deng G, Li Z, Tian G, Li Y, Jiao P, Zhang L, Liu Z, Webster RG, Yu K (2004) The evolution of H5N1 influenza viruses in ducks in southern China. Proc Natl Acad Sci U S A 101:10452–10457

Hiono T, Okamatsu M, Yamamoto N, Ogasawara K, Endo M, Kuribayashi S, Shichinohe S, Motohashi Y, Chu D-H, Suzuki M, Ichikawa T, Nishi T, Abe Y, Matsuno K, Tanaka K, Tanigawa T, Kida H, Sakoda Y (2016) Experimental infection of highly and low pathogenic avian influenza viruses to chickens, ducks, tree sparrows, jungle crows, and black rats for the evaluation of their roles in virus transmission. Vet Microbiol 182:108–115

Puranik A, Slomka MJ, Warren CJ, Thomas SS, Mahmood S, Byrne AMP, Ramsay AM, Skinner P, Watson S, Everett HE, Núñez A, Brown IH, Brookes SM (2020) Transmission dynamics between infected waterfowl and terrestrial poultry: differences between the transmission and tropism of H5N8 highly pathogenic avian influenza virus (clade 2.3.4.4a) among ducks, chickens and turkeys. Virology 541:113–123

Spackman E, Pantin-Jackwood MJ, Lee SA, Prosser D (2023) The pathogenesis of a 2022 North American highly pathogenic clade 2.3.4.4b H5N1 avian influenza virus in mallards ( Anas platyrhynchos ). Avian Pathol 52:219–228

Lambert S, Durand B, Andraud M, Delacourt R, Scoizec A, Le Bouquin S, Rautureau S, Bauzile B, Guinat C, Fourtune L, Guérin J, Paul MC, Vergne T (2022) Two major epidemics of highly pathogenic avian influenza virus H5N8 and H5N1 in domestic poultry in France, 2020–2022. Transbound Emerg Dis 69:3160–3166

Vergne T, Gubbins S, Guinat C, Bauzile B, Delpont M, Chakraborty D, Gruson H, Roche B, Andraud M, Paul M, Guérin J (2021) Inferring within-flock transmission dynamics of highly pathogenic avian influenza H5N8 virus in France, 2020. Transbound Emerg Dis 68:3151–3155

Gaide N, Foret-Lucas C, Figueroa T, Vergne T, Lucas M-N, Robertet L, Souvestre M, Croville G, Le Loch G, Delverdier M, Guérin J-L (2021) Viral tropism and detection of clade 2.3.4.4b H5N8 highly pathogenic avian influenza viruses in feathers of ducks and geese. Sci Rep 11:5928

Nuradji H, Bingham J, Payne J, Harper J, Lowther S, Wibawa H, Long NT, Meers J (2017) Highly pathogenic avian influenza (H5N1) virus in feathers. Vet Pathol 54:226–233

Yamamoto Y, Nakamura K, Okamatsu M, Miyazaki A, Yamada M, Mase M (2008) Detecting avian influenza virus (H5N1) in domestic duck feathers. Emerg Infect Dis 14:1671–1672

Pantin-Jackwood MJ, Costa-Hurtado M, Bertran K, DeJesus E, Smith D, Swayne DE (2017) Infectivity, transmission and pathogenicity of H5 highly pathogenic avian influenza clade 2.3.4.4 (H5N8 and H5N2) United States index viruses in Pekin ducks and Chinese geese. Vet Res 48:33

Pantin-Jackwood M, Swayne DE, Smith D, Shepherd E (2013) Effect of species, breed and route of virus inoculation on the pathogenicity of H5N1 highly pathogenic influenza (HPAI) viruses in domestic ducks. Vet Res 44:62

Spackman E, Pantin-Jackwood MJ, Kapczynski DR, Swayne DE, Suarez DL (2016) H5N2 highly pathogenic avian influenza viruses from the US 2014–2015 outbreak have an unusually long pre-clinical period in turkeys. BMC Vet Res 12:260

DeJesus E, Costa-Hurtado M, Smith D, Lee D-H, Spackman E, Kapczynski DR, Torchetti MK, Killian ML, Suarez DL, Swayne DE, Pantin-Jackwood MJ (2016) Changes in adaptation of H5N2 highly pathogenic avian influenza H5 clade 2.3.4.4 viruses in chickens and mallards. Virology 499:52–64

Yamamoto Y, Nakamura K, Yamada M, Mase M (2016) Corneal opacity in domestic ducks experimentally infected with H5N1 highly pathogenic avian influenza virus. Vet Pathol 53:65–76

Bui VN, Dao TD, Nguyen TTH, Nguyen LT, Bui AN, Trinh DQ, Pham NT, Inui K, Runstadler J, Ogawa H, Nguyen KV, Imai K (2014) Pathogenicity of an H5N1 avian influenza virus isolated in Vietnam in 2012 and reliability of conjunctival samples for diagnosis of infection. Virus Res 179:125–132

Bui VN, Ogawa H, Ngo LH, Baatartsogt T, Abao LNB, Tamaki S, Saito K, Watanabe Y, Runstadler J, Imai K (2013) H5N1 highly pathogenic avian influenza virus isolated from conjunctiva of a whooper swan with neurological signs. Arch Virol 158:451–455

Usui T, Soda K, Sumi K, Ozaki H, Tomioka Y, Ito H, Murase T, Kawamoto T, Miura M, Komatsu M, Imanishi T, Kurobe M, Ito T, Yamaguchi T (2020) Outbreaks of highly pathogenic avian influenza in zoo birds caused by HA clade 2.3.4.4 H5N6 subtype viruses in Japan in winter 2016. Transbound Emerg Dis 67:686–697

Abolnik C, Ostmann E, Woods M, Wandrag DBR, Grewar J, Roberts L, Olivier AJ (2021) Experimental infection of ostriches with H7N1 low pathogenic and H5N8 clade 2.3.4.4B highly pathogenic influenza A viruses. Vet Microbiol 263:109251

Liang Y, Hjulsager CK, Seekings AH, Warren CJ, Lean FZX, Núñez A, James J, Thomas SS, Banyard AC, Slomka MJ, Brown IH, Larsen LE (2022) Pathogenesis and infection dynamics of high pathogenicity avian influenza virus (HPAIV) H5N6 (clade 2.3.4.4b) in pheasants and onward transmission to chickens. Virology 577:138–148

Sánchez-González R, Ramis A, Nofrarías M, Wali N, Valle R, Pérez M, Perlas A, Majó N (2020) Experimental infection of domestic geese ( Anser anser var. domesticus ) with H5N8 Gs/GD and H7N1 highly pathogenic avian influenza viruses. Avian Pathol 49:642–657

Kwon J, Bertran K, Lee D, Criado MF, Killmaster L, Pantin-Jackwood MJ, Swayne DE (2023) Diverse infectivity, transmissibility, and pathobiology of clade 2.3.4.4 H5Nx highly pathogenic avian influenza viruses in chickens. Emerg Microbes Infect 12:2218945

Stallknecht DE, Shane SM, Kearney MT, Zwank PJ (1990) Persistence of avian influenza viruses in water. Avian Dis 34:406

Brown JD, Goekjian G, Poulson R, Valeika S, Stallknecht DE (2009) Avian influenza virus in water: infectivity is dependent on pH, salinity and temperature. Vet Microbiol 136:20–26

Brown JD, Swayne DE, Cooper RJ, Burns RE, Stallknecht DE (2007) Persistence of H5 and H7 avian influenza viruses in water. Avian Dis 51:285–289

Leung YHC, Zhang L-J, Chow C-K, Tsang C-L, Ng C-F, Wong C-K, Guan Y, Peiris JSM (2007) Poultry drinking water used for avian influenza surveillance. Emerg Infect Dis 13:1380–1382

Wille M, Bröjer C, Lundkvist Å, Järhult JD (2018) Alternate routes of influenza A virus infection in Mallard ( Anas platyrhynchos ). Vet Res 49:110

Bertran K, Dolz R, Busquets N, Gamino V, Vergara-Alert J, Chaves AJ, Ramis A, Abad XF, Höfle U, Majó N (2013) Pathobiology and transmission of highly and low pathogenic avian influenza viruses in European quail ( Coturnix c. coturnix ). Vet Res 44:23

Forrest HL, Kim J-K, Webster RG (2010) Virus shedding and potential for interspecies waterborne transmission of highly pathogenic H5N1 influenza virus in sparrows and chickens. J Virol 84:3718–3720

Delogu M, De Marco MA, Di Trani L, Raffini E, Cotti C, Puzelli S, Ostanello F, Webster RG, Cassone A, Donatelli I (2010) Can preening contribute to influenza A virus infection in wild waterbirds ? . PLoS One 5:e11315

Spekreijse D, Bouma A, Koch G, Stegeman JA (2011) Airborne transmission of a highly pathogenic avian influenza virus strain H5N1 between groups of chickens quantified in an experimental setting. Vet Microbiol 152:88–95

Spekreijse D, Bouma A, Koch G, Stegeman A (2013) Quantification of dust-borne transmission of highly pathogenic avian influenza virus between chickens. Influenza Other Respir Viruses 7:132–138

Torremorell M, Alonso C, Davies PR, Raynor PC, Patnayak D, Torchetti M, McCluskey B (2016) Investigation into the airborne dissemination of H5N2 highly pathogenic avian influenza virus during the 2015 spring outbreaks in the Midwestern United States. Avian Dis 60:637–643

Zhao Y, Richardson B, Takle E, Chai L, Schmitt D, Xin H (2019) Airborne transmission may have played a role in the spread of 2015 highly pathogenic avian influenza outbreaks in the United States. Sci Rep 9:11755

Asadi S, ben Hnia NG, Barre RS, Wexler AS, Ristenpart WD, Bouvier NM (2020) Influenza A virus is transmissible via aerosolized fomites. Nature Commun 11:4062

Ssematimba A, Hagenaars TJ, de Jong MCM (2012) Modelling the wind-borne spread of highly pathogenic avian influenza virus between farms. PLoS One 7:e31114

Gaide N, Filaire F, Bertran K, Crispo M, Dirat M, Secula A, Foret-Lucas C, Payré B, Perlas A, Cantero G, Majó N, Soubies S, Guérin JL (2023) The feather epithelium contributes to the dissemination and ecology of clade 2.3.4.4b H5 high pathogenicity avian influenza viruses in ducks. Emerg Microbes Infect 12:2272644

Yamamoto Y, Nakamura K, Yamada M, Mase M (2010) Persistence of avian influenza virus (H5N1) in feathers detached from bodies of infected domestic ducks. Appl Environ Microbiol 76:5496–5499

Download references

Acknowledgements

This study was performed in the framework of the “Chaire de Biosécurité & Santé Aviaires” funded by the Direction Generale de l’Alimentation, Ministère de l’Agriculture et de la Souveraineté Alimentaire, France, and hosted by the National Veterinary College of Toulouse (ENVT). The study was partially funded by the Veterinary Biocontained facility Network (VetBioNet) [EU Grant Agreement INFRA-2016-1 No 731014]. F.F is funded by Theseo, a company of the LanXess Group, France. K.B. is funded by the Ministry of Economy and Competitiveness, Spain, program Ramón y Cajal (Grant RYC2021-033472-I). We thank Bertin Instruments, France, and the National Institute for Occupational Safety & Health (NIOSH), United States, for the loan of aerosol collectors.

Author information

Fabien Filaire and Kateri Bertran have contributed equally to this work and should both be considered first authors

Natàlia Majó and Jean-Luc Guerin have contributed equally to this work and should both be considered last authors

Authors and Affiliations

IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France

Fabien Filaire, Nicolas Gaide, Aurélie Secula, Charlotte Foret-Lucas, Guillaume Croville & Jean-Luc Guerin

LanXess Group, THESEO France, Lanxess Biosecurity, Laval, France

Fabien Filaire

Unitat Mixta d’Investigació IRTA-UAB en Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB), 08193, Bellaterra, Catalonia, Spain

Kateri Bertran, Rosa Valle, Albert Perlas, Miquel Nofrarías, Guillermo Cantero & Natàlia Majó

Programa de Sanitat Animal, IRTA, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB), 08193, Bellaterra, Catalonia, Spain

Kateri Bertran, Rosa Valle, Albert Perlas, Miquel Nofrarías & Guillermo Cantero

Departament de Sanitat i Anatomia Animals, Facultat de Veterinària, Campus de la Universitat Autònoma de Barcelona (UAB), 08193, Bellaterra, Catalonia, Spain

Natàlia Majó

You can also search for this author in PubMed Google Scholar

Contributions

FF, KB, NG, NM and JLG conceived and designed the study. FF, NG, KT, RV, CFL, AP, MN, GC, GC and JLG performed the study. FF, KB, and AS analysed the data. FF and KB wrote the original draft. FF, NG, KB, NM, and JLG secured the funding. NM and JLG supervised the study. All the authors have read and approved the final manuscript.

Corresponding author

Correspondence to Jean-Luc Guerin .

Ethics declarations

Ethics approval and consent to participate.

All animal work was reviewed and approved by the IRTA (#258-2021) and the Catalan Government (#11467) Ethics and Animal Experimentation Committees, which are subject to national and European regulations. All procedures involving viruses were performed in a biosafety level-3 (BSL-3) laboratory and animal facility at IRTA-CReSA (Barcelona) in accordance with procedures approved by the IRTA Biosafety Committee (#59-2021).

Competing interests

The authors declare that they have no competing interests.

Additional information

Handling editor: Stéphane Biacchesi

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1. cycle threshold (ct) values obtained by rt-qpcr from biological samples..

Summary table of all the cycle threshold (Ct) values obtained by RT-qPCR on all biological samples (cloacal swabs, oropharyngeal swabs, conjunctival swabs, and feather pulp) collected from H5N8/2017 and H5N8/2020 inoculated and contact birds.

Additional file 2. Serology of H5N8/2017 and H5N8/2020 experimentally infected ducks.

Summary table of the ELISA and HI titres of blood samples collected pre- and post-inoculation from all birds.

Additional file 3. Cycle threshold (Ct) values obtained by RT-qPCR from environmental samples from the H5N8/2017 experimentally infected group.

Summary table of the cycle threshold (Ct) values obtained by RT-qPCR from the environmental samples (aerosol NIOSH fraction 1, aerosol NIOSH fraction 2, aerosol NIOSH fraction 3, aerosol Coriolis, dust walls, dust feeders, pool water, and drinking water) collected from the H5N8/2017 experimentally infected group.

Additional file 4. Cycle threshold (Ct) values obtained by RT-qPCR from environmental samples from the H5N8/2020 experimentally infected group.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Filaire, F., Bertran, K., Gaide, N. et al. Viral shedding and environmental dispersion of two clade 2.3.4.4b H5 high pathogenicity avian influenza viruses in experimentally infected mule ducks: implications for environmental sampling. Vet Res 55 , 100 (2024). https://doi.org/10.1186/s13567-024-01357-z

Download citation

Received : 22 April 2024

Accepted : 02 July 2024

Published : 12 August 2024

DOI : https://doi.org/10.1186/s13567-024-01357-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Viral shedding
environment
surveillance

Veterinary Research

ISSN: 1297-9716

Submission enquiries: [email protected]
General enquiries: [email protected]

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
Ann Indian Acad Neurol
v.14(4); Oct-Dec 2011

Design, data analysis and sampling techniques for clinical research

Karthik suresh.

Department of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Trivandrum, India

Sanjeev V. Thomas

1 Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India

Geetha Suresh

2 Department of Justice Administration, University of Louisville, Louiseville, USA

Statistical analysis is an essential technique that enables a medical research practitioner to draw meaningful inference from their data analysis. Improper application of study design and data analysis may render insufficient and improper results and conclusion. Converting a medical problem into a statistical hypothesis with appropriate methodological and logical design and then back-translating the statistical results into relevant medical knowledge is a real challenge. This article explains various sampling methods that can be appropriately used in medical research with different scenarios and challenges.

Problem Identification

Clinical research often starts from questions raised at the bedside in hospital wards. Is there an association between neurocysticercosis (NCC) and epilepsy? Are magnetic resonance imaging changes good predictors of multiple sclerosis? Is there a benefit in using steroids in pyogenic meningitis? Typically, these questions lead us to set up more refined research questions. For example, do persons with epilepsy have a higher probability of having serological (or computed tomography [CT] scan) markers for NCC? What proportion of persons with multiple lesions in the brain has Multiple Sclerosis (MS) Do children with pyogenic meningitis have a lesser risk of mortality if dexamethasone is used concomitantly with antibiotics?

Designing a clinical study involves narrowing a topic of interest into a single focused research question, with particular attention paid to the methods used to answer the research question from a cost, viability and overall effectiveness standpoint. In this paper, we focus attention on residents and younger faculty who are planning short-term research projects that could be completed in 2–3 years. Once we have a fairly well-defined research question, we need to consider the best strategy to address these questions. Further considerations in clinical research, such as the clinical setting, study design, selection criteria, data collection and analysis, are influenced by the disease characteristics, prevalence, time availability, expertise, research grants and several other factors. In the example of NCC, should we use serological markers or CT scan findings as evidence of NCC? Such a question raises further questions. How good are serologic markers compared with CT scans in terms of identifying NCC? Which test (CT or blood test) is easier, safer and acceptable for this study? Do we have the expertise to carry out these laboratory tests and make interpretations? Which procedure is going to be more expensive? It is very important that the researcher spend adequate time considering all these aspects of his study and engage in discussion with biostatisticians before actually starting the study.

The major objective of this article is to explain these initial steps. We do not intend to provide a tailor-made design. Our aim is to familiarize the reader with different sampling methods that can be appropriately used in medical research with different scenarios and challenges.

One of the first steps in clinical study is choosing an appropriate setting to conduct the study (i.e., hospital, population-based). Some diseases, such as migraine, may have a different profile when evaluated in the population than when evaluated in the hospital. On the other hand, acute diseases such as meningitis would have a similar profile in the hospital and in the community. The observations in a study may or may not be generalizable, depending on how closely the sample represents the population at large.

Consider the following studies. Both De Gans et al .[ 1 ] and Scarborough et al .[ 2 ] looked at the effect of adjunctive Dexamethasone in bacterial meningitis. Both studies are good examples of using the hospital setting. Because the studies involved acute conditions, they utilize the fact that sicker patients will seek hospital care to concentrate their ability to find patients with meningitis. By the same logic, it would be inappropriate to study less-acute conditions in such a fashion as it would bias the study toward sicker patients.

On the other hand, consider the study by Holroyd et al .[ 3 ] investigating therapies in the treatment of migraine. Here, the authors intentionally chose an outpatient setting (the patients were referred to the study clinic from a network of other physician clinics as well as local advertisements) so that their population would not include patients with more severe pathology (requiring hospital admission).

If the sample was restricted to a particular age group, sex, socioeconomic background or stage of the disease, the results would be applicable to that particular group only. Hence, it is important to decide how you select your sample. After choosing an appropriate setting, attention must be turned to the inclusion and exclusion criteria. This is often locale specific. If we compare the exclusion criteria for the two meningitis studies mentioned above, we see that in the study by de Gans,[ 1 ] patients with shunts, prior neurosurgery and active tuberculosis were specifically excluded; in the Scarbrough study, however, such considerations did not apply as the locale was considerably different (sub-saharan Africa vs. Europe).

Validity (Precision) and Reliability (Consistency)

Clinical research generally requires making use of an existing test or instrument. These instruments and investigations have usually been well validated in the past, although the populations in which such validations were conducted may be different. Many such questionnaires and patient self-rating scales (MMSE or QOLIE, for instance) were developed in another part of the world. Therefore, in order to use these tests in clinical studies locally, they require validation. Socio-demographic characteristics and language differences often influence such tests considerably. For example, consider a scale that uses the ability to drive a motor car as a Quality of Life measure. Does this measure have the same relevance in India as in the USA, where only a small minority of people drive their own vehicles? Hence, it is very important to ensure that the instruments that we use have good validity.

Validity is the degree to which the investigative goals are measured accurately. The degree to which the research truly measures what it intended to measure[ 4 ] determines the fundamentals of medical research. Peace, Parrillo and Hardy[ 5 ] explain that the validity of the entire research process must be critically analyzed to the greatest extent possible so that appropriate conclusions can be drawn, and recommendations for development of sound health policy and practice can be offered.

Another measurement issue is reliability. Reliability refers to the extent to which the research measure is a consistent and dependable indicator of medical investigation. In measurement, reliability is an estimate of the degree to which a scale measures a construct consistently when it is used under the same condition with the same or different subjects. Reliability (consistency) describes the extent to which a measuring technique consistently provides the same results if the measurement is repeated. The validity (accuracy) of a measuring instrument is high if it measures exactly what it is supposed to measure. Thus, the validity and reliability together determine the accuracy of the measurement, which is essential to make valid statistical inference from a medical research.

Consider the following scenario. Kasner et al .[ 6 ] established reliability and validity of a new National Institute of Health Stroke Scale (NIHSS) generation method. This paper provides a good example of how to test a new instrument (NIH stroke score generation via retrospective chart review) with regards to its reliability and validity. To test validity, the investigators had multiple physicians review the same set of charts and compared the variability within the scores calculated by these physicians. To test reliability, the investigators compared the new test (NIHSS calculated by chart review) to the old test (NIHSS calculated at the bedside at the time of diagnosis). They reported that, overall, 88% of the estimated scores deviated by less than five points from the actual scores at both admission and discharge.

A major purpose of doing research is to infer or generalize research objectives from a sample to a larger population. The process of inference is accomplished by using statistical methods based on probability theory. A sample is a subset of the population selected, which is an unbiased representative of the larger population. Studies that use samples are less-expensive, and study of the entire population is sometimes impossible. Thus, the goal of sampling is to ensure that the sample group is a true representative of the population without errors. The term error includes sampling and nonsampling errors. Sampling errors that are induced by sampling design include selection bias and random sampling error. Nonsampling errors are induced by data collection and processing problems, and include issues related to measurement, processing and data collection errors.

Methods of sampling

To ensure reliable and valid inferences from a sample, probability sampling technique is used to obtain unbiased results. The four most commonly used probability sampling methods in medicine are simple random sampling, systematic sampling, stratified sampling and cluster sampling.

In simple random sampling, every subject has an equal chance of being selected for the study. The most recommended way to select a simple random sample is to use a table of random numbers or a computer-generated list of random numbers. Consider the study by Kamal et al .[ 7 ] that aimed to assess the burden of stroke and transient ischemic attack in Pakistan. In this study, the investigators used a household list from census data and picked a random set of households from this list. They subsequently interviewed the members of the randomly chosen households and used this data to estimate cerebrovascular disease prevalence in a particular region of Pakistan. Prevalence studies such as this are often conducted by using random sampling to generate a sampling frame from preexisting lists (such as census lists, hospital discharge lists, etc.).

A systematic random sample is one in which every k th item is selected. k is determined by dividing the number of items in the sampling frame by sample size.

A stratified random sample is one in which the population is first divided into relevant strata or subgroups and then, using the simple random sample method, a sample is drawn from each strata. Deng et al .[ 8 ] studied IV tissue Plasminogen Activator (tPA) usage in acute stroke among hospitals in Michigan. In order to enroll patients across a wide array of hospitals, they employ a stratified random sampling in order to construct the list of hospitals. They stratified hospitals by number of stroke discharges, and then randomly picked an equal number of hospitals within each stratum. Stratified random sampling such as this can be used to ensure that sampling adequately reflects the nature of current practice (such as practice and management trends across the range of hospital patient volumes, for instance).

A cluster sample results from a two-stage process in which the population is divided into clusters, and a subset of the clusters is randomly selected. Clusters are commonly based on geographic areas or districts and, therefore, this approach is used more often in epidemiologic research than in clinical studies.[ 9 ]

Random samples and randomization

Random samples and randomization (aka, random assignment) are two different concepts. Although both involve the use of the probability sampling method, random sampling determines who will be included in the sample. Randomization, or random assignment, determines who will be in the treatment or control group. Random sampling is related to sampling and external validity (generalizability), whereas random assignment is related to design and internal validity.

In experimental studies such as randomized controlled trials, subjects are first selected for inclusion in the study on the basis of appropriate criteria; they are then assigned to different treatment modalities using random assignment. Randomized controlled trials that are considered to be the most efficient method of controlling validity issues by taking into account all the potential confounding variables (such as other outside factors that could influence the variables under study) are also considered most reliable and impartial method of determining the impact of the experiment. Any differences in the outcome of the study are more likely to be the result of difference in the treatments under consideration than due to differences because of groups.

Scarborough et al .,[ 2 ] in a trial published in the New England Journal of Medicine , looked at corticosteroid therapy for bacterial meningitis in sub-saharan Africa to see whether the benefits seen with early corticosteroid administration in bacterial meningitis in the developed world also apply to the developing world. Interestingly, they found that adjuvant Dexamethasone therapy did not improve outcomes in meningitis cases in sub-saharan Africa. In this study, they performed random assignment of therapy (Dexamethasone vs. placebo). It is useful to note that the process of random assignment usually involves multiple sub-steps, each designed to eliminate confounders. For instance, in the above-mentioned study, both steroids and placebo were packaged similarly, in opaque envelopes, and given to patients (who consented to enroll) in a randomized fashion. These measures ensure the double-blind nature of the trial. Care is taken to make sure that the administrators of the therapy in question are blinded to the type of therapy (steroid vs. placebo) that is being given.

Sample size

The most important question that a researcher should ask when planning a study is “How large a sample do I need?” If the sample size is too small, even a well-conducted study may fail to answer its research question, may fail to detect important effects or associations, or may estimate those effects or associations too imprecisely. Similarly, if the sample size is too large, the study will be more difficult and costly, and may even lead to a loss in accuracy. Hence, optimum sample size is an essential component of any research. Careful consideration of sample size and power analysis during the planning and design stages of clinical research is crucial.

Statistical power is the probability that an empirical test will detect a relationship when a relationship in fact exists. In other words, statistical power explains the generalizability of the study results and its inferential power to explain population variability. Sample size is directly related to power; ceteris paribus, the bigger a sample, the higher the statistical power.[ 10 ] If the statistical power is low, this does not necessarily mean that an undetected relationships exist, but does indicate that the research is unlikely to find such links if they exist.[ 10 ] Flow chart relating research question, sampling and research design and data analysis is shown in Figure 1 .

An external file that holds a picture, illustration, etc.
Object name is AIAN-14-287-g001.jpg

Overall framework of research design

The power of a study tells us how confidently we can exclude an association between two parameters. For example, regarding the prior research question of the association between NCC and epilepsy, a negative result might lead one to conclude that there is no association between NCC and epilepsy. However, the study might not have been sufficiently powered to exclude any possible association, or the sample size might have been too small to reveal an association.

The sample sizes seen in the two meningitis studies mentioned earlier are calculated numbers. Using estimates of prevalence of meningitis in their respective communities, along with variables such as size of expected effect (expected rate difference between treated and untreated groups) and level of significance, the investigators in both studies would have calculated their sample numbers ahead of enrolling patients. Sample sizes are calculated based on the magnitude of effect that the researcher would like to see in his treatment population (compared with placebo). It is important to note variables such as prevalence, expected confidence level and expected treatment effect need to be predetermined in order to calculate sample size. As an example, Scarborough et al .[ 2 ] state that “on the basis of a background mortality of 56% and an ability to detect a 20% or greater difference in mortality, the initial sample size of 660 patients was modified to 420 patients to detect a 30% difference after publication of the results of a European trial that showed a relative risk of death of 0.59 for corticosteroid treatment.” Determining existing prevalence and effect size can be difficult in areas of research where such numbers are not readily available in the literature. Ensuring adequate sample size has impacts for the final results of a trial, particularly negative trials. An improperly powered negative trial could fail to detect an existing association simply because not enough patients were enrolled. In other words, the result of the sample analysis would have failed to reject the null hypothesis (that there is no difference between the new treatment and the alternate treatment), when in fact it should have been rejected, which is referred to as type II error. This statistical error arises because of inadequate power to explain population variability. Careful consideration of sample size and power analysis is one of the prerequisites of medical research. Another prerequisite is appropriate and adequate research design, which will be addressed in the next issue.

Source of Support: Nil,

Conflict of Interest: Nil.

ORIGINAL RESEARCH article

Causal associations between gut microbiota and premature rupture of membranes: a two-sample mendelian randomization study.

1 Department of Clinical Laboratory, Chongqing Health Center for Women and Children, Chongqing, China
2 Department of Clinical Laboratory, Women and Children's Hospital of Chongqing Medical University, Chongqing, China
3 Department of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Gaotan, Chongqing, China

The final, formatted version of the article will be published soon.

Select one of your emails

You have multiple emails registered with Frontiers:

Notify me on publication

Please enter your email address:

If you already have an account, please login

You don't have a Frontiers account ? You can register here

Previous study has indicated a potential link between gut microbiota and maternal pregnancy outcomes. However, the causal relationship between gut microbiota and premature rupture of membranes (PROM) remains a topic of ongoing debate.A two-sample Mendelian Randomization (MR) study was used to investigate the relationship between gut microbiota and PROM. Genetic data on gut microbiota was obtained from the MiBioGen consortium's largest genome-wide association study (GWAS) (n=14,306). Genetic data on PROM (3011 cases and 104247 controls) were sourced from publicly available GWAS data from the Finnish National Biobank FinnGen consortium. Various methods including Inverse variance weighted (IVW), MR-Egger, simple mode, weighted median, and weighted mode were utilized to assess the causal relationship by calculating the odd ratio (OR) value and confidence interval (CI). Sensitivity analyses for quality control were performed using MR-Egger intercept tests, Cochran's Q tests, and leave-one-out analyses. Results: The IVW method revealed that class Mollicutes (IVW, OR=0.773, 95%CI: 0.61-0.981, pval = 0.034), genus Marvinbryantia (IVW, OR=00.736, 95%CI: 0.555-0.977, pval = 0.034), genus Ruminooccaceae UCG003 (IVW, OR=0.734, 95%CI: 0.568-0.947, pval = 0.017) and phylum Tenericutes (IVW, OR=0.773, 95%CI: 0.566-1.067, pval = 0.034) were associated with a reduced risk of PROM, while genus Collinsella (IVW, OR=1.444, 95%CI: 1.028-2.026, pval = 0.034), genus Intestinibacter (IVW, OR=1.304, 95%CI: 1.047-1.623, pval = 0.018) and genus Turicibacter (IVW, OR=1.282, 95%CI: 1.02-1.611, pval = 0.033) increased the risk of PROM. Based on the other four supplementary methods, six gut microbiota may have a potential effect on PROM. Due to the presence of pleiotropy (pval=0.045), genus Lachnoclostridium should be ruled out. No evidence of horizontal pleiotropy or heterogeneity was found in other microbiota (pval >0.05).In this study, we have discovered a causal relationship between the presence of specific probiotics and pathogens in the host and the risk of PROM. The identification of specific gut microbiota associated with PROM through MR studies offers a novel approach to diagnosing and treating this condition, thereby providing a new strategy for clinically preventing PROM.

Keywords: Gut Microbiota, Premature rupture of membranes, genetic variable, Mendelian randomization, causality

Received: 29 May 2024; Accepted: 12 Aug 2024.

Copyright: © 2024 Zhang, Li, Huang, Zou, Zou, Zhang, Su and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Jia F. Huang, Department of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Gaotan, Chongqing, China Qin Zou, Department of Clinical Laboratory, Chongqing Health Center for Women and Children, Chongqing, China Xin Y. Zhang, Department of Clinical Laboratory, Chongqing Health Center for Women and Children, Chongqing, China Yan Su, Department of Clinical Laboratory, Chongqing Health Center for Women and Children, Chongqing, China Chun L. Li, Department of Clinical Laboratory, Chongqing Health Center for Women and Children, Chongqing, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

COMMENTS

Sampling methods in Clinical Research; an Educational Review
Abstract Clinical research usually involves patients with a certain disease or a condition. The generalizability of clinical research findings is based on multiple factors related to the internal and external validity of the research methods. The main methodological issue that influences the generalizability of clinical research findings is the sampling method. In this educational article, we ...
Sampling Methods
Abstract. Knowledge of sampling methods is essential to design quality research. Critical questions are provided to help researchers choose a sampling method. This article reviews probability and non-probability sampling methods, lists and defines specific sampling techniques, and provides pros and cons for consideration.
(PDF) Types of sampling in research
Abstract and Figures Sampling is one of the most important factors which determines the accuracy of a study. This article review the sampling techniques used in research including Probability ...
(PDF) Sampling Methods in Research: A Review
PDF | The accuracy of a study is heavily influenced by the process of sampling. The article provides an overview of the various sampling techniques used... | Find, read and cite all the research ...
Big enough? Sampling in qualitative inquiry
Sample sizes in qualitative research Declaring that quantitative and qualitative sampling assumptions are different doesn't result in any concrete advice for beginners.
Series: Practical guidance to qualitative research. Part 3: Sampling
Learn how to conduct qualitative research with practical guidance on sampling, data collection and analysis in this series of articles.
Methodology Series Module 5: Sampling Strategies
Abstract Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the ' Sampling Method'. There are essentially two types of sampling methods: 1) probability sampling - based on chance events (such as random numbers, flipping a coin etc.); and 2) non ...
Qualitative Sampling Methods
Abstract. Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros ...
Qualitative Sampling Methods
Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros and cons of ...
Sampling Techniques for Qualitative Research
This chapter explains how to design suitable sampling strategies for qualitative research. The focus of this chapter is purposive (or theoretical) sampling to produce credible and trustworthy explanations of a phenomenon (a specific aspect of society). A specific...
Types of Sampling in Research : Journal of the Practice of
Abstract Sampling is one of the most important factors which determines the accuracy of a study. This article review the sampling techniques used in research including Probability sampling techniques, which include simple random sampling, systematic random sampling and stratified random sampling and Non-probability sampling, which include quota sampling, self-selection sampling, convenience ...
Sampling Methods in Research Methodology; How to Choose a Sampling
Furthermore, as there are different types of sampling techniques/methods, researcher needs to understand the differences to select the proper sampling method for the research.
Sampling Methods
There are two primary types of sampling methods that you can use in your research: Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.
What are Sampling Methods? Techniques, Types, and Examples
Understand sampling methods in research, from simple random sampling to stratified, systematic, and cluster sampling. Learn how these sampling techniques boost data accuracy and representation, ensuring robust, reliable results. Check this article to learn about the different sampling method techniques, types and examples.
Purposeful sampling for qualitative data collection and analysis in
Purposeful sampling is widely used in qualitative research for the identification and selection of information-rich cases related to the phenomenon of interest. Although there are several different purposeful sampling strategies, criterion sampling appears ...
Purposive sampling: complex or simple? Research case examples
The sampling strategies clearly situate each study in terms of trustworthiness for data collection and analysis. The selected approach to purposive sampling used in each case aligns to the research methodology, aims and objectives, thus addressing each of the aspects of rigour.
Sampling Methods in Research Design
Sampling Methods in Research Design Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Address all correspondence to D.P. Turner, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA, email:
Types of Sampling Methods in Human Research: Why, When and How?
research methods. This paper focused primarily on illuminating the basic understanding of popular sampling techniques. We looked at the probability and non-probability types of sampling, the
Archaeology in space: The Sampling Quadrangle Assemblages Research
Since our work envisioned one-meter sample squares, and recognizing the use of acronyms as a persistent element of spacefaring culture, we named our payload the Sampling Quadrangle Assemblages Research Experiment (SQuARE). Permission from the ISS NL to conduct SQuARE was contingent on using equipment that was already on board the space station.
Purposive sampling: complex or simple? Research case examples
Background. Purposive sampling has a long developmental history and there are as many views that it is simple and straightforward as there are about its complexity. The reason for purposive sampling is the better matching of the sample to the aims and objectives of the research, thus improving the rigour of the study and trustworthiness of the ...
Title page setup
The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.
PET/CT targeted tissue sampling reveals intravenously administered
PET/CT targeted tissue sampling reveals intravenously administered HGN194 IgG1 affects HIV distribution after rectal exposure. Authors: Dr. Roslyn Taylor, ... University of Texas Health Science Center at San Antonio Cancer Therapy and Research Center, Research Imaging Institute, San Antonio, Texas, United States, 78229-3264; [email protected]
ADHD symptoms in autistic children linked to neighborhood conditions
The need for a larger, more diverse sample. Calub pointed out that more research is needed to determine if the results would apply to a larger group. "It will be important for future studies to be larger and more diverse. That should help us learn whether neighborhood conditions might also influence ADHD symptoms in other groups such as youth ...
(PDF) Sampling in Research
The purpose of sampling in research, dangers of sampling and how to minimize them, types of sampling and guides for deciding the sample size are discussed.
Series: Practical guidance to qualitative research. Part 3: Sampling
In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care.
Viral shedding and environmental dispersion of two clade 2.3.4.4b H5
Environmental sampling, particularly drinking water and dust sampling, could be a valuable, easy-to-perform, fast, non-invasive, cheap, and accurate strategy for active HPAIV detection and surveillance activities on farms.
Design, data analysis and sampling techniques for clinical research
Statistical analysis is an essential technique that enables a medical research practitioner to draw meaningful inference from their data analysis. Improper application of study design and data analysis may render insufficient and improper results and conclusion. Converting a medical problem into a statistical hypothesis with appropriate ...
Convenience Sampling Revisited: Embracing Its Limitations Through
The SAGE Encyclopedia of Industrial and Organizational Psychology, 2nd edition The SAGE Encyclopedia of Abnormal and Clinical Psychology The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation Encountering Unexpected Challenges and Responding With Flexibility and Teamwork: A Maternal and Newborn Health Study in Myanmar View more
The status quo and influencing factors of clinical research nurses
The status quo and influencing factors of clinical research nurses' participation in preoperative blood sampling in patients with pulmonary nodules
Frontiers
This article is part of the Research Topic Epigenetics in the microbiome-host crosstalk: from mechanisms to Therapeutics View all 6 articles. Causal associations between gut microbiota and premature rupture of membranes: A two-sample Mendelian Randomization study Provisionally accepted

Save citation to file

Add to My Bibliography

Qualitative Sampling Methods

Similar articles

LinkOut - more resources

Miscellaneous

Sampling Techniques for Qualitative Research

Cite this chapter

Access this chapter

Similar content being viewed by others

Preparation of Qualitative Research

Author information

Corresponding author

Editor information

Rights and permissions

Copyright information

About this chapter

Download citation

Share this chapter

Secondary Logo

Types of Sampling in Research

INTRODUCTION TO RESEARCH

Definition of research

WHAT IS SAMPLING

Advantages and disadvantages of sampling

Types of sampling

Probability sampling

Simple random sampling

Lottery method/envelope method

Random number table method

Types of simple random sampling

Simple random sampling with replacement

Simple random sampling without replacement

Stratified random sampling

Types of stratified sampling

Importance of this sampling

When to use stratified random sampling

Systematic sampling

Types of systematic sampling

Linear systematic sampling

Circular systematic sampling

Cluster sampling

Multistage sampling

Nonprobability sampling

Purposive sampling

Advantage of judgmental sampling

Convenience sampling

Advantage of convenience sampling

Disadvantages

Snow-ball sampling

Types of snowball sampling

Advantage of snowball sampling

Disadvantages of snowball sampling

Quota sampling

Consecutive sampling

Financial support and sponsorship

Have a language expert improve your writing

Sampling Methods | Types, Techniques & Examples

Table of contents

Sampling frame

Sample size

Receive feedback on language, structure, and formatting

1. Simple random sampling

2. Systematic sampling

3. Stratified sampling

4. Cluster sampling

1. Convenience sampling

2. Voluntary response sampling

3. Purposive sampling

4. Snowball sampling

5. Quota sampling

Cite this Scribbr article

Is this article helpful?

Shona McCombes

What are Sampling Methods? Techniques, Types, and Examples

What is sampling?

What are sampling methods or sampling techniques?

Types of sampling methods

What is probability sampling?

Types of probability sampling