Participatory research and data – Issues at stake and recommendations coming from examples of participatory research projects
Kenneth MAUSSANG (Université de Montpellier)
Hélène JOUGUET (Huma-Num (UAR 3598), CNRS, Université d’Aix-Marseille, Campus Condorcet)
Thomas JOUNEAU (Université de Lorraine)
Jean-François MARTIN (Institut Agro | Montpellier SupAgro)
Nicolas LARROUSSE (Huma-Num (UAR 3598), CNRS, Université d’Aix-Marseille, Campus Condorcet)
September 2023
Participatory research is “a means of producing scientific knowledge in which non-professional and non-scientific actors – whether individuals or groups – are involved in an active, deliberate way” [1]F. Houllier and J.-B. Merilhou-Goudard, « Les sciences participatives en France », Rapport élaboré à la demande des ministres en charge de l’Éducation nationale, de l’Enseignement supérieur et de la Recherche, 2016.
https://doi.org/10.15454/1.4606201248693647E12
. The non-scientific participants of a participatory research project can intervene at any stage of the scientific research process: data collection, data analysis, as well as during the construction of the initial research problem.
These non-scientific actors can be groups of individuals (for example in the form of associations or citizens’ collectives); primary or secondary school pupils; students; groups of professionals from a given sector, etc… Participatory research can also involve, for example, patient associations in medical research or victims’ associations.
Participatory research was initially developed in the fields of agronomy and ecology [2]F. Houllier and J.-B. Merilhou-Goudard, « Les sciences participatives en France », Rapport élaboré à la demande des ministres en charge de l’Éducation nationale, de l’Enseignement supérieur et de la Recherche, 2016.
https://doi.org/10.15454/1.4606201248693647E12
[3]
JRC technical report (2016) – Survey report: data management in Citizen Science projects,
https://digital-strategy.ec.europa.eu/en/news/survey-report-data-management-citizen-science-projects
, where the contribution of a group of non-scientific participants makes it possible to cover a significant geographical range and increase the volume of observational data produced. Within these research communities, skills have been developed to address the specific issues encountered in this type of project, notably at Cirad [4]
Recherche participative, un trait d’union entre agriculteurs et chercheurs. Crétenet Michel (2006), extract from Le coton, fil des temps, des marchés et des cultures. CIRAD-CA-DIR, DAGRIS. Montpellier: CIRAD-CA, 2 p.
https://agritrop.cirad.fr/531431/
, Inrae [5]Sciences et recherches participatives à INRAE, NOV’AE (numéro spécial #01), 2021. https://www6.inrae.fr/novae/Les-articles-parus/Les-n-Speciaux/Sciences-et-recherches-participatives-a-INRAE2, IRD, MNHN and Inserm. However, as a result of the increase in the amount of digital equipment available to the overall population (smartphones equipped with sensors, internet access, etc.), such participatory research projects are now being developed in all disciplinary fields, with a wide variety of types of implementation and operating procedures.
Participatory research is a tool that helps to establish a dialogue between science and society, one that initiates citizens into a scientific approach. It is also a means to draw on collective intelligence to produce knowledge. However, in order to fully benefit from these approaches, it is important to take account of the specifics of this type of knowledge production in order to maintain the quality and reliability of the scientific results.
The aim of this document is to put forward recommendations for all researcher(s) who would like to initiate a participatory research project in whatever discipline.
These recommendations include points to which particular attention should be paid and general entry level methodological principles. It should be remembered that the wide variety of types of participatory research projects and disciplinary practices makes it impossible to be exhaustive. For more details on any of the topics discussed here, please refer to the research support services within your own establishment or to the dedicated services such as MNHN’s MOSAIC (https://mosaic.mnhn.fr/).
This document only covers issues linked to the data from participatory research projects.
References
Table of Contents
1 Participatory research 2 Stakeholder motivations2.1 Researcher motivations
2.2 Participant motivations
2.3 Institutional and society motivations
2.3.1 Trust in science and the role of the expert
2.3.2 Participatory democracy and participatory research - Crowdsourcing and Citizen Science Act (2016)
2.3.3 Data reliability and institutional image
3 The project: structuring and steering3.1 In the design phase
3.1.1 Why take a participatory approach?
3.1.2 Understanding the stakeholders and their mutual interests
3.1.3 A range of skills in the Steering Committee
3.1.4 Referring to an ethics committee
3.2 The human resource impact of a participatory research project
3.3 Anticipating different levels of commitment
3.3.1 Accounting for participant drop out and different levels of involvement
3.3.2 Sustaining momentum over time
3.4 Steering tools
3.4.1 Data management plan
3.4.2 Building trust: clearly defining the expectations of the various parties
3.4.3 Communication plan
3.4.4 Formally define a data policy and the terms of participation in the project
4 The participants4.1 A diverse, non-expert population
4.2 Forming a community of participants
4.3 Levels of participant knowledge and skills
4.4 Raising the levels of knowledge and skills of the participants
4.4.1 Training
4.4.2 Training the trainers - trainer communities
4.4.3 Providing support
4.4.4 Importance of a wide range of materials
4.5 Case of school groups
4.6 Tasks assigned to the participants - protocols
4.7 Facilitating a community of participants
4.8 Third party - third place
4.9 Acknowledging the participants’ contribution
5 The data from a participatory research project5.1 Impact of a participatory approach
5.2 Error tolerance
5.3 Quality strategy - data reliability
5.4 Data credibility - institutional credibility
5.5 Fake data – trolling
5.6 Open data
5.7 Informing the participants
5.8 Link between data and participants
5.9 Specific case of sensors
6 The tools6.1 Importance of usability
6.2 Building trust
6.3 Upstream tools: data exchange
6.4 Downstream tools: data processing and visualisation
6.5 Communication and/or facilitation tools
7 Legal aspects 8 Conclusion 9 Acknowledgements 10 References 11 Appendix - List of recommended tools 12 Appendix - Bibliography