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Authors: Jitka Stilund Hansen, Thomas Kaarsted, Simon Worthington, Paul Ayris, Bastian Greshake Tzovaras, Kirsty Wallis

Citizen Science Skilling for Library Staff, Researchers, and the Public
Section Editor Jitka Stilund Hansen
Series: Citizen Science for Research Libraries — A Guide

DOI: 10.25815/hf0m-2a57


Management of Citizen Science Data

In the following parts of the guide, we address subjects that will aid create FAIR data, but with an emphasis on challenges particular for citizen science.

Findability is addressed in Section 2 of the guide about infrastructures. Use of Data Policies in Citizen Science Projects describes obligations related to Access and Reuse. You will learn about issues related to creating Interoperable data in Citizen Science Data and Standards. The Acknowledgment of Citizen Scientists on Research Outputs is very important for Reuse conditions. Lastly, you will find more resources and links in Planning and Securing Resources — The Data Management Plan.

Research Data Management: Quick Start Guide (eLearning course)

Introduction to research data management, the FAIR principles and writing a data management plan

Watch three short e-learning modules videos on Research Data Management, the FAIR principles, and Data Management Plans. You will get a general introduction to the concepts.


Holmstrand, K.F., S.P.A. den Boer, E. Vlachos, P.M. Martínez-Lavanchy, K.K. Hansen, A.V. Larsen, S. Zurcher, et al. “Research Data Management (ELearning Course),” 2019.

Use of Data Policies in Citizen Science Projects: A Step-by-step Guide

Librarians servicing an academic institution may be aware of legal and ethical conditions pertaining to research projects, but how will these conditions apply to citizen science? It is inevitable that project managers have to consider a myriad of issues about access to and protection of data produced or collected by citizen scientists. The checklist below may help sustain the engagement and trust of participants by adhering to ethical and legal obligations emerging in citizen science projects.

By Jitka Stilund Hansen, Technical University of Denmark, ORCID iD: 0000-0002-5888-1221 e-mail: Article DOI: 10.25815/byq6-7095

Step 1

Manage personal information according to current legislation and responsible practices

If and how data can be accessed depends on private and sensitive information being embedded in the data. In citizen science projects, personal information (name, contact information etc.) of the volunteers and often location sharing must be protected and handled according to current laws. In the EU, the GDPR applies to all handling of personal data and includes data that can identify a person, but also sensitive data such as information on health, ethnicity or religion. Not all countries outside Europe have laws protecting privacy or sensitive information of participants in citizen science projects, so follow responsible practices.

Step 2

Clarify and review ethical issues

Evaluation by ethical committees are important for clarifying issues pertaining to health reporting and perhaps collection of biological material in projects, where citizens contribute with such data. Projects based outside an academic institution may experience difficulties receiving an ethical review depending on the regulation and possibilities in individual countries. Consider how participants are protected, their risk evaluated and how accidental finding disclosure will be handled.

Step 3

Protect studied objects and populations

Sharing information about endangered species or particular populations requires special attention. Engaging specific populations in citizen science should be followed by clarifying their cultural needs during data collection and any resistance towards openly sharing (traditional) knowledge. It is the responsibility of the project manager to assess the consequences of data sharing and discuss this with the involved participants. Such issues may take time to investigate and should be planned for.

Step 4

Determine insurance coverage

National or institutional frameworks often insure and protect participants of conventional academic projects. This may not be the case for participants of citizen science. Determine if extended insurance coverage is necessary and how to inform citizens of risks related to their contribution.

Step 5

Manage intellectual property rights of the citizen scientist

Citizen scientists may produce photographs, writings, and creative selections or arrangements of scientific data. In contrast to the undisputable regulations in many countries of employees’ inventions, citizen scientists retain the intellectual property rights (IPR) to any copyrightable work they produce. Because the citizen scientists possess the right to exclude the project in using an invention they have produced, it is recommended to make transparent IPR agreements that are regularly updated with the participants. Also, the project holder should aim at sharing IPR, education or monetary value with the volunteers.

Step 6

Specify data access and license

Data without a license is not FAIR and the project manager should early in the project consider how access to data can be aligned with a usage license. Citizen science data may have broad applicability and such reuse is facilitated by choosing legally interoperable licenses for datasets.

Step 7

Create a data policy and specify the terms of participation

Aggregate the above information and requirements in a Data policy and Terms of Participation. The information should be evaluated by relevant stakeholders of the projects (participants, organisations, institutions) before it is used. Participants must be informed about the Terms of Participation in clear and accessible language before they agree to engage in the citizen science activities.


The research librarian may readily assist in the practicalities of storing, sharing, publishing and licensing research data, and therefore also data of citizen science origin. However, bodies outside the research library often deliver legal advice and ethical evaluation of relevance for citizen science. Therefore, an important service from the library is to develop a framework to clarify ethical and legal conditions particular for projects relying on co-creation and involvement of citizen scientists.

Citizen Science Data and Standards

Three recommendations to cope with inherent diversity

By Sven Schade and Chrisa Tsinaraki, European Commission – Joint Research Centre Sven Schade, ORCID iD: 0000-0001-5677-5209 e-mail: Chrisa Tsinaraki, ORCID iD: 0000-0002-6012-0835 e-mail: Article DOI: 10.25815/jqjc-qp38

Applying standards to citizen science data is important for several reasons. First, the use of standard methods and tools to gather data helps to ensure the fit for purpose property, i.e., that the collected data meets the quality criteria of an intended use. Second, they can ensure that the data collected, validated or analysed in citizen science activities are provided under the appropriate access and use conditions — which hold to the participants but also to others (i.e., any third party that might be interested in re-using or replicating the work). Hence, standardization is essential for data to be reusable in other contexts. Third, the use of (domain-specific) data standards helps to ensure that the citizen science activity covers all important elements (attributes) of the phenomenon under investigation.

When it comes to specific (data) standards, there are many to choose from. In the context of citizen science projects, this can be considered an asset, because citizen science approaches are very rich and diverse, so that there cannot be a one-size-fits-all solution. However, a few things should be said about standards and citizen science data, and a few recommendations might help in advising practitioners and managers of citizen science standardisation initiatives.

Before anything else, the first recommendation is to avoid assumptions about any citizen science activity. It cannot be assumed that a citizen science activity aims at or would benefit from applying a particular standard, or that the participants are interested in data being re-used by others. Instead, the participants of any existing or new project need to be consulted to clarify their intentions and needs. Which (kind of) data will be collected or analysed by the project? For which purpose? What are requirements in terms of data accuracy, spatial temporal coverage? Who should have access to the data collected by the project? Are there any privacy concerns and which agreements need to be put in place to protect those contributing?

Regardless of the standard(s) that would be used in the project, it is essential to provide extensive instructions about their use, and to make sure standards are really understood and adopted by the different participants. Homogeneous data collection and representation are key for the overall scientific quality of the activity, so adequate training of participants will be utterly important. You can include information about standardisation in your communication strategy.

The second recommendation is to apply those few standards that indeed hold across any (citizen science) activity — and especially to the data it might create — those cover topics, such as:

  • Assessment of the need for an ethical review of data gathering and treatment methodologies, including the protection of personal data. Existing forms (for example, the dedicated online guide of the European Commission) might provide valuable insights. If required, ethical reviews might take particular attention, for example to control if the people involved are informed in the clearest way possible (i.e., not only being directed to long and complicated legal texts).
  • A careful assessment of the inclusiveness of the foreseen engagement methods and the way those are communicated. Here, it is important to clarify and clearly inform about conditions for participation but also which barriers this might create to particular communities.
  • The explicit use of standards and, if applicable, of machine-readable data licenses (for example, Creative Commons might be used).
  • Generic elements for describing data sets (metadata, such as Dublin Core) might be used in addition to any topic specific schemes. A first set of elements that are tailored to citizen science are under development by the community.
  • For data exchange and access, it might be assessed if the use of de facto standards for the machine-based exchange of data over the web (e.g., JSON and APIs) would add value to the project. This should include an assessment of the available technical capabilities of the team. In any case, it is also important to ensure that data is provided in ways that are appropriate for the targeted participants.

Any additional advice on the use of dedicated standards for data itself has to be put into context. Standards for citizen science data are highly topic specific, for example, different approaches have to be taken when using air quality sensors (see e.g., the SamenMeten infrastructure — soon also in English), observing birds (see e.g., the European Bird Indexes), or collecting and reporting data about litter on beaches (see e.g., Marine Litter Watch). The odour pollution case description provides some more details for one selected example, but generally it is advisable (third recommendation) to engage with research institutions and/or public administrations that are parts of the dedicated thematic communities, to learn about their standards and if and how those might apply to a given citizen science project. The best entity to engage with will depend not only on the topic, but also on the intended outcome/purpose of the citizen science project. Public institutions, for example, should be engaged if there is an ambition that the created knowledge will affect policy-making – and the right level of administration depends on the intended outreach (local, regional, national, European or global).

Standards support interoperability among systems

The university library can be a hub of knowledge for working with metadata and data standards. It aids the academic disciplines to identify and introduce existing domain and community dependent metadata standards. Find links and further resources in the DMP part. collects policies, standards and ontologies from different disciplines that may be useful also for citizen science.


Schade, Sven, Chrisa Tsinaraki, and Elena Roglia. “Scientific Data from and for the Citizen.” First Monday, July 31, 2017.

Turbé, Anne, Jorge Barba, Maite Pelacho, Shailendra Mugdal, Lucy D. Robinson, Fermin Serrano-Sanz, Francisco Sanz, Chrysa Tsinaraki, Jose-Miguel Rubio, and Sven Schade. “Understanding the Citizen Science Landscape for European Environmental Policy: An Assessment and Recommendations.” Citizen Science: Theory and Practice 4, no. 1 (December 2, 2019): 34.

Hecker, Susanne, Mordechai Haklay, Anne Bowser, Zen Makuch, Johannes Vogel, and Aletta Bonn. Citizen Science Innovation in Open Science, Society and Policy. London: UCL Press, 2018: 321-336.

Project Highlight: Defining New Data Standards with Citizen Science

The D-NOSES project applies citizen science and co-creation approaches to set new standards for odour pollution — globally.

The EU-funded project Distributed Network for Odour Sensing, Empowerment and Sustainability (D-NOSES) is an excellent example of the diversity of citizen science and data standards. It addresses odour pollution with the strong aim to influence policies — within countries, the EU and across the globe. Accounting for around 30% of the environmental complaints globally, odour pollution is an unregulated issue in many countries. D-NOSES aims to create scientific references and replicability guidelines for defining new regulatory frameworks. Among others, the project reviewed odour pollution and measurement techniques and compiled a list of good practices in handling odour pollution. A related Massive Open Online Course (MOOC) was developed to support capacity building. The project highlights that citizen science can be an integral part in developing standards, especially in domains where those standards have yet to be defined.

However, a few particularities should be noted:

  • In most thematic fields, measurement standards for data gathering and exchange already exist. Therefore, citizen science projects have different starting points;
  • Regulatory impacts are not always the main driver for a citizen science project. Thus, different standards might be needed to address different ambitions; and
  • Not all citizen science projects follow the same funding model. Available funds and related timelines do constrain the possibilities to learn to use existing or even to develop new standards.

Acknowledgment of Citizen Scientists on Research Outputs

Providing credit where it’s due, and how to achieve the ‘Reuse’ principle with information on data provenance.

By Georgia Ward-Fear, Macquarie University, ORCID iD: 0000-0002-4808-1933, e-mail: Article DOI: 10.25815/rkzw-z561

The ‘Reuse’ component of the FAIR principles dictates that data should maintain its initial complexity and have clear provenance information on how and with whom the data was formed. This, coupled with discipline-specific data and metadata standards, allows for data reuse in the future. Citizen science projects present unique challenges for accommodating the Reuse principle. This is because there is currently no single or standardised way for crediting citizen science collaborators on publications or datasets, citing collaborative works or providing Intellectual Property (IP) rights to the citizen scientists involved. Depending on the methodology used by the authors (or imposed by research journals), data provenance can thus be a subjective and sometimes mis-represented aspect of research involving citizen scientists.

Official acknowledgment for contribution to research is a cornerstone of academia and researchers should strive always to credit citizen scientists in appropriate ways. There are myriad benefits to official acknowledgment, some of which may only apply to the citizen science groups (e.g., unique funding opportunities; support to create formal structures around the citizen science group). In a collaborative project, citizen scientists/groups can be credited via the official acknowledgements, or via authorship on manuscripts/datasets. The latter often requires justification that citizen scientists have met the International Committee of Medical Journal Editors (ICMJE) standards which warrant academic authorship. Citizen science does not always lend itself to meeting these rigorous academic criteria. The case study below outlines a common situation in this respect and the diversity of considerations (cultural, legal, ethical) when deciding the best form of acknowledgement for citizen science collaborators.

Project Highlight: Lizard Conservation with the Balanggarra Rangers in Australia

To mitigate the impact of an invasive toad on a native apex predator in tropical Australia, a collaboration between conservation researchers and indigenous Traditional Owners (the Balanngarra Rangers, of the Balanggarra people) was formed. Each group brought their unique skills, experience, and knowledge to the project; an excellent example of the synergy between ‘Western science’ and Traditional Ecological Knowledge and skills. The research was highly successful and culminated in the development of a new national and international conservation strategy. The Balanggarra Rangers were pivotal to the success of this project in unique ways, yet crediting the Rangers was not simple. The group consisted of many individuals with varying degrees of input, and the group wanted to be identified collectively by their cultural name. However, adding the ‘Balanggarra Rangers’ to the author byline was rejected by some scientific publishing outlets due to editorial or ethical protocols, or because the Rangers’ lacked academic affiliation. Even those that did, reduced the author name to ‘B. Rangers’ in citations, an unintended but nevertheless culturally inappropriate practice. This experience motivated the researchers to petition for more inclusive academic authorship protocols that keep pace with the changing socio-political nature of research. ‘Group co-authorship’ is an option for including citizen science groups as authors, providing strong public acknowledgment and Intellectual Property rights. It also avoids some of the ethical pitfalls for adding individuals by name, who have not met ICMJE guidelines (which can be viewed as academic fraudulence). A summary of the suggested qualifiers for citizen science group authorship can be found in the infobox.

Research programs that engage with citizen scientists should identify the most appropriate method for credit at the outset. Not only is this ethical, but also helps to explore and achieve legal interoperability early on, i.e., the ability of organisations with different legal frameworks to work together. This may be especially pertinent in citizen science projects where disparate organisations, industry sectors or subsets of the community come together in a research setting. When assessing the best way to credit citizen scientists in a project, these are some of the questions to consider:

  1. What are the wishes of the citizen scientists involved in the program?
  2. Is the citizen science group readily identifiable by a collective name or could they create one?
  3. Is the dataset in use ‘static’ (i.e., finished, whole and attributable to one group), is this a ‘living’ dataset and/or is only a portion of it being used?
  4. Is it ethical or appropriate to identify individual citizen scientists by name?
  5. Which form of citizen scientist identification will best support the ‘Reuse’ principle?


Ward-Fear, Georgia, Balanggarra Rangers, David Pearson, Melissa Bruton, and Rick Shine. “Sharper Eyes See Shyer Lizards: Collaboration with Indigenous Peoples Can Alter the Outcomes of Conservation Research.” Conservation Letters 12, no. 4 (July 2019).

Ward-Fear, Georgia, Gregory B. Pauly, Jann E. Vendetti, and Richard Shine. “Authorship Protocols Must Change to Credit Citizen Scientists.” Trends in Ecology & Evolution 35, no. 3 (March 2020): 187–90.

Hunter, Jane, and Chih-Hsiang Hsu. “Formal Acknowledgement of Citizen Scientists’ Contributions via Dynamic Data Citations.” In Digital Libraries: Providing Quality Information, edited by Robert B. Allen, Jane Hunter, and Marcia L. Zeng, 9469:64–75. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2015.

Group Co-authorship for Citizen Scientists: Recommendations for Using, Listing, and Citing

Group co-authorship should be used:

  1. When the group in question expresses a desire for authorship.
  2. When groups cannot meet ICMJE or journal specific standards but their contribution was deemed essential to the success of the project. For citizen science projects, this should include ‘data acquisition’, which would not normally warrant authorship alone.
  3. Only for established groups (e.g., the ‘Balanggarra Rangers’) not for amorphous groups who engage with generic surveys or medical studies. Such groups are best recognised in the Acknowledgment section.

Group co-author names should:

  1. Be as short as possible, and
  2. be listed in full at all times (e.g., ‘Balanggarra Rangers’ not ‘B. Rangers’) i.e., both in the author byline and also when citing and indexing in references. This is also the responsibility of the publisher and indexing programs/institutions.

All authors collectively decide:

  1. On the best form of acknowledgment and whether it meets the ‘Reuse’ principle.
  2. The most appropriate order of authorship (groups co-authors can be anywhere in that order).
  3. Whether to list individual citizen scientist names elsewhere in the text (e.g., supplementary materials or appendices), remembering that some citizen scientists may be eligible for individual authorship independent of the group. Group co-authorship doesn’t replace this option.

Adapted from Ward-Fear et al. 2020

Planning and Securing Resources — The Data Management Plan

By Iryna Kuchma, Electronic Information for Libraries, ORCID iD: 0000-0002-2064-3439 e-mail: Article DOI: 10.20389/tpap-md16

A data management plan (DMP) can support citizen science data in becoming FAIR. It should be created early in the research process and updated regularly to prepare for data deposit, sharing and reuse. University libraries already have knowledge of FAIR data. So in this final part about the DMP, we emphasize resources useful for citizen science projects and in which sections of the guide, you can find related information. A common understanding of how data will be managed is particularly important in collaborative projects that involve researchers, institutions and groups with different ways of working and expectations.

This guidance follows the six Science Europe core requirements for DMPs (Science Europe 2021). Also, refer to Wiggins et al. (2013) for writing a DMP for citizen science projects.

Find a curated collection of Horizon 2020 DMPs where several address citizen science projects. If your research library does not provide a DMP tool, use free online tools for writing DMPs such as ARGOS or DMPOnline.

  1. Data description and collection or re-use of existing data

The data description is the core part that you build upon to make decisions on data management. In this section you include information on the type of data that will be gathered. To increase the value of citizen science data from the perspective of the general public (community interoperability) or regulatory authorities, interoperable data should be planned for. This is important for integration with existing data or when using existing technologies for data collection.

Learn more about Citizen Science Data and Standards and about open science.

  1. Documentation and data quality

Describing why and how data was collected is important for documenting the data quality. The data handling skills of the participants may be unknown, therefore, describe methods used to collect and treat data, data provenance, and quality-assurance steps taken. Explain how you will guarantee consistency within your dataset. Metadata standards from relevant disciplines (if existing) are key for data interoperability and reuse. Ventures in new technology should aim at following community standards and being open source so later users can implement and further develop the tools to their needs.

Learn more about Citizen Science Data and Standards. Search FAIRsharing for standards, ontologies and policies.

  1. Storage and backup during the research process

It is good practice to store data in at least one non-proprietary format. Project managers often use their personal data storage and the library role could be to help with the institutional storage provisions or identifying infrastructures fit for citizen science data. Seek storage solutions, which offer flexibility and protection for sensitive data or data with disclosure risk. Best practice is to store data without direct identifiers and replace personal identifiers with a randomly assigned identifier (ask researchers to create a separate file, to be kept apart from the rest of the data, which provides the linking relationship between any personal identifiers and the randomly assigned unique identifiers). Where possible, select a storage solution that allows an easy way to maintain version control.

Learn more about infrastructures for Citizen Science in Section 2 of this guide.

  1. Legal and ethical requirements, codes of conduct

Citizen science projects might not have access to legal and ethical advice, and may need help to establish approval mechanisms for sharing data (via consent, regulation, institutional agreements and other systematic data governance mechanisms, including restricted access conditions and embargoes if required). Acknowledge data provenance in metadata and any limitations or obligations in secondary use, inclusive of issues of consent.

Learn more in Use of Data Policies in Citizen Science Projects and Acknowledgment of Citizen Scientists on Research Outputs.

  1. Data sharing and long-term preservation

Where possible, advice to provide immediate open access to citizen science data and recommend Creative Commons Attribution 4.0 International License (CC BY 4.0), a Creative Commons Public Domain Dedication or equivalent. Also, clarify whether any project funder has specific data access requirements. Know the needs of the participants before you share any data and determine methods for sharing. If immediate open access is not possible, consider creating a metadata record in a repository where a persistent identifier and license can be assigned. If data cannot be open, indicate how they can be made accessible, and under which conditions. Describe which measures you will take to enable long-term preservation.

Remember to consult your participants during the project planning and with regard to their expectations of data sharing (see Citizen Science Data and Standards).
Get more information about Use of Data Policies in Citizen Science Projects and about infrastructures for sharing data in Section 2.

  1. Data management responsibilities and resources

Describe who (for example role, position, and institution) will be responsible for data management. What resources (for example financial and time) will be dedicated to data management and ensuring that data will be FAIR. Apps or technologies for data collection and participant interaction may require regular maintenance and updates – and therefore, funding for long-term support. By planning early, costs can be significantly reduced.

Identify and assess RDM costs and include them in the project planning.


Science Europe. Practical Guide to the International Alignment of Research Data Management. (Extended Edition). Brussels: Science Europe, 2021.

Wiggins, A, Bonney, R, Graham, E, Henderson, S, Kelling, S, Littauer, R, Lebuhn, G, Lotts, G, Michener, W, Newman, G, Russel, E, Stevenson, R, Weltzin, J. Data Management Guide for Public Participation in Scientific Research. DataOne, 2013

Project Highlight: FAIR Data in a Citizen Science Project

Article DOI: 10.25815/tnrh-zg50

Fangstjournalen is a citizen science project highlighting several of the points from this section: it demonstrates how good communication, project and data management create value to citizen scientists and also to scientific data. Learn how the project manager makes his data FAIR and share data with the citizens in this short video (Holmstrand et al. 2020). The collected data are relevant for reuse in projects about biodiversity, behavior and recreation, but also for national fishery regulation and policy development. Due to the content of personal data, the database is not shared openly but via a metadata record (Skov 2021).


Holmstrand, Katrine Flindt, Asger Væring Larsen, Signe Gadegaard, Jitka Stilund Hansen, Karsten Kryger Hansen, and Gertrud Stougård Thomsen. “FAIR Data in a Citizen Science Project ‘Fangstjournalen,’” 2020.

Skov, Christian. “Database from Citizen Science Project ‘Fangstjournalen.’” Technical University of Denmark, 2021.

Project Highlight: The INOS Project

Article DOI: 10.25815/aayw-w097

The INOS project is funded under the Erasmus+ KA2 Strategic Partnerships program and aims at integrating open science and citizen science into active learning approaches in Higher Education. INOS is a partnership of four universities (Aalborg University, Tallinn University, University of Oulu, University of Bordeaux), an SME (Web2Learn) and LIBER the Association of European Research Libraries.

Upskilling through open knowledge activities

In its three year duration and among its other goals and activities, INOS aims to expose academic and library staff and students to participatory methods in fostering open science, and consequently upskill them, so that they reflect on updating pedagogical models in Higher Education through citizen science. It does so by encouraging universities and university libraries to co-create and participate in Open Knowledge and Open Innovation activities. In this context LIBER co-organised four Open Knowledge Activities (OKAs) with five LIBER participant libraries and with the support of the LIBER Citizen Science Working Group and the LIBER Copyright and Legal Matters Working Group. The LIBER OKAs aimed at co-creating and debating on citizen science concepts, thus upskilling the participants in open and citizen science.

Towards a Roadmap on Capacity Building on Open Science and Citizen Science for Research Libraries — Co-creating a vision

Building up on the project’s overall intellectual outputs and activities, LIBER is leading activities on engagement, awareness raising and fostering policy change. In this framework, LIBER has recently organised two highly interactive vision-building workshops that led to the publication of a report on co-creating a vision for citizen science in higher education. This activity, together with the previous OKAs, are the starting point for a workshop dedicated to research libraries, aiming to co-create a Roadmap on Capacity Building on Open Science and Citizen Science for Research Libraries. The project will wrap up its activities with a report the the final Stakeholders’ Consultation of the project and its final Vision and Policy Recommendations for Higher Education.



User Type
  • Citizen scientist/civil society organization
  • Educator/museum
  • Researcher/research institution
  • Teacher/school
Resource type
  • Collecting and processing data
  • Getting started
  • Step by step guides
Research Field