- Convenors:
-
Jorge Barba
(Fundación Ibercivis)
Karen Soacha (Institut de Ciències del Mar (ICM-CSIC))
David Cuartielles (Malmö University)
Rosy Mondardini (Citizen Science Zurich)
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- Format:
- Workshop
Short Abstract
This workshop will address data management in citizen science: harmonisation, ethics, and AI readiness. Experts in climate, biodiversity, and health will identify gaps and priorities to foster interoperability and reuse of citizen-generated data within infrastructures and the RIECS initiative.
Description
Citizen science generates and uses vast amounts of data across disciplines, from biodiversity and climate change to health and environmental monitoring. However, the data ecosystems that support these efforts have different levels of maturity and often remain siloed, lack harmonisation, and face challenges in integration with established research infrastructures and AI systems. This workshop aims to critically examine how geographic, disciplinary and infrastructural peripheries shape the way citizen science data are collected, managed and used, and explore pathways to achieve greater interoperability, ethical governance, and AI readiness.
Structured into two interactive 105-minute sessions, the workshop will bring together data stewards, infrastructure providers, citizen science practitioners, and users from a range of initiatives—such as Copernicus, LandSense, GenBank, GBIF, LifeWatch and COACT. Together, we will reflect on emerging challenges related to data and metadata standards, responsible data sharing, access and ownership, GDPR compliance, and the ethical use of citizen-generated data in machine learning and automated systems.
Participants will engage in group discussions, case-based reflections, and collaborative mapping of needs and solutions, addressing key questions such as: What frameworks exist for data standardisation across citizen science domains? How can we balance openness with privacy, consent and recognition? What does AI-readiness mean for CS data, and who defines the criteria?
The outcomes will contribute to the long-term goal of creating a trusted, federated data ecosystem that supports open science and cross-domain collaboration in RIECS. This workshop offers a unique space for participants to collaboratively shape the future of citizen science data.
Accepted contributions
Short Abstract
Refined Guidelines with Checklist for Research Ethics and Research Integrity in Citizen Science will keep the guidelines that are sustainable and introduce those that are most relevant for the current situation of CS internationally.
Abstract
In 2022 we as a group of researchers created Guidelines for Research Ethics and Research Integrity in Citizen Science dedicated to Masters and Doctoral students and their supervisors, to facilitate CS-related research activities for citizen science (CS) in line with the values of academic integrity. We reviewed a pool of 85 papers to identify nine topics covering 22 guidelines. To dive deeper into each guideline further readings were recommended. However, due to intensive changes in social and virtual realities since the beginning of 2022 (e.g., rapid spread and development AI, Open science policies, increased relevance of science security etc.) these Guidelines should be updated and refined in a tangible manner.
A variety of methods will be used to respond to current realities of CS. The Guidelines will be complemented not only by literature review of last 5 five years (since last literature review date of the Guidelines were made in 2021), but also by conducting expert interviews with researchers from academia (e.g., dealing with issues in science security, open science, AI) highlighting the issues that are still important and those which arose and became more relevant due to the societal and technological changes. The review experienced citizen scientist will also be included to receive the feedback of the refined version to finalize the document.
The guidelines will also include a checklist for those who want to initiate the CS research in line with academic ethics.
Short Abstract
Urban ReLeaf brings case-based insights on the practical, ethical, and institutional challenges of integrating citizen-generated data into urban information systems, contributing directly to discussions on data flow, ethics, and system readiness.
Abstract
Urban ReLeaf is an ongoing Horizon Europe project focused on integrating citizen observations with urban authoritative data across six diverse European cities. Our work directly engages with many of the workshop's core themes, including data interoperability, ethics, and institutional readiness. We have encountered critical challenges such as balancing opportunistic versus structured data collection approaches and ensuring ethical governance when handling socio-demographic data for inclusivity assessments. Cross-sectoral collaboration has also revealed gaps in institutional capacity for data integration and divergent ethical and legal interpretations across countries. These cases show real-world complexities that influence the integration and re-use of citizen science data. At the workshop, we intend to introduce Urban ReLeaf as a case, and share reflections from Urban ReLeaf’s data governance practices, including our approach to operationalising data flows across public authorities and citizen platforms. A specific focus will be on data ethics—exploring tensions between openness and privacy, informed consent, and participant recognition across varied institutional settings. We are also interested in contributing to post-workshop efforts, such as co-developing a publishable perspective article that summarises key insights and pathways forward as discussed in the workshop. This aligns with our ongoing interest in shaping inclusive, interoperable and ethically robust frameworks for urban citizen science data.
Short Abstract
Ten years after examining the openness of data in biodiversity citizen science, this review assesses progress in data sharing and argues for involving experienced data scientists early to ensure open, sustainable and compliant data stewardship
Abstract
In 2016, we examined whether citizen science in biodiversity monitoring truly embodied the principles of open science. Our analysis of Global Biodiversity Information Facility (GBIF) datasets showed a paradox: while citizen scientists were often held up as an example of Open Science, their datasets were among the least openly licensed. At that time, many projects lacked licensing, imposed non-commercial restrictions, or obfuscated data. Although open data sharing is absolutely needed to resolve the biodiversity crisis, we concluded that voluntary data collection did not necessarily result in open sharing.
A decade later, this talk revisits that analysis to assess whether openness of biodiversity citizen science data have improved. Using updated GBIF metadata and examples from ongoing projects, I reevaluate licensing, attribution norms, and data governance practices. I also review how new frameworks, such as the FAIR principles, GDPR compliance, and Access and Benefit Sharing obligations are potentially influencing the data sharing in citizen science.
Beyond quantifying change, this presentation argues that sustainable open science requires embedding data science expertise from the conceptual stages of biodiversity projects. Data scientists who understand both the technical, legislative and ethical dimensions of open data, including privacy protection, community data ownership, and equitable benefit sharing, are critical for designing systems that are simultaneously open, compliant, and trusted.
This reflection highlights the need for transparent data policies, recognition systems aligned with contributor motivations, and interdisciplinary teams that can navigate the interplay between openness, ethics, and sustainability.
Abstract
The research I’ve been working on under the supervision of Prof. Dr. Sarita Albagli focuses on the legal challenges surrounding the use and governance of data in citizen science (CS) projects. We examine how diverse legal frameworks, including copyright, personal data protection, and the protection of traditional knowledge, interact and often conflict in the management and reuse of citizen-generated data. Our findings indicate that current literature tends to prioritise privacy and IP concerns while overlooking collective rights and contexts from the Global South. They also reveal the inadequacy of the current legal framework when applied to CS projects, including the narrow scope of copyright limitations and exceptions and the uncertainty as to whether CS initiatives qualify as “scientific research” or academic endeavours for applying legal flexibilities.
Recently, our work has also explored how artificial intelligence (AI) technologies are used both within and around CS. While AI can support species identification and data classification, the same citizen-generated content may later be reused to train generative AI systems, raising legal questions across copyright, data protection, and collective rights. We are also investigating the possible outcomes of the AI Bill currently being discussed in the Brazilian Congress, which introduces new provisions on transparency, remuneration, and data use for AI training. Considering that generative AI systems increasingly rely on large-scale data scraping, collaborative platforms and repositories face growing pressures. Thus, we seek to explore how legal frameworks may foster the sustainability and governance of commons-based infrastructures and support the ethical reuse of citizen-generated data.
Short Abstract
Practical technical arrangements for crowdsourcing environmental observations jointly from groups of different level of professionalism, on site and from satellite Earth observation products, utilizing Open311 based API platform Citobs by Syke. Ice, snow and algae blooms as examples.
Abstract
Information on e. g. ice and snow are relevant to very many different user groups, and it would be very difficult to set up a single mobile application service for all of them. Instead, for crowdsourcing of observations should be made available as a feature in varied services. Crowdsourcing is the technical term for gathering input and requesting for activities e. g. to observe the surrounding environment by those who are willing to participate: Common citizen observers, observations from people travelling across certain areas or at work on location, even those engaged in scientific field work on a site. Crowdsourcing can be participated by voluntary inexperienced laymen and professionals alike, and thus citizen observations can be of professional quality. However, the observations submitted via vesi.fi (waterinfo.fi) or jarviwiki.fi are typically very simple and basic, as they are aimed for common people. Syke has developed the Open311 based Citobs platform and several associated methods to release and manage more varied questionnaires and practices to assign roles to different types of participants, in voluntary, semi-professional organizer and professional roles. This ongoing development for gathering data from dedicated and more advanced observers up to professional level is presented with examples on snow, ice and algae bloom observations as well as on aquatic environment restauration planning activities. One of the primary objectives is to integrate pseudonymized observations on site to methods of observation by satellite, in order to provide match-up data and to complement cloudy or other periods of missing satellite data.