ABRIR Hackathon Proposal – Decolonizing Data Governance in Big Team Science

Decolonizing Data Governance in BTS | HACKATHON PLAN | 1 Decolonizing Data Governance in Big Team Science Hackathon / Open Discussion Proposal 1. Purpose & Rationale 2 2. Intended Audience 3 3. Format & Logistics 3 4. Structure of the Session 4 5. Possible Outputs 7 6. Glossary & Suggested Resources 8 7. Acknowledgements 13 Image by freepik This content was developed through a project funded by the Open Scholarship Catalytic Awards Program (Award Number: #20160-2025-10), with support from the Open Research Community Accelerator (ORCA) and the Chan Zuckerberg Initiative. It is licensed under CC BY NC SA 4.0. Decolonizing Data Governance in BTS | HACKATHON PLAN | 2 1. Purpose & Rationale This session would aim to open a collective conversation on equit a ble a nd non - extr a ctive d a t a govern a nce within Big Team Science projects, especially when working across Minority World and Majority World1 countries. This topic connects directly to ongoing work by ABRIR (Advancing Big-team Reproducible Science through Increased Representation), and builds on insights from previous global ABRIR events and hackathons. Why it matters ● Persistent power imb a l a nces , which often create conditions that feel inequitable for researchers working in Majority World contexts even within apparently egalitarian Big Team Science collaborations. ● These dynamics raise concerns about equit a ble a uthorship , f a ir recognition of contributions , and keeping rese a rch a gend a s relev a nt to the loc a l contexts . ● F unding a nd resource inequities create additional barriers to equitable collaboration. ● Practices like helicopter science and ethics dumping lead to exploitative or extractive research. “A certain insensitivity to cultural specificities and a persistent search for universals . . . our local data was excluded” “Another concern is managing the transparency and data-sharing aspects of Open Science, ensuring that sensitive or proprietary data is handled responsibly” “Most funding programs from the Global North ignore the Global South partners' working conditions and realities” Quotes from 2024 ABRIR events 1 We use the term "Majority World" to refer not only to countries in Africa, Asia, Latin America, the Middle East, and to Indigenous peoples (as an alternative to "Developing World" or "Third World"), but also to other underrepresented and/or under-resourced research contexts, such as those in Eastern and South-Eastern Europe. In contrast, the term "Minority World" refers to countries that are typically overrepresented in (open) science and have well-resourced research environments. This content was developed through a project funded by the Open Scholarship Catalytic Awards Program (Award Number: #20160-2025-10), with support from the Open Research Community Accelerator (ORCA) and the Chan Zuckerberg Initiative. It is licensed under CC BY NC SA 4.0. Decolonizing Data Governance in BTS | HACKATHON PLAN | 3 Overall goal Create a collaborative space to identify challenges, generate ideas, and lay the foundation for future guidelines on trustworthy/responsible sharing, ownership, and reuse of data collected by Majority World researchers during Big Team Science projects. 2. Intended Audience ● Big Team Science project leaders and members (psychology/social sciences focus, but open to all disciplines) ● Researchers collecting or curating data in Majority World countries ● Data/Ethics/Open Scholarship professionals ● Students and early-career researchers are welcome, provided they have at least a basic familiarity with open data sharing and Big Team Science ● Journal and infrastructure partners both in the Majority and Minority World 3. Format & Logistics Length: 60-90 minutes per session (the hackathon can include multiple sessions if needed) Modes: Online or in-person Possible venues: ● Online SIPS 2026 (May 6-7, 2026) ● Big Team Science Conference (online; Fall 2026) ● Self-hosted through ABRIR or, if we are partners, CONNECT (Coordinated Network of Networks for Enhancing Collaborative Teams) Tools: Miro, Google Docs or similar for collaborative notes; Google Forms, Google Sheets or similar to collect participant info for networking and future crediting; a video platform with breakout rooms (e.g., Zoom). This content was developed through a project funded by the Open Scholarship Catalytic Awards Program (Award Number: #20160-2025-10), with support from the Open Research Community Accelerator (ORCA) and the Chan Zuckerberg Initiative. It is licensed under CC BY NC SA 4.0. Decolonizing Data Governance in BTS | HACKATHON PLAN | 4 4. Structure of the Session A. Welcome & Context (25 mins) ● Brief overview of ABRIR. ● What Big Team Science is and why data governance matters. ● Introduce the following problematic research practices that can occur in Big Team Science projects: ethics dumping , extr a ctive rese a rch , and helicopter rese a rch . ○ N ote for moder a tors : Definitions and further resources on these practices as well as FAIR/CARE/JEDI/TRUST are provided in the Glossary at the end of this plan. ● Introduce the FAIR Da t a principles (Findability, Accessibility, Interoperability, and Reuse), then explain that they focus mainly on data management and do not account for power differentials or historical contexts, and therefore are desirable but not sufficient to address the problematic research practices introduced earlier. ● Briefly acknowledge the CARE P rinciples for I ndigenous Da t a G overn a nce (Collective benefit, Authority to control, Responsibility, Ethics). While they were not developed with Big Team Science projects in mind, they nonetheless help move the discussion toward considering people and purpose in open data sharing. ● Begin by explaining what JEDI ( J ustice , E quity , D iversity , a nd I nclusion ) is, then discuss why striving for JEDI in Big Team Science projects benefits everyone involved. ● Introduce the TRUST C ode (Global Code of Conduct for Equitable Research Partnerships, predicated on Fairness, Respect, Care and Honesty). ○ N ote for moder a tors : If possible, encourage registered participants to read The TRUST Code – A Global Code of Conduct for Equitable Research Partnerships (4 pages) before the event. This content was developed through a project funded by the Open Scholarship Catalytic Awards Program (Award Number: #20160-2025-10), with support from the Open Research Community Accelerator (ORCA) and the Chan Zuckerberg Initiative. It is licensed under CC BY NC SA 4.0. Decolonizing Data Governance in BTS | HACKATHON PLAN | 5 ● Explain that the purpose of the session is to start a conversation, not deliver solutions; participants are encouraged to bring their own ideas, experiences, scenarios, and questions. ○ N ote for moder a tors : Steer discussion away from generic “Good Open Science Practices” and toward power dynamics; focus on ethical and equitable data use. Ensure Majority World voices are heard. Allow disagreement and encourage sharing personal examples. Encourage reflection on structural change rather than individual compliance. Emphasize that CARE principles / JEDI / TRUST code should not seen as mutually competing; they could be framed as complementary and reinforcing. B. Discussions (45 mins) Format Depending on attendance, time, and facilitator preference, this segment can be run in one of several ways: ● Pa r a llel groups by topic (different groups work on different topics at the same time; e.g. 4-6 participants per group), or ● S equenti a l whole - group discussions (each session focuses on one topic; participants may vary across sessions), or ● H ybrid / a ll - together (everyone discusses all topics together if time permits; not all questions need to be used, moderators may select those they deem most important). Discussion prompts Participants engage with the prompts below, focusing on concrete examples, principles, or recommendations. Each group/participant documents key points, examples, and proposed practices directly on the shared board/document. This content was developed through a project funded by the Open Scholarship Catalytic Awards Program (Award Number: #20160-2025-10), with support from the Open Research Community Accelerator (ORCA) and the Chan Zuckerberg Initiative. It is licensed under CC BY NC SA 4.0. Decolonizing Data Governance in BTS | HACKATHON PLAN | 6 E xtr a ctive pr a ctices & decoloniz a tion ● What are examples of extractive practices in Big Team Science, and how can they be prevented? ● How can Big Team Science avoid helicopter research? ● Which parts of the Big Team Science workflow most urgently need decolonial redesign? Da t a ownership , govern a nce & sh a ring ● What counts as ethical and equitable data ownership in Big Team Science projects? (Prompt participants to identify concrete examples and/or guiding principles, such as shared decision-making over data use, local data stewardship, or agreements on authorship and benefit-sharing.) ● What would trustworthy and responsible sharing of data collected in the Majority World look like? ● How do FAIR Data principles fall short without CARE / JEDI / TRUST additions? ● What responsibilities do Minority World vs. Majority World researchers have in data lifecycle decisions? C oll a bor a tion principles & institution a l incentives ● Using the TRUST Code, how can these principles be adapted to Big Team Science collaborations? ● How can journals, funders, and consortiums change their policies or expectations to support equitable research partnerships? C. Synthesis (15 mins) ● If the hackathon was run in a parallel-groups format: Each group shares 1-3 key insights or tensions. ● Moderators will provide a brief closing overview of the contributions captured on the shared board/document and invite additional comments or reflections. This content was developed through a project funded by the Open Scholarship Catalytic Awards Program (Award Number: #20160-2025-10), with support from the Open Research Community Accelerator (ORCA) and the Chan Zuckerberg Initiative. It is licensed under CC BY NC SA 4.0. Decolonizing Data Governance in BTS | HACKATHON PLAN | 7 D. Closing (5 mins) ● Clarify next steps: transforming the discussion into early guidelines or a position statement. ● Invite participants to leave their contact details for future collaboration and crediting. 5. Possible Outputs ● A shared board of ideas, principles, tensions, and initial directions. ● A short summary document (post-event) capturing contributions. ● A foundation for future guidelines for equitable Big Team Science data governance (for example, in the form of “10 guiding questions for ethical data governance in Big Team Science”, adapted from the list of ten questions from Nature Portfolio journals Authorship policies - Inclusion & ethics in global research). ● A possible follow-up working group under ABRIR / CONNECT. This content was developed through a project funded by the Open Scholarship Catalytic Awards Program (Award Number: #20160-2025-10), with support from the Open Research Community Accelerator (ORCA) and the Chan Zuckerberg Initiative. It is licensed under CC BY NC SA 4.0. Decolonizing Data Governance in BTS | HACKATHON PLAN | 8 6. Glossary & Suggested Resources JEDI (Justice, Equity, Diversity, Inclusion) “ J ustice : dismantling barriers to resources and opportunities in society so that all individuals and communities can live a full and dignified life. E quity : allocating resources to ensure that everyone has access to the same opportunities. Equity recognizes that advantages and barriers—the “isms”—exist. D iversity : all the differences between us based on which we experience advantages or encounter barriers to opportunities. Diversity is not just about racial differences. I nclusion : fostering a sense of belonging by centering, valuing, and amplifying the voices, perspectives, and styles of those who experience more barriers based on their identities.” Source: JEDI Collaborative (https://jedicollaborative.com/) JEDI in Big Team Science ● Materials from 2024 ABRIR events tackling Open and Big Team Science in the Majority World (available Open Access on Zenodo). ● Jeftic, A., Lucas, M. Y., Corral-Frías, N., & Azevedo, F. (2024). Bridging the majority and minority worlds: Liminal researchers as catalysts for inclusive open and big-team science. In P. S. Forscher & M. Schmidt (Eds.), A better how: Notes on developmental meta-research (pp. 48–63). Busara. https://doi.org/10.62372/ISCI6112 (Open Access) ● Adetula, A., Forscher, P. S., Basnight-Brown, D., Azouaghe, S., & IJzerman, H. (2022). Psychology should generalize from — not just to — Africa. Nature Reviews Psychology , 1 (7), 370–371. https://doi.org/10.1038/s44159-022-00070-y (Open Access: https://doi.org/10.31730/osf.io/xd269) This content was developed through a project funded by the Open Scholarship Catalytic Awards Program (Award Number: #20160-2025-10), with support from the Open Research Community Accelerator (ORCA) and the Chan Zuckerberg Initiative. It is licensed under CC BY NC SA 4.0. Decolonizing Data Governance in BTS | HACKATHON PLAN | 9 FAIR Data principles (Findability, Accessibility, Interoperability, and Reuse) “ F ind a ble and a ccessible are mainly concerned with where data are uploaded. Considerations include the availability of persistent DOI s , met a d a t a , tr a cking of d a t a re - use , licensing , a ccess control , and long term a v a il a bility . One service that provides these features is the Open Science Framework (https://osf.io). I nteroper a ble and reus a ble highlight the importance of thinking about d a t a form a t (proprietary, e.g., Microsoft Word, vs. non-proprietary, e.g., text files) and how such formats might change in the future. One important way to ensure that materials, data, and code are reusable is through detailed document a tion . This helps future users (including the original researcher, at a later point in time) understand the functioning of the materials, the structure and format of the data, and how the code processes this data to arrive at the statistical results in a paper.” Source: Crüwell et al., 2019 ● https://www.go-fair.org/fair-principles/ (Website) CARE Principles for Indigenous Data Governance (Collective benefit, Authority to control, Responsibility, Ethics) “The CARE Principles for Indigenous Data Governance are people and purpose-oriented, reflecting the crucial role of data in advancing Indigenous innovation and self-determination. These principles complement the existing FAIR principles (www.go-fair.org) encouraging open and other data movements to consider both people and purpose in their advocacy and pursuits.” “ C ollective B ene fi t . Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data.” This content was developed through a project funded by the Open Scholarship Catalytic Awards Program (Award Number: #20160-2025-10), with support from the Open Research Community Accelerator (ORCA) and the Chan Zuckerberg Initiative. It is licensed under CC BY NC SA 4.0.