Reflections on the ECR-organized session ‘Perspectives on Data Repositories across Disciplines, Geographies, and Cultures’ from CODATA Connect leads Pragya Chaube, Louis Mapatagane, and Cyrus Walther.

At International Data Week 2025, the Early Career Researcher (ECR) session, “Perspectives on Data Repositories Across Disciplines, Geographies, and Cultures,” was more than just a panel; it served as a quiet manifesto. It reminded us that data repositories are not storage systems; they are knowledge goods. And knowledge goods, by their very nature, depend on participation, inclusion, and shared stewardship.

The session presented a rare cross-section of real-world frictions: genomic data implicate sovereignty, particle-physics data that overwhelm infrastructure, human rights data requiring machine readability for accountability, disparities in climate science data, and open research data cultures where practice lags behind aspiration. The ECR panel converged on a single truth: if data are to serve humanity, then the design architecture of the next generation of repositories that hold them must overcome these frictions and reflect humanity’s diversity.

Sovereignty Before Structure

The scientific and infrastructural requirements of genomics highlights to us that repositories are never merely technical artefacts. But as Claire Rye reminded us through her work on genomic data in Aotearoa New Zealand, no repository is ethically sound if it fails to respect sovereignty. Te Tiriti o Waitangi (‘Treaty of Waitangi’ – founding document of New Zealand, signifies partnership between the British Crown and Māori) affirms that taonga Māori (full chieftainship of indigenous possessions), including genetic data, remain under Indigenous authority. This is not an afterthought to FAIR; it is the foundation of what the CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) principles demand. When we speak of openness, we must first ask: open for whom, and on whose terms? Without community control and benefit-sharing encoded in governance and metadata, openness risks becoming another form of extraction.

Scale Without Participation Reproduces Inequality

In high-energy physics, the problem is not a lack of collaboration or data but the asymmetry of participation. As Cyrus Walther illustrated, experiments such as the Large Hadron Collider (LHC) or Square Kilometer Array Observatory (SKAO) generate petabytes of data — but access to those data, and to the computational ecosystems required to analyse them, is not always given. Here lies a paradox: the most open scientific projects can still be closed in practice. Membership fees, specialised software, and critical know-how determine who gets to participate in knowledge production. If repositories are to be truly open, they must democratise capability. That means designing open collaboration structures such as Cherenkov Telescope Array Observatory (CTAO) and open methods that allow researchers from the Global South and early-career scientists to contribute meaningfully as co-authors of discovery.

Machine Readability as a Justice Technology

Adriana Bora’s Project AI against Modern Slavery (AIMS) demonstrated a different frontier: how data infrastructure can define accountability itself. By converting thousands of corporate modern slavery statements into machine-readable datasets, AIMS transforms what was once bureaucratic reporting into a living, searchable archive for justice. In this context, machine readability is not pedantic; it is ethical. A PDF is a barrier; a CSV serves as an invitation – — because when information is locked in static documents, it excludes most people from engaging with it, but when data is structured and open, it empowers anyone to analyse, question, and act on it. Metadata and semantics in line with the FAIR principles provide a guide and make the data easier to use consistently and programmatically. When governments require and resource standardised, machine-readable reporting, they shift the cost of use from the vulnerable to the powerful. That is what participatory infrastructure looks like: when format embodies ethics.

Regional Data as Global Equity

Louis Mapatagane’s call to action on climate data inequalities strikes at the core of global disparity. Most climate data reside in the Global North, while communities suffering the harshest effects remain invisible within global systems. Without locally produced and managed data, adaptation policies are based on borrowed evidence. His call for universities to host regional repositories grounded in FAIR + CARE principles redefines such repositories as civic institutions that connect research, education, and local resilience. In this perspective, data justice begins with agency, not access. Regional capacity forms the foundation.

Culture Eats Policy

Pragya Chaube’s reflections on open research data in India brought the conversation full circle: even the best global frameworks falter without supportive cultures. India is the world’s third-largest research producer, yet data-sharing remains low, with fewer than one in ten researchers depositing data in repositories. The barrier is not just technical, it is institutional and cultural. Many institutions lack the policies, incentives, and infrastructure that enable researchers to share confidently and be recognised for doing so. Mandates alone do not create a culture of sharing — incentives, literacy, and recognition do. When researchers are rewarded for curation, not just publication, repositories begin to serve their true purpose: collective advancement.

Toward Participatory Data Futures

The throughline across these talks is unmistakable: data repositories must evolve from being mere collections to become commons. By commons, we refer to data repositories conceived as community-governed public goods, enabling shared stewardship and equitable access for the common benefit. Their legitimacy will depend not on the terabytes they host but on the diversity of those empowered to use them. Participatory design is not an embellishment; it is the architecture of trust.

As we look towards International Data Week 2027 in South Africa, the challenge before us is clear: to shift from access as a mere slogan to the reality shared analytical power as a global norm. That is how we turn repositories into living knowledge goods, and knowledge goods into instruments of equity.

L to R: Louis Mapatgane, Adriana Bora, Pragya Chaube, Claire Rye, Cyrus Walther

Reflections on Legal and Organisational Interoperability at IDW2025 from Matti Heikkurinen, LOI Lead and Project Portfolio Manager, CODATA

I’ll start with a confession: until quite recently, whenever I heard the word “interoperability”, I thought of the ability of any two systems to reproduce the same bit sequence across a connecting medium (wire, fibre, or airwaves). Formidable challenge, when you consider all the possible combinations of network vendors, device types, software stacks, noise, hardware failures, and human factors at play. But the engineering community has developed standards and processes to test technical interoperability through protocol analysis, plugtests, fault-tolerant software and hardware, among others. The blackouts are rare, worms usually stay in the cans, and Cloud outages shouldn’t cause insomnia (we hope). 

MoUSLA à la réalité

Travelling towards Brisbane, I had a high degree of trust in the technical and semantic interoperability of the aircraft components, backed by Byzantine Fault tolerance. I was less certain about finding pure algorithmic solutions or engineering approaches to provide complete solutions for the topics of the IDW 2025 session at the destination: Legal and Organisational Interoperability (LOI). This covers the ability of organisations not only to exchange information (Technical interoperability) and understand its meaning (Semantic interoperability), but also use the shared meaning to achieve mutually beneficial goals (Organisational interoperability) in a manner that defines and complies with the relevant legal basis for data sharing (Legal interoperability). 

Two randomly connected organisations are unlikely to reproduce the same policy decisions, given their unique governance models, legal frameworks, and standard operating procedures just because they can reproduce bit sequences sent by the other party. Nevertheless, organisational and legal interoperability are crucial to society, and mechanisms to achieve mutually beneficial goals have been developed. 

Memorandums of Understanding (MoUs) and Service Level Agreements (SLAs) have received significant attention as promising solutions for these challenges. And indeed, they do provide formal components to support and document Organisational and Legal interoperability. However, one of the motivations for the session was the (possibly controversial) view that, even if the agreements themselves were fully machine-actionable, they wouldn’t guarantee interoperability. They are the fiber-optic cables of LOI that need infrastructure around them to become operational; MoUs and SLAs are the desserts, not the whole meal.

CAREless whisper – Guilty machines have got no rhythm 

The opening plenary put any worries about the fit of our session topic to rest. The talks covered different aspects of indigenous data governance (presented in more detail in another blog), covering the different aspects of the CARE principles (Collective benefit, Authority to control, Responsibility, Ethics). The interesting weak signals in the session were the immediate pushback on governance models that were seen as too simplistic and the mutual benefits of adhering to CARE principles based on the SODA principles. The latter seemed important from the sustainable CARE point of view; ethics backed by enlightened self-interest tends to be much stronger LOI component than ethics alone.

The broader context of the LOI issues were covered by several other sessions and talks: the session on “Increasing Resilience of Global Earth and Environmental Science Data Supply Chains” brought new kinds of hazard scenarios into the spotlight, the RDA plenary session  “Balancing ethical considerations and Open Science principles when sharing community relevant data” presented the challenges in determining when sharing – or collecting – data might do more harm than good. 

“Our” session –  “Legal and organisational aspects of data interoperability: climate adaptation case studies” – included talks and discussions that were thought-provoking without exception. Some of the highlights included: 

• O(N2) challenge of sharing environmental data in Australia: nine jurisdictions, multiple overlapping laws, inter-state data exchange requiring compliance with federal and different state legislations (Kheeran Dharmawardena, Australian Dataspaces). Kheeran’s presentation included a table comparing different regulations in the Australian context.
• Legal and organisational interoperability in the ARDC People Thematic Data Research Commons (Adrian Burton, ARDC). Adrian described work in the ARDC People Commons and the wider OHDSI-OMOP community to establish specific terminologies for sharing agreements and consent.
• Urban research combining high-value (including high commercial value) and sensitive datasets (Pascal Perez, Director, AURIN). Pascal emphasised the importance of detailed agreements among participating organisations that deal with specifics in a way proportionate to risk and consonant with benefits. 
• Thanasis Sfetsos’s presentation of the Urban Heat Island use case of the Climate-Adapt4EOSC project, described the enablers for organisational and legal interoperability that have been identified in the initial analysis.
• Supporting CARE principles using RAID and local contexts (Rebecca Farrington, Director of Research Data Systems, AuScope), including the use of specific metadata to allow the retrospective application of usage conditions.

The session could perhaps be summarised by Adrian Burton’s quote from the first day of the event: “Data travels at the speed of trust”.

LOI Interoperability – Fade to (shades of) grey?

Reading the above summary, one might wonder where the FAIR data community should focus its efforts amid the growing number of data-sharing scenarios that no longer clearly fall into the binary “allowed-forbidden” categories? Will the response to critical hazards be hampered by the need to put the benefits and risks on a scale with an arbitrary (and context-dependent) approval line? Should we just reinforce and expand ethics boards to cope with the growing challenges?

Personally, I would argue that growing awareness of the need to operate in this “grey area” makes it more important to develop semantic frameworks and tools to describe CARE issues. Humans undoubtedly need to stay in the loop, but robust, consistent methods for describing the issues are crucial for making decision-making efficient, consistent, and CAREful. The work of initiatives such as the Cross-Domain Interoperability Framework (CDIF) and Climate-Adapt4EOSC received a major boost at the IDW, with the idea of reviving the RDA-CODATA Legal Interoperability IG gaining considerable support. As a result, we hope to observe and report an increase in the speed of trust at IDW 2027! 

References

• F. Schiller, L. van Beethoven: Ode to Joy 
• N. Lawson: Instant Chocolate Mousse
• G. Michael et al: Careless Whisper (Official Video) 
• J. Gum et at: Increasing Resilience of Global Earth and Environmental Science Data Supply Chains
• L. Bezuidenhout et al: Balancing ethical considerations and Open Science principles when sharing community relevant data
• Burton et al: Legal and organisational aspects of data interoperability: climate adaptation case studies
• Visage: Fade to Grey
• World Fair CDIF working group: Introducing CDIF

Reflections on Data Science and the role of the CODATA Data Science Journal from IDW and SciDataCon 2025, Brisbane, 15 October 2025, by Gita Yadav, DSJ Editorial Board, Matthew Mayernik, DSJ Editor-in-Chief, and Mark Parsons, past DSJ Editor-in-Chief.

What is the evolving role of data scientists in ensuring that automation and AI serve the long-term goals of science, integrity, and openness? 

This question shaped a lively and deeply reflective session at the 2025 International Data Week (IDW) in Brisbane, Australia, organised by members of the CODATA Data Science Journal (DSJ) editorial board together with partners from CODATA, the World Data System (WDS), and the International Science Council (ISC).

What made this session particularly rewarding was the high level of engagement from both speakers and audience participants, who actively examined practical pathways to support a new generation of research-aware, ethically grounded, and infrastructure-integrated data scientists. Their discussions demonstrated that the evolution of data science is not only a technical or institutional shift, but also a shared community project; one that redefines how knowledge, responsibility, and innovation intersect in the age of intelligent automation.

The session explored how data scientists must adapt to ethical, infrastructural, and community-centric expectations, and how organisations such as CODATA, WDS, ISC, and the Research Data Alliance (RDA) can collectively guide this evolution. Speakers reflected on the historical roots of data science while identifying forward-looking strategies for building capacity, governance, and trust in the expanding data ecosystem.

Background: Data Science as a Field in Motion!

The Data Science Journal was launched by CODATA in 2002 as a peer-reviewed venue to publish, share, and preserve knowledge on data-focused topics. As far as can be determined, it was the first scholarly journal to include the term “data science” in its title. In a retrospective essay, founding editor F. Jack Smith reflected that the title was initially contentious, with some members of the CODATA Publications Committee concerned that “data science” might be misunderstood. Ultimately, the committee agreed that “it was up to CODATA to ensure that it became understood” (Smith, 2023).

Over two decades later, that mission remains both prophetic and relevant. Data science has evolved dramatically, becoming central to academic research, policy, and industrial practice. The term itself has multiplied in meaning, encompassing everything from machine learning and analytics to data curation, visualization, and ethics. Today, as the volume, variety, and velocity of data continue to expand, the field faces another inflection point, while data veracity has become a critical fourth “V” in the landscape.

In an era of intelligent automation, large language models (LLMs) and ubiquitous sensing, data science has become not only a technical discipline but also a pillar of policy, infrastructure, and ethics. Yet, as the IDW2025 meeting made clear, the scientific community places distinct demands on data science, emphasizing provenance, traceability, transparency, and FAIR principles that are not always prioritized in commercial or governmental applications.
This tension raises an important question: What does it now mean to be a data scientist working in and for science, rather than for profit or production?

Mark Parson’s talk elucidated this tension very effectively, and Figure 1 is a screenshot of his half century plot depicting how the data ecosystem has continuously evolved from the establishment of the World Data Centres (1952) to the launch of the Data Science Journal (2002) and the creation of the Research Data Alliance (2013). Alongside these milestones, Mark also placed linguistic trends that reveal the rise of data science and data management and the gradual decline of information science, signalling a profound conceptual and cultural shift.

Figure 1:  Tracing the Arc of Data Science (1952–2022): This historical trajectory by Mark Parsons in his opening talk at the session illustrates how scientific data work has grown from information handling to knowledge stewardship. The Data Science Journal emerged as both a marker and a driver of this shift, grounding new technical vocabularies in the ethics and infrastructures of open science.

Insights from the Session

Evolution of Roles

Over the past three decades, data work has evolved from isolated roles (data managers, analysts, curators) into more integrated and interdisciplinary practice. In the 1990s, data management was often seen as a technical support activity. In the 2000s, as open data initiatives gained momentum, the need for stewardship became clearer: data were of little use unless structured, documented, and discoverable.

The 2010s witnessed large-scale infrastructure investments for sharing and integrating heterogeneous data. Now, in the 2020s, this infrastructure is being reconfigured in light of new AI/ML capabilities, emerging data governance policies, and community-driven data movements. Gitanjali Yadav highlighted this evolution in her talk,  emphasizing that scientific data work is inseparable from issues of responsibility and provenance (Figure 2). The emerging concept of semantic data science extends this arc, emphasizing multilingual, context-aware, and ethically informed practices that keep the human in the loop even as automation advances. 

Across all these phases, a recurring insight stands out: Automation can process data, but only humans can interpret meaning.

Figure 2: The Expanding Role of the Data Scientist by Gitanjali Yadav, reflecting on how the data scientist’s identity has evolved, from analyst (focused on computation and inference), to architect (building systems and workflows), to steward of integrity (ensuring ethics, transparency, and trust), making the present day data scientist a custodian of integrity and a community bridge.

Preservation and Provenance

A major theme during the session was preservation, not just of data, but of the expertise and communities that sustain it. Many repositories worldwide, especially smaller or domain-specific ones, face an existential crisis as funding cycles end or institutional support wanes. The panel emphasized that ownership and stewardship for such repositories should be distributed, balancing control with access, and ensuring that knowledge of the systems themselves is not lost.

Scientific data work depends on formal trust: verification through unambiguous reference to persistently accessible datasets. Provenance graphs, representing relationships between datasets, people, organizations, instruments, and software are central to this trust. They enable reproducibility, attribution, and understanding of how knowledge is constructed. Examples such as the #SemanticClimate initiative were cited (see references), showing how semantic annotation and linked data frameworks can connect disparate data sources and narratives in ethically transparent ways. Presented on behalf of Dr. Debasisa Mohanty, member of the CODATA National Committee in India, Figure 3 is a case study from India demonstrating how large-scale, ethically governed data ecosystems can transform biomedical science. This example underscored the dual role of data scientists as technical innovators and ethical stewards of sensitive information (Figure 3).

Figure 3. AI-Guided Genomic Discovery in India:  Screenshot of the Genome India project slide presented by Gitanjali Yadav on behalf of Debasisa Mohanty, depicting AI/ML-guided exploration of genomic diversity across India, discovering over 27 million rare variants, identifying population-specific drug-response markers, and constructing a genome-wide imputation panel that improves precision for Indian genotypes.

Ethics, Equity, and Access

Data derived from people raises enduring tensions between the ideal of openness and the necessity of privacy. Participants discussed methods such as anonymization and statistical obfuscation, which allow analysis without reidentification. A broader consensus emerged that equity precedes ethics: before one can speak of fairness or accountability in AI, data access itself must be equitable. Otherwise, ethical deliberations risk reinforcing existing asymmetries in who gets to produce, own, and use data.

Invisible Labour and Recognition

Several participants highlighted the invisible labour that underpins open science, particularly curation, cleaning, and metadata preparation. This work remains undervalued in traditional reward systems that prioritize publications and citations. As one discussant noted, “Everyone celebrates analysis, but few remember who made the data analyzable.”

This invisibility also shapes the status of data professionals. Data scientists and stewards are often told, “you’re just the data person,” despite their central role in making science interoperable and reproducible. The Data Science Journal and CODATA recognise that much of the world’s data ecosystem is sustained by invisible or undervalued labour from data curation, annotation, and metadata stewardship to community-driven capacity building and open infrastructure maintenance. These contributions are essential to the integrity and longevity of scientific data but often fall outside traditional academic reward systems.

Moving forward, the discussion ranged around how the Data Science Journal can address these issues, for instance by:

• Encouraging submissions that explicitly acknowledge data stewardship, curation, and technical maintenance as forms of scholarly contribution.

• Promoting author contribution statements and citation practices that make invisible labour visible.

• Partnering with CODATA and WDS initiatives to explore frameworks for credit attribution and recognition of data professionals and infrastructure teams.

• Supporting cross-sector discussions on how open data infrastructures can better align with equity, inclusion, and fair recognition principles.

By doing so, the Data Science Journal aims to model a more just and transparent data culture,  one that values all contributors to the data lifecycle, not only those visible in authorship lines or algorithmic outputs.

Rethinking What “Data Science” Means

There was broad agreement that the term data science has become so expansive as to risk losing coherence. It now covers technical, social, and managerial dimensions. The panel argued for a renewed synthesis: data stewardship needs to become more data sciency, integrating computational and AI techniques, while data science must become more stewardship-aware, incorporating principles of provenance, ethics, and transparency.

Participants also noted the value of philosophical perspectives — as raised by a reference to Naomi Oreskes’ Why Trust Science? — in understanding how we know what we know. This epistemological reflection is increasingly vital as AI systems are trained on limited, often opaque, corpora of data.

Training as ‘Reciprocal Learning’

The conversation repeatedly returned to the challenge of training and upskilling. Many organizations are hiring data stewards from disciplinary backgrounds rather than data or information science programmes, and vice versa. The result is uneven literacy across both communities.

Some argued it is easier to train scientists in data management than to train data managers in domain science; others disagreed. What was clear is that curricula for both data science and data stewardship could benefit from closer integration,  potentially as a future CODATA initiative and that going forwards, we should use the term ‘reciprocal learning’ instead of ‘capacity building’. Embedding data stewards within research teams was highlighted as a successful model: it strengthens stewardship within science and builds new skills within the stewardship community itself.

In a forward looking stance, the Data Science Journal editors and CODATA executive team in the room, discussed with audience how we could commit to advancing visibility and equity in data science by championing reciprocal learning as a guiding principle for global data collaboration,  moving beyond the traditional notion of “capacity building,” which can imply a one-sided transfer of knowledge. Instead, we emphasise mutual exchange, contextual expertise, and co-creation between diverse data communities. We can also encourage publications and dialogues that highlight the human and infrastructural labour underpinning scientific data.

By embracing both the recognition of invisible labour and the ethos of reciprocal learning, the Data Science Journal seeks to nurture a more just, inclusive, and reflexive data culture, one that recognises data science as a community of practice rather than a hierarchy of expertise.

Global Parallels and Technological Impacts

While automation and AI are disrupting employment patterns worldwide, the panel noted that the challenges and opportunities faced by data professionals are strikingly similar across countries. Early-career researchers tend to adopt AI tools faster, while mid-career professionals face greater displacement risks. As history shows, new technologies often reinforce existing power structures, making it even more important to foreground inclusivity and human judgment in the automation age.

Data Scientists as Connectors

A unifying theme across the session was that data scientists serve as connectors,  linking disciplines, infrastructures, and communities. They mediate between automated systems and human interpretation, ensuring that AI augments rather than replaces expertise.

This connective role is both technical and social: it requires understanding algorithms, policies, and people. As automation accelerates, these skills, including empathy, interdisciplinarity, ethical awareness, will define the next generation of scientific data professionals.

Concluding Reflections

The panel concluded that the evolution of data science cannot be understood purely through the lens of tools or technologies. It must also be seen as a cultural and institutional transformation in how science organizes, values, and preserves its knowledge.

As automation, AI, and semantic infrastructures reshape the research landscape, the task of data scientists, and the mission of the CODATA Data Science Journal, remains clear: we aim to uphold transparency, equity, and openness in the systems that increasingly govern knowledge itself.

References

• Smith, F. J. (2023). The Launch of the Data Science Journal in 2002. Data Science Journal, 22, 11. https://doi.org/10.5334/dsj-2023-11

• The #SemanticClimate Initiative: A global citizen science movement for extracting knowledge form locked literature (https://semanticclimate.github.io/p/en/)

—–About the Session——

Session Title: Evolving Roles for Data Scientists in the Age of Intelligent Automation
Event: International Data Week (IDW) 2025 – SciDataCon 2025, Brisbane, Australia
Date: 24–27 October 2025
Organizers: Data Science Journal Editorial Board (CODATA, ISC, WDS)
Moderator: Dr. Gita Yadav (National Institute of Plant Genome Research, India)
Speakers:

• Matt Mayernik – NSF National Center for Atmospheric Research, USA
• Mark Parsons – Research Data Alliance / Arctic Data Community
• Debasisa Mohanty – Director National Institute of Immunology, India (presentation by GY)
• Gitanjali Yadav, National Institute of Plant Genome Research (NIPGR) India and #semanticClimate

Affiliated Organisations: CODATA, World Data System (WDS), International Science Council (ISC), Research Data Alliance (RDA)
Session Link: https://scidatacon.org/event/9/contributions/40/

Blog Post by Gitanjali Yadav, India

The hum of conversation this week at the sprawling and impressive Brisbane Convention & Exhibition Centre carried a familiar cadence; the sound of the global data community converging once again under the banner of International Data Week!  There’s something quietly transformative about being in a room where the world’s leading minds in data gather, not just to talk about datasets and infrastructures, but about what responsibility means in the age of digital abundance. That’s what it felt like at the IDW 2025: a coming together of scientists, policymakers, technologists, and communities, each holding a different piece of the world’s data puzzle. This year’s event drew 807 participants, with more than a hundred joining online from across continents;  a testament to how inclusive the global data community has become.

Across plenaries and working sessions, one thread remained unbroken: how we, as stewards of data, can embed responsibility, equity, and interoperability at the heart of global science :)

As a member of the CODATA International Data Policy Committee (IDPC), I have been in many such rooms before. But this week was somehow more grounded and more self-aware. Maybe it was the unmistakable presence of the First Nations voices that opened our discussions, or maybe it was the realization that data policy is no longer a backroom dialogue but center stage in how we imagine a fair and sustainable scientific future..

Beginning with CARE, and the Human Face of Data

The opening plenary, “CAREful Indigenous Data Governance,” set a powerful tone. Alfred Lin’s exploration of Taiwan’s Indigenous Peoples’ data practices, Niklas Labba’s Sami’ stories and Marcia Langton’s reflections on Australian Indigenous governance frameworks underscored how data justice is becoming a core tenet of global policy. 

For those of us in global data policy, this wasn’t just an inspiring beginning; it was a call to listen more carefully, to communities, to context, to the histories embedded in our datasets. The conversations that followed about the CARE and FAIR principles weren’t merely academic, but about dignity, reciprocity, and trust, primarily about placing humanity back into the architecture of data governance. 

From the CODATA and IDPC perspectives, these discussions reinforce our ongoing work on developing policy frameworks that operationalize CARE Principles alongside FAIR, ensuring inclusivity without compromising scientific rigor. The need to balance cultural sovereignty with open science ideals is no longer a conceptual debate; it’s a governance imperative.

From FAIR Maturity to Data Stewardship

The technical sessions that followed, covering metadata frameworks, persistent identifiers, and FAIR maturity models, jointly revealed how far the data world has come since these terms first entered our vocabulary. But beyond the acronyms and schemas, what struck us most was a subtle shift in language: from compliance to stewardship.The National PID Strategy case studies and the FAIR Data Maturity Model WG session both illustrated the policy dimension behind technical progress. They asked the hard questions, ranging from how national frameworks align with global infrastructures, to who holds accountability for cross-border interoperability?

As policy actors, we must ensure that these models translate into equitable access and sustained trust, not just technological alignment. Stewardship, after all, is not a technical act; it’s an ethical one that materialises when researchers, repositories, and policymakers see themselves as part of a living ecosystem of data care.

When AI Walks into the Room! 

Artificial intelligence hovered like a quiet undercurrent throughout the week, not as hype, but as a reckoning. The second plenary on Rigorous, Responsible, and Reproducible Science in the Era of FAIR Data and AI captured this policy challenge; governing machine learning and generative AI systems through the lens of openness and ethics. Presentations on FAIR for Machine Learning, AI-Ready Data Workflows, and Documenting LLM Interactions in Research showed a welcome move toward transparency. The session by the Artificial Intelligence and Data Visitation (AIDV) Working Group went further, connecting AI governance to international policy harmonization. 

This is a theme directly tied to CODATA’s strategic focus on AI policy interoperability!  We didn’t just celebrate what AI can do; we questioned what it should do, and how we might govern it.  CODATA’s role, and that of the IDPC, is to ensure that this understanding is shared, equitable, and globally informed.

Policy as Practice, Not Paper

By the time we reached Day 03, it was clear that “policy” is no longer a distant set of guidelines, but the pulse that runs through collaboration. The third day’s sessions from “Policy and Practice of Data in Research” to “From Guidance to Practice: Implementing Open Science Policies in Crisis Situations”, revealed the increasing role of research data policy as a bridge between disciplines, sectors, and nations. Hearing examples from health data infrastructures, crisis response networks, and university governance frameworks reminded me that policy, at its best, is lived and negotiated in real time, across borders and disciplines.

It also reminded us of how delicate this work is: good policy isn’t just written, but trusted: and that trust, once built, must be constantly renewed.

As the IDPC, we have long argued that policy coherence is essential to avoid fragmentation between AI regulation, Indigenous data principles, and open science standards. Seeing these conversations converge across RDA and CODATA sessions was both validating and urgent. The question is no longer whether data policy matters, but how fast we can make it responsive to evolving technologies and societal needs.

Towards a Global Commons for Data Policy: From Brisbane to the World

Across the plenaries and working group sessions, I kept returning to one idea: the vision of a policy commons for data.This recurring vision across sessions was unmistakable as a tangible thread that connects FAIR infrastructures, CARE governance, AI ethics, and SDG-aligned data frameworks into a coherent ecosystem. Initiatives like the Global Open Research Commons (GORC) and cross-continental collaborations on metadata standards reflect what such a commons could look like in practice: A shared space where ethical frameworks, AI governance, Indigenous sovereignty, and open science standards can coexist without hierarchy. 

It’s an ambitious dream,  but one that feels closer than ever.

A Subtle Shift

In the corridors between plenaries and coffee breaks, I sensed a subtle shift;  from technical to human, from compliance to care. Maybe that’s what this week was about, realizing that our modern data systems, no matter how complex, are still part of that same continuum of knowledge care! Policy, at its best, is about tending to that continuum and ensuring that as we build the future of data, we don’t forget its human roots. 

The conversations were about more than open data; they were about open hearts and about the humility it takes to govern wisely in an interconnected world. Leaving the southern hemisphere, I carry with me a renewed sense of purpose, that data policy, when done right, is not about control, but about connection. 

I am also reminded that policy work in data is as much about listening as legislating, about bringing voices from the periphery to the center, ensuring that our data futures are inclusive, ethical, and sustainable. That, perhaps, is the real legacy of International Data Week 2025!

Looking Ahead: Our shared Data Futures!

As the week closed, there was a sense of momentum; that the conversations begun in Brisbane would ripple outward. The Research Data Alliance (RDA) announced that its next plenary will be hosted at the Oval Cricket Ground in London, with the call for sessions opening in March 2026 and registrations from April 2026.

In the closing plenary, Mercè Crosas, President of CODATA, shared an evocative AI-generated “Data Word Cloud”;  a collective snapshot of the ideas that shaped International Data Week 2025. Created from the titles of every talk presented during the week, the cloud distills the essence of our shared conversations where Indigenous data governance, open science, FAIR principles, community empowerment, metadata standards, and responsible research practices emerged as themes, painting a portrait of what global data stewardship truly means today. This visual tapestry is a representation of collective language of the week, where words like Earth, CAREful, AI, Indigenous, FAIR, responsible and Community pulse among others. To me, it captures something more than any summary or report: the living vocabulary of a global data movement in motion! 

AI-generated Data Word Cloud presented by Mercè Crosas, President of CODATA, at the IDW 2025 Closing Plenary. Based on the titles of all conference talks.

Going forwards, the community is already looking to Cape Town, South Africa, where the next International Data Week will take place in September 2027. The symbolism of moving from Brisbane to the African continent is powerful; a reminder that the future of data must be as global, diverse, and connected as the challenges it seeks to address. 

I think back to the week’s first words; the acknowledgment of the traditional custodians of this land, their knowledge systems, and their deep relationship to data in its most ancient form: “Telling a Story!”

Dr. Gitanjali Yadav is a senior scientist at the National Institute of Plant Genome Research (NIPGR), India and the cofounder of #semanticClimate, a global citizen science movement for climate action. She also serves as co-chair of the International Data Policy Committee (IDPC) of CODATA and strongly advocates FAIR principles and Open Access. As an editor of CODATA’s Data Science Journal (DSJ), she chaired the IDW2025 session on “An evolving role of data scientists in the age of intelligent automation”. With CODATA India, Gita in working towards shaping the next generation of open, interoperable, and globally FAIR research assessment tools and welcomes participation from researchers, data scientists, policy experts, infrastructure developers, and early-career professionals interested in AI-ready, federated data access (data visitation) through Metadata, ontology, and semantic enrichment, as well as in reciprocal learning for equitable data ecosystems, specially across the Global South. Gita can be found on Linkedin, and contacted via email (gy@nipgr.ac.in). She will present her reflections on ‘Who Owns Our Knowledge’ at the New York Tech Libraries (NYIT) Open Access Week on October 24, 2025.

Blog Post by Gitanjali Yadav, India

The opening plenary on CAREful Indigenous Data Governance at the International Data Week 2025 in Brisbane promised to be one of the most important conversations of the week long biennial data conference, and it delivered! The session opened with a respectful acknowledgement of the Traditional Custodians of the land on which we met; the Thurrbal and Yaagera peoples. For me, on my first visit to the southern hemisphere, this was a particularly heartwarming moment as Prof. Tony Heymet, the Australian chief scientist and Dr. Margaret Sheil, Vice Chancellor of the Queensland University of Technology paid respects to Elders past and present, all Aboriginal and Islander peoples, honouring their enduring connection to knowledge, and to storytelling, the world’s oldest continuous data system, recognising how their wisdom continues to guide how we gather, share, and care for knowledge.

This opening plenary brought together global efforts to empower indigenous communities based on CARE foundational principles. For me, this session resonated very well with my own continuing reflections on “Who owns our knowledge?”, the theme of this year’s global ‘Open Access Week’, not only as a call to action but also as an inspiration for important conversations and collaborations around the globe. The answer can be found in the systems we build, the policies we uphold, and the communities we empower. Chaired with characteristic grace by Rosie Hicks, the session brought together three very distinct voices: Marcia Langton (Australia), Niklas Lábba (Sápmi, Sweden), and Alfred J. P. Lin (Taiwan) , each addressing how data, culture, and governance intersect in profoundly different ways. It was, in many ways, a masterclass in contrast! 

When Alfred J. P. Lin opened his talk with a background on Taiwan’s Indigenous Peoples (TIP), expectations were high. His talk, “How Scientific Computing, Data Science, and Open Science Revitalize and Empower Hard-to-Reach Populations” promised a 20-year perspective on digital empowerment. Yet to me, his presentation felt curiously detached from the communities it claimed to serve. The TIP data science framework was impressive in scope but seemed to prioritize system architecture over sovereignty. My concern is that even as we strive to foster self-determination, promote equitable partnerships, and address historical injustices, can scientific computing and open data ever truly empower Indigenous communities, or are we merely empowering institutions that have the infrastructure to process and publish those data? When I asked who owns the Indigenous knowledge generated in his projects; his research institute? the Taiwanese government? Or was it owned by the TIPs themselves, there was no clear answer. It was an unsettling moment. The question went to the heart of the session’s theme: CAREful governance requires clarity of authority and accountability. Without that, “empowerment” risks becoming just another research deliverable.

Still, this moment of discomfort was revealing. It reminded us that CARE, Collective Benefit, Authority to Control, Responsibility, and Ethics cannot be retrofitted onto systems that were never co-designed with communities in the first place. And this point was brought home beautifully by Niklas Lábba, whose talk “Knowledge, Culture and Business Data” was a revelation to me and easily among the most memorable talks of the entire week. Speaking as both a Sámi scholar and practitioner, he illustrated how cultural and business data are not opposing domains but co-dependent systems. His articulation of “data as kinship”, the idea that information can maintain or disrupt relationships, deeply resonated with me. Niklas didn’t just speak about data; he embodied a worldview in which data governance is a living, relational practice. His message was practical yet poetic: data governance that doesn’t nurture relationships will eventually fail. 

The final talk of the session by Marcia Langton was delivered with huge intellectual clarity and unmistakable authority, as the author spoke from the lived realities of Australian Indigenous communities and the long journey toward data sovereignty. I loved Marica, and her unapologetically political framing! She emphasized (and the audience agreed) that governance begins with recognition of power; who holds it, who enforces it, and who benefits from the flow of data. She reminded us that Indigenous data governance is not a technical framework but a political project, one that demands trust, time, and truth-telling. Her clarity on the stakes of sovereignty, that no principle of FAIR or CARE can substitute for Indigenous control, set a high bar for the entire conference.

Taken together, the #IDW2025 opening plenary offered a vivid panorama of where we stand in the global conversation on Indigenous data:

• Langton grounded us in the politics of definition: that “governance” itself must be Indigenous-led.
• Lábba showed us the poetry and pragmatism of data sovereignty lived every day.
• Lin exposed, perhaps unintentionally, how far we still need to go when scientific ambition outpaces ethical reflection.

As a member of the CODATA International Data Policy Committee, I left the room convinced that our policy work must continue to challenge not just how data is shared, but who decides what sharing means. This session and its conversations reminded us all that policy isn’t about perfect frameworks; it’s about courage. The courage to ask uncomfortable questions, to hold silence when answers don’t come, and to keep insisting that “open” must never mean “unaccountable.” The future of data governance won’t be built in servers or standards: it will be built in trust.

Dr. Gitanjali Yadav is a senior scientist at the National Institute of Plant Genome Research (NIPGR), India and the cofounder of #semanticClimate, a global citizen science movement for climate action. She also serves as co-chair of the International Data Policy Committee (IDPC) of CODATA and strongly advocates FAIR principles and Open Access. As an editor of CODATA’s Data Science Journal (DSJ), she chaired the IDW2025 session on “An evolving role of data scientists in the age of intelligent automation”. With CODATA India, Gita in working towards shaping the next generation of open, interoperable, and globally FAIR research assessment tools and welcomes participation from researchers, data scientists, policy experts, infrastructure developers, and early-career professionals interested in AI-ready, federated data access (data visitation) through Metadata, ontology, and semantic enrichment, as well as in reciprocal learning for equitable data ecosystems, specially across the Global South. Gita can be found on Linkedin, and contacted via email (gy@nipgr.ac.in). She will present her reflections on ‘Who Owns Our Knowledge’ at the New York Tech Libraries (NYIT) Open Access Week on October 24, 2025.