{"id":3171,"date":"2025-10-31T16:15:08","date_gmt":"2025-10-31T16:15:08","guid":{"rendered":"https:\/\/codata.org\/blog\/?p=3171"},"modified":"2025-10-31T16:16:56","modified_gmt":"2025-10-31T16:16:56","slug":"evolving-roles-for-data-scientists-in-the-age-of-intelligent-automation","status":"publish","type":"post","link":"https:\/\/codata.org\/blog\/2025\/10\/31\/evolving-roles-for-data-scientists-in-the-age-of-intelligent-automation\/","title":{"rendered":"Evolving Roles for Data Scientists in the Age of Intelligent Automation"},"content":{"rendered":"

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.<\/span><\/i><\/p>\n

\"\"<\/a><\/p>\n

What is the evolving role of data scientists in ensuring that automation and AI serve the long-term goals of science, integrity, and openness?\u00a0<\/span><\/p>\n

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

What made this session particularly rewarding was the high level of engagement from both speakers and audience participants, who actively examined <\/span>practical pathways<\/span><\/i> 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.<\/span><\/p>\n

The session explored how data scientists must adapt to ethical, infrastructural, and community-centric expectations, and how organisations such as <\/span>CODATA<\/b>, <\/span>WDS<\/b>, <\/span>ISC<\/b>, and the <\/span>Research Data Alliance (RDA)<\/b> 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.<\/span><\/p>\n

Background: Data Science as a Field in Motion!<\/b><\/h4>\n

The <\/span>Data Science Journal<\/span><\/i> 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 <\/span>\u201cdata science\u201d<\/span><\/i> 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 \u201cdata science\u201d might be misunderstood. Ultimately, the committee agreed that \u201cit was up to CODATA to ensure that it became understood\u201d (<\/span>Smith, 2023<\/span><\/a>).<\/span><\/p>\n

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 <\/span>data veracity<\/span><\/i> has become a critical fourth \u201cV\u201d in the landscape.<\/span><\/p>\n

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.<\/span>
\n<\/span>This tension raises an important question: <\/span>What does it now mean to be a data scientist working in and for science, rather than for profit or production?<\/span><\/i><\/p>\n

Mark Parson\u2019s<\/b> 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.<\/span><\/p>\n

\"\"<\/a><\/p>\n

Figure 1: <\/i><\/b>\u00a0Tracing the Arc of Data Science (1952\u20132022): This historical trajectory by <\/span><\/i>Mark Parsons<\/i><\/b> 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.<\/span><\/i><\/p>\n

Insights from the Session<\/b><\/h3>\n

Evolution of Roles<\/b><\/h4>\n

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.<\/span><\/h4>\n

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. <\/span>Gitanjali Yadav<\/b> highlighted this evolution in her talk,\u00a0 emphasizing that scientific data work is inseparable from issues of responsibility and provenance (Figure 2). The emerging concept of <\/span>semantic data science<\/span><\/i> extends this arc, emphasizing multilingual, context-aware, and ethically informed practices that keep the human in the loop even as automation advances.\u00a0<\/span><\/h4>\n

Across all these phases, a recurring insight stands out: <\/span>Automation can process data, but only humans can interpret meaning.<\/b><\/h4>\n

\"\"<\/a><\/p>\n

Figure 2: The Expanding Role of the Data Scientist <\/b>by<\/span> Gitanjali<\/i><\/b> Yadav<\/i><\/b>, reflecting on how the data scientist\u2019s 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.<\/span><\/i><\/h3>\n

Preservation and Provenance<\/b><\/h4>\n

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.<\/span><\/h4>\n

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 <\/span>Dr. Debasisa Mohanty<\/b>, 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).<\/span><\/h4>\n

\"\"<\/a><\/p>\n

Figure 3. AI-Guided Genomic Discovery in India: <\/b>\u00a0<\/i><\/b>Screenshot of the Genome India project slide presented by Gitanjali Yadav on behalf of <\/span><\/i>Debasisa Mohanty<\/i><\/b>, 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.<\/span><\/i><\/p>\n

Ethics, Equity, and Access<\/b><\/h4>\n

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.<\/span><\/h4>\n

Invisible Labour and Recognition<\/b><\/h4>\n

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, <\/span>\u201c<\/span><\/i>Everyone celebrates analysis, but few remember who made the data analyzable.\u201d<\/i><\/b><\/h4>\n

This invisibility also shapes the status of data professionals. Data scientists and stewards are often told, \u201cyou\u2019re just the data person,\u201d despite their central role in making science interoperable and reproducible. The <\/span>Data Science Journal<\/span><\/i> and CODATA recognise that much of the world\u2019s 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.<\/span><\/h4>\n

Moving forward, the discussion ranged around how the <\/span>Data Science Journal<\/span><\/i> can address these issues, for instance by:<\/span><\/h4>\n