Why It Takes a Village to Manage and Share Data is a research paper published in Harvard Data Science Review (2022). On theSindex it has a DataRank of 0.454. It has been cited 11 times, with 2 citing works in its 1-hop citation network.
Implementation plans for the National Institutes of Health policy for data management and sharing, which takes effect in 2023, provide an opportunity to reflect on the stakeholders, infrastructures, practice, economics, and sustainability of data sharing. Responsibility for fulfilling data sharing requirements tends to fall on principal investigators, whereas it takes a village of stakeholders to construct, manage, and sustain the necessary knowledge infrastructure for disseminating data products. Individual scientists have mixed incentives, and many disincentives to share data, all of which vary by research domain, methods, resources, and other factors. Motivations and investments for data sharing also vary widely among academic institutional stakeholders such as university leadership, research computing, libraries, and individual schools and departments. Stakeholder concerns are interdependent along many dimensions, seven of which are explored: what data to share; context and credit; discovery; methods and training; intellectual property; data science programs; and international tensions. Data sharing is not a simple matter of individual practice, but one of infrastructure, institutions, and economics. Governments, funding agencies, and international science organizations all will need to invest in commons approaches for data sharing to develop into a sustainable international ecosystem.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.373
From this paper's citation signal
Citation Network Contribution
0.0817
From 2 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 82% comes from its base citations and 18% from the citation network (2 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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