Data Communities: A New Model for Supporting STEM Data Sharing

Cooper, Danielle; Springer, Rebecca

doi:10.18665/sr.311396

Cited by 16 publications

(16 citation statements)

References 5 publications

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“…Fifty percent also indicated needing data outside of their domains of expertise, perhaps reflecting funders' efforts to promote interdisciplinary research (Allmendinger, 2015). Data communities can also be thought of in terms of the type of data that a particular group uses (Cooper & Springer, 2019;Gregory et al, 2019b).…”

Section: Communities Of Data Seekersmentioning

confidence: 99%

Lost or Found? Discovering Data Needed for Research

Gregory

Groth

Scharnhorst

et al. 2020

Harvard Data Science Review

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Finding or discovering data is a necessary precursor to being able to reuse data, although relatively little large-scale empirical evidence exists about how researchers discover, make sense of and (re)use data for research. This study presents evidence from the largest known survey investigating how researchers discover and use data that they do not create themselves. We examine the data needs and discovery strategies of respondents, propose a typology for data (re)use and probe the role of social interactions and other research practices in data discovery, with the aim of informing the design of community-centric solutions and policies.

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Section: Communities Of Data Seekersmentioning

confidence: 99%

Lost or Found? Discovering Data Needed for Research

Gregory

Groth

Scharnhorst

et al. 2020

Harvard Data Science Review

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“…We recommend that journals craft data policies that use language to stipulate conditions and levels of enforcement as prescribed by prevailing standards such as the TOP Guidelines and with respect to the particular norms of the community. Considered together with the specific norms and issues within the specific research communities [28], policy language will be more effective in aligning principles with practice.…”

Section: Plos Onementioning

confidence: 99%

Journal data policies: Exploring how the understanding of editors and authors corresponds to the policies themselves

et al. 2020

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Despite the increase in the number of journals issuing data policies requiring authors to make data underlying reporting findings publicly available, authors do not always do so, and when they do, the data do not always meet standards of quality that allow others to verify or extend published results. This phenomenon suggests the need to consider the effectiveness of journal data policies to present and articulate transparency requirements, and how well they facilitate (or hinder) authors' ability to produce and provide access to data, code, and associated materials that meet quality standards for computational reproducibility. This article describes the results of a research study that examined the ability of journal-based data policies to: 1) effectively communicate transparency requirements to authors, and 2) enable authors to successfully meet policy requirements. To do this, we conducted a mixed-methods study that examined individual data policies alongside editors' and authors' interpretation of policy requirements to answer the following research questions. Survey responses from authors and editors along with results from a content analysis of data policies found discrepancies among editors' assertion of data policy requirements, authors' understanding of policy requirements, and the requirements stated in the policy language as written. We offer explanations for these discrepancies and offer recommendations for improving authors' understanding of policies and increasing the likelihood of policy compliance.

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“…What might these look like? Here, too, more research is needed, but one possible model can be found in recent calls for building the capacity of emergent data communities [ 57 ]. When a loosely connected group of researchers shares a stake in working with a particular type of data, then institutional support has the potential to intensify experimentation and exchange.…”

Section: Discussionmentioning

confidence: 99%

Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science

2022

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Considerable resources are being invested in strategies to facilitate the sharing of data across domains, with the aim of addressing inefficiencies and biases in scientific research and unlocking potential for science-based innovation. Still, we know too little about what determines whether scientific researchers actually make use of the unprecedented volume of data being shared. This study characterizes the factors influencing researcher data reuse in terms of their relationship to a specific research project, and introduces subjectification as the mechanism by which these influencing factors are activated. Based on our analysis of semi-structured interviews with a purposive sample of 24 data reusers and intermediaries, we find that while both project-independent and project-dependent factors may have a direct effect on a single instance of data reuse, they have an indirect effect on recurring data reuse as mediated by subjectification. We integrate our findings into a model of recurring data reuse behavior that presents subjectification as the mechanism by which influencing factors are activated in a propensity to engage in data reuse. Our findings hold scientific implications for the theorization of researcher data reuse, as well as practical implications around the role of settings for subjectification in bringing about and sustaining changes in researcher behavior.

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Data Communities: A New Model for Supporting STEM Data Sharing

Cited by 16 publications

References 5 publications

Lost or Found? Discovering Data Needed for Research

Lost or Found? Discovering Data Needed for Research

Journal data policies: Exploring how the understanding of editors and authors corresponds to the policies themselves

Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science

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