Macro and micro pressures in data sharing

Kervin, Karina; Finholt, Thomas A.; Hedstrom, Margaret

doi:10.1109/iri.2012.6303054

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…Traditionally, research data has been shared through professional networks and by personal request [32,36,38]. These 'traditions' were incorporated into research processes as early-career researchers were indoctrinated by mentors and senior researchers [52]. This approach allowed those who owned datasets to scrutinize requests and all aspects of the requestor, including the reputation of their institution, their publications, and any other factors they felt important.…”

Section: Discussionmentioning

confidence: 99%

The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

2020

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BackgroundFunding agencies and research journals are increasingly demanding that researchers share their data in public repositories. Despite these requirements, researchers still withhold data, refuse to share, and deposit data that lacks annotation. We conducted a meta-synthesis to examine the views, perspectives, and experiences of academic researchers on data sharing and reuse of research data. MethodsWe searched the published and unpublished literature for studies on data sharing by researchers in academic institutions. Two independent reviewers screened citations and abstracts, then full-text articles. Data abstraction was performed independently by two investigators. The abstracted data was read and reread in order to generate codes. Key concepts were identified and thematic analysis was used for data synthesis. ResultsWe reviewed 2005 records and included 45 studies along with 3 companion reports. The studies were published between 2003 and 2018 and most were conducted in North America (60%) or Europe (17%). The four major themes that emerged were data integrity, responsible conduct of research, feasibility of sharing data, and value of sharing data. Researchers lack time, resources, and skills to effectively share their data in public repositories. Data quality is affected by this, along with subjective decisions around what is considered to be worth sharing. Deficits in infrastructure also impede the availability of research data. Incentives for sharing data are lacking. ConclusionResearchers lack skills to share data in a manner that is efficient and effective. Improved infrastructure support would allow them to make data available quickly and seamlessly. The

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Section: Discussionmentioning

confidence: 99%

The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

2020

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“…However, despite organizations and CI's that make it possible to share data and other computational resources, policies are still misaligned to incentivize sharing. Kervin, Finholt, and Hedstrom () cite the lack of research that explores the “normative socialization” aspects of scientific work and the top‐down factors that is guide scientific data practices. Essentially, they argue that studies about data sharing practice fail to take into account the institutional ecology of science that enforce macro and the micro factors that influence data sharing practices.…”

Section: Network Resources: Infrastructure For Sharing Data In Open Smentioning

confidence: 99%

“…Framing of interoperability is guided by Star and Griesmer () observation that science is not conducted in a vacuum but in a web of institutional ecologies that create tensions and heterogeneous means to do work. Some studies suggest that coherence and cooperation of an infrastructure to that of practice entails looking at publication norms and expectations, tenure opportunities and professionalization along with the micro and macro factors of science culture together not separately including social ties of collaboration, funding agencies, lab culture, publication expectations, university norms and policies (Lagoze, Edwards, Sandvig, ; Plantin, ; Kervin, Finholt, Hedstrom, )…”

Section: Comparing Digital Infrastructuresmentioning

confidence: 99%

Comparing internal and external interoperability of digital infrastructures

Sharma

Sawyer

2016

Proc. Assoc. Info. Sci. Tech.

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This research study compares the internal and external interoperability of digital infrastructures across two cyberinfrastructure organizations. Internal interoperability refers to the work that goes into developing seamless relations to align people and resources for a CI provision whereas external interoperability refers to the linkages the CI provision makes to the larger institutional ecology of science. The case study is designed to look at the governance of digital infrastructures to understand policy decisions in relation to three institutional aspects of science: funding, publication norms, and training and professionalization. Initial findings suggest that internal interoperability is similar for both digital infrastructures whereas external interoperability differs and is shaped by the domain of science that is stewarding the development of the provision. The study concludes by suggesting that linking digital infrastructure development to the components of science practice may be a starting point to understand how they become embedded (or not) in scientific work.

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“…Certainly, international and US agencies have implemented data sharing practices that are partially effective. A deeper look reveals that researchers' data practices, including data sharing, are more often guided by individual benefit [11]. The reality is that many researchers do not budget adequate time for metadata generation, and perceive that this task is not a high priority.…”

Section: Introductionmentioning

confidence: 99%

The Backstage Work of Data Sharing

Kervin

Cook

Michener

2014

Proceedings of the 18th International Conference on Supporting Group Work

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Conventional wisdom suggests that there are benefits to the creation of shared repositories of scientific data. Funding agencies require that the data from sponsored projects be shared publicly, but individual researchers often see little personal benefit to offset the work of creating easily sharable data. These conflicting forces have led to the emergence of a new role to support researchers: data managers. This paper identifies key differences between the sociotechnical context of data managers and other "human infrastructure" roles articulated previously in Computer Supported Cooperative Work (CSCW) literature and summarizes the challenges that data managers face when accepting data for archival and reuse. While data managers' work is critical for advancing science and science policy, their work is often invisible and under-appreciated since it takes place behind the scenes.

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Macro and micro pressures in data sharing

Cited by 5 publications

References 28 publications

The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

Comparing internal and external interoperability of digital infrastructures

The Backstage Work of Data Sharing

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