Data Management Practices of Health Sciences Researchers

Authors: Melissa Ratajeski, Carrie Iwema, Andrea Ketchum

Abstract: Librarians at the University of Pittsburgh Health Sciences Library System conducted a 25-question online survey of the data management practices of researchers within the six schools of the health sciences (School of Medicine, School of Health and Rehabilitation Sciences, Graduate School of Public Health, School of Nursing, School of Pharmacy, and School of Dental Medicine).

The survey was administered via SurveyMonkey.  Questions included researchers’ demographics and data management practices such as the use of file naming conventions, assignment of metadata to data files, storage of working and back-up data, data accessibility, and the use of data management plans (survey instrument provided). All multiple choice questions required a response and the majority were “check all that apply.”

Citation: Ratajeski, Melissa; Iwema, Carrie; Ketchum, Andrea (2017): Data Management Practices of Health Sciences Researchers. figshare. Fileset. https://doi.org/10.6084/m9.figshare.1600993.v1

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Source: Data Management Practices of Health Sciences Researchers

ORCID Annual Report 2016

Authors: Alice Meadows, Josh Brown, Laurel Haak, Laura Paglione, Robert Peters, Douglas Wright

Abstract: This is the 2016 annual report for ORCID, which includes information about membership, usage and adoption, engagement activities, integrations, technical updates, financials, and more. For more information, visit orcid.org.

Citation: Meadows, Alice; Brown, Josh; Haak, Laurel; Paglione, Laura; Peters, Robert; Wright, Douglas (2017): ORCID Annual Report 2016.pdf. figshare. https://doi.org/10.6084/m9.figshare.4810213.v2. Retrieved: 12 40, Apr 07, 2017 (GMT).

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Adopting a Distributed Model for Data Services

Authors: Casey Gibbs, Marcos Hernandez, Pongracz Sennyey

Abstract: This article describes how the Saint Edward’s University Library implemented a distributed model for the Institutional Repository. Based on Cloud Based platforms and APIs, the Library has created an Institutional Repository that is scaleable and modular, considerably lowering its implementation and maintenance costs, while lowering its technical complexity.

Casey Gibbs, Marcos Hernandez, Pongracz Sennyey. (2017). Adopting a Distributed Model for Data Services. Code4Lib Journal. Issue 35.

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