Archives des mots-clés : Data Management

Understanding the Data Management Plan as a Boundary Object through a Multi-stakeholder perspective

Posté le par

Authors : Live Kvale, Nils Pharo

A three-phase Delphi study was used to investigate an emerging community for research data management in Norway and their understanding and application of data management plans (DMPs). The findings reveal visions of what the DMP should be as well as different practice approaches, yet the stakeholders present common goals.

This paper discusses the different perspectives on the DMP by applying Star and Griesemer’s theory of boundary objects (Star & Griesemer, 1989). The debate on what the DMP is and the findings presented are relevant to all research communities currently implementing DMP procedures and requirements. The current discussions about DMPs tend to be distant from the active researchers and limited to the needs of funders and institutions rather than to the usefulness for researchers.

By analysing the DMP as a boundary object, plastic and adaptable yet with a robust identity (Star & Griesemer, 1989), and by translating between worlds where collaboration on data sharing can take place we expand the perspectives and include all stakeholders. An understanding of the DMP as a boundary object can shift the focus from shaping a DMP which fulfils funders’ requirements to enabling collaboration on data management and sharing across domains using standardised forms.

URL : Understanding the Data Management Plan as a Boundary Object through a Multi-stakeholder perspective

DOI: https://doi.org/10.2218/ijdc.v15i1.729

 

A Review of the History, Advocacy and Efficacy of Data Management Plans

Posté le par

Authors: Nicholas Andrew Smale, Kathryn Unsworth, Gareth Denyer, Elise Magatova, Daniel Barr

Data management plans (DMPs) have increasingly been encouraged as a key component of institutional and funding body policy. Although DMPs necessarily place administrative burden on researchers, proponents claim that DMPs have myriad benefits, including enhanced research data quality, increased rates of data sharing, and institutional planning and compliance benefits.

In this article, we explore the international history of DMPs and describe institutional and funding body DMP policy. We find that economic and societal benefits from presumed increased rates of data sharing was the original driver of mandating DMPs by funding bodies.

Today, 86% of UK Research Councils and 63% of US funding bodies require submission of a DMP with funding applications. Given that no major Australian funding bodies require DMP submission, it is of note that 37% of Australian universities have taken the initiative to internally mandate DMPs. Institutions both within Australia and internationally frequently promote the professional benefits of DMP use, and endorse DMPs as ‘best practice’.

We analyse one such typical DMP implementation at a major Australian institution, finding that DMPs have low levels of apparent translational value. Indeed, an extensive literature review suggests there is very limited published systematic evidence that DMP use has any tangible benefit for researchers, institutions or funding bodies.

We are therefore led to question why DMPs have become the go-to tool for research data professionals and advocates of good data practice. By delineating multiple use-cases and highlighting the need for DMPs to be fit for intended purpose, we question the view that a good DMP is necessarily that which encompasses the entire data lifecycle of a project.

Finally, we summarise recent developments in the DMP landscape, and note a positive shift towards evidence-based research management through more researcher-centric, educative, and integrated DMP services.

URL : A Review of the History, Advocacy and Efficacy of Data Management Plans

DOI : https://doi.org/10.2218/ijdc.v15i1.525

Digging into data management in public‐funded, international research in digital humanities

Posté le par

Authors : Alex H. Poole, Deborah A. Garwood

Path‐breaking in theory and practice alike, digital humanities (DH) not only secures a larger public audience for humanities and social sciences research, but also permits researchers to ask novel questions and to revisit familiar ones. Public‐funded, international, and collaborative research in DH furthers institutional research missions and enriches networked knowledge.

The Digging into Data 3 challenge (DID3) (2014–2016), an international and interdisciplinary grant initiative embracing big data, included 14 teams sponsored by 10 funders from four nations.

A qualitative case study that relies on purposive sampling and grounded analysis, this article centers on the information practices of DID3 participants. Semistructured interviews were conducted with 53 participants on 11 of the 14 DID3 projects.

The study explores how Data Management Plan requirements affect work practices in public‐funded DH, how scholars grapple with key data management challenges, and how they plan to reuse and share their data. It concludes with three recommendations and three directions for future research.

DOI : https://doi.org/10.1002/asi.24213

Replicable Services for Reproducible Research: A Model for Academic Libraries

Posté le par

Authors : Franklin Sayre, Amy Riegelman

Over the past decade, evidence from disciplines ranging from biology to economics has suggested that many scientific studies may not be reproducible. This has led to declarations in both the scientific and lay press that science is experiencing a “reproducibility crisis” and that this crisis has consequences for the extent to which students, faculty, and the public at large can trust research.

Faculty build on these results with their own research, and students and the public use these results for everything from patient care to public policy. To build a model for how academic libraries can support reproducible research, the authors conducted a review of major guidelines from funders, publishers, and professional societies. Specific recommendations were extracted from guidelines and compared with existing academic library services and librarian expertise.

The authors believe this review shows that many of the recommendations for improving reproducibility are core areas of academic librarianship, including data management, scholarly communication, and methodological support for systematic reviews and data-intensive research.

By increasing our knowledge of disciplinary, journal, funder, and society perspectives on reproducibility, and reframing existing librarian expertise and services, academic librarians will be well positioned to be leaders in supporting reproducible research.

URL : Replicable Services for Reproducible Research: A Model for Academic Libraries

DOI : https://doi.org/10.5860/crl.80.2.260

Data Management Practices in Academic Library Learning Analytics: A Critical Review

Posté le par

Author : Kristin A Briney

INTRODUCTION

Data handling in library learning analytics plays a pivotal role in protecting patron privacy, yet the landscape of data management by librarians is poorly understood.

METHODS

This critical review examines data-handling practices from 54 learning analytics studies in academic libraries and compares them against the NISO Consensus Principles on User’s Digital Privacy in Library, Publisher, and Software-Provider Systems and data management best practices.

RESULTS

A number of the published research projects demonstrate inadequate data protection practices including incomplete anonymization, prolonged data retention, collection of a broad scope of sensitive information, lack of informed consent, and sharing of patron-identified information.

DISCUSSION

As with researchers more generally, libraries should improve their data management practices. No studies aligned with the NISO Principles in all evaluated areas, but several studies provide specific exemplars of good practice.

CONCLUSION

Libraries can better protect patron privacy by improving data management practices in learning analytics research.

URL : Data Management Practices in Academic Library Learning Analytics: A Critical Review

DOI : https://doi.org/10.7710/2162-3309.2268