Étiquette : research data

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Peer Review of Research Data Submissions to ScholarsArchive@OSU: How can we improve the curation of research datasets to enhance reusability?

Authors : Clara Llebot, Steven Van Tuyl

Objective

Best practices such as the FAIR Principles (Findability, Accessibility, Interoperability, Reusability) were developed to ensure that published datasets are reusable. While we employ best practices in the curation of datasets, we want to learn how domain experts view the reusability of datasets in our institutional repository, ScholarsArchive@OSU.

Curation workflows are designed by data curators based on their own recommendations, but research data is extremely specialized, and such workflows are rarely evaluated by researchers.

In this project we used peer-review by domain experts to evaluate the reusability of the datasets in our institutional repository, with the goal of informing our curation methods and ensure that the limited resources of our library are maximizing the reusability of research data.

Methods

We asked all researchers who have datasets submitted in Oregon State University’s repository to refer us to domain experts who could review the reusability of their data sets. Two data curators who are non-experts also reviewed the same datasets.

We gave both groups review guidelines based on the guidelines of several journals. Eleven domain experts and two data curators reviewed eight datasets.

The review included the quality of the repository record, the quality of the documentation, and the quality of the data. We then compared the comments given by the two groups.

Results

Domain experts and non-expert data curators largely converged on similar scores for reviewed datasets, but the focus of critique by domain experts was somewhat divergent.

A few broad issues common across reviews were: insufficient documentation, the use of links to journal articles in the place of documentation, and concerns about duplication of effort in creating documentation and metadata. Reviews also reflected the background and skills of the reviewer.

Domain experts expressed a lack of expertise in data curation practices and data curators expressed their lack of expertise in the research domain.

Conclusions

The results of this investigation could help guide future research data curation activities and align domain expert and data curator expectations for reusability of datasets.

We recommend further exploration of these common issues and additional domain expert peer-review project to further refine and align expectations for research data reusability.

URL : Peer Review of Research Data Submissions to ScholarsArchive@OSU: How can we improve the curation of research datasets to enhance reusability?

DOI : https://doi.org/10.7191/jeslib.2019.1166

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

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

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Data Management Planning: How Requirements and Solutions are Beginning to Converge

Authors : Sarah Jones, Robert Pergl, Rob Hooft, Tomasz Miksa, Robert Samors, Judit Ungvari, Rowena I. Davis, Tina Lee

Effective stewardship of data is a critical precursor to making data FAIR. The goal of this paper is to bring an overview of current state of the art of data management and data stewardship planning solutions (DMP).

We begin by arguing why data management is an important vehicle supporting adoption and implementation of the FAIR principles, we describe the background, context and historical development, as well as major driving forces, being research initiatives and funders. Then we provide an overview of the current leading DMP tools in the form of a table presenting the key characteristics.

Next, we elaborate on emerging common standards for DMPs, especially the topic of machine-actionable DMPs. As sound DMP is not only a precursor of FAIR data stewardship, but also an integral part of it, we discuss its positioning in the emerging FAIR tools ecosystem. Capacity building and training activities are an important ingredient in the whole effort.

Although not being the primary goal of this paper, we touch also the topic of research workforce support, as tools can be just as much effective as their users are competent to use them properly.

We conclude by discussing the relations of DMP to FAIR principles, as there are other important connections than just being a precursor.

URL : Data Management Planning: How Requirements and Solutions are Beginning to Converge

Alternative location : https://www.mitpressjournals.org/doi/pdf/10.1162/dint_a_00043

 

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Playing Well on the Data FAIRground: Initiatives and Infrastructure in Research Data Management

Authors : Danielle Descoteaux, Chiara Farinelli, Marina Soares e Silva, Anita de Waard

Over the past five years, Elsevier has focused on implementing FAIR and best practices in data management, from data preservation through reuse. In this paper we describe a series of efforts undertaken in this time to support proper data management practices.

In particular, we discuss our journal data policies and their implementation, the current status and future goals for the research data management platform Mendeley Data, and clear and persistent linkages to individual data sets stored on external data repositories from corresponding published papers through partnership with Scholix.

Early analysis of our data policies implementation confirms significant disparities at the subject level regarding data sharing practices, with most uptake within disciplines of Physical Sciences. Future directions at Elsevier include implementing better discoverability of linked data within an article and incorporating research data usage metrics.

URL : Playing Well on the Data FAIRground: Initiatives and Infrastructure in Research Data Management

DOI : https://doi.org/10.1162/dint_a_00020

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Publishers’ Responsibilities in Promoting Data Quality and Reproducibility

Author : Iain Hrynaszkiewicz

Scholarly publishers can help to increase data quality and reproducible research by promoting transparency and openness.

Increasing transparency can be achieved by publishers in six key areas: (1) understanding researchers’ problems and motivations, by conducting and responding to the findings of surveys; (2) raising awareness of issues and encouraging behavioural and cultural change, by introducing consistent journal policies on sharing research data, code and materials; (3) improving the quality and objectivity of the peer-review process by implementing reporting guidelines and checklists and using technology to identify misconduct; (4) improving scholarly communication infrastructure with journals that publish all scientifically sound research, promoting study registration, partnering with data repositories and providing services that improve data sharing and data curation; (5) increasing incentives for practising open research with data journals and software journals and implementing data citation and badges for transparency; and (6) making research communication more open and accessible, with open-access publishing options, permitting text and data mining and sharing publisher data and metadata and through industry and community collaboration.

This chapter describes practical approaches being taken by publishers, in these six areas, their progress and effectiveness and the implications for researchers publishing their work.

URL : Publishers’ Responsibilities in Promoting Data Quality and Reproducibility

Alternative location : https://link.springer.com/chapter/10.1007%2F164_2019_290

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Making FAIR Easy with FAIR Tools: From Creolization to Convergence

Authors : Mark Thompson, Kees Burger, Rajaram Kaliyaperumal, Marco Roos, Luiz Olavo Bonino da Silva Santos

Since their publication in 2016 we have seen a rapid adoption of the FAIR principles in many scientific disciplines where the inherent value of research data and, therefore, the importance of good data management and data stewardship, is recognized.

This has led to many communities asking “What is FAIR?” and “How FAIR are we currently?”, questions which were addressed respectively by a publication revisiting the principles and the emergence of FAIR metrics.

However, early adopters of the FAIR principles have already run into the next question: “How can we become (more) FAIR?” This question is more difficult to answer, as the principles do not prescribe any specific standard or implementation.

Moreover, there does not yet exist a mature ecosystem of tools, platforms and standards to support human and machine agents to manage, produce, publish and consume FAIR data in a user-friendly and efficient (i.e., “easy”) way. In this paper we will show, however, that there are already many emerging examples of FAIR tools under development.

This paper puts forward the position that we are likely already in a creolization phase where FAIR tools and technologies are merging and combining, before converging in a subsequent phase to solutions that make FAIR feasible in daily practice.

DOI : https://doi.org/10.1162/dint_a_00031

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The History and Future of Data Citation in Practice

Authors : Mark A. Parsons, Ruth E. Duerr, Matthew B. Jones

In this review, we adopt the definition that ‘Data citation is a reference to data for the purpose of credit attribution and facilitation of access to the data’ (TGDCSP 2013: CIDCR6). Furthermore, access should be enabled for both humans and machines (DCSG 2014).

We use this to discuss how data citation has evolved over the last couple of decades and to highlight issues that need more research and attention.

Data citation is not a new concept, but it has changed and evolved considerably since the beginning of the digital age. Basic practice is now established and slowly but increasingly being implemented.

Nonetheless, critical issues remain. These issues are primarily because we try to address multiple human and computational concerns with a system originally designed in a non-digital world for more limited use cases.

The community is beginning to challenge past assumptions, separate the multiple concerns (credit, access, reference, provenance, impact, etc.), and apply different approaches for different use cases.

URL : The History and Future of Data Citation in Practice

DOI : http://doi.org/10.5334/dsj-2019-052