Category: Data Curation, Open Data, and Research Data Management

The Data Citation Synthesis Working Group has released the "Joint Declaration of Data Citation Principles."

Here's an excerpt:

The Data Citation Principles cover purpose, function and attributes of citations. These principles recognize the dual necessity of creating citation practices that are both human understandable and machine-actionable.

See also "An Introduction to the Joint Principles for Data Citation."

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Benedikt Fecher et al. have self-archived "A Reputation Economy: Results from an Empirical Survey on Academic Data Sharing."

Here's an excerpt:

We surveyed 2661 individual academic researchers across all disciplines on their dealings with data, their publication practices, and motives for sharing or withholding research data. The results for 1564 valid responses show that researchers across disciplines recognise the benefit of secondary research data for their own work and for scientific progress as a whole-still they only practice it in moderation. An explanation for this evidence could be an academic system that is not driven by monetary incentives, nor the desire for scientific progress, but by individual reputation-expressed in (high ranked journal) publications. We label this system a Reputation Economy. This special economy explains our findings that show that researchers have a nuanced idea how to provide adequate formal recognition for making data available to others-namely data citations. We conclude that data sharing will only be widely adopted among research professionals if sharing pays in form of reputation. Thus, policy measures that intend to foster research collaboration need to understand academia as a reputation economy. Successful measures must value intermediate products, such as research data, more highly than it is the case now.

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W3C has released a draft of Data on the Web Best Practices.

Here's an excerpt:

This document provides best practices related to the publication and usage of data on the Web designed to help support a self-sustaining ecosystem. Data should be discoverable and understandable by humans and machines. Where data is used in some way, whether by the originator of the data or by an external party, such usage should also be discoverable and the efforts of the data publisher recognized. In short, following these best practices will facilitate interaction between publishers and consumers.

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John Ernest Kratz and Carly Strasser have published "Researcher Perspectives on Publication and Peer Review of Data" in PLOS ONE.

Here's an excerpt:

Data "publication" seeks to appropriate the prestige of authorship in the peer-reviewed literature to reward researchers who create useful and well-documented datasets. The scholarly communication community has embraced data publication as an incentive to document and share data. But, numerous new and ongoing experiments in implementation have not yet resolved what a data publication should be, when data should be peer-reviewed, or how data peer review should work. While researchers have been surveyed extensively regarding data management and sharing, their perceptions and expectations of data publication are largely unknown. To bring this important yet neglected perspective into the conversation, we surveyed ~ 250 researchers across the sciences and social sciences—asking what expectations "data publication" raises and what features would be useful to evaluate the trustworthiness, evaluate the impact, and enhance the prestige of a data publication. We found that researcher expectations of data publication center on availability, generally through an open database or repository. Few respondents expected published data to be peer-reviewed, but peer-reviewed data enjoyed much greater trust and prestige. The importance of adequate metadata was acknowledged, in that almost all respondents expected data peer review to include evaluation of the data's documentation. Formal citation in the reference list was affirmed by most respondents as the proper way to credit dataset creators. Citation count was viewed as the most useful measure of impact, but download count was seen as nearly as valuable. These results offer practical guidance for data publishers seeking to meet researcher expectations and enhance the value of published data.

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ERCIM has released a special issue of ERCIM News on scientific data sharing and re-use.

Here's an excerpt from "Introduction to the Special Theme Scientific Data Sharing and Re-use":

This special issue features a keynote paper from an EU funding organization, an invited paper from a global organization that aims to accelerate and facilitate research data sharing and exchange, an invited paper from a prominent US scientist and an invited paper from a large Australian data organization. The core part of this issue presents several contributions of European researchers that address the different aspects of the data sharing and (re)use problem.

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Margaret Henderson and Teresa L. Knott have self-archived "Starting a Research Data Management Program Based in a University Library."

Here's an excerpt:

As the need for research data management grows, many libraries are considering adding data services to help with the research mission of their institution. The Virginia Commonwealth University (VCU) Libraries created a position and hired a director of research data management in September 2013. The position was new to the libraries and the university. With the backing of the library administration, a plan for building relationships with VCU faculty, researchers, students, service and resource providers, including grant administrators, was developed to educate and engage the community in data management plan writing and research data management training.

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Nicolas Robinson-Garcia et al. have self-archived "Analyzing Data Citation Practices According to the Data Citation Index."

Here's an excerpt:

The findings of this study show that data citation practices are far from common in most research fields. Some differences have been reported on the way researchers cite data: while in the areas of Science and Engineering and Technology data sets were the most cited, in Social Sciences and Arts and Humanities data studies play a greater role. 88.1 percent of the records have received no citations, but some repositories show very low uncitedness rates. While data citation practices are rare in most fields, they have expanded in disciplines such as Crystallography or Genomics. We conclude by emphasizing the role that the DCI could play in encouraging the consistent, standardized citation of research data—a role that would enhance its value as a means of following the research process from data collection to publication.

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Heather L. Coates has published "Building Data Services from the Ground Up: Strategies and Resources" in the Journal of eScience Librarianship.

Here's an excerpt:

There is a scarcity of practical guidance for developing data services in an academic library. Data services, like many areas of research, require the expertise and resources of teams spanning many disciplines. While library professionals are embedded into the teaching activities of our institutions, fewer of us are embedded in research activities occurring across the full life cycle. The significant challenges of managing, preserving, and sharing data for reuse demand that we take a more active role. Providing support for funder data management plans is just one option in the data services landscape. Awareness of the institutional and library culture in which we operate places an emphasis on the importance of relationships. Understanding the various cultures in which our researchers operate is crucial for delivering data services that are relevant and utilized. The goal of this article is to guide data specialists through this landscape by providing key resources and strategies for developing locally relevant services and by pointing to active communities of librarians and researchers tackling the challenges associated with digital research data.

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Stephen Pinfield, Andrew M. Cox, and Jen Smith have published "Research Data Management and Libraries: Relationships, Activities, Drivers and Influences " in PLOS ONE.

Here's an excerpt:

This paper analyses the contribution of academic libraries to research data management (RDM) in the wider institutional context. In particular it: examines the roles and relationships involved in RDM, identifies the main components of an RDM programme, evaluates the major drivers for RDM activities, and analyses the key factors influencing the shape of RDM developments. The study is written from the perspective of library professionals, analysing data from 26 semi-structured interviews of library staff from different UK institutions. This is an early qualitative contribution to the topic complementing existing quantitative and case study approaches. Results show that although libraries are playing a significant role in RDM, there is uncertainty and variation in the relationship with other stakeholders such as IT services and research support offices. Current emphases in RDM programmes are on developments of policies and guidelines, with some early work on technology infrastructures and support services. Drivers for developments include storage, security, quality, compliance, preservation, and sharing with libraries associated most closely with the last three. The paper also highlights a 'jurisdictional' driver in which libraries are claiming a role in this space. A wide range of factors, including governance, resourcing and skills, are identified as influencing ongoing developments. From the analysis, a model is constructed designed to capture the main aspects of an institutional RDM programme. This model helps to clarify the different issues involved in RDM, identifying layers of activity, multiple stakeholders and drivers, and a large number of factors influencing the implementation of any initiative. Institutions may usefully benchmark their activities against the data and model in order to inform ongoing RDM activity.

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