Archives des mots-clés : research data

What Drives Academic Data Sharing?

Statut

“Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher’s point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.”

URL : What Drives Academic Data Sharing?

DOI :10.1371/journal.pone.0118053

Issues in Open Research Data

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“In 2010 the Panton Principles for Open Data in Science were published. These principles were founded upon the idea that ‘Science is based on building on, reusing and openly criticising the published body of scientific knowledge’ (http://pantonprinciples.org) and they provide a succinct list of the fundamentals to observe when making your data open. Intended for a broad audience of academics, publishers and librarians, Issues in Research Data explores the implications of the Panton Principles through a number of perspectives on open research data in the sciences and beyond.

The book features chapters by open data experts in a range of academic disciplines, covering practical information on licensing, ethics, and advice for data curators, alongside more theoretical issues surrounding the adoption of open data. As the book is open access, each chapter can stand alone from the main volume so that communities can host, distribute, build upon and remix the content that is relevant to them.”

URL : https://microblogging.infodocs.eu/wp-content/uploads/2015/01/moore2014.pdf

DOI: http://dx.doi.org/10.5334/ban

Incentives and motivations for sharing research data: researcher’s perspectives

Statut

“This study, commissioned by Knowledge Exchange, has gathered evidence, examples and opinions on current and future incentives for research data sharing from the researcher’s point of view, in order to provide recommendations for policy and practice development on how best to incentivise data access and reuse. Whilst most researchers appreciate the benefits of sharing research data, on an individual basis they may be reluctant to share their own data. This study is based on qualitative interviews with 22 selected researchers of five research teams that have established data sharing cultures, in the partner countries of Knowledge Exchange: Denmark, Finland, Germany, the Netherlands and the United Kingdom. The five case studies span various academic disciplines: arts and humanities, social sciences, biomedicine, chemistry and biology.”

URL : Incentives and motivations for sharing research data: researcher’s perspectives

Alternative URL : http://knowledge-exchange.info/Default.aspx?ID=733

Data without Peer: Examples of Data Peer Review in the Earth Sciences

Statut

“Peer review of data is an important process if data is to take its place as a first class research output. Much has been written about the theoretical aspects of peer review, but not as much about the actual process of doing it. This paper takes an experimental view, and selects seven datasets, all from the Earth Sciences and with DOIs from DataCite, and attempts to review them, with varying levels of success. Key issues identified from these case studies include the necessity of human readable metadata, accessibility of datasets, and permanence of links to and accessibility of metadata stored in other locations.”

URL : http://www.dlib.org/dlib/january15/callaghan/01callaghan.html

Research Data Management and Libraries: Relationships, Activities, Drivers and Influences

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“The management of research data is now a major challenge for research organisations. Vast quantities of born-digital data are being produced in a wide variety of forms at a rapid rate in universities. 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.”

URL : Research Data Management and Libraries: Relationships, Activities, Drivers and Influences

DOI : 10.1371/journal.pone.0114734

Issues in the development of open access to research data

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This paper explores key issues in the development of open access to research data. The use of digital means for developing, storing and manipulating data is creating a focus on ‘data-driven science’. One aspect of this focus is the development of ‘open access’ to research data.

Open access to research data refers to the way in which various types of data are openly available to public and private stakeholders, user communities and citizens. Open access to research data, however, involves more than simply providing easier and wider access to data for potential user groups. The development of open access requires attention to the ways data are considered in different areas of research.

We identify how open access is being unevenly developed across the research environment and the consequences this has in terms of generating data gaps. Data gaps refer to the way data becomes detached from published conclusions. To address these issues, we examine four main areas in developing open access to research data: stakeholder roles and values; technological requirements for managing and sharing data; legal and ethical regulations and procedures; institutional roles and policy frameworks.

We conclude that problems of variability and consistency across the open access ecosystem need to be addressed within and between these areas to ensure that risks surrounding a data gap are managed in open access.

URL : http://dx.doi.org/10.1080/08109028.2014.956505

Publishing without Publishers: a Decentralized Approach to Dissemination, Retrieval, and Archiving of Data

Statut

“Making available and archiving scientific results is for the most part still considered the task of classical publishing companies, despite the fact that classical forms of publishing centered around printed narrative articles no longer seem well-suited in the digital age. In particular, there exist currently no efficient, reliable, and agreed-upon methods for publishing scientific datasets, which have become increasingly important for science. Here we propose to design scientific data publishing as a Web-based bottom-up process, without top-down control of central authorities such as publishing companies. We present a protocol and a server network to decentrally store and archive data in the form of nanopublications, an RDF-based format to represent scientific data with formal semantics. We show how this approach allows researchers to produce, publish, retrieve, address, verify, and recombine datasets and their individual nanopublications in a reliable and trustworthy manner, and we argue that this architecture could be used for the Semantic Web in general. Our evaluation of the current small network shows that this system is efficient and reliable, and we discuss how it could grow to handle the large amounts of structured data that modern science is producing and consuming.”

URL : http://arxiv-web3.library.cornell.edu/abs/1411.2749