scholarly journals Surveying the landscape of CIHR-funded research data sharing practices: An analysis of the published literature

2021 ◽  
Author(s):  
Kevin B Read ◽  
Heather Ganshorn ◽  
Sarah Rutley ◽  
David R. Scott
Keyword(s):  
Best Practices ◽  
Data Sharing ◽  
Descriptive Analysis ◽  
Research Data ◽  
Data Discovery ◽  
Pubmed Central ◽  
Funded Research ◽  
Shared Data ◽  
Research Data Management ◽  
Is Value

Background:As Canada increases requirements for research data management (RDM) and sharing, there is value in identifying how research data are shared, and what has been done to make them findable and reusable. This study aims to understand Canada’s data sharing landscape by reviewing how Canadian Institutes of Health Research (CIHR) funded data are shared, and comparing researchers’ data sharing practices to RDM and sharing best practices. Methods:We performed a descriptive analysis of CIHR-funded publications from PubMed and PubMed Central that were published between 1946 and Dec 31, 2019 and that indicated the research data underlying the results of the publication were shared. Each publication was analyzed to identify how and where data were shared, who shared data, and what documentation was included to support data reuse.Results:Of 4,144 CIHR-funded publications, 45.2% (n=1,876) included accessible data, 21.9% (n=909) stated data were available by request, 7.3% (n=304) stated data sharing was not applicable/possible, and we found no evidence of data sharing in 37.6% (n=1,558) of publications. Frequent data sharing methods included via a repository (n=1,549, 37.3%), within supplementary files (n=1,048, 25.2%), and by request (n=919, 22.1%). 13.1% (n=554) of publications included documentation that would facilitate data reuse.Interpretation:Our findings reveal that CIHR-funded publications largely lack the metadata, access instructions, and documentation to facilitate data discovery and reuse. Without measures to address these concerns, and enhanced support for researchers seeking to implement RDM and sharing best practices, most CIHR-funded research data will remain hidden, inaccessible, and unusable.

scholarly journals Tuuli project: accelerating data management planning in Finnish research organisations

2018 ◽  
Vol 12 (2) ◽  
pp. 107-115 ◽  
Author(s):  
Minna Ahokas ◽  
Mari Elisa Kuusniemi ◽  
Jari Friman
Keyword(s):  
Best Practices ◽  
Data Management ◽  
Data Sharing ◽  
Research Data ◽  
Planning Tool ◽  
Management Planning ◽  
Research Data Management ◽  
Management Plans ◽  
Research Organisations ◽  
And Training

Many research funders have requirements for data sharing and data management plans (DMP). DMP tools are services built to help researchers to create data management plans fitting their needs and based on funder and/or organisation guidelines. Project Tuuli (2015–2017) has provided DMPTuuli, a data management planning tool for Finnish researchers and research organisations offering DMP templates and guidance. In this paper we describe how project has helped both Finnish researchers and research organisations adopt research data management best practices. As a result of the project we have also created a national Tuuli network. With growing competence and collaboration of the network, the project has reached most of its goals. The project has also actively promoted DMP support and training in Finnish research organisations.

scholarly journals Making Research Data Accessible

2021 ◽  
Author(s):  
Diana Kapiszewski ◽  
Sebastian Karcher
Keyword(s):  
Data Sharing ◽  
Research Method ◽  
Secondary Analysis ◽  
Research Data ◽  
Data Generation ◽  
Shared Data

This chapter argues that the benefits of data sharing will accrue more quickly, and will be more significant and more enduring, if researchers make their data “meaningfully accessible.” Data are meaningfully accessible when they can be interpreted and analyzed by scholars far beyond those who generated them. Making data meaningfully accessible requires that scholars take the appropriate steps to prepare their data for sharing, and avail themselves of the increasingly sophisticated infrastructure for publishing and preserving research data. The better other researchers can understand shared data and the more researchers who can access them, the more those data will be re-used for secondary analysis, producing knowledge. Likewise, the richer an understanding an instructor and her students can gain of the shared data being used to teach and learn a particular research method, the more useful those data are for that pedagogical purpose. And the more a scholar who is evaluating the work of another can learn about the evidence that underpins its claims and conclusions, the better their ability to identify problems and biases in data generation and analysis, and the better informed and thus stronger an endorsement of the work they can offer.

scholarly journals Research Data Management - Current status and future challenges for German non-university research institutions

2020 ◽  
Vol 6 ◽  
Author(s):  
Mareike Petersen ◽  
Bianca Pramann ◽  
Ralf Toepfer ◽  
Janna Neumann ◽  
Harry Enke ◽  
...  
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Working Group ◽  
Research Data ◽  
University Research ◽  
Current Status ◽  
Research Data Management ◽  
World Cafe ◽  
Future Challenges ◽  
Scientific Fields

This report describes the results of a workshop on research data management (RDM) that took place in June 2019. More than 50 experts from 46 different non-university institutes covering all Leibniz Sections participated. The aim of the workshop was the intra- and transdisciplinary exchange among RDM experts of different institutions and sections within the Leibniz Association on current questions and challenges but also on experiences and activities with respect to RDM. The event was structured in inspiring talks, a World Café to discuss ideas and solutions related to RDM and an exchange of experts following their affiliation to the different Leibniz sections. The workshop revealed that most institutions, independent of scientific fields, face similar overarching problems with respect to RDM, e.g. missing incentives and no awareness of the benefits that would arise from a proper RDM and data sharing. The event also endorsed that the Research Data Working Group of the Leibniz Association (AK Forschungsdaten) is a place for the exchange of all topics around RDM and enables discussions on how to refine RDM at all institutions and in all scientific fields.

scholarly journals Research Data Management: A proposed framework to boost research in Higher Educational Institutes.

10.29173/iq12 ◽  
2017 ◽  
Vol 41 (1-4) ◽  
pp. 12
Author(s):  
Bhojaraju Gunjal ◽  
Panorea Gaitanou
Keyword(s):  
Literature Review ◽  
Best Practices ◽  
Data Management ◽  
Skill Development ◽  
Research Process ◽  
Research Data ◽  
Research Data Management ◽  
Research Activities ◽  
Educational Institute ◽  
Higher Educational

This paper attempts to present a brief overview of several Research Data Management (RDM) issues and a detailed literature review regarding the RDM aspects adopted in libraries globally. Furthermore, it will describe several tendencies concerning the management of repository tools for research data, as well as the challenges in implementing the RDM. The proper planned training and skill development for all stakeholders by mentors to train both staff and users are some of the issues that need to be considered to enhance the RDM process. An effort will be also made to present the suitable policies and workflows along with the adoption of best practices in RDM, so as to boost the research process in an organisation. This study will showcase the implementation of RDM processes in the Higher Educational Institute of India, referring particularly to the Central Library @ NIT Rourkela in Odisha, India with a proposed framework. Finally, this study will also propose an area of opportunities that can boost research activities in the Institute.

scholarly journals A collective challenge: Open Science from the perspective of Science Europe

2019 ◽  
Vol 72 (2) ◽  
pp. 424-430
Author(s):  
Marie Timmermann
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Open Science ◽  
Research Data ◽  
Major Research ◽  
Research Data Management ◽  
Management Policies ◽  
Good Research ◽  
Quality Of Research

Open Science aims to enhance the quality of research by making research and its outputs openly available, reproducible and accessible. Science Europe, the association of major Research Funding Organisations and Research Performing Organisations, advocates data sharing as one of the core aspects of Open Science and promotes a more harmonised approach to data sharing policies. Good research data management is a prerequisite for Open Science and data management policies should be aligned as much as possible, while taking into account discipline-specific differences. Research data management is a broad and complex field with many actors involved. It needs collective efforts by all actors to work towards aligned policies that foster Open Science.

scholarly journals The 2014 DAF Survey at the University of Sheffield

2015 ◽  
Vol 10 (1) ◽  
pp. 210-229 ◽  
Author(s):  
Andrew Cox ◽  
Laurian Williamson
Keyword(s):  
Data Sharing ◽  
Target Population ◽  
Research Data ◽  
Management Policy ◽  
Research Data Management ◽  
Data Practices ◽  
Positive Attitudes ◽  
The Uk ◽  
The University ◽  
Local Research

The Data Asset Framework methodology has evolved to provide a model for institutional surveys of researchers’ data practices and attitudes. At least 13 such studies have been published in the UK and internationally. The aim of this paper is to analyse the results from the 2014 DAF survey at the University of Sheffield and to reflect on the comparability of this with previous published studies. 432 researchers responded to the survey representing 8% of the target population. Researchers at Sheffield collect multiple types of data and a significant number have accumulated very large amounts of data. Data was backed up on a diverse basis. Only 25% of respondents had a DMP. Eighteen months after its creation most respondents were still not aware of the local research data management policy. Fortunately, most respondents were favourable to the idea of training in many aspects of RDM. Researchers had generally had no experience of sharing data, but attitudes were positive, both in terms of a significant minority seeing a lack of data sharing as an obstacle to the progress of research and also desire to reuse the data of others and share their own with a broad group of researchers. Comparison of the Sheffield results with those of other institutions is difficult particularly because of the divergence of questions asked in the different studies. Nevertheless, in terms of data practices and identifying training priorities there are common patterns. This institutional survey showed less positive attitudes to data sharing than the results of cross-institutional studies, such as conducted by Tenopir et al. (2011).

scholarly journals A descriptive analysis of the data availability statements accompanying medRxiv preprints and a comparison with their published counterparts

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250887
Author(s):  
Luke A. McGuinness ◽  
Athena L. Sheppard
Keyword(s):  
Data Sharing ◽  
Descriptive Analysis ◽  
Open Data ◽  
System Change ◽  
Research Data ◽  
Data Availability ◽  
Published Data ◽  
Editorial Policies ◽  
Journal Editors ◽  
Closed Data

Objective To determine whether medRxiv data availability statements describe open or closed data—that is, whether the data used in the study is openly available without restriction—and to examine if this changes on publication based on journal data-sharing policy. Additionally, to examine whether data availability statements are sufficient to capture code availability declarations. Design Observational study, following a pre-registered protocol, of preprints posted on the medRxiv repository between 25th June 2019 and 1st May 2020 and their published counterparts. Main outcome measures Distribution of preprinted data availability statements across nine categories, determined by a prespecified classification system. Change in the percentage of data availability statements describing open data between the preprinted and published versions of the same record, stratified by journal sharing policy. Number of code availability declarations reported in the full-text preprint which were not captured in the corresponding data availability statement. Results 3938 medRxiv preprints with an applicable data availability statement were included in our sample, of which 911 (23.1%) were categorized as describing open data. 379 (9.6%) preprints were subsequently published, and of these published articles, only 155 contained an applicable data availability statement. Similar to the preprint stage, a minority (59 (38.1%)) of these published data availability statements described open data. Of the 151 records eligible for the comparison between preprinted and published stages, 57 (37.7%) were published in journals which mandated open data sharing. Data availability statements more frequently described open data on publication when the journal mandated data sharing (open at preprint: 33.3%, open at publication: 61.4%) compared to when the journal did not mandate data sharing (open at preprint: 20.2%, open at publication: 22.3%). Conclusion Requiring that authors submit a data availability statement is a good first step, but is insufficient to ensure data availability. Strict editorial policies that mandate data sharing (where appropriate) as a condition of publication appear to be effective in making research data available. We would strongly encourage all journal editors to examine whether their data availability policies are sufficiently stringent and consistently enforced.

scholarly journals Archaeological Analysis in the Information Age: Guidelines for Maximizing the Reach, Comprehensiveness, and Longevity of Data

2019 ◽  
Vol 8 (1) ◽  
pp. 40-52 ◽  
Author(s):  
Sarah W. Kansa ◽  
Levent Atici ◽  
Eric C. Kansa ◽  
Richard H. Meadow
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Information Age ◽  
Research Data ◽  
Primary Data ◽  
Reproducible Research ◽  
Research Data Management ◽  
The Past ◽  
Archaeological Analysis ◽  
The Web

ABSTRACTWith the advent of the Web, increased emphasis on “research data management,” and innovations in reproducible research practices, scholars have more incentives and opportunities to document and disseminate their primary data. This article seeks to guide archaeologists in data sharing by highlighting recurring challenges in reusing archived data gleaned from observations on workflows and reanalysis efforts involving datasets published over the past 15 years by Open Context. Based on our findings, we propose specific guidelines to improve data management, documentation, and publishing practices so that primary data can be more efficiently discovered, understood, aggregated, and synthesized by wider research communities.

Knowledge management, research data management, and university scholarship

VINE ◽  
2015 ◽  
Vol 45 (3) ◽  
pp. 344-359 ◽  
Author(s):  
Joyline Makani
Keyword(s):  
Knowledge Management ◽  
Data Management ◽  
Research Data ◽  
Management Support ◽  
Data Discovery ◽  
Content Type ◽  
Research On Research ◽  
Research Data Management ◽  
Academic Scholarship ◽  
Theoretical Perspectives

Purpose – The purpose of this paper is to synthesize existing research on research data management (RDM), academic scholarship and knowledge management and provide a conceptual framework for an institutional research data management support-system (RDMSS) for systems development, managerial and academic use. Design/methodology/approach – Viewing RDMSS from multiple theoretical perspectives, including data management, knowledge management, academic scholarship and the practice-based perspectives of knowledge and knowing, this paper conceptually explores the systems’ elements needed in the development of an institutional RDM service by considering the underlying data discovery and application issues, as well as the nature of academic scholarship and knowledge creation, discovery, application and sharing motivations in a university environment. Findings – The paper provides general criteria for an institutional RDMSS framework. It suggests that RDM in universities is at the very heart of the knowledge life cycle and is a central ingredient to the academic scholarships of discovery, integration, teaching, engagement and application. Research limitations/implications – This is a conceptual exploration and as a result, the research findings may lack generalisability. Researchers are therefore encouraged to further empirically examine the proposed propositions. Originality/value – The broad RDMSS framework presented in this paper can be compared with the actual situation at universities and eventually guide recommendations for adaptations and (re)design of the institutional RDM infrastructure and knowledge discovery services environment. Moreover, this paper will help to address some of the identified underlying scholarship and RDM disciplinary divides and confusion constraining the effective functioning of the modern day university’s RDM and data discovery environment.

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