scholarly journals Research data management: a bibliometric and altmetric study based on Dimensions

2021 ◽  
Vol 1 (3) ◽  
pp. 1-19
Author(s):  
Marília Catarina Andrade Gontijo ◽  
Raíssa Yuri Hamanaka ◽  
Ronaldo Ferreira De Araujo
Keyword(s):  
Information Systems ◽  
Data Management ◽  
Data Science ◽  
Research Data ◽  
Scientific Production ◽  
Research Data Management ◽  
Research Areas ◽  
Library And Information Studies ◽  
Information Studies ◽  
Online Attention

Objective. This study aims to analyze the scientific production on research data management indexed in the Dimensions database. Design/Methodology/Approach. Using the term “research data management” in the Dimensions database, 677 articles were retrieved and analyzed employing bibliometric and altmetric indicators. The Altmetrics.com system was used to collect data from alternative virtual sources to measure the online attention received by the retrieved articles. Bibliometric networks from journals bibliographic coupling and keywords co-occurrence were generated using the VOSviewer software. Results/Discussion. Growth in scientific production over the period 1970-2021 was observed. The countries/regions with the highest rates of publications were the USA, Germany, and the United Kingdom. Among the most productive authors were Andrew Martin Cox, Stephen Pinfield, Marta Teperek, Mary Anne Kennan, and Amanda L. Whitmire. The most productive journals were the International Journal of Digital Curation, Journal of eScience Librarianship, and Data Science Journal, while the most representative research areas were Information and Computing Sciences, Information Systems, and Library and Information Studies. Conclusions. The multidisciplinarity in research data management was demonstrated by publications occurring in different fields of research, such as Information and Computing Sciences, Information Systems, Library and Information Studies, Medical and Health Sciences, and History and Archeology. About 60% of the publications had at least one citation, with a total of 3,598 citations found, featuring a growing academic impact. Originality/Value. This bibliometric and altmetric study allowed the analysis of the literature on research data management. The theme was investigated in the Dimensions database and analyzed using productivity, impact, and online attention indicators.

Data librarianship in South African academic and research libraries: a survey

2020 ◽  
Vol 41 (6/7) ◽  
pp. 401-416
Author(s):  
Elisha R.T. Chiware
Keyword(s):  
Data Management ◽  
Online Survey ◽  
Full Range ◽  
Research Data ◽  
Content Type ◽  
Research Libraries ◽  
Research Data Management ◽  
Library And Information Studies ◽  
Information Studies ◽  
Management Services

PurposeThe purpose of this study was to establish the current skills base of librarians working in research data management services in academic and research libraries in South Africa. The purpose was also to determine the relevance of courses and programmes that are currently being offered by library and information studies programmes in response to the needs of research data management services and make recommendations on curriculum improvement.Design/methodology/approachAbout 13 institutions which were considered early adopters of research data management services were identified as participants in an online survey. In addition, a review of Web pages of existing library and information studies schools was carried to establish courses that would support research data management services. Data collected through the two approaches were analysed and presented quantitatively and qualitatively.FindingsThe findings reveal an environment in a developmental stage, with limited skilled personnel to run research data management services. The findings also show an absence of specific data librarianship courses within existing library and information studies programmes and a very limited scope for the full range of data management courses within professional development programmes.Originality/valueThe paper provides information on approaches to further develop existing curriculum and contribute to the data management needs and support governments, funders and publishers' requirements for the discoverability and re-use of research data across research domains.

scholarly journals “How Do I Do That?” A Literature Review of Research Data Management Skill Gaps of Canadian Health Sciences Information Professionals

2019 ◽  
Vol 40 (2) ◽  
pp. 51-60
Author(s):  
Justin Fuhr
Keyword(s):  
Literature Review ◽  
Data Management ◽  
Health Sciences ◽  
Research Data ◽  
Research Data Management ◽  
Skill Gaps ◽  
Library And Information Studies ◽  
Information Studies ◽  
Canadian Health ◽  
Information Professionals

Abstract: There is a recognized need to provide research data management (RDM) services in health sciences libraries. A review of the literature reveals numerous strategies to provide training for health sciences librarians as they provide RDM services to health sciences researchers, faculty, and students. However, no consensus emerges through this literature review with respect to RDM training initiatives. With training initiatives being developed and documented, more in-depth research will emerge that verifies which initiatives have the greatest success for upskilling information professionals in managing research data. This is an area where future library and information studies research can be conducted. It is the hope that with this literature review, I can conduct my own survey to gain more perspective on RDM in a Canadian health sciences library context.

scholarly journals A critical analysis of lifecycle models of the research process and research data management

2018 ◽  
Vol 70 (2) ◽  
pp. 142-157 ◽  
Author(s):  
Andrew Martin Cox ◽  
Winnie Wan Ting Tam
Keyword(s):  
Data Management ◽  
Design Methodology ◽  
Research Process ◽  
Research Data ◽  
Systematic Analysis ◽  
Content Type ◽  
Research Data Management ◽  
Library And Information Studies ◽  
Information Studies ◽  
Practical Implications

Purpose Visualisations of research and research-related activities including research data management (RDM) as a lifecycle have proliferated in the last decade. The purpose of this paper is to offer a systematic analysis and critique of such models. Design/methodology/approach A framework for analysis synthesised from the literature presented and applied to nine examples. Findings The strengths of the lifecycle representation are to clarify stages in research and to capture key features of project-based research. Nevertheless, their weakness is that they typically mask various aspects of the complexity of research, constructing it as highly purposive, serial, uni-directional and occurring in a somewhat closed system. Other types of models such as spiral of knowledge creation or the data journey reveal other stories about research. It is suggested that we need to develop other metaphors and visualisations around research. Research limitations/implications The paper explores the strengths and weaknesses of the popular lifecycle model for research and RDM, and also considers alternative ways of representing them. Practical implications Librarians use lifecycle models to explain service offerings to users so the analysis will help them identify clearly the best type of representation for particular cases. The critique offered by the paper also reveals that because researchers do not necessarily identify with a lifecycle representation, alternative ways of representing research need to be developed. Originality/value The paper offers a systematic analysis of visualisations of research and RDM current in the Library and Information Studies literature revealing the strengths and weaknesses of the lifecycle metaphor.

scholarly journals NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

2020 ◽  
Vol 6 ◽  
Author(s):  
Christoph Steinbeck ◽  
Oliver Koepler ◽  
Felix Bach ◽  
Sonja Herres-Pawlis ◽  
Nicole Jung ◽  
...  
Keyword(s):  
Data Management ◽  
Data Science ◽  
Open Data ◽  
Research Data ◽  
Data Standards ◽  
Data Repositories ◽  
Data Infrastructure ◽  
Research Data Management ◽  
Wide Range ◽  
Chemistry Community

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

scholarly journals Reproducibility Librarianship

2017 ◽  
Author(s):  
Vicky Steeves
Keyword(s):  
New York ◽  
Data Management ◽  
Data Science ◽  
Research Data ◽  
Reproducible Research ◽  
New York University ◽  
Research Data Management ◽  
Digital Scholarship ◽  
The Many

This is a self-archived version of an article published in Collaborative Librarianship. The content of this article is not different from what is in the journal (found here: http://digitalcommons.du.edu/collaborativelibrarianship/vol9/iss2/4)Recommended CitationSteeves, Vicky (2017) "Reproducibility Librarianship," Collaborative Librarianship: Vol. 9 : Iss. 2 , Article 4. Available at: https://digitalcommons.du.edu/collaborativelibrarianship/vol9/iss2/4Over the past few years, research reproducibility has been increasingly highlighted as a multifaceted challenge across many disciplines. There are socio-cultural obstacles as well as a constantly changing technical landscape that make replicating and reproducing research extremely difficult. Researchers face challenges in reproducing research across different operating systems and different versions of software, to name just a few of the many technical barriers. The prioritization of citation counts and journal prestige has undermined incentives to make research reproducible.While libraries have been building support around research data management and digital scholarship, reproducibility is an emerging area that has yet to be systematically addressed. To respond to this, New York University (NYU) created the position of Librarian for Research Data Management and Reproducibility (RDM & R), a dual appointment between the Center for Data Science (CDS) and the Division of Libraries. This report will outline the role of the RDM & R librarian, paying close attention to the collaboration between the CDS and Libraries to bring reproducible research practices into the norm.

scholarly journals PUBLICATION OF RESEARCH DATA MANAGEMENT IN OPEN ACCESS JOURNAL ANALYSIS BASED ON SCOPUS DATA

2020 ◽  
Vol 41 (2) ◽  
pp. 215
Author(s):  
Tupan Tupan ◽  
Kamaludin Kamaludin
Keyword(s):  
New York ◽  
Open Access ◽  
Data Management ◽  
Data Science ◽  
The United States ◽  
Open Access Journal ◽  
Research Data ◽  
New York University ◽  
Research Data Management ◽  
Open Access Resources

The study aims to determine: (1) the number of open access resources for research data management publications indexed by Scopus, including the year of publication, source of publication, authors, institutions, countries, types of documents and funding agencies; (2) mapping research data management based on keywords. The results of the study showed that the number of open access resources for research data management publications has started since 1981 and the number has continued to increase starting in 2014 and the highest number occurred in 2019, namely 49 publications. The most publicized journals that open access to research data management was the Data Science Journal, which was 11 publications. The most productive author of conducting research data management publications was Cox, A.M. and Pinfield, S. The largest institutions contributing to the publication of open access research data management were the University of Toronto and New York University. The countries that contributed the most were the United States with 50 publications, then China with 38 publications. The most open access research data management in the form of articles as many as 107 and 37 conference paper publications. The institutions that provided the most funding sponsors were the Deutsche Forschungsgemeinschaft and the National Science Foundation. The results of keyword mapping with VOSViewer showed that big data, research data management, information management, data management, medical research topics, software, information processing, and metadata were the most researched topics.

scholarly journals Data meets history: A research data management strategy for the historically oriented humanities

2021 ◽  
pp. 155-178
Author(s):  
Fabian Cremer ◽  
Silvia Daniel ◽  
Marina Lemaire ◽  
Katrin Moeller ◽  
Matthias Razum ◽  
...  
Keyword(s):  
Data Management ◽  
Management Strategy ◽  
Research Data ◽  
Research Data Management

In defense of decentralized research data management

Neuroforum ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michael Hanke ◽  
Franco Pestilli ◽  
Adina S. Wagner ◽  
Christopher J. Markiewicz ◽  
Jean-Baptiste Poline ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
Research Data ◽  
Data Archive ◽  
Individual Scientist ◽  
Computing Platform ◽  
Research Data Management ◽  
Cloud Computing Platform ◽  
Research Objects ◽  
Central Services

Abstract Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.

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