scholarly journals Data Citation Index: Promoting attribution, use and discovery of research data

2014 ◽  
Vol 34 (1-2) ◽  
pp. 97-98 ◽  
Author(s):  
Megan M. Force ◽  
Daniel M. Auld
Keyword(s):  
Citation Index ◽  
Research Data ◽  
Data Citation

Data Documents

2021 ◽  
Vol 48 (4) ◽  
pp. 307-328
Author(s):  
Dominic Farace ◽  
Hélène Prost ◽  
Antonella Zane ◽  
Birger Hjørland ◽  
◽  
...  
Keyword(s):  
Citation Index ◽  
The Other ◽  
Data Sets ◽  
Bibliometric Data ◽  
Model Data ◽  
Other Hand ◽  
Data Citation ◽  
Philosophical Assumption ◽  
The One

This article presents and discusses different kinds of data documents, including data sets, data studies, data papers and data journals. It provides descriptive and bibliometric data on different kinds of data documents and discusses the theoretical and philosophical problems by classifying documents according to the DIKW model (data documents, information documents, knowl­edge documents and wisdom documents). Data documents are, on the one hand, an established category today, even with its own data citation index (DCI). On the other hand, data documents have blurred boundaries in relation to other kinds of documents and seem sometimes to be understood from the problematic philosophical assumption that a datum can be understood as “a single, fixed truth, valid for everyone, everywhere, at all times”

scholarly journals Data catalog cards: simplifying article/data linking

2016 ◽  
Author(s):  
Martin Fenner
Keyword(s):  
Research Data ◽  
Data Citation

Data citation is core to DataCite's mission and DataCite is involved in several projects that try to facilitate data citation, including THOR, Data Citation Implementation Pilot (DCIP), Research Data Alliance (RDA), and COPDESS. ...

scholarly journals The reproducibility challenge – what researchers need

2017 ◽  
Author(s):  
Federica Rosetta
Keyword(s):  
Scientific Discovery ◽  
Open Data ◽  
Science Research ◽  
Open Science ◽  
Research Data ◽  
Data Sets ◽  
Scientific Practices ◽  
Data Citation ◽  
Materials Used ◽  
High Level

Watch the VIDEO here.Within the Open Science discussions, the current call for “reproducibility” comes from the raising awareness that results as presented in research papers are not as easily reproducible as expected, or even contradicted those original results in some reproduction efforts. In this context, transparency and openness are seen as key components to facilitate good scientific practices, as well as scientific discovery. As a result, many funding agencies now require the deposit of research data sets, institutions improve the training on the application of statistical methods, and journals begin to mandate a high level of detail on the methods and materials used. How can researchers be supported and encouraged to provide that level of transparency? An important component is the underlying research data, which is currently often only partly available within the article. At Elsevier we have therefore been working on journal data guidelines which clearly explain to researchers when and how they are expected to make their research data available. Simultaneously, we have also developed the corresponding infrastructure to make it as easy as possible for researchers to share their data in a way that is appropriate in their field. To ensure researchers get credit for the work they do on managing and sharing data, all our journals support data citation in line with the FORCE11 data citation principles – a key step in the direction of ensuring that we address the lack of credits and incentives which emerged from the Open Data analysis (Open Data - the Researcher Perspective https://www.elsevier.com/about/open-science/research-data/open-data-report ) recently carried out by Elsevier together with CWTS. Finally, the presentation will also touch upon a number of initiatives to ensure the reproducibility of software, protocols and methods. With STAR methods, for instance, methods are submitted in a Structured, Transparent, Accessible Reporting format; this approach promotes rigor and robustness, and makes reporting easier for the author and replication easier for the reader.

scholarly journals Analyzing data citation practices using the data citation index

2015 ◽  
Vol 67 (12) ◽  
pp. 2964-2975 ◽  
Author(s):  
Nicolas Robinson-García ◽  
Evaristo Jiménez-Contreras ◽  
Daniel Torres-Salinas
Keyword(s):  
Citation Index ◽  
Data Citation ◽  
Citation Practices

scholarly journals DataCite: Lessons Learned on Persistent Identifiers for Research Data

2017 ◽  
Vol 11 (2) ◽  
pp. 39-47 ◽  
Author(s):  
Laura Rueda ◽  
Martin Fenner ◽  
Patricia Cruse
Keyword(s):  
Collaborative Research ◽  
Research Data ◽  
Lessons Learned ◽  
Data Reuse ◽  
Service Development ◽  
Key Factors ◽  
Data Publication ◽  
Data Citation ◽  
Share Information ◽  
Insight Into

Data are the infrastructure of science and they serve as the groundwork for scientific pursuits. Data publication has emerged as a game-changing breakthrough in scholarly communication. Data form the outputs of research but also are a gateway to new hypotheses, enabling new scientific insights and driving innovation. And yet stakeholders across the scholarly ecosystem, including practitioners, institutions, and funders of scientific research are increasingly concerned about the lack of sharing and reuse of research data. Across disciplines and countries, researchers, funders, and publishers are pushing for a more effective research environment, minimizing the duplication of work and maximizing the interaction between researchers. Availability, discoverability, and reproducibility of research outputs are key factors to support data reuse and make possible this new environment of highly collaborative research. An interoperable e-infrastructure is imperative in order to develop new platforms and services for to data publication and reuse. DataCite has been working to establish and promote methods to locate, identify and share information about research data. Along with service development, DataCite supports and advocates for the standards behind persistent identifiers (in particular DOIs, Digital Object Identifiers) for data and other research outputs. Persistent identifiers allow different platforms to exchange information consistently and unambiguously and provide a reliable way to track citations and reuse. Because of this, data publication can become a reality from a technical standpoint, but the adoption of data publication and data citation as a practice by researchers is still in its early stages. Since 2009, DataCite has been developing a series of tools and services to foster the adoption of data publication and citation among the research community. Through the years, DataCite has worked in a close collaboration with interdisciplinary partners on these issues and we have gained insight into the development of data publication workflows. This paper describes the types of different actions and the lessons learned by DataCite. 

Citation of evolving data in distributed asynchronous infrastructures

2019 ◽  
Vol 15 (S350) ◽  
pp. 392-393
Author(s):  
C. M. Zwölf ◽  
N. Moreau ◽  
Y. A. Ba ◽  
M. L. Dubernet
Keyword(s):  
Working Group ◽  
Research Data ◽  
Dynamic Data ◽  
Data Citation ◽  
Evolving Data ◽  
Set Up ◽  
Data Centres

AbstractThe VAMDC Consortium intended to find a way for users to cite the datasets accessed through the infrastructure. The Research Data Alliance Data citation working group provided the researchers and data centres communities with a recommendation to identify and cite dynamic data. This recommendation perfectly matched the VAMDC needs: the proposed solution relies on a query centric view and the set-up of a Query Store. Data should be stored in a versioned time-stamped manner and accessed through queries. The Query Store we implemented for VAMDC is interlinked with Zenodo. Since Zenodo is indexed in OpenAIRE and since the latter implements Scholix, VAMDC indirectly implements Scholix via its Query Store. The paper outlines the successes and limitations of the above approach.

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