re3data COREF – Enhancing the re3data service as a community-driven and trustworthy resource for research data repositories and portals

2021 ◽  
Author(s):  
Nina Weisweiler ◽  
Kirsten Elger ◽  
Robert Ulrich ◽  
Michael Witt ◽  
Lea Maria Ferguson ◽  
...  
Keyword(s):  
Research Data ◽  
Third Party ◽  
Model Concept ◽  
German Research Foundation ◽  
Data Repositories ◽  
Digital Repositories ◽  
Metadata Schema ◽  
Scientific Organizations ◽  
Scientific Disciplines ◽  
Global Registry

<p>re3data is the global registry for research data repositories. As of January 2021, the service lists over 2620 digital repositories across all scientific disciplines and provides an extensive description of repositories based on a detailed metadata schema (https://doi.org/10.2312/re3.008). A variety of funders, publishers, and scientific organizations around the world refer to re3data within their guidelines and policies, recommending the service to researchers looking for appropriate repositories for storage and discovery of research data. With over 750 entries the field of geosciences is one of the most strongly represented subject groups in the registry.</p><p>The re3data COREF project (Community Driven Open Reference for Research Data Repositories) started in January 2020 and receives funding from the German Research Foundation (DFG) for 36 months. With its main focus on the current project the presentation will outline the further professionalization of re3data and the provision of reliable and individualizable descriptions of research data repositories. This includes updates and revisions of the metadata schema, the advancement of the technical infrastructure as well as an enhanced overall (technical) service model concept to embed and connect the service within the research data landscape as a community-driven source and reference for trustworthy repositories.</p><p>In addition, outcomes from the first re3data COREF stakeholder survey and workshop held in November 2020 will be presented, introducing diverse use cases of the re3data service and examples for the reuse of its metadata. The presentation will address how re3data currently interlinks with external parties and how more advanced options for easier and trustworthy integration of third-party information can be facilitated.</p>

scholarly journals Análisis de los principios para la descripción de datos de investigación mediante Datacite Metadata Schema

2020 ◽  
Vol 23 (1) ◽  
Author(s):  
Eder Ávila-Barrientos
Keyword(s):  
Discourse Analysis ◽  
Open Access ◽  
Data Access ◽  
Research Data ◽  
Data Sets ◽  
Data Repositories ◽  
Metadata Schema ◽  
Acceso Abierto ◽  
Methodological Principles ◽  
Access Policies

El objetivo de este trabajo consiste en analizar los principios teórico-metodológicos relacionados con la descripción de los datos de investigación. Se realizó un análisis sobre el estado de la cuestión de los datos de investigación, en cual se abordan aspectos de su citación, descripción y sistematización. Se identificaron y analizaron los elementos de metadatos para la descripción de conjuntos de datos de investigación que se incluyen en el DataCite Metadata Schema, con el propósito de crear una propuesta de perfil descriptivo aplicable a estos conjuntos. Se estima que, si los datos de investigación se encuentran debidamente descritos, entonces se fomentará en mayor grado su accesibilidad y reutilización. Para ello, es necesario que las instituciones académicas y de investigación participen en la generación de políticas de acceso abierto a sus datos de investigación. The objective of this work is to analyze the theoretical-methodological principles related to the description and accessibility of research data. Hermeneutics and discourse analysis were applied to literature specialized in: research data; access and description of research data; data repositories. Metadata elements for the description of research datasets that are included in the DataCite Metadata Schema were identified and analyzed, in order to create a descriptive profile proposal for research data sets, which can be applied in the data repositories. If the research data is properly described, then its accessibility and reuse will be further promoted. To do this, it is necessary for academic and research institutions to participate in the generation of open access policies for their research data.

scholarly journals Crosswalk among Prominent Open Research Data Repositories

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 60-67
Author(s):  
Dr.M. Krishnamurthy ◽  
Dr. Bhalachandra S. Deshpande ◽  
Dr.C. Sajana
Keyword(s):  
Open Access ◽  
Open Data ◽  
Research Data ◽  
Data Repositories ◽  
Digital Repositories ◽  
Open Research ◽  
Global Movement ◽  
Access To Knowledge ◽  
Metadata Harvesting ◽  
Data Upload

Open Access is a synergised global movement using Internet to provide equal access to knowledge that once hid behind the subscription paywalls. Many new models for scholarly communication have emerged in recent past. One among them is institutional or digital repositories which archive the scholarly content of an organization. While the concept of Open Access opened new arena for institutional or digital repositories in the form of Open repositories. Likewise, the Open repositories for Research Data Management (RDM) are initiative to organize, store, cite, preserve, and share the collected data derived from the research. There are many multidisciplinary and subject specific open repositories for RDM offering exquisite features for perpetual management of research data. The objective of the present study is to evaluate features of popular Open Data Repositories-Zenodo, FigShare, Harvard Dataverse and Mendeley Data. The evaluation provided insights about the key features of the selected Open Data Repositories and which enable us to select the best among them. Zenodo provides maximum data upload limit. While the major features required by a researcher like DOI, File Types, citation support, licenses, search (metadata harvesting) are provided by all three repositories.

scholarly journals Re3data.org: Discovery Tool for Reusable Research Data

2020 ◽  
Author(s):  
Charles J Greenberg ◽  
Sangeeta Narang
Keyword(s):  
Disease Transmission ◽  
Disaster Preparedness ◽  
Research Data ◽  
Medical Sciences ◽  
German Research Foundation ◽  
Data Repositories ◽  
International Partnership ◽  
Clinical Investigations ◽  
Open Datasets ◽  
A Site

Introduction: Research data is a representation of raw statistical or visual data collected from sources considered vital during a scientific investigation. In the medical sciences, there is enormous data generated from observations, experiments, and clinical investigations. Re3data.org is a global registry of research data, a site supported by DataOne. Re3data.org was originally founded in 2012 and funded by the DFG German Research Foundation, and currently features an international partnership. Discussion: re3data.org currently indexes over 2000 repositories, representing all subjects. In medicine there are 568 listed repositories, divided into 48 sub-categories. A small number of registered repositories are noted as closed, and there are also subjects with no currently listed data repositories. Conclusion: Availability and sharing of research data can facilitate disaster preparedness, model disease transmission, track health outcomes and provide reusable data for new emerging research. Re3data.org provides visibility for existing and emerging open datasets available in a variety of eScience repositories.

Improvements for research data repositories: The case of text spam

2021 ◽  
pp. 016555152199863
Author(s):  
Ismael Vázquez ◽  
María Novo-Lourés ◽  
Reyes Pavón ◽  
Rosalía Laza ◽  
José Ramón Méndez ◽  
...  
Keyword(s):  
Web Application ◽  
Research Data ◽  
Data Sets ◽  
Data Repositories ◽  
Software Applications ◽  
Public Data ◽  
Protection Mechanisms ◽  
Experimental Protocols ◽  
Learning Research ◽  
Processing Steps

Current research has evolved in such a way scientists must not only adequately describe the algorithms they introduce and the results of their application, but also ensure the possibility of reproducing the results and comparing them with those obtained through other approximations. In this context, public data sets (sometimes shared through repositories) are one of the most important elements for the development of experimental protocols and test benches. This study has analysed a significant number of CS/ML ( Computer Science/ Machine Learning) research data repositories and data sets and detected some limitations that hamper their utility. Particularly, we identify and discuss the following demanding functionalities for repositories: (1) building customised data sets for specific research tasks, (2) facilitating the comparison of different techniques using dissimilar pre-processing methods, (3) ensuring the availability of software applications to reproduce the pre-processing steps without using the repository functionalities and (4) providing protection mechanisms for licencing issues and user rights. To show the introduced functionality, we created STRep (Spam Text Repository) web application which implements our recommendations adapted to the field of spam text repositories. In addition, we launched an instance of STRep in the URL https://rdata.4spam.group to facilitate understanding of this study.

scholarly journals GAMS – An infrastructure for the long-term preservation and publication of research data from the Humanities

2018 ◽  
Vol 71 (1) ◽  
pp. 207-216 ◽  
Author(s):  
Johannes Hubert Stigler ◽  
Elisabeth Steiner
Keyword(s):  
System Architecture ◽  
Academic Research ◽  
Research Data ◽  
Data Repository ◽  
Data Repositories ◽  
Domain Specific ◽  
Preservation Policy ◽  
Long Term Preservation ◽  
Data Centres

Research data repositories and data centres are becoming more and more important as infrastructures in academic research. The article introduces the Humanities’ research data repository GAMS, starting with the system architecture to preservation policy and content policy. Challenges of data centres and repositories and the general and domain-specific approaches and solutions are outlined. Special emphasis lies on the sustainability and long-term perspective of such infrastructures, not only on the technical but above all on the organisational and financial level.

scholarly journals Connecting Data Publication to the Research Workflow: A Preliminary Analysis

2017 ◽  
Vol 12 (1) ◽  
pp. 88-105 ◽  
Author(s):  
Sünje Dallmeier-Tiessen ◽  
Varsha Khodiyar ◽  
Fiona Murphy ◽  
Amy Nurnberger ◽  
Lisa Raymond ◽  
...  
Keyword(s):  
Working Group ◽  
Research Data ◽  
Data Publishing ◽  
Data Repository ◽  
Research Activity ◽  
Data Repositories ◽  
Data Publication ◽  
Loosely Coupled ◽  
Definition Of ◽  
Data Documentation

The data curation community has long encouraged researchers to document collected research data during active stages of the research workflow, to provide robust metadata earlier, and support research data publication and preservation. Data documentation with robust metadata is one of a number of steps in effective data publication. Data publication is the process of making digital research objects ‘FAIR’, i.e. findable, accessible, interoperable, and reusable; attributes increasingly expected by research communities, funders and society. Research data publishing workflows are the means to that end. Currently, however, much published research data remains inconsistently and inadequately documented by researchers. Documentation of data closer in time to data collection would help mitigate the high cost that repositories associate with the ingest process. More effective data publication and sharing should in principle result from early interactions between researchers and their selected data repository. This paper describes a short study undertaken by members of the Research Data Alliance (RDA) and World Data System (WDS) working group on Publishing Data Workflows. We present a collection of recent examples of data publication workflows that connect data repositories and publishing platforms with research activity ‘upstream’ of the ingest process. We re-articulate previous recommendations of the working group, to account for the varied upstream service components and platforms that support the flow of contextual and provenance information downstream. These workflows should be open and loosely coupled to support interoperability, including with preservation and publication environments. Our recommendations aim to stimulate further work on researchers’ views of data publishing and the extent to which available services and infrastructure facilitate the publication of FAIR data. We also aim to stimulate further dialogue about, and definition of, the roles and responsibilities of research data services and platform providers for the ‘FAIRness’ of research data publication workflows themselves.

scholarly journals PsychData – Experiences from 12 Years of Research Data Archiving

2015 ◽  
Author(s):  
Peter Weiland ◽  
Ina Dehnhard
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Research Data ◽  
Data Reuse ◽  
German Research Foundation ◽  
Cross Sectional ◽  
Data Archiving ◽  
Wide Range ◽  
Domain Specific Knowledge ◽  
Meta Analyses

See video of the presentation.The benefits of making research data permanently accessible through data archives is widely recognized: costs can be reduced by reusing existing data, research results can be compared and validated with results from archived studies, fraud can be more easily detected, and meta-analyses can be conducted. Apart from that, authors may gain recognition and reputation for producing the datasets. Since 2003, the accredited research data center PsychData (part of the Leibniz Institute for Psychology Information in Trier, Germany) documents and archives research data from all areas of psychology and related fields. In the beginning, the main focus was on datasets that provide a high potential for reuse, e.g. longitudinal studies, large-scale cross sectional studies, or studies that were conducted during historically unique conditions. Presently, more and more journal publishers and project funding agencies require researchers to archive their data and make them accessible for the scientific community. Therefore, PsychData also has to serve this need.In this presentation we report on our experiences in operating a discipline-specific research data archive in a domain where data sharing is met with considerable resistance. We will focus on the challenges for data sharing and data reuse in psychology, e.g.large amount of domain-specific knowledge necessary for data curationhigh costs for documenting the data because of a wide range on non-standardized measuressmall teams and little established infrastructures compared with the "big data" disciplinesstudies in psychology not designed for reuse (in contrast to the social sciences)data protectionresistance to sharing dataAt the end of the presentation, we will provide a brief outlook on DataWiz, a new project funded by the German Research Foundation (DFG). In this project, tools will be developed to support researchers in documenting their data during the research phase.

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