Understanding Research Data Repositories as Infrastructures

2021 ◽  
Vol 58 (1) ◽  
pp. 25-35
Author(s):  
Ceilyn Boyd
Keyword(s):  
Research Data ◽  
Data 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 A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question"

2019 ◽  
Vol 5 ◽  
Author(s):  
Matthew Murray ◽  
Megan O'Donnell ◽  
Mark Laufersweiler ◽  
John Novak ◽  
Betty Rozum ◽  
...  
Keyword(s):  
United States ◽  
Academic Libraries ◽  
The United States ◽  
Research Data ◽  
The State ◽  
Executive Summary ◽  
Data Repositories ◽  
Data Services ◽  
Survey Questions ◽  
State Of Research

This report shares the results of a Spring 2018 survey of 35 academic libraries in the United States in regard to the research data services (RDS) they offer. An executive summary presents key findings while the results section provides detailed information on the answers to specific survey questions related to data repositories, metadata, workshops, and polices.

scholarly journals DataverseNO: A National, Generic Repository and its Contribution to the Increased FAIRness of Data from the Long Tail of Research

Ravnetrykk ◽  
2020 ◽  
Author(s):  
Philipp Conzett
Keyword(s):  
Open Source Software ◽  
Crucial Role ◽  
Research Data ◽  
Background Information ◽  
Data Repositories ◽  
Long Tail ◽  
Open Research ◽  
Research Organizations ◽  
Digital Objects ◽  
Organizational Features

Research data repositories play a crucial role in the FAIR (Findable, Accessible, Interoperable, Reusable) ecosystem of digital objects. DataverseNO is a national, generic repository for open research data, primarily from researchers affiliated with Norwegian research organizations. The repository runs on the open-source software Dataverse. This article presents the organization and operation of DataverseNO, and investigates how the repository contributes to the increased FAIRness of small and medium sized research data. Sections 1 to 3 present background information about the FAIR Data Principles (section 1), how FAIR may be turned into reality (section 2), and what these principles and recommendations imply for data from the so-called long tail of research, i.e. small and medium-sized datasets that are often heterogenous in nature and hard to standardize (section 3). Section 4 gives an overview of the key organizational features of DataverseNO, followed by an evaluation of how well DataverseNO and the repository application Dataverse as such support the FAIR Data Principles (section 5). Section 6 discusses how sustainable and trustworthy the repository is. The article is rounded up in section 7 by a brief summary including a look into the future of the repository.

scholarly journals A Content Analysis of Indian Research Data Repositories Prospects and Possibilities

2019 ◽  
Vol 39 (06) ◽  
pp. 280-289 ◽  
Author(s):  
Raj Kumar Bhardwaj
Keyword(s):  
Application Programming Interface ◽  
Research Data ◽  
Identification System ◽  
Microsoft Excel ◽  
Data Repositories ◽  
Data Formats ◽  
Author Identification ◽  
Metadata Standards ◽  
Application Programming ◽  
Content Coverage

The study aims to trace the development of Indian research data repositories (RDRs) and explore their content with the view of identifying prospects and possibilities. Further, it analyses the distribution of data repositories on the basis of content coverage, types of content, author identification system followed, software and the application programming interface used, subject wise number of repositories etc. The study is based on data repositories listed on the registry of data repositories accessible at http://www.re3data.org.The dataset was exported in Microsoft Excel format for analysis. A simple percentage method was followed in data analyses and results are presented through Tables and Figures. The study found a total of 2829 data repositories in existence worldwide. Further, it was seen that 1526 (53.9 %) are open and 924 (32.4 %) are restricted data repositories. Also, there are embargoed data repositories numbering 225 (8.0 %) and closed ones numbering 154 (5.4 %). There are 2829 RDRs covering 72 countries in the world. The study found that out of total 45 Indian RDRs, only 30 (67 %) are open, followed by restricted 12 (27 %) and 3 (6 %) that are closed. Majority of Indian RDRs (20) were developed in the year 2014. The study found that the majority of Indian RDRs (17) are‘disciplinary’. Further, the study also revealed that statistical data formats are available in a maximum of 31 (68.9 %) Indian RDRs. It was also seen that the majority of Indian RDRs (28) has datasets relating to ‘Life Sciences’. It was identified that only 20% of data repositories have been using metadata standards in metadata; the remaining 80% do not use any standards in metadata entry. This study covered only the research data repositories in India registered on the registry of data repositories. RDRs not listed in the registry of data repositories are left out.

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.

Sharing Open Data in Agriculture

2021 ◽  
pp. 244-266
Author(s):  
Liah Shonhe
Keyword(s):  
Agricultural Sector ◽  
Open Data ◽  
Research Data ◽  
Data Sets ◽  
Research Activity ◽  
African Countries ◽  
Data Set ◽  
Data Repositories ◽  
Bibliographic Data ◽  
Prolific Authors

The main focus of the study was to explore the practices of open data sharing in the agricultural sector, including establishing the research outputs concerning open data in agriculture. The study adopted a desktop research methodology based on literature review and bibliographic data from WoS database. Bibliometric indicators discussed include yearly productivity, most prolific authors, and enhanced countries. Study findings revealed that research activity in the field of agriculture and open access is very low. There were 36 OA articles and only 6 publications had an open data badge. Most researchers do not yet embrace the need to openly publish their data set despite the availability of numerous open data repositories. Unfortunately, most African countries are still lagging behind in management of agricultural open data. The study therefore recommends that researchers should publish their research data sets as OA. African countries need to put more efforts in establishing open data repositories and implementing the necessary policies to facilitate OA.

scholarly journals Openness in Big Data and Data Repositories

2019 ◽  
Vol 11 (3) ◽  
pp. 255-273 ◽  
Author(s):  
Vicki Xafis ◽  
Markus K. Labude
Keyword(s):  
Big Data ◽  
Ethical Issues ◽  
Open Science ◽  
Research Data ◽  
Data Repositories ◽  
Ethics Framework ◽  
Full Explanation ◽  
Health Related Research ◽  
Health Related ◽  
Privacy And Confidentiality

Abstract There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and re-use of research data. This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit. To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of Openness in Big Data and Data Repositories. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

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