Research Data Sharing and Reuse Through Open Data

2021 ◽  
pp. 284-306
Author(s):  
Abel Christopher M'kulama ◽  
Akakandelwa Akakandelwa
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Data Storage ◽  
Mixed Methods Research ◽  
Open Data ◽  
Research Process ◽  
Research Data ◽  
Research Data Management ◽  
Inadequate Knowledge ◽  
Self Administered Questionnaire

Research data management is considered a critical step in the research process among researchers. Researchers are required to submit RDM plans with details about data storage, data sharing, and reuse procedures when submitting research proposals for grants. This chapter presents findings of an investigation into the perceptions and practices of ZARI researchers towards research data management. Mixed methods research using a self-administered questionnaire was adopted for data collection. Fifty-one researchers were sampled and recruited for participation into the study. The study established that the majority of the researchers were not depositing their research data in central repositories; data was kept on individual's devices and was therefore not readily available for sharing. The major challenges being faced by researchers included lack of a policy, lack of a repository, and inadequate knowledge in RDM. The study concludes that research data at ZARI was not being professionally managed. The study recommends for formulation of policies, establishment of repository and staff training.

scholarly journals Researchers at Arab Universities Hold Positive Views on Research Data Management and Data Sharing

2020 ◽  
Vol 15 (2) ◽  
pp. 168-170
Author(s):  
Jennifer Kaari
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Data Privacy ◽  
Management Practices ◽  
Open Data ◽  
Scientific Progress ◽  
Management Plan ◽  
Research Data ◽  
Data Repository ◽  
Research Data Management

A Review of: Elsayed, A. M., & Saleh, E. I. (2018). Research data management and sharing among researchers in Arab universities: An exploratory study. IFLA Journal, 44(4), 281–299. https://doi.org/10.1177/0340035218785196 Abstract Objective – To investigate researchers’ practices and attitudes regarding research data management and data sharing. Design – Email survey. Setting – Universities in Egypt, Jordan, and Saudi Arabia. Subjects – Surveys were sent to 4,086 academic faculty researchers. Methods – The survey was emailed to faculty at three Arab universities, targeting faculty in the life sciences and engineering. The survey was created using Google Docs and remained open for five months. Participants were asked basic demographic questions, questions regarding their research data and metadata practices, and questions regarding their data sharing practices. Main Results – The authors received 337 responses, for a response rate of 8%. The results showed that 48.4% of respondents had a data management plan and that 97% were responsible for preserving their own data. Most respondents stored their research data on their personal storage devices. The authors found that 64.4% of respondents reported sharing their research data. Respondents most frequently shared their data by publishing in a data research journal, sharing through academic social networks such as ResearchGate, and providing data upon request to peers. Only 5.1% of respondents shared data through an open data repository.  Of those who did not share data, data privacy and confidentiality were the most common reasons cited. Of the respondents who did share their data, contributing to scientific progress and increased citation and visibility were the primary reasons for doing so. A total of 59.6% of respondents stated that they needed more training in research data management from their universities. Conclusion – The authors conclude that researchers at Arab universities are still primarily responsible for their own data and that data management planning is still a new concept to most researchers. For the most part, the researchers had a positive attitude toward data sharing, although depositing data in open repositories is still not a widespread practice. The authors conclude that in order to encourage strong data management practices and open data sharing among Arab university researchers, more training and institutional support is needed.

scholarly journals The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Daniel Arend ◽  
Patrick König ◽  
Astrid Junker ◽  
Uwe Scholz ◽  
Matthias Lange
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Best Practice ◽  
Research Process ◽  
Research Data ◽  
Primary Data ◽  
Data Repository ◽  
Plant Genomics ◽  
Quality Of Data

Abstract Background The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. Results To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a “bring the infrastructure to the data” approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. Conclusion The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process.

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.

Designing an information architecture for data management technologies: Introducing the DIAMANT model

2019 ◽  
Vol 52 (2) ◽  
pp. 592-600
Author(s):  
Katarina Blask ◽  
André Förster
Keyword(s):  
Data Management ◽  
Reference Model ◽  
Implementation Process ◽  
Research Process ◽  
Research Data ◽  
Information Architecture ◽  
Central Research ◽  
Research Data Management ◽  
Comprehensive Framework ◽  
The Ideal

Although research institutions take on increased responsibility for providing infrastructures and services around the proper handling of research data, there is no comprehensive framework addressing the ideal conditions of this implementation process. To overcome this gap, we present the DIAMANT model, a reference model aimed at providing an orientation framework for the implementation of research data management guided by the research process itself. It builds upon a central research data management information unit controlling the information flow between all other organizational units involved in research data management. Due to the possibility of outsourcing organizational units, the implementation process is maximally flexible and efficient.

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 eLabFTW as an Open Science tool to improve the quality and translation of preclinical research

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 292
Author(s):  
Michael Hewera ◽  
Daniel Hänggi ◽  
Björn Gerlach ◽  
Ulf Dietrich Kahlert
Keyword(s):  
Data Management ◽  
Open Source ◽  
Academic Research ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
Preclinical Research ◽  
Research Data Management ◽  
New Methods ◽  
Set Up

Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small preclinical labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research.

scholarly journals Research Data Management Initiatives at University of Edinburgh

2011 ◽  
Vol 6 (2) ◽  
pp. 232-244 ◽  
Author(s):  
Robin Rice ◽  
Jeff Haywood
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Academic Staff ◽  
Research Data ◽  
Lessons Learned ◽  
The Public ◽  
Research Data Management ◽  
Online Tools ◽  
University Of Edinburgh ◽  
Future Direction

During the last decade, national and international attention has been increasingly focused on issues of research data management and access to publicly funded research data. The pressure brought to bear on researchers to improve their data management and data sharing practice has come from research funders seeking to add value to expensive research and solve cross-disciplinary grand challenges; publishers seeking to be responsive to calls for transparency and reproducibility of the scientific record; and the public seeking to gain and re-use knowledge for their own purposes using new online tools. Meanwhile higher education institutions have been rather reluctant to assert their role in either incentivising or supporting their academic staff in meeting these more demanding requirements for research practice, partly due to lack of knowledge as to how to provide suitable assistance or facilities for data storage and curation/preservation. This paper discusses the activities and drivers behind one institution’s recent attempts to address this gap, with reflection on lessons learned and future direction.

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.

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