scholarly journals FAIR and transparent research data

2021 ◽  
Author(s):  
Kenneth Ruud ◽  
Per Pippin Aspaas
Keyword(s):  
Research And Development ◽  
Data Management ◽  
Computational Chemistry ◽  
Research Data ◽  
The Arctic ◽  
Transferable Skills ◽  
Research Data Management ◽  
Transferable Skill ◽  
Fair Principles ◽  
Funded Project

This interview was recorded in July 2020 for DocEnhance, an EU-funded project that aims to broaden the expertise of PhDs by developing courses in transferable skills. One such transferable skill is how to manage your research data in a transparent manner and as much as possible in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reproducible). Professor of computational chemistry and prorector for research and development at UiT The Arctic University of Norway, Kenneth Ruud gives an introduction to FAIR and transparent research data management, emphasizing that this will not only help Science develop, but also help the career of individual researchers. First published online: July 9, 2021.

scholarly journals Plano de gestão de dados fair: uma proposta para a Fiocruz | Fair data management plan: a proposal for Fiocruz

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Viviane Santos de Oliveira Veiga ◽  
Patricia Henning ◽  
Simone Dib ◽  
Erick Penedo ◽  
Jefferson Da Costa Lima ◽  
...  
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Open Science ◽  
Management Plan ◽  
Research Data ◽  
Research Institutions ◽  
Research Data Management ◽  
Management Plans ◽  
Fair Principles

RESUMO Este artigo trás para discussão o papel dos planos de gestão de dados como instrumento facilitador da gestão dos dados durante todo o ciclo de vida da pesquisa. A abertura de dados de pesquisa é pauta prioritária nas agendas científicas, por ampliar tanto a visibilidade e transparência das investigações, como a capacidade de reprodutibilidade e reuso dos dados em novas pesquisas. Nesse contexto, os princípios FAIR, um acrônimo para ‘Findable’, ‘Accessible’, ‘Interoperable’ e ‘Reusable’ é fundamental por estabelecerem orientações basilares e norteadoras na gestão, curadoria e preservação dos dados de pesquisa direcionados para o compartilhamento e o reuso. O presente trabalho tem por objetivo apresentar uma proposta de template de Plano de Gestão de Dados, alinhado aos princípios FAIR, para a Fundação Oswaldo Cruz. A metodologia utilizada é de natureza bibliográfica e de análise documental de diversos planos de gestão de dados europeus. Concluímos que a adoção de um plano de gestão nas práticas cientificas de universidades e instituições de pesquisa é fundamental. No entanto, para tirar maior proveito dessa atividade é necessário contar com a participação de todos os atores envolvidos no processo, além disso, esse plano de gestão deve ser machine-actionable, ou seja, acionável por máquina.Palavras-chave: Plano de Gestão de Dados; Dado de Pesquisa; Princípios FAIR; PGD Acionável por Máquina; Ciência Aberta.ABSTRACT This article proposes to discuss the role of data management plans as a tool to facilitate data management during researches life cycle. Today, research data opening is a primary agenda at scientific agencies as it may boost investigations’ visibility and transparency as well as the ability to reproduce and reuse its data on new researches. Within this context, FAIR principles, an acronym for Findable, Accessible, Interoperable and Reusable, is paramount, as it establishes basic and guiding orientations for research data management, curatorship and preservation with an intent on its sharing and reuse. The current work intends to present to the Fundação Oswaldo Cruz a new Data Management Plan template proposal, aligned with FAIR principles. The methodology used is bibliographical research and documental analysis of several European data management plans. We conclude that the adoption of a management plan on universities and research institutions scientific activities is paramount. However, to be fully benefited from this activity, all actors involved in the process must participate, and, on top of that, this plan must be machine-actionable.Keywords: Data Management Plan; Research Data; FAIR Principles; DMP Machine-Actionable; Open Science.

scholarly journals Teaching Open Science

2020 ◽  
Author(s):  
Helene N. Andreassen ◽  
Erik Lieungh
Keyword(s):  
Open Access ◽  
Data Management ◽  
Teaching And Learning ◽  
Open Science ◽  
Research Data ◽  
Open Access Publishing ◽  
The Arctic ◽  
Learning Support ◽  
Research Data Management ◽  
The University

In this episode, we are discussing how to teach open science to PhD students. Helene N. Andreassen, head of Library Teaching and Learning Support at the University Library of UiT the Arctic University of Norway shares her experiences with the integration of open science in a special, tailor-made course for PhD's that have just started their project. An interdisciplinary, discussion-based course, "Take Control of Your PhD Journey: From (P)reflection to Publishing" consists of a series of seminars on research data management, open access publishing and other subject matters pertaining to open science. First published online February 26, 2020.

scholarly journals Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic

2021 ◽  
Author(s):  
Núria Queralt-Rosinach ◽  
Rajaram Kaliyaperumal ◽  
César H. Bernabé ◽  
Qinqin Long ◽  
Simone A. Joosten ◽  
...  
Keyword(s):  
Semantic Web ◽  
Data Management ◽  
Research Data ◽  
Patient Data ◽  
Research Data Management ◽  
Hospitalised Patients ◽  
The World ◽  
Data Points ◽  
Fair Principles ◽  
Digital Objects

AbstractBackgroundThe COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data ‘silos’ that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR.ResultsIn this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors’ research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital.ConclusionsOur work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR digital objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.

scholarly journals Artificial intelligence in research and development for sustainability: the centrality of explicability and research data management

AI and Ethics ◽  
2021 ◽  
Author(s):  
Erik Hermann ◽  
Gunter Hermann
Keyword(s):  
Artificial Intelligence ◽  
Research And Development ◽  
Data Management ◽  
Social Contexts ◽  
Research Data ◽  
Chemical Research ◽  
Research Data Management ◽  
Innovation Potential ◽  
Open Research ◽  
Collaborative Efforts

AbstractSustainability constitutes a focal challenge and objective of our time and requires collaborative efforts. As artificial intelligence brings forth substantial opportunities for innovations across industry and social contexts, so it provides innovation potential for pursuing sustainability. We argue that (chemical) research and development driven by artificial intelligence can substantially contribute to sustainability if it is leveraged in an ethical way. Therefore, we propose that the ethical principle explicability combined with (open) research data management systems should accompany artificial intelligence in research and development to foster sustainability in an equitable and collaborative way.

Research data management at the University of Bristol

2015 ◽  
Vol 49 (4) ◽  
pp. 475-493 ◽  
Author(s):  
Debra Hiom ◽  
Dom Fripp ◽  
Stephen Gray ◽  
Kellie Snow ◽  
Damian Steer
Keyword(s):  
Data Management ◽  
Design Methodology ◽  
Research Data ◽  
Content Type ◽  
Sustainability Planning ◽  
Research Data Management ◽  
Funded Project ◽  
University Of Bristol ◽  
The University

Purpose – The purpose of this paper is to chart the development of research data management services within the University of Bristol, from the initial Jisc-funded project, through to pilot service and planned core funding of the service. Design/methodology/approach – The paper provides a case study of the approach of the University of Bristol Library service to develop a sustainable Research Data Service. Findings – It outlines the services developed during the project and pilot phases of the service. In particular it focuses on the sustainability planning to ensure that research data management is embedded as a core university service. Originality/value – The case study provides practical advice and valuable insights into the issues and experiences of ensuring that research data management is properly valued and supported within universities.

scholarly journals Research data management support

2019 ◽  
Author(s):  
Abdurhman Kelil Ali
Keyword(s):  
Data Management ◽  
Support Services ◽  
Research Data ◽  
The Arctic ◽  
Management Support ◽  
Research Data Management ◽  
Road Map ◽  
Senior Research ◽  
Research Meeting

Good management and sharing of research data is a key principle for UiT The Arctic University of Norway, rooted in the value of increased transparency, reproducibility and reuse as well as increased quality of research. Meeting this aspiration requires operational support services, infrastructure, competence and a road map for different stakeholders. In line with these requirements, UiT has taken important steps to implement the ambition of FAIR research data management. These include the establishment of UiT Open Research Data archive in September 2016. Since then, more than 600 datasets with more than 5000 files have been uploaded, curated and made openly available. Moreover, UiT has been conducting a senior research data project that aims to preserve research data from senior researchers and make them available for future use. Additionally, UiT has adopted a policy for research data management that came into effect in September 2017. The poster outlines and reviews these and other efforts by UiT The Arctic University of Norway to provide support services for FAIR research data management.

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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