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 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.

Global presence of open-source research data management platform for libraries: the Dataverse project

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vineet Jamwal ◽  
Simran Kaur
Keyword(s):  
Data Management ◽  
Open Source ◽  
Local Level ◽  
Research Data ◽  
Content Type ◽  
Management Platform ◽  
Research Data Management ◽  
The World ◽  
Global Presence ◽  
The Usa

Purpose This paper aims to provide statistical information on the worldwide spread of the open-source research data management application, the Dataverse Project, to librarians, data managers and information managers who are considering using the application at their own institution. Design/methodology/approach To produce a list of dataverse repositories, the official Dataverse website was evaluated, and JSON data were downloaded and parsed. Data standardisation was performed to assess the state of installations in various nations and continents across the world. Findings Globally, the Dataverse repositories have seen a rise in overall installations. The year 2020 alone saw a 23.21% rise. In a country-by-country comparison, the USA (13) has the most dataverse installations, while Europe (25) has the highest number of installations worldwide. Originality/value This research will be useful to librarians, data managers and information managers, among others, who want to learn more about Dataverse repositories throughout the world before deploying at their local level.

Research Data Management Practices in University libraries: A study

2017 ◽  
Vol 37 (6) ◽  
pp. 417 ◽  
Author(s):  
Manorama Tripathi ◽  
Archana Shukla ◽  
Sharad Kumar Sonkar
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Data Reuse ◽  
University Libraries ◽  
Research Data Management ◽  
The World ◽  
The University

<p>The paper has studied the research data management (RDM) services implemented by different university libraries for managing, organizing, curating and preserving research data generated at their universities’ departments and laboratories, for data reuse and sharing. It has surveyed the central university libraries and the best 20 university libraries of the world to highlight how RDM is extended to the researchers. Further, it has suggested a model for the university libraries in the country to follow for actually deploying RDM services. </p>

Research Data Management Practices in University libraries: A study

2017 ◽  
Vol 37 (6) ◽  
pp. 417 ◽  
Author(s):  
Manorama Tripathi ◽  
Archana Shukla ◽  
Sharad Kumar Sonkar
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Data Reuse ◽  
University Libraries ◽  
Research Data Management ◽  
The World ◽  
The University

<p>The paper has studied the research data management (RDM) services implemented by different university libraries for managing, organizing, curating and preserving research data generated at their universities’ departments and laboratories, for data reuse and sharing. It has surveyed the central university libraries and the best 20 university libraries of the world to highlight how RDM is extended to the researchers. Further, it has suggested a model for the university libraries in the country to follow for actually deploying RDM services. </p>

scholarly journals Reference models for e-infrastructures and related research data management: A use case from Austrian Universities

2015 ◽  
Author(s):  
Raman Ganguly
Keyword(s):  
Data Management ◽  
Research Data ◽  
Use Case ◽  
It Services ◽  
Sensitive Data ◽  
Related Research ◽  
Reference Models ◽  
Data Preservation ◽  
Research Data Management ◽  
The World

One goal of e-Infrastructures Austria, a project involving Austrian universities and non-university research institutions, is to find strategies for managing research data. We established a network of experts from libraries and IT services to develop architecture for efficient e-infrastructures and related reproach data management.Based on our experience from years of running a repository project for research data, we have developed models for designing e-infrastructures for research data management. This model helps us to design technical and non-technical services for preserving data. In this poster, we will present ways of defining the world of data, workflows for data preservation and models for defining roles in the entire process.These models help to structure different aspects for data preservation based on each data type. It is necessary to have different solutions for the different kinds of needs that researchers may have, whereas there must be alternate solutions for preventing sensitive data from being misused when handling petabytes of data.

How to Extend your Data Lifetime: Research Data Management in Indonesia’s Contexts

2018 ◽  
Author(s):  
Dasapta Erwin Irawan ◽  
Santirianingrum Soebandhi ◽  
Fierly Hayati ◽  
Cahyo Darujati ◽  
Deffy Ayu Puspito Sari
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Intellectual Property Rights ◽  
Research Data ◽  
The Other ◽  
Know How ◽  
Research Data Management ◽  
The World ◽  
Data Ownership ◽  
Data Lifetime

Data is the basis of research. On the other side, the world has a problem of replication. The first problem is we don’t really know how to manage our own data to able to reanalyze it at some point after the research has been finished. The lifetime of data is very short, in only one or two fiscal years. In this article we will describe on how to write a research data management in order to extend the lifetime of data. There are seven basic components to remember before writing a proper research data management: (1) Data storage and software, (2) Metadata, (3) Structure, (4) Persistent link, (5) Licensing, (6) Data maintainer, (7) Indexing. In several fields, including medicine, an anomyzation strategy will be needed. We also need to put into account the Intellectual Property Rights and data ownership in to the equation, as Indonesian scientists are not properly exposed to those subjects.

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 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
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