scholarly journals Making FAIR Easy with FAIR Tools: From Creolization to Convergence

2020 ◽  
Vol 2 (1-2) ◽  
pp. 87-95 ◽  
Author(s):  
Mark Thompson ◽  
Kees Burger ◽  
Rajaram Kaliyaperumal ◽  
Marco Roos ◽  
Luiz Olavo Bonino da Silva Santos
Keyword(s):  
Data Management ◽  
Daily Practice ◽  
Research Data ◽  
Early Adopters ◽  
Scientific Disciplines ◽  
Data Stewardship ◽  
Fair Principles ◽  
Specific Standard ◽  
Subsequent Phase ◽  
User Friendly

Since their publication in 2016 we have seen a rapid adoption of the FAIR principles in many scientific disciplines where the inherent value of research data and, therefore, the importance of good data management and data stewardship, is recognized. This has led to many communities asking “What is FAIR?” and “How FAIR are we currently?”, questions which were addressed respectively by a publication revisiting the principles and the emergence of FAIR metrics. However, early adopters of the FAIR principles have already run into the next question: “How can we become (more) FAIR?” This question is more difficult to answer, as the principles do not prescribe any specific standard or implementation. Moreover, there does not yet exist a mature ecosystem of tools, platforms and standards to support human and machine agents to manage, produce, publish and consume FAIR data in a user-friendly and efficient (i.e., “easy”) way. In this paper we will show, however, that there are already many emerging examples of FAIR tools under development. This paper puts forward the position that we are likely already in a creolization phase where FAIR tools and technologies are merging and combining, before converging in a subsequent phase to solutions that make FAIR feasible in daily practice.

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 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 Data Management Planning: How Requirements and Solutions are Beginning to Converge

2020 ◽  
Vol 2 (1-2) ◽  
pp. 208-219 ◽  
Author(s):  
Sarah Jones ◽  
Robert Pergl ◽  
Rob Hooft ◽  
Tomasz Miksa ◽  
Robert Samors ◽  
...  
Keyword(s):  
Data Management ◽  
Driving Forces ◽  
Management Planning ◽  
Current State ◽  
Adoption And Implementation ◽  
Key Characteristics ◽  
Data Stewardship ◽  
Fair Principles ◽  
Research Workforce ◽  
Research Initiatives

Effective stewardship of data is a critical precursor to making data FAIR. The goal of this paper is to bring an overview of current state of the art of data management and data stewardship planning solutions (DMP). We begin by arguing why data management is an important vehicle supporting adoption and implementation of the FAIR principles, we describe the background, context and historical development, as well as major driving forces, being research initiatives and funders. Then we provide an overview of the current leading DMP tools in the form of a table presenting the key characteristics. Next, we elaborate on emerging common standards for DMPs, especially the topic of machine-actionable DMPs. As sound DMP is not only a precursor of FAIR data stewardship, but also an integral part of it, we discuss its positioning in the emerging FAIR tools ecosystem. Capacity building and training activities are an important ingredient in the whole effort. Although not being the primary goal of this paper, we touch also the topic of research workforce support, as tools can be just as much effective as their users are competent to use them properly. We conclude by discussing the relations of DMP to FAIR principles, as there are other important connections than just being a precursor.

scholarly journals Solving legal puzzles is not easy – supporting creating Data Management Plans in three scientific disciplines: chemistry, economics, and civil engineering

2020 ◽  
Author(s):  
Magdalena Szuflita-Żurawska ◽  
Anna Wałek
Keyword(s):  
Data Management ◽  
Civil Engineering ◽  
Academic Staff ◽  
Open Science ◽  
Management Plan ◽  
Research Data ◽  
Academic Community ◽  
Legal Issues ◽  
Scientific Disciplines ◽  
Management Plans

Open Science Competence Center at the Gdańsk University of Technology Library was established upon the Bridge of Data project at the end of 2018. Our main goals include providing support for the academic community for broad issues associated with Open Science, especially with Open Research Data. Our team of professionals help researchers in many topics such as: "what kinds of data you need to share", "how to make your data openly available to others", or "how to create a Data Management Plan" – that recently has been the most popular and demanding service.  One of the main challenges to support academic staff with Data Management Plans is dealing with the legal impediments to provide open access and reusing of research data for publicly funded scientific projects. The lack of understanding the legal issues in opening research is a significant barrier to facilitate Open Science. Much public-funded research requires to prepare a Data Management Plan that, among other items, provides information about ownership and user rights. One of the most common activity for scholars is choosing which license (if any) they are supposed to use in terms of the dissemination the scientific output. However, in many cases, resolving the right license for research data is not enough. Academic staff faces many tensions with a lack of clarity around legal requirements and obstacles. The increasing researchers' need for understanding and describing conflicting issues (e.g. patenting) results in looking for professional and knowledgeable support at the university. We examine the most frequent legal issues arising among DMPs from the three scientific disciplines: chemistry (e.g. ethical papers), economics (e.g. data value cycle), and civil engineering (e.g. complexity of construction data). In our presentation, we would like to introduce the main identified problems and show how mapping and benchmarking occurring problems among those disciplines help us to establish more efficient legal support for researchers. 

scholarly journals Research Data Stewardship for Healthcare Professionals

2018 ◽  
pp. 37-53 ◽  
Author(s):  
Paula Jansen ◽  
Linda van den Berg ◽  
Petra van Overveld ◽  
Jan-Willem Boiten
Keyword(s):  
Data Collection ◽  
Clinical Research ◽  
Data Management ◽  
Study Design ◽  
Healthcare Professionals ◽  
Research Data ◽  
Third Parties ◽  
Data Stewardship ◽  
Digital Research

AbstractResearch data stewardship refers to the long-term and sustainable care for research data, from study design to data collection, analysis, storage, and sharing. It involves all activities that are required to ensure that digital research data is findable, accessible, interoperable, and reusable (FAIR) in the long term, including data management, archiving, and reuse by third parties. This chapter provides an overview of the aspects of FAIR data stewardship that you should consider when you are involved in clinical research.

scholarly journals On a Quest for Cultural Change - Surveying Research Data Management Practices at Delft University of Technology

2019 ◽  
Author(s):  
Heather Andrews ◽  
Marta Teperek ◽  
Jasper van Dijck ◽  
Kees den Heijer ◽  
Robbert Eggermont ◽  
...  
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Management Training ◽  
Research Data ◽  
Data Repositories ◽  
Automatic Data ◽  
Awareness Campaigns ◽  
Research Data Management ◽  
University Of Technology ◽  
Data Stewardship

The Data Stewardship project is a new initiative from the Delft University of Technology (TU Delft) in the Netherlands. Its aim is to create mature working practices and policies regarding research data management across all TU Delft faculties. The novelty of this project relies on having a dedicated person, the so-called ‘Data Steward’, embedded in each faculty to approach research data management from a more discipline-specific perspective. It is within this framework that a research data management survey was carried out at the faculties that had a Data Steward in place by July 2018. The goal was to get an overview of the general data management practices, and use its results as a benchmark for the project. The total response rate was 11 to 37% depending on the faculty. Overall, the results show similar trends in all faculties, and indicate lack of awareness regarding different data management topics such as automatic data backups, data ownership, relevance of data management plans, awareness of FAIR data principles and usage of research data repositories. The results also show great interest towards data management, as more than ~80% of the respondents in each faculty claimed to be interested in data management training and wished to see the summary of survey results. Thus, the survey helped identified the topics the Data Stewardship project is currently focusing on, by carrying out awareness campaigns and providing training at both university and faculty levels.

scholarly journals Views on Data Stewardship - report of preliminary findings at the Faculty of Technology, Policy and Management (TPM) at TU Delft

2018 ◽  
Author(s):  
Marta Teperek
Keyword(s):  
Data Management ◽  
Technology Policy ◽  
Management Practice ◽  
Critical Evaluation ◽  
Management Training ◽  
Research Data ◽  
Management Policy ◽  
First Year ◽  
Data Stewardship

Qualitative interviews with nine researchers at the Faculty of Technology, Policy and Management (TPM) at TU Delft were undertaken in order to get an understanding of data management needs at the faculty in advance of appointing a dedicated Data Steward. The purpose of this was to aid the recruitment of the Data Steward and to define the skills and experience of an ideal candidate, as well as help deciding on the work priority areas for the Data Steward. The results of this research can be also used as a point in time reference to monitor changes in data management practice at the faculty.The main data management challenges identified were: handling personal sensitive research data; working with big data, managing and sharing commercially confidential information and software management issues. Despite the diversity of problems, some common issues were identified as well: need for improving daily data management practice, as well as the need for revising workflows for students’ research data. With the exception of one researcher, who was in opposition to the Data Stewardship project, all other researchers expressed their support for the project and welcomed the idea of having a dedicated Data Steward at the faculty.Additionally, several follow up actions were already undertaken as a follow up of these interviews: the Data Stewardship Coordinator was invited to give two talks about Data Stewardship to two different groups of researchers;a member of the Research Data Support from the Library team was asked to deliver a training course for students;the Data Stewardship Coordinator was asked to discuss the best way of rolling our data management training for PhD students at TPM in coordination with the TPM Graduate School.Given that the financial allocation for the Data Steward at TPM faculty is currently at 0,5 FTE for the first year and 1,0 FTE for the two subsequent years (until December 2020), it is recommended that the first year is spent on continuing and extending this research to better understand the needs of the faculty. It is suggested that at the same time, the Data Steward starts addressing the most urgent data management needs at TPM faculty, in particular the development of a data management policy, as well as the development of solutions and recommendations for working with personal sensitive research data.The two subsequent years could be devoted to developing resources and solutions for the remaining problems and for critical evaluation of the project and its effect on data management practice at the faculty. This approach should provide the faculty with enough resources and information to decide on the best strategy for Data Stewardship beyond December 2020.

scholarly journals Implementação de serviços institucionais de administração de dados | Implementation of Institutional Data Stewardship Services

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Paloma Marín-Arraiza ◽  
Silvana Vidotti
Keyword(s):  
Data Management ◽  
Research Process ◽  
Research Data ◽  
Research Support ◽  
Research Data Management ◽  
Institutional Services ◽  
Bibliographic Search ◽  
Data Stewardship ◽  
Data Policy ◽  
High Production

RESUMO As tarefas de gestão de dados de pesquisa ao longo do processo de pesquisa têm se tornado muito importantes devido à alta produção de dados e à exigência da sua preservação. Tanto bibliotecas quanto seções de apoio à pesquisa de diversas instituições de ensino e pesquisa têm começado a implementar serviços para a gestão de dados e a profissionalização desta gestão. Com um caráter qualitativo, e após um levantamento bibliográfico em bases de dados abertas, contextualiza-se a gestão de dados de pesquisa, analisam-se os perfis profissionais e determinam-se três fases para a implementação institucional destes serviços: elaboração de uma política, estabelecimento de uma unidade de informação e integração de profissionais da gestão de dados.Palavras-chave: Administração de Dados; Dados de Pesquisa; Gestão de Dados de Pesquisa; Política de Dados; Serviços Institucionais.   ABSTRACT The tasks of managing research data throughout the research process have become very important due to the high production of data and the requirement for its preservation. Both libraries and research support sections of various research institutions have started to implement services for data management and the professionalization of this management. With a qualitative character, and after a bibliographic search in open databases, research data management is contextualized, professional profiles are analyzed, and three phases are determined for the institutional implementation of these services: the elaboration of a policy, the establishment of an information unit and the integration of data management professionals.Keywords: Data Stewardship; Research Data; Research Data Management; Data Policy; Institutional Services.

Diffusion and adoption of research data management services

2019 ◽  
Vol 69 (3) ◽  
pp. 117-133
Author(s):  
Jinfang Niu
Keyword(s):  
Data Management ◽  
Diffusion Of Innovations ◽  
Communication Channels ◽  
Research Data ◽  
Original Research ◽  
Content Type ◽  
Early Adopters ◽  
Research Data Management ◽  
Organization Networks ◽  
Management Services

Purpose This paper aims to identify the diffusion patterns, especially the communication channels, in the diffusion and adoption of research data management services (RDMS) among libraries. Design/methodology/approach Literature about the RDMS in individual libraries was gathered and analyzed. Data relevant to the research questions were extracted and analyzed. Findings Early adopters conduct much original research to create RDMS and they often serve as change agents in diffusing their RDMS and related innovations to other libraries. In contrast, late adopters usually learn from early adopters and use their innovations for establishing their own RDMS. Communication channels used in diffusing RDMS deviate slightly from those reported in general diffusion of innovations (DOI) theories. Research limitations/implications Gathered literature provides incomplete and uneven information for RDMS adopters. This makes it difficult to identify adopter categories and test many generalizations in DOI theories. To overcome these limitations, surveys and interviews will be conducted in the future. Originality/value Findings from this project contribute to general DOI theories because RDMS is unique compared with many other innovations. The diffusion of RDMS is a decentralized process that involves a high-degree of reinvention and it involves the generation and diffusion of many relevant innovations. The project also identified scholarly communication and inter-organization networks as new types of communication channels that are not well accounted for in existing DOI theories.

Sign in / Sign up

Export Citation Format

Share Document