Sustainable FAIR Data management is challenging for RIs and it is challenging to solid Earth scientists

2020 ◽  
Author(s):  
Massimo Cocco ◽  
Daniele Bailo ◽  
Keith G. Jeffery ◽  
Rossana Paciello ◽  
Valerio Vinciarelli ◽  
...  
Keyword(s):  
Data Management ◽  
Solid Earth ◽  
Service Management ◽  
Open Science ◽  
Adoption Process ◽  
Scientific Communities ◽  
Research Infrastructures ◽  
Data Products ◽  
Data Stewardship ◽  
Fair Principles

<p>Interoperability has long been an objective for research infrastructures dealing with research data to foster open access and open science. More recently, FAIR principles (Findability, Accessibility, Interoperability and Reusability) have been proposed. The FAIR principles are now reference criteria for promoting and evaluating openness of scientific data. FAIRness is considered a necessary target for research infrastructures in different scientific domains at European and global level.</p><p>Solid Earth RIs have long been committed to engage scientific communities involved in data collection, standardization and quality management as well as providing metadata and services for qualification, storage and accessibility. They are working to adopt FAIR principles, thus addressing the onerous task of turning these principles into practices. To make FAIR principles a reality in terms of service provision for data stewardship, some RI implementers in EPOS have proposed a FAIR-adoption process leveraging a four stage roadmap that reorganizes FAIR principles to better fit to scientists and RI implementers mindset. The roadmap considers FAIR principles as requirements in the software development life cycle, and reorganizes them into data, metadata, access services and use services. Both the implementation and the assessment of “FAIRness” level by means of questionnaire and metrics is made simple and closer to day-to-day scientists works.</p><p>FAIR data and service management is demanding, requiring resources and skills and more importantly it needs sustainable IT resources. For this reason, FAIR data management is challenging for many Research Infrastructures and data providers turning FAIR principles into reality through viable and sustainable practices. FAIR data management also includes implementing services to access data as well as to visualize, process, analyse and model them for generating new scientific products and discoveries.</p><p>FAIR data management is challenging to Earth scientists because it depends on their perception of finding, accessing and using data and scientific products: in other words, the perception of data sharing. The sustainability of FAIR data and service management is not limited to financial sustainability and funding; rather, it also includes legal, governance and technical issues that concern the scientific communities.</p><p>In this contribution, we present and discuss some of the main challenges that need to be urgently tackled in order to run and operate FAIR data services in the long-term, as also envisaged by the European Open Science Cloud initiative: a) sustainability of the IT solutions and resources to support practices for FAIR data management (i.e., PID usage and preservation, including costs for operating the associated IT services); b) re-usability, which on one hand requires clear and tested methods to manage heterogeneous metadata and provenance, while on the other hand can be considered a frontier research field; c) FAIR services provision, which presents many open questions related to the application of FAIR principles to services for data stewardship, and to services for the creation of data products taking in input FAIR raw data, for which is not clear how FAIRness compliancy of data products can be still guaranteed.</p>

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 pISA-tree - a data management framework for life science research projects using a standardised directory tree

2021 ◽  
Author(s):  
Marko Petek ◽  
Maja Zagorscak ◽  
Andrej Blejec ◽  
Ziva Ramsak ◽  
Anna Coll ◽  
...  
Keyword(s):  
Data Management ◽  
Life Science ◽  
Source Code ◽  
Science Research ◽  
Open Science ◽  
Reproducible Research ◽  
Public Repository ◽  
Management Framework ◽  
Fair Principles ◽  
R Packages

We have developed pISA-tree, a straightforward and flexible data management solution for organisation of life science project-associated research data and metadata. It enables on-the-fly creation of enriched directory tree structure (project/Investigation/Study/Assay) via a series of sequential batch files in a standardised manner based on the ISA metadata framework. The system supports reproducible research and is in accordance with the Open Science initiative and FAIR principles. Compared with similar frameworks, it does not require any systems administration and maintenance as it can be run on a personal computer or network drive. It is complemented with two R packages, pisar and seekr, where the former facilitates integration of the pISA-tree datasets into bioinformatic pipelines and the latter enables synchronisation with the FAIRDOMHub public repository using the SEEK API. Source code and detailed documentation of pISA-tree and its supporting R packages are available from https://github.com/NIB-SI/pISA-tree.

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 Methods for Open and Reproducible Materials Science

2019 ◽  
Author(s):  
Sara L Wilson ◽  
Micah Altman ◽  
Rafael Jaramillo
Keyword(s):  
Data Management ◽  
Data Science ◽  
Management Practices ◽  
Materials Science ◽  
Critical Role ◽  
Structured Interview ◽  
Open Science ◽  
Manuscript Submission ◽  
Attitudes And Practices ◽  
Data Stewardship

Data stewardship in experimental materials science is increasingly complex and important. Progress in data science and inverse-design of materials give reason for optimism that advances can be made if appropriate data resources are made available. Data stewardship also plays a critical role in maintaining broad support for research in the face of well-publicized replication failures (in different fields) and frequently changing attitudes, norms, and sponsor requirements for open science. The present-day data management practices and attitudes in materials science are not well understood. In this article, we collect information on the practices of a selection of materials scientists at two leading universities, using a semi-structured interview instrument. An analysis of these interviews reveals that although data management is universally seen as important, data management practices vary widely. Based on this analysis, we conjecture that broad adoption of basic file-level data sharing at the time of manuscript submission would benefit the field without imposing substantial burdens on researchers. More comprehensive solutions for lifecycle open research in materials science will have to overcome substantial differences in attitudes and practices.

scholarly journals Measuring FAIR Principles to Inform Fitness for Use

2018 ◽  
Vol 13 (1) ◽  
pp. 35-46
Author(s):  
Carolyn Hank ◽  
Bradley Wade Bishop
Keyword(s):  
Best Practices ◽  
Data Quality ◽  
Data Management ◽  
Open Science ◽  
Conceptual Level ◽  
Data Types ◽  
Good Data ◽  
Secondary Purpose ◽  
Fair Principles ◽  
Data Quality Metrics

For open science to flourish, data and any related digital outputs should be discoverable and re-usable by a variety of potential consumers. The recent FAIR Data Principles produced by the Future of Research Communication and e-Scholarship (FORCE11) collective provide a compilation of considerations for making data findable, accessible, interoperable, and re-usable. The principles serve as guideposts to ‘good’ data management and stewardship for data and/or metadata. On a conceptual level, the principles codify best practices that managers and stewards would find agreement with, exist in other data quality metrics, and already implement. This paper reports on a secondary purpose of the principles: to inform assessment of data’s FAIR-ness or, put another way, data’s fitness for use. Assessment of FAIR-ness likely requires more stratification across data types and among various consumer communities, as how data are found, accessed, interoperated, and re-used differs depending on types and purposes. This paper’s purpose is to present a method for qualitatively measuring the FAIR Data Principles through operationalizing findability, accessibility, interoperability, and re- usability from a re-user’s perspective. The findings may inform assessments that could also be used to develop situationally-relevant fitness for use frameworks.

scholarly journals Perspectives on the Implementation of FAIR Principles in Solid Earth Research Infrastructures

2020 ◽  
Vol 8 ◽  
Author(s):  
Daniele Bailo ◽  
Rossana Paciello ◽  
Manuela Sbarra ◽  
Riccardo Rabissoni ◽  
Valerio Vinciarelli ◽  
...  
Keyword(s):  
Solid Earth ◽  
Research Infrastructures ◽  
Fair Principles

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.

Open research data in African academic and research libraries: a literature analysis

2020 ◽  
Vol 41 (6/7) ◽  
pp. 383-399
Author(s):  
Elisha R.T. Chiware
Keyword(s):  
Data Management ◽  
Open Science ◽  
Research Data ◽  
Content Type ◽  
Research Libraries ◽  
Research Data Management ◽  
Open Research ◽  
Research Infrastructures ◽  
Data Infrastructures ◽  
Management Services

PurposeThe paper presents a literature review on research data management services in African academic and research libraries on the backdrop of the advancing open science and open research data infrastructures. It provides areas of focus for library to support open research data.Design/methodology/approachThe literature analysis and future role of African libraries in research data management services were based on three areas as follows:open science, research infrastructures and open data infrastructures. Focussed literature searches were conducted across several electronic databases and discovery platforms, and a qualitative content analysis approach was used to explore the themes based on a coded list.FindingsThe review reports of an environment where open science in Africa is still at developmental stages. Research infrastructures face funding and technical challenges. Data management services are in formative stages with progress reported in a few countries where open science and research data management policies have emerged, cyber and data infrastructures are being developed and limited data librarianship courses are being taught.Originality/valueThe role of the academic and research libraries in Africa remains important in higher education and the national systems of research and innovation. Libraries should continue to align with institutional and national trends in response to the provision of data management services and as partners in the development of research infrastructures.

scholarly journals The FAIRness of data management plans: an assessment of some European DMPs

RECIIS ◽  
2021 ◽  
Vol 15 (3) ◽  
Author(s):  
Patricia Henning ◽  
Luis Olavo Bonino Da Silva ◽  
Luís Ferreira Pires ◽  
Marten Van Sinderen ◽  
João Luís Rebelo Moreira
Keyword(s):  
Best Practices ◽  
Data Management ◽  
Research Policy ◽  
Management Plan ◽  
Scientific Data ◽  
Funding Agencies ◽  
Address Data ◽  
Management Plans ◽  
Data Stewardship ◽  
Fair Principles

The FAIR principles have become a data management instrument for the academic and scientific community, since they provide a set of guiding principles to bring findability, accessibility, interoperability and reusability to data and metadata stewardship. Since their official publication in 2016 by Scientific Data – Nature, these principles have received worldwide recognition and have been quickly endorsed and adopted as a cornerstone of data stewardship and research policy. However, when put into practice, they occasionally result in organisational, legal and technological challenges that can lead to doubts and uncertainty as to whether the effort of implementing them is worthwhile. Soon after their publication, the European Commission and other funding agencies started to require that project proposals include a Data Management Plan (DMP) based on the FAIR principles. This paper reports on the adherence of DMPs to the FAIR principles, critically evaluating ten European DMP templates. We observed that the current FAIRness of most of these DMPs is only partly satisfactory, in that they address data best practices, findability, accessibility and sometimes preservation, but pay much less attention to metadata and interoperability.

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