scholarly journals Data stewardship for open science: implementing FAIR principles / Data-driven storytelling

2018 ◽  
Vol 88 (16) ◽  
pp. 3249-3250
Author(s):  
Ramalingam Shanmugam
Keyword(s):  
Open Science ◽  
Data Driven ◽  
Data Stewardship ◽  
Fair Principles

scholarly journals Data Papers as a New Form of Knowledge Organization in the Field of Research Data

2019 ◽  
Vol 46 (8) ◽  
pp. 622-638
Author(s):  
Joachim Schöpfel ◽  
Dominic Farace ◽  
Hélène Prost ◽  
Antonella Zane
Keyword(s):  
Business Models ◽  
Open Science ◽  
Research Data ◽  
Data Driven ◽  
Academic Publishing ◽  
Science Data ◽  
Research Data Management ◽  
Fair Principles ◽  
New Form ◽  
Machine Readable

Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e., disciplines, publishers and business models, and about their structure, length, formats, metadata, and licensing. Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machine-readable. Data papers are essentially information, i.e., description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing.

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>

Computational implementation and formalism of FAIR data stewardship principles

2020 ◽  
Vol 54 (2) ◽  
pp. 193-214
Author(s):  
Kushal Ajaybhai Anjaria
Keyword(s):  
Data Sharing ◽  
Data Storage ◽  
Petri Net ◽  
Life Science ◽  
Formal Model ◽  
Content Type ◽  
Computational Implementation ◽  
Data Stewardship ◽  
Fair Principles ◽  
Existing Data

PurposeThe progress of life science and social science research is contingent on effective modes of data storage, data sharing and data reproducibility. In the present digital era, data storage and data sharing play a vital role. For productive data-centric tasks, findable, accessible, interoperable and reusable (FAIR) principles have been developed as a standard convention. However, FAIR principles have specific challenges from computational implementation perspectives. The purpose of this paper is to identify the challenges related to computational implementations of FAIR principles. After identification of challenges, this paper aims to solve the identified challenges.Design/methodology/approachThis paper deploys Petri net-based formal model and Petri net algebra to implement and analyze FAIR principles. The proposed Petri net-based model, theorems and corollaries may assist computer system architects in implementing and analyzing FAIR principles.FindingsTo demonstrate the use of derived petri net-based theorems and corollaries, existing data stewardship platforms – FAIRDOM and Dataverse – have been analyzed in this paper. Moreover, a data stewardship model – “Datalection” has been developed and conversed about in the present paper. Datalection has been designed based on the petri net-based theorems and corollaries.Originality/valueThis paper aims to bridge information science and life science using the formalism of data stewardship principles. This paper not only provides new dimensions to data stewardship but also systematically analyzes two existing data stewardship platforms FAIRDOM and Dataverse.

scholarly journals Open Science and information literacy

2021 ◽  
Vol 17 (1) ◽  
pp. 4-8
Author(s):  
Maria Luz Antunes ◽  
Carlos Lopes ◽  
Tatiana Sanches
Keyword(s):  
Information Literacy ◽  
Scientific Knowledge ◽  
Applied Research ◽  
Open Science ◽  
Research Center ◽  
Academic Impact ◽  
Training Support ◽  
The Social ◽  
Fair Principles ◽  
The Creation

The APPsyCI, a Portuguese research center, decided to incorporate, in all its areas of activity, a research line within Open Science articulated with information literacy (IL). The Open Science assumptions were implemented through several actions: repository management, teacher and researcher training, support for choosing the journals where to publish, dissemination, and promotion of scientific knowledge within FAIR principles. The social and academic impact of the research line provides some light on the national landscape for research innovation and broadens horizons and sheds when combining IL with Open Science. Thus, the creation of this research line within the research center shows that the association of Open Science with IL can be considered as the path and object of applied research.

scholarly journals CoreTrustSeal

2018 ◽  
Vol 71 (1) ◽  
pp. 162-170 ◽  
Author(s):  
Ingrid Dillo ◽  
Lisa De Leeuw
Keyword(s):  
Open Data ◽  
Open Science ◽  
Self Assessment ◽  
Data Repositories ◽  
Non Profit ◽  
Digital Repositories ◽  
Long Term Storage ◽  
Fair Principles ◽  
Core Characteristics

Open data and data management policies that call for the long-term storage and accessibility of data are becoming more and more commonplace in the research community. With it the need for trustworthy data repositories to store and disseminate data is growing. CoreTrustSeal, a community based and non-profit organisation, offers data repositories a core level certification based on the DSA-WDS Core Trustworthy Data Repositories Requirements catalogue and procedures. This universal catalogue of requirements reflects the core characteristics of trustworthy data repositories. Core certification involves an uncomplicated process whereby data repositories supply evidence that they are sustainable and trustworthy. A repository first conducts an internal self-assessment, which is then reviewed by community peers. Once the self-assessment is found adequate the CoreTrustSeal board certifies the repository with a CoreTrustSeal. The Seal is valid for a period of three years. Being a certified repository has several external and internal benefits. It for instance improves the quality and transparency of internal processes, increases awareness of and compliance with established standards, builds stakeholder confidence, enhances the reputation of the repository, and demonstrates that the repository is following good practices. It is also offering a benchmark for comparison and helps to determine the strengths and weaknesses of a repository. In the future we foresee a larger uptake through different domains, not in the least because within the European Open Science Cloud, the FAIR principles and therefore also the certification of trustworthy digital repositories holding data is becoming increasingly important. Next to that the CoreTrustSeal requirements will most probably become a European Technical standard which can be used in procurement (under review by the European Commission).

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.

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