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 DS Wizard Meets DAISY: A Romance Solving Data Protection Requirements in Data Management Planning

2021 ◽  
Author(s):  
Marek Suchánek ◽  
Pinar Alper ◽  
Jan Slifka ◽  
Vilém Děd ◽  
Nene DJenaba Barry ◽  
...  
Keyword(s):  
Information System ◽  
Data Management ◽  
Data Protection ◽  
Research Projects ◽  
Knowledge Model ◽  
Management Planning ◽  
On Demand ◽  
New Knowledge ◽  
Data Stewardship ◽  
Document Generation

This report summarises our activities and achievements in integrating the Data Stewardship Wizard (DSW) and Data Information System (DAISY) tools during the ELIXIR BioHackathon Europe 2021. As a data information system for GDPR compliance, DAISY is focused on a single goal – gathering all information required for GDPR accountability of biomedical research projects. On the other hand, DSW is very flexible and can be used beyond data management planning. We worked on the integration between both tools on two fronts. Firstly, we created a new Knowledge Model in DSW together with a document output template to be able to generate a data protection impact assessment (DPIA). Secondly, we introduced a new integration type between projects in DSW and DAISY that allows the querying of DAISY data upon document generation in DSW. Both of these independent activities brought successful results that were polished and published after the actual BioHackathon. Finally, we provide the related materials as an on-demand training course in the ELIXIR eLearning Platform.

scholarly journals Data Management Education from the Perspective of Science Educators

2016 ◽  
Vol 11 (1) ◽  
pp. 232-251 ◽  
Author(s):  
Carol Tenopir ◽  
Suzie Allard ◽  
Priyanki Sinha ◽  
Danielle Pollock ◽  
Jess Newman ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Management ◽  
Management Education ◽  
Management Planning ◽  
Current State ◽  
Lack Of Information ◽  
Science Educators

In order to better understand the current state of data management education in multiple fields of science, this study surveyed scientists, including information scientists, about their data management education practices, including at what levels they are teaching data management, which topics they covering, and what barriers they experience in teaching these topics. We found that a handful of scientists are teaching data management in undergraduate, graduate, and other types of courses, as well as outside of classroom settings. Commonly taught data management topics included quality control, protecting data, and management planning. However, few instructors felt they were covering data management topics thoroughly, and respondents cited barriers such as lack of time, lack of necessary expertise, and lack of information for teaching data management. We offer some potential explanations for the existing state of data management education and suggest areas for further research.

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.

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 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.

Product Data Management Systems: State-of-the-Art and the Future

1997 ◽  
Author(s):  
Taner Bilgiç ◽  
Dennis Rock
Keyword(s):  
Data Management ◽  
State Of The Art ◽  
Product Data Management ◽  
Management Systems ◽  
Systems Management ◽  
Engineering Project ◽  
Product Data ◽  
Current State ◽  
Functional Representation ◽  
Near Future

Abstract We survey the current state-of-the-art in (commercial) Product Data Management (PDM) systems. After identifying the major functions of PDM systems, we indicate various shortcomings of the current PDM technology. An important shortcoming is in the representation and use of functions. We review the functional representation literature in the context of PDM technology. Systems management aspects of an engineering project is also commented on. We believe these two areas are the next two challenges awaiting PDM technology in the near future.

scholarly journals Data Stewardship – addressing disciplinary data management needs

2018 ◽  
Author(s):  
Marta Teperek ◽  
Maria J. Cruz ◽  
Ellen Verbakel ◽  
Jasmin K. Böhmer ◽  
Alastair Dunning
Keyword(s):  
Data Management ◽  
Common Denominator ◽  
Management Support ◽  
Research Institutions ◽  
Multidisciplinary Research ◽  
University Of Technology ◽  
Address Data ◽  
Subject Specific ◽  
Data Stewardship ◽  
The Common

One of the biggest challenges for multidisciplinary research institutions which provide data management support to researchers is addressing disciplinary differences1. Centralised services need to be general enough to cater for all the different flavours of research conducted in an institution. At the same time, focusing on the common denominator means that subject-specific differences and needs may not be effectively addressed. In 2017, Delft University of Technology (TU Delft) embarked on an ambitious Data Stewardship project, aiming to comprehensively address data management needs across a multi-disciplinary campus. In this practice paper, we describe the principles behind the Data Stewardship project at TU Delft, the progress so far, we identify the key challenges and explain our plans for the future.

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