Community curation in PomBase: enabling fission yeast experts to provide detailed, standardized, sharable annotation from research publications

Authors : Antonia Lock, Midori A Harris, Kim Rutherford, Jacqueline Hayles, Valerie Wood

Maximizing the impact and value of scientific research requires efficient knowledge distribution, which increasingly depends on the integration of standardized published data into online databases.

To make data integration more comprehensive and efficient for fission yeast research, PomBase has pioneered a community curation effort that engages publication authors directly in FAIR-sharing of data representing detailed biological knowledge from hypothesis-driven experiments.

Canto, an intuitive online curation tool that enables biologists to describe their detailed functional data using shared ontologies, forms the core of PomBase’s system.

With 8 years’ experience, and as the author response rate reaches 50%, we review community curation progress and the insights we have gained from the project.

We highlight incentives and nudges we deploy to maximize participation, and summarize project outcomes, which include increased knowledge integration and dissemination as well as the unanticipated added value arising from co-curation by publication authors and professional curators.

URL : Community curation in PomBase: enabling fission yeast experts to provide detailed, standardized, sharable annotation from research publications

DOI : https://doi.org/10.1093/database/baaa028

FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Authors : Koenraad De Smedt, Dimitris Koureas, Peter Wittenburg

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability.

As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers.

In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data.

The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions.

We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

URL : FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Alternative location : https://www.mdpi.com/2304-6775/8/2/21

Digital Objects – FAIR Digital Objects: Which Services Are Required?

Author : Ulrich Schwardmann

Some of the early Research Data Alliance working groups reused the notion of digital objects as digital entities described by metadata and referenced by a persistent identifier. In recent times the FAIR principles became a prominent role as framework for the sustainability of scientific data.

Both approaches had always machine actionability, the capability of computational systems to use services on data without human intervention, in their focus. The more technical approach of digital objects turned out to provide a complementary view on several aspects of the policy framework of FAIR from a technical perspective.

After a deeper analysis and integration of these concepts by a group of European data experts the discussion intensified on so called FAIR digital objects. But they need to be accompanied by services as building blocks for automated processes. We will describe the components of this framework and its potentials here, and also which services inside this framework are required.

URL : Digital Objects – FAIR Digital Objects: Which Services Are Required?

DOI : http://doi.org/10.5334/dsj-2020-015

The Heritage Data Reuse Charter: from principles to research workflows

Authors : Erzsébet Tóth-Czifra, Laurent Romary

There is a growing need to establish domain-or discipline-specific approaches to research data sharing workflows. A defining feature of data and data workflows in the arts and humanities domain is their dependence on cultural heritage sources hosted and curated in museums, libraries, galleries and archives.

A major difficulty when scholars interact with heritage data is that the nature of the cooperation between researchers and Cultural Heritage Institutions (henceforth CHIs) is often constrained by structural and legal challenges but even more by uncertainties as to the expectations of both parties.

The Heritage Data Reuse Charter aims to address these by designing a common environment that will enable all the relevant actors to work together to connect and improve access to heritage data and make transactions related to the scholarly use of cultural heritage data more visible and transparent.

As a first step, a wide range of stakeholders on the Cultural Heritage and research sector agreed upon a set of generic principles, summarized in the Mission Statement of the Charter, that can serve as a baseline governing the interactions between CHIs, researchers and data centres.

This was followed by a long and thorough validation process related to these principles through surveys 1 and workshops 2. As a second step, we now put forward a questionnaire template tool that helps researchers and CHIs to translate the 6 core principles into specific research project settings.

It contains questions about access to data, provenance information, preferred citation standards, hosting responsibilities etc. on the basis of which the parties can arrive at mutual reuse agreements that could serve as a starting point for a FAIR-by-construction data management, right from the project planning/application phase.

The questionnaire template and the resulting mutual agreements can be flexibly applied to projects of different scale and in platform-independent ways. Institutions can embed them into their own exchange protocols while researchers can add them to their Data Management Plans.

As such, they can show evidence for responsible and fair conduct of cultural heritage data, and fair (but also FAIR) research data management practices that are based on partnership with the holding institution.

URL : https://halshs.archives-ouvertes.fr/halshs-02475692

L’éthique des données de la recherche en sciences humaines et sociales. Une introduction

Auteurs/Authors : Bernard Jacquemin, Joachim Schöpfel, Stéphane Chaudiron, Eric Kergosien

L’organisation de l’accès libre aux données scientifiques fait partie des objectifs de la recherche publique de la France. La volonté d’ouvrir les données de la recherche a été confirmée par le plan d’action national 2018-2020 dont l’engagement 18 vise à construire un écosystème de la science ouverte.

Sur le terrain, la politique d’ouverture s’accompagne d’une forte incitation à mettre en œuvre des bonnes pratiques scientifiques compatibles avec certains principes définis au niveau européens comme « FAIR Guiding Principles » de la gestion et du pilotage des données de la recherche. Quelle est la dimension éthique d’une gestion « FAIR » des données de la recherche?

À partir d’une sélection de publications récentes, d’enquêtes, travaux et activités menées autour des données de la recherche, notre communication essaie de synthétiser plusieurs aspects de la dimension éthique de la gestion des données de la recherche, dans l’environnement français, dont la place de l’éthique dans les plans de gestion, les données personnelles, la crédibilité ou encore la sécurité des données.

URL : https://hal.archives-ouvertes.fr/GERIICO/hal-01958472v1

“Data Stewardship Wizard”: A Tool Bringing Together Researchers, Data Stewards, and Data Experts around Data Management Planning

Authors: Robert Pergl, Rob Hooft, Marek Suchánek, Vojtěch Knaisl, Jan Slifka

The Data Stewardship Wizard is a tool for data management planning that is focused on getting the most value out of data management planning for the project itself rather than on fulfilling obligations.

It is based on FAIR Data Stewardship, in which each data-related decision in a project acts to optimize the Findability, Accessibility, Interoperability and/or Reusability of the data.

The background to this philosophy is that the first reuser of the data is the researcher themselves. The tool encourages the consulting of expertise and experts, can help researchers avoid risks they did not know they would encounter by confronting them with practical experience from others, and can help them discover helpful technologies they did not know existed.

In this paper, we discuss the context and motivation for the tool, we explain its architecture and we present key functions, such as the knowledge model evolvability and migrations, assembling data management plans, metrics and evaluation of data management plans.

URL : “Data Stewardship Wizard”: A Tool Bringing Together Researchers, Data Stewards, and Data Experts around Data Management Planning

DOI : http://doi.org/10.5334/dsj-2019-059

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

Authors : Sarah Jones, Robert Pergl, Rob Hooft, Tomasz Miksa, Robert Samors, Judit Ungvari, Rowena I. Davis, Tina Lee

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.

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