Étiquette : research data

Catégories
EN

Cluster Analysis of Open Research Data: A Case for Replication Metadata

Author : Ana Trisovic

Research data are often released upon journal publication to enable result verification and reproducibility. For that reason, research dissemination infrastructures typically support diverse datasets coming from numerous disciplines, from tabular data and program code to audio-visual files. Metadata, or data about data, is critical to making research outputs adequately documented and FAIR.

Aiming to contribute to the discussions on the development of metadata for research outputs, I conducted an exploratory analysis to determine how research datasets cluster based on what researchers organically deposit together. I use the content of over 40,000 datasets from the Harvard Dataverse research data repository as my sample for the cluster analysis.

I find that the majority of the clusters are formed by single-type datasets, while in the rest of the sample, no meaningful clusters can be identified. For the result interpretation, I use the metadata standard employed by DataCite, a leading organization for documenting a scholarly record, and map existing resource types to my results.

About 65% of the sample can be described with a single-type metadata (such as Dataset, Software orReport), while the rest would require aggregate metadata types. Though DataCite supports an aggregate type such as a Collection, I argue that a significant number of datasets, in particular those containing both data and code files (about 20% of the sample), would be more accurately described as a Replication resource metadata type. Such resource type would be particularly useful in facilitating research reproducibility.

URL : Cluster Analysis of Open Research Data: A Case for Replication Metadata

DOI : https://doi.org/10.2218/ijdc.v17i1.833

Catégories
EN

Data Management Plans: Implications for Automated Analyses

Authors : Ngoc-Minh Pham, Heather Moulaison-Sandy, Bradley Wade Bishop, Hannah Gunderman

Data management plans (DMPs) are an essential part of planning data-driven research projects and ensuring long-term access and use of research data and digital objects; however, as text-based documents, DMPs must be analyzed manually for conformance to funder requirements.

This study presents a comparison of DMPs evaluations for 21 funded projects using 1) an automated means of analysis to identify elements that align with best practices in support of open research initiatives and 2) a manually-applied scorecard measuring these same elements.

The automated analysis revealed that terms related to availability (90% of DMPs), metadata (86% of DMPs), and sharing (81% of DMPs) were reliably supplied. Manual analysis revealed 86% (n = 18) of funded DMPs were adequate, with strong discussions of data management personnel (average score: 2 out of 2), data sharing (average score 1.83 out of 2), and limitations to data sharing (average score: 1.65 out of 2).

This study reveals that the automated approach to DMP assessment yields less granular yet similar results to manual assessments of the DMPs that are more efficiently produced. Additional observations and recommendations are also presented to make data management planning exercises and automated analysis even more useful going forward.

URL : Data Management Plans: Implications for Automated Analyses

DOI : http://doi.org/10.5334/dsj-2023-002

Catégories
EN

The Role of Metadata and Vocabulary Standards in Enabling Scientific Data Interoperability: A Study of Earth System Science Data Facilities

Authors : Matthew S. Mayernik, Yauheniya Liapich

Objective

Journal publishers within many sciences are increasingly expecting data to be deposited into repositories that support the FAIR principles. Data repositories are thus needing to determine what implications the FAIR principles have on their existing services and systems. Metadata standards and controlled vocabularies are specifically called out as core components of the FAIR principles related to interoperability.

Methods

This paper looks specifically at the ways that metadata standards and controlled vocabularies are used by Earth system science data repositories. Data sets from 55 data facilities were examined to determine which metadata standards and controlled subject / keyword vocabularies were used.

Results

The findings indicate that only the ISO 19115:2003 and DataCite metadata standards are used by more than 40% of the data facilities, and the NASA Global Change Master Directory (GCMD) keywords are the only keyword vocabulary of broad use within this community.

Conclusions

These findings raise questions about the extent to which metadata standards and keyword vocabularies can facilitate interoperability beyond narrow sub-sections of the data facility communities. This study also points to systematic challenges related to migration to new standards.

URL : The Role of Metadata and Vocabulary Standards in Enabling Scientific Data Interoperability: A Study of Earth System Science Data Facilities

DOI : https://doi.org/10.7191/jeslib.619

Catégories
FR

Prise de décision par les pouvoirs publics et partage des données de la recherche, une approche par le risque

Autrice/Author : Fleur Nadine Ndjock

Si la recherche scientifique est prioritairement financée par l’État au travers des dotations budgétaires, il est logique que les résultats de cette recherche aide (contribue) à la prise de décision efficace par les pouvoirs publics pour le développement d’un pays.

Pour ce faire, les données issues de la recherche doivent être partagées s’il est vrai que pour décider, l’on a besoin d’informations et les données de la recherche qu’elles soient d’observation, expérimentales ou de simulation, sont importantes dans le processus décisionnel stratégique.

Cet article vise un double objectif : Il s’agit d’une part d’établir une typologie des risques qu’encourt le partage de données de la recherche avec les pouvoirs publics, mais aussi, de questionner les concepts à mobiliser pour rendre compte des enjeux de ces risques, car ils sont déterminants et peuvent influencer les motivations de leur partage.

DOI : https://doi.org/10.4000/ctd.8301

 

Catégories
EN

An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data

Authors : Romain David, Christian Ohmann, Jan‑Willem Boiten, Mónica Cano Abadía, Florence Bietrix, Steve Canham, Maria Luisa Chiusano, Walter Dastrù, Arnaud Laroquette, Dario Longo, Michaela Th. Mayrhofer, Maria Panagiotopoulou, Audrey S. Richard, Sergey Goryanin, Pablo Emilio Verde

For life science infrastructures, sensitive data generate an additional layer of complexity. Cross-domain categorisation and discovery of digital resources related to sensitive data presents major interoperability challenges. To support this FAIRification process, a toolbox demonstrator aiming at support for discovery of digital objects related to sensitive data (e.g., regulations, guidelines, best practice, tools) has been developed.

The toolbox is based upon a categorisation system developed and harmonised across a cluster of 6 life science research infrastructures. Three different versions were built, tested by subsequent pilot studies, finally leading to a system with 7 main categories (sensitive data type, resource type, research field, data type, stage in data sharing life cycle, geographical scope, specific topics).

109 resources attached with the tags in pilot study 3 were used as the initial content for the toolbox demonstrator, a software tool allowing searching of digital objects linked to sensitive data with filtering based upon the categorisation system.

Important next steps are a broad evaluation of the usability and user-friendliness of the toolbox, extension to more resources, broader adoption by different life-science communities, and a long-term vision for maintenance and sustainability.

URL : An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data

DOI : https://doi.org/10.1038/s41598-022-25278-z

Catégories
EN

Data Quality Assurance at Research Data Repositories

Authors : Maxi Kindling, Dorothea Strecker

This paper presents findings from a survey on the status quo of data quality assurance practices at research data repositories. The personalised online survey was conducted among repositories indexed in re3data in 2021. It covered the scope of the repository, types of data quality assessment, quality criteria, responsibilities, details of the review process, and data quality information and yielded 332 complete responses.

The results demonstrate that most repositories perform data quality assurance measures, and overall, research data repositories significantly contribute to data quality. Quality assurance at research data repositories is multifaceted and nonlinear, and although there are some common patterns, individual approaches to ensuring data quality are diverse.

The survey showed that data quality assurance sets high expectations for repositories and requires a lot of resources. Several challenges were discovered: for example, the adequate recognition of the contribution of data reviewers and repositories, the path dependence of data review on review processes for text publications, and the lack of data quality information. The study could not confirm that the certification status of a repository is a clear indicator of whether a repository conducts in-depth quality assurance.

URL : Data Quality Assurance at Research Data Repositories

DOI : http://doi.org/10.5334/dsj-2022-018

Catégories
EN

Putting FAIR principles in the context of research information: FAIRness for CRIS and CRIS for FAIRness

Authors : Otmane Azeroual, Joachim Schöpfel, Janne Pölönen, Anastasija Nikiforova

Digitization in the research domain refers to the increasing integration and analysis of research information in the process of research data management. However, it is not clear whether it is used and, more importantly, whether the data are of sufficient quality, and value and knowledge could be extracted from them.

FAIR principles (Findability, Accessibility, Interoperability, Reusability) represent a promising asset to achieve this. Since their publication, they have rapidly proliferated and have become part of (inter-)national research funding programs.

A special feature of the FAIR principles is the emphasis on the legibility, readability, and understandability of data. At the same time, they pose a prerequisite for data for their reliability, trustworthiness, and quality. In this sense, the importance of applying FAIR principles to research information and respective systems such as Current Research Information Systems (CRIS), which is an underrepresented subject for research, is the subject of the paper.

Supporting the call for the need for a ”one-stop-shop and register-onceuse-many approach”, we argue that CRIS is a key component of the research infrastructure landscape, directly targeted and enabled by operational application and the promotion of FAIR principles.

We hypothesize that the improvement of FAIRness is a bidirectional process, where CRIS promotes FAIRness of data and infrastructures, and FAIR principles push further improvements to the underlying CRIS.

URL https://hal.archives-ouvertes.fr/hal-03836525