scholarly journals openEHR is FAIR-Enabling by Design

2021 ◽  
Author(s):  
Francesca Frexia ◽  
Cecilia Mascia ◽  
Luca Lianas ◽  
Giovanni Delussu ◽  
Alessandro Sulis ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Implementation Strategy ◽  
Contextual Information ◽  
Structured Data ◽  
Data Repository ◽  
Domain Specific ◽  
Research Outcomes ◽  
Fair Principles ◽  
Clinical Data Repository ◽  
Machine Readable

AbstractThe FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of structured data, coupled with contextual information about their creation, expressed in domain-specific standards and machine readable formats. This paper analyses the potential support to FAIRness of the openEHR e-health standard, by theoretically assessing the compliance with each of the 15 FAIR principles of a hypothetical Clinical Data Repository (CDR) developed according to the openEHR specifications. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant CDRs.

scholarly journals openEHR Is FAIR-Enabling by Design

2021 ◽  
Author(s):  
Francesca Frexia ◽  
Cecilia Mascia ◽  
Luca Lianas ◽  
Giovanni Delussu ◽  
Alessandro Sulis ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Clinical Data ◽  
Implementation Strategy ◽  
Contextual Information ◽  
Data Repositories ◽  
Domain Specific ◽  
Research Outcomes ◽  
Fair Principles ◽  
Reference Implementation ◽  
Machine Readable

The FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of data, coupled with contextual information about their creation, expressed in domain-specific standards and machine-readable formats. This paper analyses the potential support to FAIRness of the openEHR specifications and reference implementation, by theoretically assessing their compliance with each of the 15 FAIR principles. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant Clinical Data Repositories (CDRs).

scholarly journals The ATMODAT Standard enhances FAIRness of Atmospheric Model data

2021 ◽  
Author(s):  
Angelika Heil ◽  
Anette Ganske ◽  
Andrea Lammert ◽  
Daniel Heydebreck ◽  
Hannes Thiemann
Keyword(s):  
Climate Models ◽  
Urban Climate ◽  
Atmospheric Model ◽  
Data Repository ◽  
Model Data ◽  
Core Element ◽  
File Formats ◽  
Fair Principles ◽  
Machine Readable ◽  
Wide Scientific Community

<p>Atmospheric Model data form the basis to understand and predict weather, climate and air quality phenomena. Access to this data is not only of interest to a wide scientific community but also to public services, companies, politicians and citizens. One way to make the data available is to publish them via a data repository. To ensure that datasets in a repository are indeed <strong>F</strong>indable, <strong>A</strong>ccessible, <strong>I</strong>nteroperable, and <strong>R</strong>eusable (i.e. FAIR<sup>1</sup>), it is essential that the data are stored together with detailed metadata and that the file structure and metadata follow an established standard. Furthermore, datasets are easier to find and reuse if  the corresponding metadata is machine-readable and uses a standardised vocabulary. While data standardization is well established in large, internationally coordinated model intercomparison projects (e.g. for climate models in CMIP<sup>2</sup>), joint standards are still lacking in many atmospheric modelling sub-disciplines, such as e.g. urban climate or cloud-resolving modelling. </p><p>The AtMoDat project (<strong>At</strong>mospheric <strong>Mo</strong>del <strong>Dat</strong>a)<sup>3</sup>, led by a team of atmospheric scientists and infrastructure providers, aims to improve the overall FAIRness of atmospheric model data and thus promote their re-use. Within the project, the ATMODAT standard<sup>4</sup> has been developed which includes precise recommendations to achieve enhanced FAIRness of atmospheric model data in repositories. A prerequisite of this standard is that the data are published with a DataCite DOI<sup>5</sup>. The ATMODAT standard specifies requirements for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF) and the structure within files. Human- and machine-readable landing pages holding discipline-specific metadata are a core element of this standard. </p><p>The ATMODAT standard is easy to implement and provides checklists for data curators and data producers. In addition, to facilitate the compliance check with the ATMODAT standard, the <em>atmodat data checker</em><sup>6</sup> has been developed. A dataset that complies with this standard will follow the FAIR principles and its metadata will be of high quality. If this compliance has been verified by the respective repository, the dataset can be labelled with the <strong>Ea</strong>rth <strong>Sy</strong>stem <strong>Da</strong>ta <strong>B</strong>randing (EASYDAB)<sup>7</sup>. This branding makes it easy for users to verify that the data are properly curated and the metadata has been quality assured.</p><p><sup>1</sup>  Juckes et al., 2020: https://doi.org/10.5194/gmd-13-201-2020 <br><sup>2</sup>  <span>Eyring</span> et al., 2016: https://doi.org/10.5194/gmd-9-1937-2016<br><sup>3</sup>  www.atmodat.de<br><sup>4</sup>  https://doi.org/10.35095/WDCC/atmodat_standard_en_v3_0<br><sup>5</sup>  https://datacite.org<br><sup>6</sup>  https://github.com/AtMoDat/atmodat_data_checker <br><sup>7</sup>  https://easydab.de</p>

scholarly journals The C-BIG Repository: an Institution-Level Open Science Platform

2021 ◽  
Author(s):  
Samir Das ◽  
Rida Abou-Haidar ◽  
Henri Rabalais ◽  
Sonia Denise Lai Wing Sun ◽  
Zaliqa Rosli ◽  
...  
Keyword(s):  
Neurological Diseases ◽  
Open Data ◽  
Open Science ◽  
Data Repository ◽  
Multiple Stakeholders ◽  
Montreal Neurological Institute ◽  
Multimodal Data ◽  
Small Patient ◽  
Clinical Data Repository ◽  
Access Data

AbstractIn January 2016, the Montreal Neurological Institute-Hospital (The Neuro) declared itself an Open Science organization. This vision extends beyond efforts by individual scientists seeking to release individual datasets, software tools, or building platforms that provide for the free dissemination of such information. It involves multiple stakeholders and an infrastructure that considers governance, ethics, computational resourcing, physical design, workflows, training, education, and intra-institutional reporting structures. The C-BIG repository was built in response as The Neuro’s institutional biospecimen and clinical data repository, and collects biospecimens as well as clinical, imaging, and genetic data from patients with neurological disease and healthy controls. It is aimed at helping scientific investigators, in both academia and industry, advance our understanding of neurological diseases and accelerate the development of treatments. As many neurological diseases are quite rare, they present several challenges to researchers due to their small patient populations. Overcoming these challenges required the aggregation of datasets from various projects and locations. The C-BIG repository achieves this goal and stands as a scalable working model for institutions to collect, track, curate, archive, and disseminate multimodal data from patients. In November 2020, a Registered Access layer was made available to the wider research community at https://cbigr-open.loris.ca, and in May 2021 fully open data will be released to complement the Registered Access data. This article outlines many of the aspects of The Neuro’s transition to Open Science by describing the data to be released, C-BIG’s full capabilities, and the design aspects that were implemented for effective data sharing.

scholarly journals MMoOn Core – the Multilingual Morpheme Ontology

Semantic Web ◽  
2020 ◽  
pp. 1-29
Author(s):  
Bettina Klimek ◽  
Markus Ackermann ◽  
Martin Brümmer ◽  
Sebastian Hellmann
Keyword(s):  
Semantic Web ◽  
Linked Data ◽  
Semantic Model ◽  
Lexical Resources ◽  
Domain Specific ◽  
Word Forms ◽  
Language Data ◽  
Core Ontology ◽  
Machine Readable ◽  
Rapid Emergence

In the last years a rapid emergence of lexical resources has evolved in the Semantic Web. Whereas most of the linguistic information is already machine-readable, we found that morphological information is mostly absent or only contained in semi-structured strings. An integration of morphemic data has not yet been undertaken due to the lack of existing domain-specific ontologies and explicit morphemic data. In this paper, we present the Multilingual Morpheme Ontology called MMoOn Core which can be regarded as the first comprehensive ontology for the linguistic domain of morphological language data. It will be described how crucial concepts like morphs, morphemes, word forms and meanings are represented and interrelated and how language-specific morpheme inventories can be created as a new possibility of morphological datasets. The aim of the MMoOn Core ontology is to serve as a shared semantic model for linguists and NLP researchers alike to enable the creation, conversion, exchange, reuse and enrichment of morphological language data across different data-dependent language sciences. Therefore, various use cases are illustrated to draw attention to the cross-disciplinary potential which can be realized with the MMoOn Core ontology in the context of the existing Linguistic Linked Data research landscape.

scholarly journals GAMS – An infrastructure for the long-term preservation and publication of research data from the Humanities

2018 ◽  
Vol 71 (1) ◽  
pp. 207-216 ◽  
Author(s):  
Johannes Hubert Stigler ◽  
Elisabeth Steiner
Keyword(s):  
System Architecture ◽  
Academic Research ◽  
Research Data ◽  
Data Repository ◽  
Data Repositories ◽  
Domain Specific ◽  
Preservation Policy ◽  
Long Term Preservation ◽  
Data Centres

Research data repositories and data centres are becoming more and more important as infrastructures in academic research. The article introduces the Humanities’ research data repository GAMS, starting with the system architecture to preservation policy and content policy. Challenges of data centres and repositories and the general and domain-specific approaches and solutions are outlined. Special emphasis lies on the sustainability and long-term perspective of such infrastructures, not only on the technical but above all on the organisational and financial level.

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.

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