A Generic Workflow for the Data FAIRification Process

Jacobsen, Annika; Kaliyaperumal, Rajaram; Santos, Luiz Olavo Bonino da Silva; Mons, Barend; Schultes, Erik; Roos, Marco; Thompson, Mark

doi:10.1162/dint_a_00028

Cited by 83 publications

(82 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data FAIRification is a complex process, requiring several areas of expertise—domain knowledge on the specifics of the data generation process: physicochemical characterization, eco and human toxicology, exposure, material science, as well as knowledge from the computer science domain—data modelling, software architecture, software services and their secure deployment, data protection and licenses. The need for multidisciplinary team involvement is obvious and, together with strong data stewardship skills, is recommended by several guides and publications [ 6 , 32 ]. However, this is a heavyweight process and data providers are frequently reluctant and slow to adopt workflows, tools, and data formats outside of their daily routine.…”

Section: Discussionmentioning

confidence: 99%

Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database

et al. 2020

View full text Add to dashboard Cite

The field of nanoinformatics is rapidly developing and provides data driven solutions in the area of nanomaterials (NM) safety. Safe by Design approaches are encouraged and promoted through regulatory initiatives and multiple scientific projects. Experimental data is at the core of nanoinformatics processing workflows for risk assessment. The nanosafety data is predominantly recorded in Excel spreadsheet files. Although the spreadsheets are quite convenient for the experimentalists, they also pose great challenges for the consequent processing into databases due to variability of the templates used, specific details provided by each laboratory and the need for proper metadata documentation and formatting. In this paper, we present a workflow to facilitate the conversion of spreadsheets into a FAIR (Findable, Accessible, Interoperable, and Reusable) database, with the pivotal aid of the NMDataParser tool, developed to streamline the mapping of the original file layout into the eNanoMapper semantic data model. The NMDataParser is an open source Java library and application, making use of a JSON configuration to define the mapping. We describe the JSON configuration syntax and the approaches applied for parsing different spreadsheet layouts used by the nanosafety community. Examples of using the NMDataParser tool in nanoinformatics workflows are given. Challenging cases are discussed and appropriate solutions are proposed.

show abstract

Section: Discussionmentioning

confidence: 99%

Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database

et al. 2020

View full text Add to dashboard Cite

show abstract

“…MeSH annotations were collected from (Caniza, Romero & Paccanaro, 2015) (https: //paccanarolab.org/disease_similarity) and annotations were also obtained by NCBO annotator API (Noy et al, 2009) using the OMIM disease description. The data that was not yet in a Linked Data format were converted to RDF with a FAIRification process (Jacobsen et al, 2019). We kept the copies of the retrieved non-RDF datasets in our GitHub repository to prevent the data access issues that may arise if data sources are unavailable.…”

Section: Fairified Data Collectionmentioning

confidence: 99%

Towards FAIR protocols and workflows: the OpenPREDICT use case

Çelebi

Moreira

Hassan

et al. 2020

PeerJ Computer Science

View full text Add to dashboard Cite

It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

show abstract

“…Second, the expertise required to achieve the objectives were identified. Conducting the FAIRification process requires a highly multidisciplinary team guided by FAIR data steward(s) [8].…”

Section: Step 1 -Identify Fairification Objectives and Expertisementioning

confidence: 99%

“…Also, research expertise is applicable throughout the table and is for simplicity not specified. The areas of expertise have been learned from a previous project [8], and further advanced here. Table 1.…”

Section: Step 1 -Identify Fairification Objectives and Expertisementioning

confidence: 99%

The de novo FAIRification process of a registry for vascular anomalies

Groenen¹,

Jacobsen

Kersloot

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

BackgroundPatient data registries that are FAIR - Findable, Accessible, Interoperable, and Reusable for humans and computers - facilitate research across multiple resources. This is particularly relevant to rare diseases, where data often are scarce and scattered. Specific research questions can be asked across FAIR rare disease registries and other FAIR resources without physically combining the data.ResultsWe successfully developed and implemented a process of making a rare disease registry for vascular anomalies FAIR from its conception - de novo. Here, we describe the five phases of this process in detail: i) pre-FAIRification, ii) facilitating FAIRification, iii) data collection, iv) generating FAIR data in real-time, and v) using FAIR data. This includes the creation of an electronic case report form and a semantic data model of the “Set of Common Data Elements for Rare Disease Registration” released by the European Commission, and the technical implementation in the Electronic Data Capture (EDC) system for real-time, automatic data FAIRification. Further, we describe how we contribute to the four facets of FAIR, and how our FAIRification process can be reused by other registries.ConclusionsIn conclusion, a detailed de novo FAIRification process of a registry for vascular anomalies is described. To a large extent, the process may be reused by other rare disease registries, and we envision this work to be a substantial contribution to an ecosystem of FAIR rare disease resources.

show abstract

A Generic Workflow for the Data FAIRification Process

Cited by 83 publications

References 18 publications

Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database

Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database

Towards FAIR protocols and workflows: the OpenPREDICT use case

The de novo FAIRification process of a registry for vascular anomalies

Contact Info

Product

Resources

About