Towards FAIR protocols and workflows: the OpenPREDICT use case

Abstract

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.

Developing and reusing bioinformatics data analysis pipelines using scientific workflow systems

“…The ’FAIR Guiding Principles for scientific data management and stewardship’ [61] provides guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. Applying FAIR principles to scientific workflows [62] , [63] is particularly important as it includes defining metrics e.g. to assess the ability of a workflow to be reproduced or reused, thus providing key quality information to workflow (re)users.…”

MGATRx: Discovering Drug Repositioning Candidates Using Multi-View Graph Attention

“…These baseline models are openly available in GitHub repositories. We did not use PREDICT [2] as a baseline because of the reproducibility issues (reported recently in OpenPREDICT case study [33]). To ensure fairness, we split the same size of training, validation, and test sets during 10-fold cross-validation.…”

User-friendly Composition of FAIR Workflows in a Notebook Environment

“…For some time, researchers have been combining existing ontologies to describe workflows and help prevent their 'decay' into non-reusability [5]. In our previous work [8], we proposed a semantic data model that can describe the workflow steps and their execution order (prospective provenance) and the concrete activities that happened during execution (retrospective provenance). Here we continue on from the…”

“…The retrospective nanopublications are linked to the prospective nanopublications that they are derived from, step to step and workflow to workflow, as shown in Figure 4. Both retrospective and prospective nanopublications are based on the Plex ontology described in [8].…”

“…Sharing of workflows in more detail, including information about the individual steps, their parameters, and provenance, is essential for reproducibility. In our previous work [8], we proposed a semantic data model that can describe the workflow steps and their execution order (prospective provenance) and the concrete activities that happened during execution (retrospective provenance). Building upon this earlier work, we present here a concrete implementation and user interface, and show evaluation results that demonstrate the feasibility of this approach.…”