Non-reproducibility of scientiﬁc studies (e.g. 64% in pharmacological studies [2], 89% in cancer [3], and 66% in psychology [4]) is often due to imprecise speciﬁcation of the scientiﬁc protocol in publications. To address this issue, we develop methods to apply the FAIR data principles to protocols and workﬂows. Speciﬁc research questions are: 🡪 How can workﬂow and protocol speciﬁcations, e.g. standard operating procedures, be published as FAIR data? 🡪 How to abstract from concrete workﬂow to general protocol speciﬁcations (e.g., with semantic templates) to improve reproducibility? 🡪 How to model and integrate non-computational steps in workﬂow and protocol speciﬁcations? FAIR Workflows Data publishing of scientific protocols João Moreira1, Tobias Kuhn1, Michel Dumontier2, Remzi Celebi2 , Ahmed Hassan2, Harald Schmidt2, Lars Ridder3 , Valentina Maccatrozzo3, Roel Zinkstok3, Carlos Martinez3 1 University VU Amsterdam, Netherlands. 2 Maastricht University, Netherlands 3 Netherlands eScience Center, Netherlands Motivation and research questions [1] Vasilevsky, N. A., Brush, M. H., Paddock, H., Ponting, L., Tripathy, S. J., LaRocca, G. M., & Haendel, M. A. (2013). On the reproducibility of science: unique identiﬁcation of research resources in the biomedical literature. PeerJ, 1, e148. doi:10.7717/peerj.148 [2] Prinz, F., Schlange, T., & Asadullah, K. (2011). Believe it or not: how much can we rely on published data on potential drug targets? Nature Reviews Drug Discovery, 10(9), 712–712. doi:10.1038/nrd3439-c1 [3] Begley, C. G., & Ellis, L. M. (2012). Drug development: Raise standards for preclinical cancer research. Nature, 483(7391), 531–533. doi:10.1038/483531a [4] Klein, R. A., Ratliﬀ, K. A., Vianello, M., Adams, R. B., Bahník, Š., Bernstein, M. J., Brumbaugh, C. C. (2014). Investigating Variation in Replicability. Social Psychology, 45(3), 142–152. doi:10.1027/1864-9335/a000178[ Requirements based on user stories 🡪 Platform that supports reproducing experiments and their results 🡪 Share workﬂows in ﬁndable data repositories (FAIR data points) 🡪 Use community standards and tools to share workﬂows 🡪 Detailed speciﬁcation of computational and manual workﬂow steps 🡪 Track workﬂow executions with versioning and provenance Preliminary results 🡪 Workﬂow ontology and semantic templates for metadata management 🡪 Common Workﬂow Language (CWL) for interoperability of workﬂow systems 🡪 FAIR technologies: Data Points, Nanopublications, Projectors and Accessors 🡪 Prototype plug-in for IPython/Jupyter notebooks Validation 🡪 Case studies and user study with drug repositioning and systematic reviews Impact Domain-agnostic: applicable to other ﬁelds to improve reproducibility in a vast area of scientiﬁc research Pharmaceutical research eﬃciency: improve identiﬁcation of cardiovascular drug candidates Approach Workﬂow step operations edam:QueryAndRetrieval edam:Parsing edam:Mapping edam:DataHandling edam:Aggregation edam:NetworkSimulation obi:SimilarityCalculation mls:HyperParameterSetting mls:ModelEvaluation obi:WesternBlotAnalysis edam:EnrichmentAnalysis edam:PathwayOrNetworkVisualisation P-PLAN SIO: Semanticscience Integrated Ontology Ontology engineering 🡪 Systematic literature review of existing ontologies and gap analysis 🡪 Ontology-driven conceptual modelling and ontology alignments 🡪 Proof-of-concept: semantic wrapper (proxy) DOLCE+DnS Ultralite (DUL) OpenPREDICT use case 🡪 Machine learning algorithm for cardiovascular drug repositioning 🡪 IPython/Jupyter notebook