Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods, and Results

Manir, Sadnan Al; Niestroy, Justin; Levinson, Maxwell Adam; Clark, Tim W.

doi:10.1101/2021.03.29.437561

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…The W3C PROV model (Gil et al, 2013;Lebo et al, 2013;Moreau et al, 2013) was developed initially to support interoperability across the transformation logs of workflow systems. Our prior work on Micropublications (Clark et al, 2014) extending and repurposing several core classes and predicates from W3C PROV, were preliminary work forming a basis for the EVI ontology (Al Manir et al, 2021aManir et al, , 2021b.…”

Section: Related Workmentioning

confidence: 99%

FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics

et al. 2021

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Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result’s metadata. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.

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Section: Related Workmentioning

confidence: 99%

FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The W3C PROV model (Gil et al 2013;Lebo et al 2013;Moreau et al 2013) was developed initially to support interoperability across the transformation logs of workflow systems. Our prior work on Micropublications (Tim Clark et al 2014) extending and repurposing several core classes and predicates from W3C PROV, were preliminary work forming a basis for the EVI ontology (Al Manir et al 2021aManir et al , 2021b.…”

Section: Related Workmentioning

confidence: 99%

FAIRSCAPE: A Framework for FAIR and Reproducible Biomedical Analytics

Levinson

Niestroy

Manir

et al. 2020

Preprint

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Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis consists of accessible data and software with runtime environment and personnel involved. Evidence graphs - a derivation of argumentation frameworks adapted to biological science - can provide this disclosure as machine-readable metadata resolvable from persistent identifiers for computationally generated graphs, images, or tables, that can be archived and cited in a publication including a persistent ID. We have built a cloud-based, computational research commons for predictive analytics on biomedical time series datasets with hundreds of algorithms and thousands of computations using a reusable computational framework we call FAIRSCAPE. FAIRSCAPE computes a complete chain of evidence on every result, including software, computations, and datasets. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves the provenance graph across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software. FAIRSCAPE is a reusable computational framework, enabling simplified access to modern scalable cloud-based components. It fully implements the FAIR data principles and extends them to provide FAIR Evidence, including provenance of datasets, software and computations, as metadata for all computed results

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AI-readiness for Biomedical Data: Bridge2AI Recommendations

Clark,

Caufield,

Parker

et al. 2024

Preprint

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Biomedical research and clinical practice are in the midst of a transition toward significantly increased use of artificial intelligence (AI) and machine learning (ML) methods. These advances promise to enable qualitatively deeper insight into complex challenges formerly beyond the reach of analytic methods and human intuition while placing increased demands on ethical and explainable artificial intelligence (XAI), given the opaque nature of many deep learning methods. The U.S. National Institutes of Health (NIH) has initiated a significant research and development program, Bridge2AI, aimed at producing new "flagship" datasets designed to support AI/ML analysis of complex biomedical challenges, elucidate best practices, develop tools and standards in AI/ML data science, and disseminate these datasets, tools, and methods broadly to the biomedical community. An essential set of concepts to be developed and disseminated in this program along with the data and tools produced are criteria for AI-readiness of data, including critical considerations for XAI and ethical, legal, and social implications (ELSI) of AI technologies. NIH Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group members prepared this article to present methods for assessing the AI-readiness of biomedical data and the data standards perspectives and criteria we have developed throughout this program. While the field is rapidly evolving, these criteria are foundational for scientific rigor and the ethical design and application of biomedical AI methods.

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Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods, and Results

Cited by 3 publications

References 55 publications

FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics

FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics

FAIRSCAPE: A Framework for FAIR and Reproducible Biomedical Analytics

AI-readiness for Biomedical Data: Bridge2AI Recommendations

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