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BackgroundThe Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open-source community specifications and software tools for enabling discovery, exchange and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab – a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, a JSON serialization ISA-JSON was developed.ResultsIn this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python object classes. We describe the ISA API feature set, early adopters and its growing user community.ConclusionsThe ISA API provides users with rich programmatic metadata handling functionality to support automation, a common interface and an interoperable medium between the two ISA formats, as well as with other life science data formats required for depositing data in public databases.

Scientific innovation is increasingly reliant on data and computational resources. Much of today’s life science research involves generating, processing, and reusing heterogeneous datasets that are growing exponentially in size. Demand for technical experts (data scientists and bioinformaticians) to process these data is at an all-time high, but these are not typically trained in good data management practices. That said, we have come a long way in the last decade, with funders, publishers, and researchers themselves making the case for open, interoperable data as a key component of an open science philosophy. In response, recognition of the FAIR Principles (that data should be Findable, Accessible, Interoperable and Reusable) has become commonplace. However, both technical and cultural challenges for the implementation of these principles still exist when storing, managing, analysing and disseminating both legacy and new data.COPO is a computational system that attempts to address some of these challenges by enabling scientists to describe their research objects (raw or processed data, publications, samples, images, etc.) using community-sanctioned metadata sets and vocabularies, and then use public or institutional repositories to share it with the wider scientific community. COPO encourages data generators to adhere to appropriate metadata standards when publishing research objects, using semantic terms to add meaning to them and specify relationships between them. This allows data consumers, be they people or machines, to find, aggregate, and analyse data which would otherwise be private or invisible. Building upon existing standards to push the state of the art in scientific data dissemination whilst minimising the burden of data publication and sharing.AvailabilityCOPO is entirely open source and freely available on GitHub at https://github.com/collaborative-open-plant-omics. A public instance of the platform for use by the community, as well as more information, can be found at copo-project.org.