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Making Neural Networks FAIR

Making Neural Networks FAIR

Published: 2019 Juli

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Research on neural networks has gained significant momentum over the past few years. A plethora of neural networks is currently being trained on available data in research as well as in industry. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a recent trend to attempt to re-use already-trained neural networks. As such, neural networks themselves have become research data. In this paper, we present the Neural Network Ontology, an ontology to make neural networks findable, accessible, interoperable and reusable as suggested by the well-established FAIR guiding principles for scientific data management and stewardship. We created the new FAIRnets Dataset that comprises about 2,000 neural networks openly accessible on the internet and uses the Neural Network Ontology to semantically annotate and represent the neural networks. For each of the neural networks in the FAIRnets Dataset, the relevant properties according to the Neural Network Ontology such as the description and the architecture are stored. Ultimately, the FAIRnets Dataset can be queried with a set of desired properties and responds with a set of neural networks that have these properties. We provide the service FAIRnets Search which is implemented on top of a SPARQL endpoint and allows for querying, searching and finding trained neural networks annotated with the Neural Network Ontology. The service is demonstrated by a browser-based frontend to the SPARQL endpoint.

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