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This presentation was given to NERC’s Digital Gathering 23,https://digitalenvironment.org/events/dg23-digital-gathering-2023, on Monday 10th July 2023. Further details of the complete list of authors and abstract below. Scivision authors: Coca Castro, A. (1), Conner, A. (1), Corcoran, E. (1), Costa Gomes, B., Fenton, I. (1), Famili, M. (1), Mehonic, A. (1), Strickson, O. (1), Van Zeeland, L. (1), Anhert, S. (1, 2), Lowe, A. (1, 3), Hosking, J. S (1, 4) EDS book authors: Coca Castro, A. (1), Hosking, J. S (1, 4), EDS book community (5) Affiliations (1 – The Alan Turing Institute, 2 – University College London, 3 – University of Cambridge, 4 – British Antarctic Survey, 5 – Multiple) Supported by interdisciplinary collaborations between teams from environmental, statistics and computer sciences, the past decade has seen accelerated development of environmental data, models and pipelines. This talk will highlight how two community-driven initiatives, created and maintained by the Alan Turing Institute, make research products in environmental science more accessible and discoverable. Scivision (https://sci.vision) is an open-source software tool, an open catalogue of datasets and models, and a community of computer vision experts and users. Scivision aims to accelerate scientific computer vision by sharing and matching models and datasets through the Scivision catalogue. The models in the catalogue have a common interface and are designed to be installable and runnable by someone without a computer science background; the datasets indicate their domain of application and any tasks that they may be suitable for, so that they are discoverable by computer vision model developers. Scivision has been applied to environmental use cases to analyse image datasets across different scales and formats including tree crown detection from drone imagery, coastal vegetation edge detection from satellite imagery, automated extraction of plant phenotype data from multiple 2D views of whole plants, among others. Scivision has also been utilised in one of Turing’s data study groups, which involved identification of plankton species using computer vision. EDS book (http://www.edsbook.org) is an online resource leveraging executable notebooks, cloud computing resources and technical implementations of the FAIR (Findable, Accessible, Interoperable and Reusable) principles to support the publication of datasets, innovative research and open-source tools in environmental science. EDS book provides practical guidelines and templates that maximise open infrastructure services to translate research outputs into curated, interactive, shareable and reproducible executable notebooks which benefit from a collaborative and transparent reviewing process. To date, the community has published multiple python-based notebooks covering a wide range of topics in environmental data science. More recently, EDS book successfully partnered with the 12th International Conference on Climate Informatics and Environmental Data Science Journal to deliver hands-on training and the underpinning framework for a reproducibility hackathon to support open-source climate research. In future work, we expect to increase contributions showcasing scalable and interoperable open-source developments in Julia and R programming languages and engage research networks interested in improving scientific software practices in environmental science. |
