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FAIRification and AI-Readiness for Biodiversity Digital Twins

Weiland, Claus; Bauer, Daniel; Chala, Desalegn; Endresen, Dag; Grieb, Jonas; Kehel, Zakaria; Orwick-Rydmark, Marcella; Puechmaille, Danaële; Zuquim, Gabriela

Publisher
Zenodo
DOI
10.3897/biss.9.181439
Repository
zenodo.org
Language
en

Abstract

The European Green Deal addresses a wide array of critical topics regarding the interaction and exchange between society and ecosystems involving human-induced climate change, biodiversity loss, and deforestation. To support the development of appropriate and sustainable mitigation strategies, both political frameworks as well as large-scale scenario forecast and simulation infrastructures such as Destination Earth Data Lake (DEDL, Puechmaille et al. 2025) and the Green Deal Data Space (Penninga et al. 2021) are currently implemented. Setting up these data spaces requires the consolidation and harmonization of vast amounts of data, among them data from long-term biodiversity monitoring (e.g. eDNA, Abarenkov et al. 2023), near real-time data captured by IoT devices (e.g. camera traps, Grieb et al. (2025)) and large-scale Earth observation data (e.g. ERA5 global reanalysis, Hersbach et al. (2020)). Key tools and research methodologies for integration of such data have been developed in the Destination Earth-linked Biodiversity Digital Twin project (BioDT). Biodiversity digital twins provide digital replicas of biodiversity patterns and processes (Khan et al. 2025), for FAIR-compliant reuse and cross-platform portability, they are packaged in BioDT using the RO-Crate specification. Resulting datasets as well as computational workflows are bundled as digital objects and annotated with rich machine-interpretable metadata using schema.org and its extensions Bioschemas and Croissant (high-level metadata specification supporting AI-Readiness, Bolleman et al. 2025).The workflow.earth pilot was developed to migrate and functionally integrate BioDT's digital twin for crop wild relatives (BioDT CWR) into DEDL. BioDT CWR is designed to identify genetic resources in crop wild relatives (CWR) and aid in creating strategies to mitigate food-related crises linked to climate change (Chala et al. 2024). CWR are crucial sources of untapped genetic diversity, offering greater adaptability to extreme weather events such as droughts, cold waves, and heavy precipitation. They are therefore of particular importance to ensure food security with regard to Sustainable Development Goals (notably Goal 2 "End hunger, achieve food security and improved nutrition and promote sustainable agriculture", United Nations 2023).Major objectives for workflow.earth areto enhance visibility and long-term availability of the BioDT CWR,to improve its prediction capabilities using DEDL's near data processing services, andto facilitate (in perspective) the orchestrated interplay with other digital twins such as the Climate Change Adaptation DT (Weiland et al. 2024).On top of RO-Crate, workflow.earth employs FAIR Signposting to add machine-interpretable links to human-readable digital objects using link relation types (RFC8288, Fig. 1) to present the web topology of a resource (Soiland-Reyes et al. 2024).In this presentation, we will demonstrate the deployment of agrobiodiversity workflows in DEDL using workflow.earth and the publication of model and results as webby FDOs.

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Biodiversity Information Science and Standards 9: e181439 doi: 10.3897/biss.9.181439 Conference Abstract FAIRification and AI-Readiness for Biodiversity Digital Twins Claus Weiland , Daniel Bauer , Desalegn Chala , Dag Endresen , Jonas Grieb , Zakaria Kehel , Marcella Orwick-Rydmark , Danaële Puechmaille , Gabriela Zuquim ‡ Senckenberg – Leibniz Institution for Biodiversity and Earth System Research, Frankfurt am Main, Germany § Natural History Museum, University of Oslo, Oslo, Norway | ICARDA - The International Center for Agricultural Research in the Dry Areas, Rabat, Morocco ¶ EUMETSAT - European Organisation for the Exploitation of Meteorological Satellites, Darmstadt, Germany # CSC - IT Center for Science, Espoo, Finland Corresponding author: Claus Weiland ([email protected]) Received: 05 Dec 2025 | Published: 09 Dec 2025 Citation: Weiland C, Bauer D, Chala D, Endresen D, Grieb J, Kehel Z, Orwick-Rydmark M, Puechmaille D, Zuquim G (2025) FAIRification and AI-Readiness for Biodiversity Digital Twins. Biodiversity Information Science and Standards 9: e181439. https://doi.org/10.3897/biss.9.181439 Abstract The European Green Deal addresses a wide array of critical topics regarding the interaction and exchange between society and ecosystems involving human-induced climate change, biodiversity loss, and deforestation. To support the development of appropriate and sustainable mitigation strategies, both political frameworks as well as large-scale scenario forecast and simulation infrastructures such as Destination Earth Data Lake (DEDL, Puechmaille et al. 2025) and the Green Deal Data Space (Penninga et al. 2021) are currently implemented. Setting up these data spaces requires the consolidation and harmonization of vast amounts of data, among them data from longterm biodiversity monitoring (e.g. eDNA, Abarenkov et al. 2023), near real-time data captured by IoT devices (e.g. camera traps, Grieb et al. (2025)) and large-scale Earth observation data (e.g. ERA5 global reanalysis, Hersbach et al. (2020)). Key tools and research methodologies for integration of such data have been developed in the Destination Earth-linked Biodiversity Digital Twin project (BioDT). Biodiversity digital twins provide digital replicas of biodiversity patterns and processes (Khan et al. 2025), for FAIR-compliant reuse and cross-platform portability, they are packaged in BioDT using the RO-Crate specification. Resulting datasets as well as computational ‡ ‡ § § ‡ | § ¶ # © Weiland C et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. workflows are bundled as digital objects and annotated with rich machine-interpretable metadata using schema.org and its extensions Bioschemas and Croissant (high-level metadata specification supporting AI-Readiness, Bolleman et al. 2025). The workflow.earth pilot was developed to migrate and functionally integrate BioDT's digital twin for crop wild relatives (BioDT CWR) into DEDL. BioDT CWR is designed to identify genetic resources in crop wild relatives (CWR) and aid in creating strategies to mitigate food-related crises linked to climate change (Chala et al. 2024). CWR are crucial sources of untapped genetic diversity, offering greater adaptability to extreme weather events such as droughts, cold waves, and heavy precipitation. They are therefore of particular importance to ensure food security with regard to Sustainable Development Goals (notably Goal 2 “End hunger, achieve food security and improved nutrition and promote sustainable agriculture”, United Nations 2023). Major objectives for workflow.earth are 1. to enhance visibility and long-term availability of the BioDT CWR, 2. to improve its prediction capabilities using DEDL’s near data processing services, and 3. to facilitate (in perspective) the orchestrated interplay with other digital twins such as the Climate Change Adaptation DT (Weiland et al. 2024). On top of RO-Crate, workflow.earth employs FAIR Signposting to add machineinterpretable links to human-readable digital objects using link relation types (RFC8288, Fig. 1) to present the web topology of a resource (Soiland-Reyes et al. 2024). In this presentation, we will demonstrate the deployment of agrobiodiversity workflows in DEDL using workflow.earth and the publication of model and results as webby FDOs. Keywords climate change, crop wild relatives, SDG, Destination Earth, data lake, Earth observation, RO-Crate, FAIR, signposting, species distribution model Presenting author Claus Weiland Presented at Living Data 2025 2Weiland C et al Funding program Biodiversity Digital Twin for Advanced Modelling, Simulation and Prediction Capabilities (BioDT), Horizon Europe, GA no. 101057437 FAIRagro - FAIR Data Infrastructure for Agrosystems, DFG, GA no. 501899475 Agroclim - Agriculture and Climate Change DEDL Use Case, EUMETSAT procurement no. 500025286 Conflicts of interest The authors have declared that no competing interests exist. Figure 1. FAIR Signposting in workflow.earth using link relation types to indicate resources and attribution of a CWR species distribution model, i.a. "describedBy" to link to the RO-Crate describing the resource, "license" to link to a machine-interpretable license and "author" to link of the author's ORCID or Wikidata Person ID (all graphics by Myriam Ries/weltalle and Claus Weiland under CC-BY-4.0). FAIRification and AI-Readiness for Biodiversity Digital Twins 3 References • Abarenkov K, Andersson AF, Bissett A, Finstad AG, Fossøy F, Grosjean M, Hope M, Jeppesen TS, Kõljalg U, Lundin D, Nilsson RH, Prager M, Provoost P, Schigel D, Suominen S, Svenningsen C, Frøslev TG (2023) Publishing DNA-derived data through biodiversity data platforms. Copenhagen: GBIF Secretariat https://doi.org/10.35035/docvf1a-nr22 • Bolleman J, Castro LJ, Gaignard A, Kalampaliki A, Kalaš M, Ong EJK, Queralt-Rosinach N, Quiñones N, Ravinder R, Solanki D, Steinberg D, Weiland C, Wijnbergen D (2025) An assessment of Croissant ML metadata descriptors for AI-ready datasets. BioHackathon Europe https://doi.org/10.37044/osf.io/4sgdq_v1 • Chala D, Kusch E, Weiland C, Andrew C, Grieb J, Rossi T, Martinovic T, Endresen D (2024) Prototype biodiversity digital twin: crop wild relatives genetic resources for food security. Research Ideas and Outcomes 10 https://doi.org/10.3897/rio.10.e125192 • Grieb J, Weiland C, Wolodkin A, Bauer D, Beukes M, Biber M, Jansen M, Wesche K (2025) AI4WildLIVE: Integrating Biodiversity Monitoring and Earth Observation. AGILE: GIScience Series 6: 1‑6. https://doi.org/10.5194/agile-giss-6-24-2025 • Hersbach H, Bell B, Berrisford P, et al. (2020) The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146 (730): 1999‑2049. https://doi.org/10.1002/ qj.3803 • Khan T, de Koning K, Endresen D, Chala D, Kusch E (2025) TwinEco: A unified framework for dynamic data-driven digital twins in ecology. Ecological Informatics 91 https://doi.org/10.1016/j.ecoinf.2025.103407 • Penninga F, Lutz M, Minghini M, Cetl V, Robbrecht J (2021) INSPIRE, a public sector contribution to the European green deal data space - A vision for the technological evolution of Europe’s spatial data infrastructures for 2030. Publications Office of the European Union https://doi.org/10.2760/8563 • Puechmaille D, Schick M, Saulyak B, Dillmann M, Wolf L (2025) Destination Earth Data Lake unlocking Big Earth Data processing. EGU General Assembly 2024, Vienna, Austria https://doi.org/10.5194/egusphere-egu24-11115 • Soiland-Reyes S, Castro LJ, Ravinder R, Weiland C, Grieb J, Rogers A, Blanchi C, Van de Sompel H (2024) BioHackEU23 report: Enabling FAIR Digital Objects with RO-Crate, Signposting and Bioschemas. Biohackathon Europe https://doi.org/10.37044/osf.io/gmk2h • United Nations (2023) UN SDGs (UN Sustainable Development Goals). Encyclopedia of Sustainable Management3820‑3820. https://doi.org/10.1007/978-3-031-25984-5_302271 • Weiland C, Grieb J, Bauer D, Chala D, Kusch E, Andrew C, Endresen D (2024) Dataspace Integration for Agrobiodiversity Digital Twins with RO-Crate. Biodiversity Information Science and Standards 8 https://doi.org/10.3897/biss.8.134479 4Weiland C et al