Walsh, I. et al. DOME: recommendations for supervised machine learning validation in biology. Nat. Methods 18, 1122–1127 (2021).
Google Scholar
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
Luo, M. et al. Artificial intelligence for life sciences: a comprehensive guide and future trends. Innov. Life 2, 100105 (2024).
Google Scholar
Paysan-Lafosse, T. et al. The Pfam protein families database: embracing AI/ML. Nucleic Acids Res. 53, D523–D534 (2025).
Google Scholar
Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2022).
Google Scholar
Kapoor, S. & Narayanan, A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4, 100804 (2023).
Google Scholar
Clark, T. et al. AI-readiness for biomedical data: Bridge2AI recommendations. Preprint at bioRxiv https://doi.org/10.1101/2024.10.23.619844 (2024).
Tedersoo, L. et al. Data sharing practices and data availability upon request differ across scientific disciplines. Sci. Data 8, 192 (2021).
Google Scholar
Laurinavichyute, A., Yadav, H. & Vasishth, S. Share the code, not just the data: a case study of the reproducibility of articles published in the Journal of Memory and Language under the open data policy. J. Mem. Lang. 125, 104332 (2022).
Google Scholar
Alper, P. et al. RDMkit: A research data management toolkit for life sciences. Patterns 6, 101345 (2025).
Google Scholar
Pistoia Alliance. The FAIR toolkit for life science industry. https://fairtoolkit.pistoiaalliance.org (2020).
Ouyang, W. et al. BioImage Model Zoo: a community-driven resource for accessible deep learning in bioimage analysis. Preprint at bioRxiv https://doi.org/10.1101/2022.06.07.495102 (2022).
Avsec, Ž et al. The Kipoi repository accelerates community exchange and reuse of predictive models for genomics. Nat. Biotechnol. 37, 592–600 (2019).
Google Scholar
Akhtar, M. et al. Croissant: a metadata format for ML-ready datasets. In Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning (eds Hulsebos, M., Interlandi, M. & Shankar, S.) 1–6 (Association for Computing Machinery, 2024).
Research Data Alliance. RDA FAIR for Machine Learning (FAIR4ML) Interest Group. https://www.rd-alliance.org/groups/fair-machine-learning-fair4ml-ig/activity (2022).
Beam, A. L., Manrai, A. K. & Ghassemi, M. Challenges to the reproducibility of machine learning models in health care. JAMA 323, 305–306 (2020).
Google Scholar
Unsal, S. et al. Learning functional properties of proteins with language models. Nat. Mach. Intell. 4, 227–245 (2022).
Google Scholar
Sapkota, R., Roumeliotis, K. I. & Karkee, M. AI agents vs. agentic AI: A conceptual taxonomy, applications and challenges. Inf. Fusion 126, 103599 (2026).
Google Scholar
Schwartz, R., Dodge, J., Smith, N. A. & Etzioni, O. Green AI. ACM 63, 54–63 (2020).
Google Scholar
White, M. et al. The Model Openness Framework: promoting completeness and openness for reproducibility, transparency, and usability in artificial intelligence. Preprint at https://doi.org/10.48550/arXiv.2403.13784 (2024).
Lekadir, K. et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ 388, e081554 (2025).
Google Scholar
Kapoor, S. et al. REFORMS: consensus-based recommendations for machine-learning-based science. Sci. Adv. 10, eadk3452 (2024).
Google Scholar
Machine Learning Commons. MLCommons: better AI for everyone. https://mlcommons.org (2025).
FAIR Advanced Research and Reproducibility (FARR) Research Coordination Network. FARR RCN. https://www.farr-rcn.org (2025).
Rai, A. Explainable AI: from black box to glass box. J. Acad. Mark. Sci. 48, 137–141 (2020).
Google Scholar
Afroogh, S., Akbari, A., Malone, E., Kargar, M. & Alambeigi, H. Trust in AI: progress, challenges, and future directions. Humanit. Soc. Sci. Commun. 11, 1568 (2024).
Google Scholar
Leslie, D. Understanding Artificial Intelligence Ethics and Safety: a Guide for the Responsible Design and Implementation of AI Systems in the Public Sector (The Alan Turing Institute, 2019).
Dignum, V. Responsible artificial intelligence: from principles to practice. Preprint at https://doi.org/10.48550/arXiv.2205.10785 (2022).
Ahdritz, G. et al. OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nat. Methods 21, 1514–1524 (2024).
Google Scholar
Collins, G. S. et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 384, e078378 (2024).
Google Scholar
Schmied, C. et al. Community-developed checklists for publishing images and image analyses. Nat. Methods 21, 170–181 (2024).
Google Scholar
Liu, X. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat. Med. 26, 1364–1374 (2020).
Google Scholar
Cruz Rivera, S. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat. Med. 26, 1351–1363 (2020).
Google Scholar
Kaggle. Kaggle: your machine learning and data science community. https://www.kaggle.com (2025).
Wolf, T. et al. HuggingFace’s Transformers: state-of-the-art natural language processing. Preprint at https://doi.org/10.48550/arXiv.1910.03771 (2019).
Turon, G., Legese, A., Arora, D. & Duran-Frigola, M. Ersilia Model Hub: a repository of AI/ML models for infectious and neglected tropical diseases. Zenodo https://doi.org/10.5281/ZENODO.7274645 (2025).
European Organization For Nuclear Research (CERN) & OpenAIRE. Zenodo https://doi.org/10.25495/7GXK-RD71 (2013).
Leo, S. et al. Recording provenance of workflow runs with RO-Crate. PLoS ONE 19, e0309210 (2024).
Google Scholar
Huerta, E. A. et al. FAIR for AI: an interdisciplinary and international community building perspective. Sci. Data 10, 487 (2023).
Google Scholar
Castro, L. J. et al. FAIR4ML-schema. Zenodo https://doi.org/10.5281/ZENODO.14002310 (2024).
Pistoia Alliance. Pistoia Alliance organisation website. https://www.pistoiaalliance.org (2025).
Open Data Institute. A framework for AI-ready data. https://theodi.hacdn.io/media/documents/A_framework_for_AI-ready_data.pdf (2025).
Scientific Computing World. Pistoia Alliance launches DataFAIRy to drive AI adoption. https://www.scientific-computing.com/news/pistoia-alliance-launches-datafairy-drive-ai-adoption (2024).
Desai, A., Abdelhamid, M. & Padalkar, N. R. What is reproducibility in artificial intelligence and machine learning research? AI Mag. 46, e70004 (2025).
Carter, R. E., Attia, Z. I., Lopez-Jimenez, F. & Friedman, P. A. Pragmatic considerations for fostering reproducible research in artificial intelligence. NPJ Digit. Med. 2, 42 (2019).
Google Scholar
Tiwari, D. D. et al. BioModelsML: building a FAIR and reproducible collection of machine learning models in life sciences and medicine for easy reuse. Preprint at bioRxiv https://doi.org/10.1101/2023.05.22.540599 (2023).
Merkel, D. Docker: lightweight Linux containers for consistent development and deployment. Linux J. 2014, 2 (2014).
Anaconda. Conda https://anaconda.org/anaconda/conda (2025).
Di Tommaso, P. et al. Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35, 316–319 (2017).
Google Scholar
Köster, J. & Rahmann, S. Snakemake: a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).
Google Scholar
Galaxy Community, T. he et al. The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update. Nucleic Acids Res. 52, W83–W94 (2024).
Google Scholar
Heil, B. J. et al. Reproducibility standards for machine learning in the life sciences. Nat. Methods 18, 1132–1135 (2021).
Google Scholar
Bisong, E. Google Colaboratory. In Building Machine Learning and Deep Learning Models on Google Cloud Platform Ch. 7, 59–64 (Apress, 2019).
Anthony, L. F. W., Kanding, B. & Selvan, R. Carbontracker: tracking and predicting the carbon footprint of training deep learning models. Preprint at https://doi.org/10.48550/arXiv.2007.03051 (2020).
Ritchie, H. et al. Hardware and energy cost to train notable AI systems. Our World in Data https://ourworldindata.org/grapher/hardware-and-energy-cost-to-train-notable-ai-systems (2023).
Gailhofer, P. et al. The Role of Artificial Intelligence in the European Green Deal (European Parliament, 2023).
Bolón-Canedo, V. et al. A review of green artificial intelligence: towards a more sustainable future. Neurocomputing 599, 128096 (2024).
Google Scholar
EMBL. Sustainability: reports and resources. https://www.embl.org/about/info/sustainability/reports-resources (2025).
Yamada, T. et al. Frugal machine learning: making AI more efficient, accessible, and sustainable. Preprint at https://doi.org/10.36227/techrxiv.173385981.11102720/v1 (2024).
Tornede, T. et al. Towards green automated machine learning: status quo and future directions. J. Artif. Intell. Res. 77, 427–457 (2023).
Google Scholar
Johnson, S. G., Simon, G. & Aliferis, C. Regulatory aspects and ethical legal societal implications (ELSI). In Artificial Intelligence and Machine Learning in Health Care and Medical Sciences (eds Simon, G. J. & Aliferis, C.) Ch. 16, 659–692 (Springer, 2024).
Jefferson, E. et al. GRAIMatter: guidelines and resources for AI model access from TrusTEd research environments (GRAIMatter). Int. J. Popul. Data Sci. 7, 2005 (2022).
Google Scholar
European Commission. AI for Health: evaluation of applications & datasets (AHEAD). CORDIS https://cordis.europa.eu/project/id/101183031 (2024).
European Commission. HORIZON Europe: ELIXIR-STEERS project. CORDIS https://cordis.europa.eu/project/id/101131096 (2024).
SustAInML. Sustainable AI and Machine Learning. https://sustainml.eu (2021).
Software Sustainability Institute. Green DiSC: a digital sustainability certification. https://www.software.ac.uk/GreenDiSC (2025).
Geoscience and Remote Sensing Society (GRSS). GeoCroissant: a metadata framework for geospatial ML-ready datasets. https://www.grss-ieee.org/events/geocroissant-a-metadata-framework-for-geospatial-ml-ready-datasets (2024).
Mitchell, M. et al. Model cards for model reporting. In Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency (eds Friedler, S. A. & Wilson, C.) 220–229 (Association for Computing Machinery, 2019).
Pushkarna, M., Zaldivar, A. & Kjartansson, O. Data cards: purposeful and transparent dataset documentation for responsible AI. Preprint at https://doi.org/10.48550/ARXIV.2204.01075 (2022).
Dasoulas, I., Yang, D. & Dimou, A. MLSea: a semantic layer for discoverable machine learning. In The Semantic Web (eds Meroño Peñuela, A. et al.) Ch. 11, 178–198 (Springer, 2024).
SciLifeLab Data Centre. SciLifeLab: funder requirements and FAIR ML models. https://serve.scilifelab.se/docs/model-serving/fair (2025).
Van Geest, G. et al. Using Glittr.org to find, compare and re-use online materials for training and education. PLoS ONE 19, e0308729 (2024).
Google Scholar
Data Carpentry. Data Carpentry lessons. https://datacarpentry.org/lessons (2025).
The Turing Way Community. The Turing way: a handbook for reproducible, ethical and collaborative research. Zenodo https://doi.org/10.5281/ZENODO.15213042 (2025).
ONNX. ONNX: Open Neural Network Exchange. https://onnx.ai/ (2025).
Attafi, O. A. et al. DOME registry: implementing community-wide recommendations for reporting supervised machine learning in biology. GigaScience 13, giae094 (2024).
Google Scholar
Kurtzer, G. M., Sochat, V. & Bauer, M. W. Singularity: scientific containers for mobility of compute. PLoS ONE 12, e0177459 (2017).
Google Scholar
Docker. Docker Hub container image library. https://hub.docker.com (2025).
Yuen, D. et al. The Dockstore: enhancing a community platform for sharing reproducible and accessible computational protocols. Nucleic Acids Res. 49, W624–W632 (2021).
Google Scholar
Clyburne-Sherin, A., Fei, X. & Green, S. A. Computational reproducibility via containers in psychology. Meta Psychol. 3, 892 (2019).
Google Scholar
Kryshtafovych, A. et al. Critical assessment of methods of protein structure prediction (CASP): round XV. Proteins 91, 1539–1549 (2023).
Google Scholar
Xiong, Z. et al. Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: the Multi-Targeting Drug DREAM Challenge. PLoS Comput. Biol. 17, e1009302 (2021).
Google Scholar
Capella-Gutierrez, S. et al. Lessons learned: recommendations for establishing critical periodic scientific benchmarking. Preprint at bioRxiv https://doi.org/10.1101/181677 (2017).
Ash, J. T. & Adams, R. P. On warm-starting neural network training. In Advances in Neural Information Processing Systems 33 (eds Larochelle, H. et al.) 3884–3894 (Curran Associates, 2020).
Tmamna, J. et al. Pruning deep neural networks for green energy-efficient models: a survey. Cogn. Comput. 16, 2931–2952 (2024).
Google Scholar
Krishnan, S. & Faust, A. Quantization for fast and environmentally sustainable reinforcement learning. Google Research Blog https://research.google/blog/quantization-for-fast-and-environmentally-sustainable-reinforcement-learning (2021).
Yuan, Y. et al. The impact of knowledge distillation on the energy consumption and runtime efficiency of NLP models. In Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering – Software Engineering for AI (eds Cleland-Huang, J., Bosch, J., Muccini, H. & Lewis, G. A.) 129–133 (Association for Computing Machinery, 2024).
Tabbakh, A. et al. Towards sustainable AI: a comprehensive framework for Green AI. Discov. Sustain. 5, 408 (2024).
Google Scholar
Guo, D. et al. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning. Nature 645, 633–638 (2025).
Google Scholar
Green Software Foundation. Green software patterns. https://patterns.greensoftware.foundation (2025).
Green Software Foundation. Green Software Foundation. https://greensoftware.foundation (2025).
TOP500.org. Green500 List: November 2023. https://top500.org/lists/green500/2023/11 (2023).
Performance Optimisation and Productivity Centre of Excellence in HPC. https://pop-coe.eu (2025).
Schmidt, V. et al. Machine learning CO2 impact calculator. https://mlco2.github.io/impact (2025).
GitHub. Official Repository of MICCAI FLARE Challenges. https://github.com/JunMa11/FLARE (2025).
Henderson, P. et al. Towards the systematic reporting of the energy and carbon footprints of machine learning. J. Mach. Learn. Res. 21, 10039–10081 (2020).
Ravi, N. et al. FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy. Sci. Data 9, 657 (2022).
Google Scholar
Farrell, G. OSAI ecosystem components data. Zenodo https://doi.org/10.5281/zenodo.15391273 (2025).
RSQKit Community. Research software quality kit (RSQKit). Zenodo https://doi.org/10.5281/zenodo.14923572 (2025).
Gavriilidis, G. I. et al. APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19. Bioinformatics 41, btaf063 (2025).
Google Scholar
D’Anna, F. et al. A research data management (RDM) community for ELIXIR. F1000Res. 13, 230 (2024).
BY-COVID. Infectious Diseases Toolkit (IDTk). https://www.infectious-diseases-toolkit.org (2025).
Mungall, C. Open knowledge bases in the age of generative AI. F1000Res. https://doi.org/10.7490/F1000RESEARCH.1120248.1 (2025).
Yiyao, L. et al. OmicsNavigator: an LLM-driven multi-agent system for autonomous zero-shot biological analysis in spatial omics. Preprint at bioRxiv https://doi.org/10.1101/2025.07.21.665821 (2025).
Huang, K. et al. Biomni: a general-purpose biomedical AI agent. Preprint at bioRxiv https://doi.org/10.1101/2025.05.30.656746 (2025).
Wei, J. et al. From AI for science to agentic science: a survey on autonomous scientific discovery. Preprint at https://doi.org/10.48550/arXiv.2508.14111 (2025).
Kim, J. et al. The cost of dynamic reasoning: demystifying AI agents and test-time scaling from an AI infrastructure perspective. Preprint at https://doi.org/10.48550/arXiv.2506.04301 (2025).
European Commission. The EU AI Act. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai (2024).
National Science Foundation. National Artificial Intelligence Research Resource (NAIRR) pilot. https://www.nsf.gov/focus-areas/artificial-intelligence/nairr (2024).
The White House. America’s AI action plan. https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf (2025).
Declaration on Research Assessment (DORA). https://sfdora.org/about-dora (2025).
CoARA. Coalition for Advancing Research Assessment. https://coara.org (2025).
Wang, Y. et al. SimpleFold: folding proteins is simpler than you think. Preprint at https://doi.org/10.48550/arXiv.2509.18480 (2025).
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Google Scholar
AlphaFold3: why did Nature publish it without its code? Nature 629, 728 (2024).
Callaway, E. AI protein-prediction tool AlphaFold3 is now more open. Nature 635, 531–532 (2024).
Google Scholar
Global Alliance for Genomics & Health (GA4GH). https://www.ga4gh.org (2025).
Pascucci, E. et al. Progressing towards personalised medicine: the Genomic Data Infrastructure (GDI) project. Eur. J. Public Health 34, ckae144.1956 (2024).
Google Scholar
Heredia, I. et al. AI4EOSC: a federated cloud platform for artificial intelligence in scientific research. Preprint at https://arxiv.org/abs/2512.16455 (2025).
