Resources

• 15 Guiding FAIR Principles

• The latest developments on FAIR are available at GO-FAIR.

FAIR in ML:

• RDA FAIR4ML schema

AI-ready data:

• Checklist to Examine AI-readiness for Open Environmental Datasets. Version 1.0. Earth Science Information Partners.

• Croissant format specification (for machine learning training dataset)

  • Geo-croissant extends Croissant for geospatial data 

• ​STAC (Spatio Temporal Asset Catalog) version 1.1.0

• AI-ready Earth Observation training dataset

• AI-readiness for biomedical data: Bridge2AI recommendations

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Reproducibility:

• RECUP: scalable metadata and provenance for reproducible hybrid workflow

• DLHub // https://www.dlhub.org/  

• OpenML // https://www.openml.org/ 

• AI Model Share // https://www.modelshare.org/

• MLCommons

• Foundry

• Reforms: Reporting Standards for Machine Learning Based Science

• Using FAIR to foster AI-readiness in Data Facilities

• Checklist to Examine AI-readiness

• How to use the AI Ready Checklist

• What does "AI-readiness" mean for geoscience data repositories? by Yuhan (Douglas) Rao (Video)

FARR Blog Posts

• Environmental Data Science book or EDS book is a living, open and community-driven online resource to showcase and support the publication of data, research and open-source tools for collaborative, reproducible and transparent Environmental Data Science.

  • Variational data assimilation with deep prior (CIRC23) notebook

  • Deep learning and variational inversion to quantify and attribute climate change (CIRC23) notebook

  • Learning the underlying physics of a simulation model of the ocean’s temperature (CIRC23) notebook

• AI Reproducibility Webinar-2: Embracing open science principles to improve reproducibility in Environmental Data Science.

• AI Reproducibility Webinar-1 Strategies for Machine Learning Reproducibility
• FARR Blog
  • Do Containers Improve the Reproducibility of Machine Learning Experiments
  • AI Reproducibility Minute: Implementation Factors
  • Reproducibility in AI, and What Computing Professionals Should Know for Supporting Researchers

• Newsletters: past issues

• Postcard  (AI Reproducibility)

• Postcard (AI readiness)

• Link to interesting reads

• NAIRR Pilot

  • Recent webinar,  slides (2/21/24)

  • Awarded projects (4/22/24)

  • Resource request to Advance AI Research (5/6/24)

• Virtual backgrounds (Open Science at bottom of page)