Work with the computational notebook community to design and implement a publishing workflow for computational notebooks.
Ethics in Artificial Intelligence and Machine Learning
Develop principles and responsibilities for Earth, space, and environmental science research employing artificial intelligence and machine learning.
Researcher-oriented guidance that improves data management practices, developed with an international, trans-disciplinary team.
Data Citations and Credit
Ensuring all papers from NSF grants published at AGU have proper data citations. Expanding to all papers.
Data Citation Community of Practice
This community of practice for data citation in the Earth, space and environmental sciences builds on discussions held at the AGU Fall Meeting 2021 Data FAIR Town Hall, “Why Is Citing Data Still Hard?”. In this community of practice, we will work to address the use case of citing a large number of datasets, to ensure that credit for individual datasets is properly assigned.
AGU’s Notebooks Now! project is supported through the Notebooks Now! Elevating notebooks into scholarly publishing project with funding provided by the Sloan Foundation.
AGU’s Ethics in AI/ML project is supported through the Ethics in Use of Artificial Intelligence (AI) and Machine Learning (ML) in Science project with funding provided by NASA, grant 21-TWSC21-0023.
The AGU Open Science Leadership team is supported through the Building New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas (PARSEC) project with funding provided by the Belmont Forum through the National Science Foundation, Grant 1929464.
The PARSEC project team is also funded by UNESCO/IGCP, Project 697.
AGU’s Open Science Leadership work connecting data and software citation in our journals through to the NSF Public Access Repository is funded through the Accelerating Open and FAIR Data Practices Across the Earth, Space, and Environmental Sciences: A Pilot with the NSF to Support Public Access to Research Data project, National Science Foundation, Grant 2025364.