High Priority Open-Source Science (HPOSS)

High Priority Open-Source Science (HPOSS) is sponsored by NASA. Under the ROSES-24 solicitation, HPOSS supports proposals for the development of open-source tools, software, frameworks, data formats, or libraries that will have a significant impact on the Science Mission Directorate (SMD) science community.

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14 High Priority Open-Source Science F. 7 Support for Open Source Tools, Frameworks, and Libraries F. 8 Supplements for Open-Source Science F.

14 Transform to Open Science Training F. 19 Multidomain Reusable Artificial Intelligence Tools Opportunities for open-source science projects that have now closed to submissions. This historical content may contain outdated information or references that may not reflect current policy or programs.

F. 14 High Priority Open-Source Science High Priority Open-Source Science (HPOSS) supports new work to advance and streamline the open sharing of scientific information. HPOSS is not currently solicited.

Under ROSES-24, HPOSS will support two types of work: Proposals for the development of open source tools, software, frameworks, data formats, or libraries that will have a significant impact to the SMD science community. Proposals for the development of capacity building materials to support the adoption of open science practices.

This includes curricula, tutorials, or other training materials that will be appropriate for one or more science disciplines supported by SMD. Awards of ~$100k support work for one year. Summaries of previously selected proposals are available under "Selections" on the HPOSS NSPIRES pages for ROSES-22 , ROSES-23 , and ROSES-24 .

Watch the recording of the HPOSS informational webinar and access the presentation slides (PDF). Read the August 20, 2024 feature about the HPOSS awardees from the ROSES-22 and ROSES-23 calls . F.

7 Support for Open Source Tools, Frameworks, and Libraries Open Source Tools, Frameworks, and Libraries (OSTFL) supports the improvement and sustainment of existing high-value, open-source tools, frameworks, and libraries that have significantly impacted the SMD science community. OSTFL is not currently solicited.

The program element looked to support two types of awards: Foundational awards: cooperative agreements for up to five years for open-source tools, frameworks, and libraries that have a significant impact on two or more divisions of the SMD.

Sustainment awards: grants or cooperative agreements of up to three years in duration for open-source tools, frameworks, and libraries that have significant impact in one or more divisions of the SMD. For previous examples of awards, see the eight awards (PDF) initially selected from the ROSES-20 solicitation. A town hall on the ROSES-24 F.

7 OSTFL solicitation was held on April 2. Here are the slides (PDF) and video recording . Read the October 24, 2024 news release about the 2024 OSTFL awardees and the summaries of the ROSES24 OSTFL selections .

F. 8 Supplements for Open-Source Science Supplements for Open-Source Science (SOSS) supports the addition of an open science component to an existing "parent" ROSES award. F.

14 Transform to Open Science Training Transform to Open Science Training (TOPS-T) supports the development of discipline-specific open science curriculum and capacity building efforts for open science training. $6. 5M awarded over 3 years based on ROSES-22 solicitation.

Read the news release and summaries of selected proposals (PDF). F. 19 Multidomain Reusable Artificial Intelligence Tools Multidomain Reusable Artificial Intelligence Tools (MRAIT) solicits proposals that would enable critically needed machine learning tools to advance both heliophysics and Earth science research.

Read summaries of selected proposals (PDF) from the ROSES-22 solicitation . Discover More Topics From NASA NASA's commitment to open science fuels groundbreaking research while maximizing transparency, innovation, and collaboration. Artificial Intelligence for Science NASA is creating artificial intelligence tools to help researchers use NASA's science data more effectively.

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