NASA reinforces commitment to share lunar data with the global scientific community through the Artemis Accords
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NASA reinforces commitment to share lunar data with the global scientific community through the Artemis Accords

NASA reaffirmed on Thursday (11th) its commitment to making data, samples, and discoveries generated by the Artemis program available to the international scientific community. This initiative is supported by the 71 countries that have signed the Artemis Accords, a set of guidelines focused on safe and transparent civil space exploration, with Brazil being one of the signatories.

To realize this commitment, the agency held a series of two virtual workshops focused on open science and data sharing. These meetings took place between July 28th and September 8th, aiming to define methods for scientific information to reach researchers from various nations in a timely manner.

Open Science Principles

The first session of the workshops concentrated on the concepts of open science and the necessary methodologies for its implementation. According to NASA, the discussions aimed to expand collaboration and interoperability among signatory nations, while also stimulating transparency, accessibility, and replicability in the scientific field.

Sharing Tools and Resources

In the second meeting, tools capable of operationalizing open science were addressed. NASA presented signatories with a reference model that allows each country to develop or improve its own data sharing systems and structures. Among the resources displayed by the agency was the Planetary Data System, one of NASA's main repositories for planetary science data. Publicly accessible lunar data, along with analysis and visualization tools and the standard information model used by the system, were also shared.

Jacob Bleacher, NASA's chief exploration scientist, stated: "We are providing data, tools, and results free of charge and inviting Artemis Accords partners to innovate with us and share their data as well." Meanwhile, Andrew Mitchell, Deputy Director of Scientific Data at NASA's Mission Directorate, emphasized that data openness transcends mere technology; he considers the change in the scientific process itself to be fundamental to making it more collaborative and transparent.

The agency stresses that making scientific results and processes as open and reproducible as possible can drive new studies and elevate both the quality and the pace of scientific advancement. In the context of the Artemis program, this includes sharing information derived from lunar rocks, datasets, and discoveries made.

The workshops followed up on debates initiated by the ISRO (Indian Space Research Organisation) in May, when signatories began evaluating paths to advance open data practices and established a common basis for future discussions on the subject.

Origin and Objectives of the Artemis Accords

The Artemis Accords were formalized in 2020 by NASA and the U.S. Department of State, along with seven other pioneering nations. Their creation occurred during a period of growing interest from governments and private corporations in activities conducted on the Moon. The established principles aim to standardize practices to enhance safety and coordination among countries during the exploration of the Moon, Mars, and other space areas. Commitments include peaceful and transparent exploration, assistance to those in need, and guaranteeing access to scientific data.

Additionally, signatories commit to ensuring that their operations do not interfere with the actions of other participants and to protecting sites and artifacts of historical relevance through the adoption of best practices. NASA concludes that adherence to the Artemis Accords creates opportunities for future lunar missions in collaboration with the agency, thus contributing to the goals of human return to the Moon.

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NASA and IBM's New AI Uses 2 Million Images to Identify Water on the Moon
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NASA and IBM's New AI Uses 2 Million Images to Identify Water on the Moon

NASA and IBM collaborated on the launch of an open-source artificial intelligence model designed to analyze the lunar surface. This system was trained using data from four distinct missions, allowing it to identify potential ice deposits, map craters, and assist in the study of volcanic formations.

The development of this tool aims to accelerate investigations based on decades of lunar observations and support plans for establishing a lasting human presence on the satellite. One of the primary focuses of this project is locating water and other crucial resources for future expeditions.

The model, named NASA-IBM Lunar Foundation Model, received its training primarily with information captured by the Lunar Reconnaissance Orbiter (LRO) over 17 years. The dataset covers approximately two million images, including over one million one-meter resolution camera records and nearly 964 thousand multispectral images with 100-meter resolution.

Additionally, data from the GRAIL, Lunar Prospector, and SELENE missions, conducted by the Japanese space agency JAXA, were incorporated. In total, the training involves more than 30 layers of data collected by nine instruments from four missions.

Although NASA has dedicated decades to creating a vast scientific record of the Moon, the data collection process is only one step; it is necessary to make this data more accessible and usable for scientists.

Potential applications of the model

The model already processes a large volume of information during training and has the capacity for subsequent adaptation to specific tasks, even with smaller volumes of classified data. Among the foreseen uses are the permanently shadowed polar regions. These areas are of great interest because they maintain temperatures low enough to preserve ice for billions of years.

Discovering locations where this ice may be stable, whether on the surface or underground, contributes both to understanding lunar history and to assessing exploration resources. Beyond scientific research, the presence of ice signals the existence of water and oxygen, substances considered vital for establishing a future lunar base and for producing propellant for trips to Mars.

Craters also play a significant role, as each is the result of an impact, and their characteristics and quantity help scientists determine the age of the lunar surface and reconstruct the chronology of the Solar System.

In tests conducted, the model demonstrated the ability to identify lunar surface features with up to 23% greater accuracy compared to conventional methods, as reported by NASA and IBM. This improvement was particularly notable in estimating the stability of polar ice.

The NASA-IBM Lunar Foundation Model has been made public on the Hugging Face platform, and its complete code is available on GitHub. Both NASA and IBM have also made machine learning-ready datasets and integrated test collections available via TerraTorch.

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