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Artemis, Architecture, and Lunar Science: SMD and ESDMD Associate Administrators visits Tokyo
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By NASA
Explore This Section Science Goddard Space Flight Center Linking Satellite Data and… Overview Learning Resources Science Activation Teams SME Map Opportunities More Science Activation Stories Citizen Science 4 min read
Linking Satellite Data and Community Knowledge to Advance Alaskan Snow Science
Seasonal snow plays a significant role in global water and energy cycles, and billions of people worldwide rely on snowmelt for water resources needs, including water supply, hydropower, agriculture, and more. Monitoring snow water equivalent (SWE) is critical for supporting these applications and for mitigating damages caused by snowmelt flooding, avalanches, and other snow-related disasters. However, our ability to measure SWE remains a challenge, particularly in northern latitudes where in situ SWE observations are sparse and satellite observations are impacted by the boreal forest and environmental conditions. Despite limited in situ SWE measurements, local residents in Arctic and sub-Arctic regions provide a vast and valuable body of place-based knowledge and observations that are essential for understanding snowpack behavior in northern regions.
As part of a joint NASA SnowEx, NASA’s Minority University Research and Education Project (MUREP) for American Indian and Alaska Native STEM (Science, Technology, Engineering, & Mathematics) Engagement (MAIANSE), and Global Learning & Observations to Benefit the Environment (GLOBE) Program partnership, a team of scientists including NASA intern Julia White (NASA Goddard Space Flight Center, University of Alaska Fairbanks), Carrie Vuyovich (NASA Goddard Space Flight Center), Alicia Joseph (NASA Goddard Space Flight Center), and Christi Buffington (University of Alaska Fairbanks, GLOBE Implementation Office) is studying snow water equivalent (SWE) across Interior Alaska. This project combines satellite-based interferometric synthetic aperture radar (InSAR) data, primarily from the Sentinel-1 satellite, with ground-based observations from the Snow Telemetry (SNOTEL) network and GLOBE (Global Learning Observations to Benefit the Environment). Together, these data sources help the team investigate how SWE varies across the landscape and how it affects local ecosystems and communities. The team is also preparing for future integration of data from NASA’s upcoming NISAR (NASA ISRO Synthetic Aperture Radar) mission, which is expected to enhance SWE retrieval capabilities.
After a collaborative visit to the classroom of Tammie Kovalenko in November 2024, Delta Junction junior and senior high school students in vocational agriculture (Vo Ag) classes, including members of Future Farmers of America (FFA), began collecting GLOBE data on a snowdrift located just outside their classroom. As the project progressed, students developed their own research questions. One student, Fianna Rooney, took the project even further — presenting research posters at both the GLOBE International Virtual Science Symposium (IVSS) and both the FFA Regional and National Conventions. Her work highlights the growing role of Alaskan youth in science, and how student-led inquiry can enrich both education and research outcomes. (This trip was funded by the NASA Science Activation Program’s Arctic and Earth SIGNs – STEM Integrating GLOBE & NASA – project at the University of Alaska Fairbanks.)
In February 2025, the team collaborated with Delta Junction Junior High and High School students, along with the Delta Junction Trails Association, to conduct a GLOBE Intensive Observation Period (IOP), “Delta Junction Snowdrifts,” to collect Landcover photos, snow depth, and snow water equivalent data. Thanks to aligned interests and research goals at the Alaska Satellite Facility (ASF), the project was further expanded into Spring 2025. Collaborators from ASF and the Alaska Center for Unmanned Aircraft Systems Integration (ACUASI) collected high resolution airborne data over the snowdrift at the Delta Junction Junior and Senior High School. This complementary dataset helped strengthen connections between satellite observations and ground-based student measurements.
This effort, led by a NASA intern, scientists, students, and Alaskan community members, highlights the power of collaboration in advancing science and education. Next steps will include collaboration with Native Alaskan communities near Delta Junction, including the Healy Lake Tribe, whose vast, generational knowledge will be of great value to deepening our understanding of Alaskan snow dynamics.
Learn more about how NASA’s Science Activation program connects NASA science experts, real content, and experiences with community leaders to do science in ways that activate minds and promote deeper understanding of our world and beyond: https://science.nasa.gov/learn/about-science-activation/
Julia White and Delta Junction student following GLOBE protocols for snow depth. Tori Brannan Share
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Last Updated Jul 14, 2025 Editor NASA Science Editorial Team Location Goddard Space Flight Center Related Terms
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By NASA
An artist’s concept design of NASA’s Lunar Terrain Vehicle.Credit: NASA NASA has selected three instruments to travel to the Moon, with two planned for integration onto an LTV (Lunar Terrain Vehicle) and one for a future orbital opportunity.
The LTV is part of NASA’s efforts to explore the lunar surface as part of the Artemis campaign and is the first crew-driven vehicle to operate on the Moon in more than 50 years. Designed to hold up to two astronauts, as well as operate remotely without a crew, this surface vehicle will enable NASA to achieve more of its science and exploration goals over a wide swath of lunar terrain.
“The Artemis Lunar Terrain Vehicle will transport humanity farther than ever before across the lunar frontier on an epic journey of scientific exploration and discovery,” said Nicky Fox, associate administrator, Science Mission Directorate at NASA Headquarters in Washington. “By combining the best of human and robotic exploration, the science instruments selected for the LTV will make discoveries that inform us about Earth’s nearest neighbor as well as benefit the health and safety of our astronauts and spacecraft on the Moon.”
The Artemis Infrared Reflectance and Emission Spectrometer (AIRES) will identify, quantify, and map lunar minerals and volatiles, which are materials that evaporate easily, like water, ammonia, or carbon dioxide. The instrument will capture spectral data overlaid on visible light images of both specific features of interest and broad panoramas to discover the distribution of minerals and volatiles across the Moon’s south polar region. The AIRES instrument team is led by Phil Christensen from Arizona State University in Tempe.
The Lunar Microwave Active-Passive Spectrometer (L-MAPS) will help define what is below the Moon’s surface and search for possible locations of ice. Containing both a spectrometer and a ground-penetrating radar, the instrument suite will measure temperature, density, and subsurface structures to more than 131 feet (40 meters) below the surface. The L-MAPS instrument team is led by Matthew Siegler from the University of Hawaii at Manoa.
When combined, the data from the two instruments will paint a picture of the components of the lunar surface and subsurface to support human exploration and will uncover clues to the history of rocky worlds in our solar system. The instruments also will help scientists characterize the Moon’s resources, including what the Moon is made of, potential locations of ice, and how the Moon changes over time.
In addition to the instruments selected for integration onto the LTV, NASA also selected the Ultra-Compact Imaging Spectrometer for the Moon (UCIS-Moon) for a future orbital flight opportunity. The instrument will provide regional context to the discoveries made from the LTV. From above, UCIS-Moon will map the Moon’s geology and volatiles and measure how human activity affects those volatiles. The spectrometer also will help identify scientifically valuable areas for astronauts to collect lunar samples, while its wide-view images provide the overall context for where these samples will be collected. The UCIS-Moon instrument will provide the Moon’s highest spatial resolution data of surface lunar water, mineral makeup, and thermophysical properties. The UCIS-Moon instrument team is led by Abigail Fraeman from NASA’s Jet Propulsion Laboratory in Southern California.
“Together, these three scientific instruments will make significant progress in answering key questions about what minerals and volatiles are present on and under the surface of the Moon,” said Joel Kearns, deputy associate administrator for Exploration, Science Mission Directorate at NASA Headquarters. “With these instruments riding on the LTV and in orbit, we will be able to characterize the surface not only where astronauts explore, but also across the south polar region of the Moon, offering exciting opportunities for scientific discovery and exploration for years to come.”
Leading up to these instrument selections, NASA has worked with all three lunar terrain vehicle vendors – Intuitive Machines, Lunar Outpost, and Venturi Astrolab – to complete their preliminary design reviews. This review demonstrates that the initial design of each commercial lunar rover meets all of NASA’s system requirements and shows that the correct design options have been selected, interfaces have been identified, and verification methods have been described. NASA will evaluate the task order proposals received from each LTV vendor and make a selection decision on the demonstration mission by the end of 2025.
Through Artemis, NASA will address high priority science questions, focusing on those that are best accomplished by on-site human explorers on and around the Moon by using robotic surface and orbiting systems. The Artemis missions will send astronauts to explore the Moon for scientific discovery, economic benefits, and build the foundation for the first crewed missions to Mars.
To learn more about Artemis, visit:
https://www.nasa.gov/artemis
-end-
Karen Fox / Molly Wasser
Headquarters, Washington
202-358-1600
karen.c.fox@nasa.gov / molly.l.wasser@nasa.gov
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Last Updated Jul 10, 2025 LocationNASA Headquarters Related Terms
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By NASA
6 min read
Smarter Searching: NASA AI Makes Science Data Easier to Find
Image snapshot taken from NASA Worldview of NASA’s Global Precipitation Measurement (GPM) mission on March 15, 2025 showing heavy rain across the southeastern U.S. with an overlay of the GCMD Keyword Recommender for Earth Science, Atmosphere, Precipitation, Droplet Size. NASA Worldview Imagine shopping for a new pair of running shoes online. If each seller described them differently—one calling them “sneakers,” another “trainers,” and someone else “footwear for exercise”—you’d quickly feel lost in a sea of mismatched terminology. Fortunately, most online stores use standardized categories and filters, so you can click through a simple path: Women’s > Shoes > Running Shoes—and quickly find what you need.
Now, scale that problem to scientific research. Instead of sneakers, think “aerosol optical depth” or “sea surface temperature.” Instead of a handful of retailers, it is thousands of researchers, instruments, and data providers. Without a common language for describing data, finding relevant Earth science datasets would be like trying to locate a needle in a haystack, blindfolded.
That’s why NASA created the Global Change Master Directory (GCMD), a standardized vocabulary that helps scientists tag their datasets in a consistent and searchable way. But as science evolves, so does the challenge of keeping metadata organized and discoverable.
To meet that challenge, NASA’s Office of Data Science and Informatics (ODSI) at the agency’s Marshall Space Flight Center (MSFC) in Huntsville, Alabama, developed the GCMD Keyword Recommender (GKR): a smart tool designed to help data providers and curators assign the right keywords, automatically.
Smarter Tagging, Accelerated Discovery
The upgraded GKR model isn’t just a technical improvement; it’s a leap forward in how we organize and access scientific knowledge. By automatically recommending precise, standardized keywords, the model reduces the burden on human curators while ensuring metadata quality remains high. This makes it easier for researchers, students, and the public to find exactly the datasets they need.
It also sets the stage for broader applications. The techniques used in GKR, like applying focal loss to rare-label classification problems and adapting pre-trained transformers to specialized domains, can benefit fields well beyond Earth science.
Metadata Matchmaker
The newly upgraded GKR model tackles a massive challenge in information science known as extreme multi-label classification. That’s a mouthful, but the concept is straightforward: Instead of predicting just one label, the model must choose many, sometimes dozens, from a set of thousands. Each dataset may need to be tagged with multiple, nuanced descriptors pulled from a controlled vocabulary.
Think of it like trying to identify all the animals in a photograph. If there’s just a dog, it’s easy. But if there’s a dog, a bird, a raccoon hiding behind a bush, and a unicorn that only shows up in 0.1% of your training photos, the task becomes far more difficult. That’s what GKR is up against: tagging complex datasets with precision, even when examples of some keywords are scarce.
And the problem is only growing. The new version of GKR now considers more than 3,200 keywords, up from about 430 in its earlier iteration. That’s a sevenfold increase in vocabulary complexity, and a major leap in what the model needs to learn and predict.
To handle this scale, the GKR team didn’t just add more data; they built a more capable model from the ground up. At the heart of the upgrade is INDUS, an advanced language model trained on a staggering 66 billion words drawn from scientific literature across disciplines—Earth science, biological sciences, astronomy, and more.
NASA ODSI’s GCMD Keyword Recommender AI model automatically tags scientific datasets with the help of INDUS, a large language model trained on NASA scientific publications across the disciplines of astrophysics, biological and physical sciences, Earth science, heliophysics, and planetary science. NASA “We’re at the frontier of cutting-edge artificial intelligence and machine learning for science,” said Sajil Awale, a member of the NASA ODSI AI team at MSFC. “This problem domain is interesting, and challenging, because it’s an extreme classification problem where the model needs to differentiate even very similar keywords/tags based on small variations of context. It’s exciting to see how we have leveraged INDUS to build this GKR model because it is designed and trained for scientific domains. There are opportunities to improve INDUS for future uses.”
This means that the new GKR isn’t just guessing based on word similarities; it understands the context in which keywords appear. It’s the difference between a model knowing that “precipitation” might relate to weather versus recognizing when it means a climate variable in satellite data.
And while the older model was trained on only 2,000 metadata records, the new version had access to a much richer dataset of more than 43,000 records from NASA’s Common Metadata Repository. That increased exposure helps the model make more accurate predictions.
The Common Metadata Repository is the backend behind the following data search and discovery services:
Earthdata Search International Data Network Learning to Love Rare Words
One of the biggest hurdles in a task like this is class imbalance. Some keywords appear frequently; others might show up just a handful of times. Traditional machine learning approaches, like cross-entropy loss, which was used initially to train the model, tend to favor the easy, common labels, and neglect the rare ones.
To solve this, NASA’s team turned to focal loss, a strategy that reduces the model’s attention to obvious examples and shifts focus toward the harder, underrepresented cases.
The result? A model that performs better across the board, especially on the keywords that matter most to specialists searching for niche datasets.
From Metadata to Mission
Ultimately, science depends not only on collecting data, but on making that data usable and discoverable. The updated GKR tool is a quiet but critical part of that mission. By bringing powerful AI to the task of metadata tagging, it helps ensure that the flood of Earth observation data pouring in from satellites and instruments around the globe doesn’t get lost in translation.
In a world awash with data, tools like GKR help researchers find the signal in the noise and turn information into insight.
Beyond powering GKR, the INDUS large language model is also enabling innovation across other NASA SMD projects. For example, INDUS supports the Science Discovery Engine by helping automate metadata curation and improving the relevancy ranking of search results.The diverse applications reflect INDUS’s growing role as a foundational AI capability for SMD.
The INDUS large language model is funded by the Office of the Chief Science Data Officer within NASA’s Science Mission Directorate at NASA Headquarters in Washington. The Office of the Chief Science Data Officer advances scientific discovery through innovative applications and partnerships in data science, advanced analytics, and artificial intelligence.
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Last Updated Jul 09, 2025 Related Terms
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By NASA
2 Min Read I Am Artemis: Joe Pavicic
Listen to this audio excerpt from Joe Pavicic, Artemis operations project engineer
0:00 / 0:00
Your browser does not support the audio element. Joe Pavicic will never forget when he told the Artemis launch director teams were NO-GO for launch.
Before Artemis I lifted off from NASA’s Kennedy Space Center in Florida in November 2022, the launch team made multiple launch attempts the months prior.
“During a previous Artemis I launch attempt, there was an issue with engine three,” said Pavicic, operations project engineer who worked on the engines console during Artemis I. “One sensor was showing that it wasn’t seeing liquid hydrogen through it. It was showing that it was at ambient temperature.”
And I had to tell the launch director, ‘We can't get there today with the current criteria that we have. My recommendation is a NO-GO.’
Joe pavicic
Operations Project Engineer
Prior to engine ignition, launch team controllers must first chill the engines before the cryogenic liquid propellant fuels and lifts the SLS (Space Launch System) rocket and Orion spacecraft into the heavens and onward to the Moon. Chilling the engines ensures the hardware doesn’t get damaged when exposed to the super-cooled liquid hydrogen at -423 degrees Fahrenheit.
NASA/Kim Shiflett “We tried everything we could think of,” Pavicic recalls. “Any procedure we could try, we tried it, and we just never saw those rates that we should have.”
Thus, Pavicic, who is originally from West Palm Beach and studied aerospace engineering at Embry Riddle Aeronautical University in Daytona Beach, Florida, went back to the drawing board with the rest of his team, working days and nights rewriting procedures and learning new lessons about the engines and sensors until they were finally able to get to a successful launch.
“I just remember after I said, 'NO-GO,' I felt like all these people came to watch the launch, all my family, and I'm like, ‘I'm the guy,' but I told myself, ‘I'm not going to be the one to say this for the next launch attempt. I'm going to do what I can to get us there.’
joe pavicic
Operations Project Engineer
NASA successfully launched and flew the Artemis I mission and now, Pavicic is working as one of the operations project engineers, continuing to help the launch team develop new launch commit criteria and procedures within the launch countdown ahead of Artemis II, the first crewed Artemis mission, which will send four astronauts around the Moon and back in 10 days next year.
About the Author
Antonia Jaramillo
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Last Updated Jul 09, 2025 Related Terms
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By NASA
2 min read
Polar Tourists Give Positive Reviews to NASA Citizen Science in Antarctica
Citizen science projects result in an overwhelmingly positive impact on the polar tourism experience. That’s according to a new paper analyzing participant experiences in the first two years of FjordPhyto, a NASA Citizen Science project..
The FjordPhyto citizen science project invites travelers onboard expedition cruise vessels to gather data and samples during the polar summer season, helping researchers understand changes in microalgae communities in response to melting glaciers. Travelers in Antarctica from November to March help collect phytoplankton and ocean data from polar regions facilitated by trained expedition guides.
The new research found that ninety-seven percent of respondents reported that participating in citizen science enriched their travel experience. The paper provides a first understanding of the impact of citizen science projects on the tourism experience.
“I was worried that I would feel guilty being a tourist in a place as remote and untouched as Antarctica,” said one anonymous FjordPhyto participant. “But being able to learn and be a part of citizen science, whilst constantly being reminded of our environmental responsibilities, made me feel less like just a visitor and more a part of keeping the science culture that Antarctica is known for alive and well.”
For more information and to sign up, visit the FjordPhyto website.
Travelers in Antarctica participate in collecting phytoplankton and ocean data from polar regions facilitated by trained expedition guides. Credit: Mathew Farrell courtesy of Robert Gilmore Share
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