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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 European Space Agency
Astronomers have discovered a huge filament of hot gas bridging four galaxy clusters. At 10 times as massive as our galaxy, the thread could contain some of the Universe’s ‘missing’ matter, addressing a decades-long mystery.
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By NASA
This NASA/ESA Hubble Space Telescope image features the remote galaxy HerS 020941.1+001557, which appears as a red arc that partially encircles a foreground elliptical galaxy.ESA/Hubble & NASA, H. Nayyeri, L. Marchetti, J. Lowenthal This NASA/ESA Hubble Space Telescope image offers us the chance to see a distant galaxy now some 19.5 billion light-years from Earth (but appearing as it did around 11 billion years ago, when the galaxy was 5.5 billion light-years away and began its trek to us through expanding space). Known as HerS 020941.1+001557, this remote galaxy appears as a red arc partially encircling a foreground elliptical galaxy located some 2.7 billion light-years away. Called SDSS J020941.27+001558.4, the elliptical galaxy appears as a bright dot at the center of the image with a broad haze of stars outward from its core. A third galaxy, called SDSS J020941.23+001600.7, seems to be intersecting part of the curving, red crescent of light created by the distant galaxy.
The alignment of this trio of galaxies creates a type of gravitational lens called an Einstein ring. Gravitational lenses occur when light from a very distant object bends (or is ‘lensed’) around a massive (or ‘lensing’) object located between us and the distant lensed galaxy. When the lensed object and the lensing object align, they create an Einstein ring. Einstein rings can appear as a full or partial circle of light around the foreground lensing object, depending on how precise the alignment is. The effects of this phenomenon are much too subtle to see on a local level but can become clearly observable when dealing with curvatures of light on enormous, astronomical scales.
Gravitational lenses not only bend and distort light from distant objects but magnify it as well. Here we see light from a distant galaxy following the curve of spacetime created by the elliptical galaxy’s mass. As the distant galaxy’s light passes through the gravitational lens, it is magnified and bent into a partial ring around the foreground galaxy, creating a distinctive Einstein ring shape.
The partial Einstein ring in this image is not only beautiful, but noteworthy. A citizen scientist identified this Einstein ring as part of the SPACE WARPS project that asked citizen scientists to search for gravitational lenses in images.
Text Credit: ESA/Hubble
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By NASA
Explore Hubble Hubble Home Overview About Hubble The History of Hubble Hubble Timeline Why Have a Telescope in Space? Hubble by the Numbers At the Museum FAQs Impact & Benefits Hubble’s Impact & Benefits Science Impacts Cultural Impact Technology Benefits Impact on Human Spaceflight Astro Community Impacts Science Hubble Science Science Themes Science Highlights Science Behind Discoveries Hubble’s Partners in Science Universe Uncovered Explore the Night Sky Observatory Hubble Observatory Hubble Design Mission Operations Missions to Hubble Hubble vs Webb Team Hubble Team Career Aspirations Hubble Astronauts Multimedia Multimedia Images Videos Sonifications Podcasts e-Books Online Activities 3D Hubble Models Lithographs Fact Sheets Posters Hubble on the NASA App Glossary News Hubble News Social Media Media Resources More 35th Anniversary Online Activities 2 min read
Hubble Images Galaxies Near and Far
This NASA/ESA Hubble Space Telescope image features the remote galaxy HerS 020941.1+001557, which appears as a red arc that partially encircles a foreground elliptical galaxy. ESA/Hubble & NASA, H. Nayyeri, L. Marchetti, J. Lowenthal This NASA/ESA Hubble Space Telescope image offers us the chance to see a distant galaxy now some 19.5 billion light-years from Earth (but appearing as it did around 11 billion years ago, when the galaxy was 5.5 billion light-years away and began its trek to us through expanding space). Known as HerS 020941.1+001557, this remote galaxy appears as a red arc partially encircling a foreground elliptical galaxy located some 2.7 billion light-years away. Called SDSS J020941.27+001558.4, the elliptical galaxy appears as a bright dot at the center of the image with a broad haze of stars outward from its core. A third galaxy, called SDSS J020941.23+001600.7, seems to be intersecting part of the curving, red crescent of light created by the distant galaxy.
The alignment of this trio of galaxies creates a type of gravitational lens called an Einstein ring. Gravitational lenses occur when light from a very distant object bends (or is ‘lensed’) around a massive (or ‘lensing’) object located between us and the distant lensed galaxy. When the lensed object and the lensing object align, they create an Einstein ring. Einstein rings can appear as a full or partial circle of light around the foreground lensing object, depending on how precise the alignment is. The effects of this phenomenon are much too subtle to see on a local level but can become clearly observable when dealing with curvatures of light on enormous, astronomical scales.
Gravitational lenses not only bend and distort light from distant objects but magnify it as well. Here we see light from a distant galaxy following the curve of spacetime created by the elliptical galaxy’s mass. As the distant galaxy’s light passes through the gravitational lens, it is magnified and bent into a partial ring around the foreground galaxy, creating a distinctive Einstein ring shape.
The partial Einstein ring in this image is not only beautiful, but noteworthy. A citizen scientist identified this Einstein ring as part of the SPACE WARPS project that asked citizen scientists to search for gravitational lenses in images.
Text Credit: ESA/Hubble
Facebook logo @NASAHubble @NASAHubble Instagram logo @NASAHubble Media Contact:
Claire Andreoli (claire.andreoli@nasa.gov)
NASA’s Goddard Space Flight Center, Greenbelt, MD
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Last Updated May 20, 2025 Editor Andrea Gianopoulos Location NASA Goddard Space Flight Center Related Terms
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By NASA
5 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Chaitén Volcano in southern Chile erupted on May 2, 2008 for the first time inn 9,000 years. NASA satellites that monitor changes in vegetation near volcanoes could aid in earlier eruption warnings.Jeff Schmaltz, MODIS Rapid Response Team, NASA Goddard Space Flight Center Scientists know that changing tree leaves can indicate when a nearby volcano is becoming more active and might erupt. In a new collaboration between NASA and the Smithsonian Institution, scientists now believe they can detect these changes from space.
As volcanic magma ascends through the Earth’s crust, it releases carbon dioxide and other gases which rise to the surface. Trees that take up the carbon dioxide become greener and more lush. These changes are visible in images from NASA satellites such as Landsat 8, along with airborne instruments flown as part of the Airborne Validation Unified Experiment: Land to Ocean (AVUELO).
Ten percent of the world’s population lives in areas susceptible to volcanic hazards. People who live or work within a few miles of an eruption face dangers that include ejected rock, dust, and surges of hot, toxic gases. Further away, people and property are susceptible to mudslides, ashfalls, and tsunamis that can follow volcanic blasts. There’s no way to prevent volcanic eruptions, which makes the early signs of volcanic activity crucial for public safety. According to the U.S. Geological Survey, NASA’s Landsat mission partner, the United States is one of the world’s most volcanically active countries.
Carbon dioxide released by rising magma bubbles up and heats a pool of water in Costa Rica near the Rincón de LaVieja volcano. Increases in volcanic gases could be a sign that a volcano is becoming more active.Josh Fisher/Chapman University When magma rises underground before an eruption, it releases gases, including carbon dioxide and sulfur dioxide. The sulfur compounds are readily detectable from orbit. But the volcanic carbon dioxide emissions that precede sulfur dioxide emissions – and provide one of the earliest indications that a volcano is no longer dormant – are difficult to distinguish from space.
The remote detection of carbon dioxide greening of vegetation potentially gives scientists another tool — along with seismic waves and changes in ground height—to get a clear idea of what’s going on underneath the volcano. “Volcano early warning systems exist,” said volcanologist Florian Schwandner, chief of the Earth Science Division at NASA’s Ames Research Center in California’s Silicon Valley, who had teamed up with Fisher and Bogue a decade ago. “The aim here is to make them better and make them earlier.”
“Volcanoes emit a lot of carbon dioxide,” said volcanologist Robert Bogue of McGill University in Montreal, but there’s so much existing carbon dioxide in the atmosphere that it’s often hard to measure the volcanic carbon dioxide specifically. While major eruptions can expel enough carbon dioxide to be measurable from space with sensors like NASA’s Orbiting Carbon Observatory 2, detecting these much fainter advanced warning signals has remained elusive. “A volcano emitting the modest amounts of carbon dioxide that might presage an eruption isn’t going to show up in satellite imagery,” he added.
Gregory Goldsmith from Chapman University launches a slingshot into the forest canopy to install a carbon dioxide sensor in the canopy of a Costa Rican rainforest near the Rincón de LaVieja volcano.Josh Fisher/Chapman University Because of this, scientists must trek to volcanoes to measure carbon dioxide directly. However, many of the roughly 1,350 potentially active volcanoes worldwide are in remote locations or challenging mountainous terrain. That makes monitoring carbon dioxide at these sites labor-intensive, expensive, and sometimes dangerous.
Volcanologists like Bogue have joined forces with botanists and climate scientists to look at trees to monitor volcanic activity. “The whole idea is to find something that we could measure instead of carbon dioxide directly,” Bogue said, “to give us a proxy to detect changes in volcano emissions.”
“There are plenty of satellites we can use to do this kind of analysis,” said volcanologist Nicole Guinn of the University of Houston. She has compared images collected with Landsat 8, NASA’s Terra satellite, ESA’s (European Space Agency) Sentinel-2, and other Earth-observing satellites to monitor trees around the Mount Etna volcano on the coast of Sicily. Guinn’s study is the first to show a strong correlation between tree leaf color and magma-generated carbon dioxide.
Confirming accuracy on the ground that validates the satellite imagery is a challenge that climate scientist Josh Fisher of Chapman University is tackling with surveys of trees around volcanoes. During the March 2025 Airborne Validation Unified Experiment: Land to Ocean mission with NASA and the Smithsonian Institution scientists deployed a spectrometer on a research plane to analyze the colors of plant life in Panama and Costa Rica.
Alexandria Pivovaroff of Occidental College measures photosynthesis in leaves extracted from trees exposed to elevated levels of carbon dioxide near a volcano in Costa Rica.Josh Fisher/Chapman University Fisher directed a group of investigators who collected leaf samples from trees near the active Rincon de la Vieja volcano in Costa Rica while also measuring carbon dioxide levels. “Our research is a two-way interdisciplinary intersection between ecology and volcanology,” Fisher said. “We’re interested not only in tree responses to volcanic carbon dioxide as an early warning of eruption, but also in how much the trees are able to take up, as a window into the future of the Earth when all of Earth’s trees are exposed to high levels of carbon dioxide.”
Relying on trees as proxies for volcanic carbon dioxide has its limitations. Many volcanoes feature climates that don’t support enough trees for satellites to image. In some forested environments, trees that respond differently to changing carbon dioxide levels. And fires, changing weather conditions, and plant diseases can complicate the interpretation of satellite data on volcanic gases.
Chapman University visiting professor Gaku Yokoyama checks on the leaf-measuring instrumentation at a field site near the Rincón de LaVieja volcano.Josh Fisher/Chapman University Still, Schwandner has witnessed the potential benefits of volcanic carbon dioxide observations first-hand. He led a team that upgraded the monitoring network at Mayon volcano in the Philippines to include carbon dioxide and sulfur dioxide sensors. In December 2017, government researchers in the Philippines used this system to detect signs of an impending eruption and advocated for mass evacuations of the area around the volcano. Over 56,000 people were safely evacuated before a massive eruption began on January 23, 2018. As a result of the early warnings, there were no casualties.
Using satellites to monitor trees around volcanoes would give scientists earlier insights into more volcanoes and offer earlier warnings of future eruptions. “There’s not one signal from volcanoes that’s a silver bullet,” Schwandner said. “And tracking the effects of volcanic carbon dioxide on trees will not be a silver bullet. But it will be something that could change the game.”
By James Riordon
NASA’s Earth Science News Team
Media contact: Elizabeth Vlock
NASA Headquarters
About the Author
James R. Riordon
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Last Updated May 15, 2025 LocationAmes Research Center Related Terms
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