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By Space Force
Colorado Springs is playing host to the DOD Warrior Games July 18-26, and for the first time, Guardians are among the nearly 200 wounded, ill and injured athletes competing in 11 adaptive sports over nine days.
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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
3 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA employee Naomi Torres sits inside the air taxi passenger ride quality simulator at NASA’s Armstrong Flight Research Center in Edwards, California, as the simulator moves during a study on Oct. 23, 2024. Research continues to better understand how humans may interact with these new types of aircraft.NASA/Steve Freeman NASA’s Advanced Air Mobility vision involves the skies above the U.S. filled with new types of aircraft, including air taxis. But making that vision a reality involves ensuring that people will actually want to ride these aircraft – which is why NASA has been working to evaluate comfort, to see what passengers will and won’t tolerate.
NASA is conducting a series of studies to understand how air taxi motion, vibration, and other factors affect ride comfort. The agency will provide the data it gathers to industry and others to guide the design and operational practices for future air taxis.
“The results of this study can guide air taxi companies to design aircraft that take off, land, and respond to winds and gusts in a way that is comfortable for the passengers,” said Curt Hanson, senior flight controls researcher for this project based at NASA’s Armstrong Flight Research Center in Edwards, California. “Passengers who enjoy their experience in an air taxi are more likely to become repeat riders, which will help the industry grow.”
The air taxi comfort research team uses NASA Armstrong’s Ride Quality Laboratory as well as the Human Vibration Lab and Vertical Motion Simulator at NASA’s Ames Research Center in California’s Silicon Valley to study passenger response to ride quality, as well as how easily and precisely a pilot can control and maneuver aircraft.
After pilots checked out the simulator setup, the research team conducted a study in October where NASA employees volunteered to participate as passengers to experience the virtual air taxi flights and then describe their comfort level to the researchers.
Curt Hanson, senior flight controls researcher for the Revolutionary Vertical Lift Technology project based at NASA’s Armstrong Flight Research Center in Edwards, California, explains the study about to begin to NASA employee and test subject Naomi Torres on Oct. 23, 2024. Behind them is the air taxi passenger ride quality simulator in NASA Armstrong’s Ride Quality Laboratory. Studies continue to better understand passenger comfort for future air taxi rides.NASA/Steve Freeman Using this testing, the team produced an initial study that found a relationship between levels of sudden vertical motion and passenger discomfort. More data collection is needed to understand the combined effect of motion, vibration, and other factors on passenger comfort.
“In the Vertical Motion Simulator, we can investigate how technology and aircraft design choices affect the handling qualities of the aircraft, generate data as pilots maneuver the air taxi models under realistic conditions, and then use this to further investigate passenger comfort in the Ride Quality and Human Vibration Labs,” said Carlos Malpica, senior rotorcraft flight dynamics researcher for this effort based at NASA Ames.
This work is managed by the Revolutionary Vertical Lift Technology project under NASA’s Advanced Air Vehicles Program in support of NASA’s Advanced Air Mobility mission, which seeks to deliver data to guide the industry’s development of electric air taxis and drones.
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Last Updated Jun 20, 2025 EditorDede DiniusContactTeresa Whitingteresa.whiting@nasa.govLocationArmstrong Flight Research Center Related Terms
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By NASA
After a decade of searching, NASA’s MAVEN (Mars Atmosphere Volatile Evolution) mission has, for the first time, reported a direct observation of an elusive atmospheric escape process called sputtering that could help answer longstanding questions about the history of water loss on Mars.
Scientists have known for a long time, through an abundance of evidence, that water was present on Mars’ surface billions of years ago, but are still asking the crucial question, “Where did the water go and why?”
Early on in Mars’ history, the atmosphere of the Red Planet lost its magnetic field, and its atmosphere became directly exposed to the solar wind and solar storms. As the atmosphere began to erode, liquid water was no longer stable on the surface, so much of it escaped to space. But how did this once thick atmosphere get stripped away? Sputtering could explain it.
Sputtering is an atmospheric escape process in which atoms are knocked out of the atmosphere by energetic charge particles.
“It’s like doing a cannonball in a pool,” said Shannon Curry, principal investigator of MAVEN at the Laboratory for Atmospheric and Space Physics at the University of Colorado Boulder and lead author of the study. “The cannonball, in this case, is the heavy ions crashing into the atmosphere really fast and splashing neutral atoms and molecules out.”
While scientists had previously found traces of evidence that this process was happening, they had never observed the process directly. The previous evidence came from looking at lighter and heavier isotopes of argon in the upper atmosphere of Mars. Lighter isotopes sit higher in the atmosphere than their heavier counterparts, and it was found that there were far fewer lighter isotopes than heavy argon isotopes in the Martian atmosphere. These lighter isotopes can only be removed by sputtering.
“It is like we found the ashes from a campfire,” said Curry. “But we wanted to see the actual fire, in this case sputtering, directly.”
To observe sputtering, the team needed simultaneous measurements in the right place at the right time from three instruments aboard the MAVEN spacecraft: the Solar Wind Ion Analyzer, the Magnetometer, and the Neutral Gas and Ion Mass Spectrometer. Additionally, the team needed measurements across the dayside and the nightside of the planet at low altitudes, which takes years to observe.
The combination of data from these instruments allowed scientists to make a new kind of map of sputtered argon in relation to the solar wind. This map revealed the presence of argon at high altitudes in the exact locations that the energetic particles crashed into the atmosphere and splashed out argon, showing sputtering in real time. The researchers also found that this process is happening at a rate four times higher than previously predicted and that this rate increases during solar storms.
The direct observation of sputtering confirms that the process was a primary source of atmospheric loss in Mars’ early history when the Sun’s activity was much stronger.
“These results establish sputtering’s role in the loss of Mars’ atmosphere and in determining the history of water on Mars,” said Curry.
The finding, published this week in Science Advances, is critical to scientists’ understanding of the conditions that allowed liquid water to exist on the Martian surface, and the implications that it has for habitability billions of years ago.
The MAVEN mission is part of NASA’s Mars Exploration Program portfolio. MAVEN’s principal investigator is based at the Laboratory for Atmospheric and Space Physics (LASP) at the University of Colorado Boulder, which is also responsible for managing science operations and public outreach and communications. NASA’s Goddard Space Flight Center in Greenbelt, Maryland, manages the MAVEN mission. Lockheed Martin Space built the spacecraft and is responsible for mission operations. NASA’s Jet Propulsion Laboratory in Southern California provides navigation and Deep Space Network support.
More information on NASA’s MAVEN mission
By Willow Reed
Laboratory for Atmospheric and Space Physics, University of Colorado Boulder
Media Contacts:
Nancy N. Jones
NASA’s Goddard Space Flight Center, Greenbelt, Md.
Karen Fox / Molly Wasser
Headquarters, Washington
202-358-1600
karen.c.fox@nasa.gov / molly.l.wasser@nasa.gov
karen.c.fox@nasa.gov / molly.l.wasser@nasa.gov
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Last Updated May 28, 2025 Related Terms
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By European Space Agency
This new snapshot from the European Space Agency’s Mars Express deftly captures the two distinct faces of Mars: ridged and rugged versus smooth and unmarked.
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