Members Can Post Anonymously On This Site
-
Similar Topics
-
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.
Share
Details
Last Updated Jul 09, 2025 Related Terms
Science & Research Artificial Intelligence (AI) Explore More
2 min read Polar Tourists Give Positive Reviews to NASA Citizen Science in Antarctica
Article
6 hours ago
2 min read Hubble Observations Give “Missing” Globular Cluster Time to Shine
Article
6 days ago
5 min read How NASA’s SPHEREx Mission Will Share Its All-Sky Map With the World
Article
7 days ago
Keep Exploring Discover Related Topics
Missions
Humans in Space
Climate Change
Solar System
View the full article
-
-
By NASA
2 Min Read NASA Announces Winners of 2025 Human Lander Challenge
NASA’s Human Lander Challenge marked its second year on June 26, awarding $18,000 in prize money to three university teams for their solutions for long-duration cryogenic, or super chilled, liquid storage and transfer systems for spaceflight.
Building on the crewed Artemis II flight test, NASA’s Artemis III mission will send astronauts to explore the lunar South Pole region with a human landing system and advanced spacesuits, preparing humanity to ultimately go to Mars. In-space propulsion systems that use cryogenic liquids as propellants must stay extremely cold to remain in a liquid state and are critical to mission success. The Artemis mission architecture will need these systems to function for several weeks or even months.
Students and advisors with the 12 finalist teams for the 2025 Human Lander Challenge competed in Huntsville, Alabama, near the agency’s Marshall Space Flight Center between June 24-26. NASA/Charles Beason NASA announced Embry-Riddle Aeronautical University, Prescott as the overall winner and recipient of the $10,000 top prize award. Old Dominion University won second place and a $5,000 award, followed by Massachusetts Institute of Technology in third place and a $3,000 award.
Before the winners were announced, 12 finalist teams selected in April gave their presentations to a panel of NASA and industry judges as part of the final competition in Huntsville. As part of the 2025 Human Lander Challenge, university teams developed systems-level solutions that could be used within the next 3-5 years for Artemis.
NASA selected Embry-Riddle Aeronautical University, Prescott as the overall winner of NASA’s 2025 Human Lander Challenge Forum June 26. Lisa Watson-Morgan, manager of NASA’s Human Landing System Program, presented the awards at the ceremony. NASA/Charles Beason “Today’s Golden Age of Innovation and Exploration students are tomorrow’s mission designers, systems engineers, and explorers,” said Juan Valenzuela, main propulsion systems and cryogenic fluid management subsystems lead for NASA’s Human Landing System Program at NASA’s Marshall Space Flight Center in Huntsville, Alabama. “The Human Lander Challenge concepts at this year’s forum demonstrate the ingenuity, passion, and determination NASA and industry need to help solve long-duration cryogenic storage challenges to advance human exploration to deep space.”
The challenge is sponsored by the agency’s Human Landing System Program within the Exploration Systems Development Mission Directorate and managed by the National Institute of Aerospace.
Through the Artemis campaign, NASA will send astronauts to explore the Moon for scientific discovery, economic benefits, and to build the foundation for the first crewed missions to Mars – for the benefit of all.
For more information about Artemis missions, visit:
https://www.nasa.gov/artemis
News Media Contact
Corinne Beckinger
Marshall Space Flight Center, Huntsville, Ala.
256.544.0034
corinne.m.beckinger@nasa.gov
Share
Details
Last Updated Jun 27, 2025 EditorLee MohonContactCorinne M. Beckingercorinne.m.beckinger@nasa.govLocationMarshall Space Flight Center Related Terms
Human Lander Challenge Artemis General Human Landing System Program Humans in Space Marshall Space Flight Center Explore More
3 min read NASA Engineers Simulate Lunar Lighting for Artemis III Moon Landing
Article 1 week ago 4 min read NASA Marshall Fires Up Hybrid Rocket Motor to Prep for Moon Landings
Article 2 months ago 3 min read NASA Selects Finalist Teams for Student Human Lander Challenge
Article 3 months ago Keep Exploring Discover More Topics From NASA
Human Landing System
Space Launch System (SLS)
Marshall Space Flight Center manages the Space Launch System (SLS), an integrated super heavy lift launch platform enabling a new…
Humans In Space
Orion Capsule
NASA’s Orion spacecraft is built to take humans farther than they’ve ever gone before. Orion will serve as the exploration…
View the full article
-
By European Space Agency
Asteroid 2024 YR4 made headlines earlier this year when its probability of impacting Earth in 2032 rose as high as 3%. While an Earth impact has now been ruled out, the asteroid’s story continues.
The final glimpse of the asteroid as it faded out of view of humankind’s most powerful telescopes left it with a 4% chance of colliding with the Moon on 22 December 2032.
The likelihood of a lunar impact will now remain stable until the asteroid returns to view in mid-2028. In this FAQ, find out why we are left with this lingering uncertainty and how ESA's planned NEOMIR space telescope will help us avoid similar situations in the future.
View the full article
-
By Space Force
A nationwide reading program was created to encourage kindergarten through eighth-grade students to read 12 books during the summer break.
View the full article
-
-
Check out these Videos
Recommended Posts
Join the conversation
You can post now and register later. If you have an account, sign in now to post with your account.
Note: Your post will require moderator approval before it will be visible.