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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.
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.

Image snippets showing Earth, the Carina nebula, Jupiter, the surface of the Sun, and a cell.
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:

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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Jul 09, 2025

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      The NISAR satellite is the first free-flying space mission to feature two radar instruments — an L-band system and an S-band system. Each system is sensitive to features of different sizes and specializes in detecting certain attributes. The L-band radar excels at measuring soil moisture, forest biomass, and motion of land and ice surfaces, while S-band radar excels at monitoring agriculture, grassland ecosystems, and infrastructure movement.
      Together, the radar instruments will enhance all of the satellite’s observations, making NISAR more capable than previous synthetic aperture radar missions. Unlike optical sensors, NISAR will be able to “see” through clouds, making it possible to monitor the surface during storms, as well as in darkness and light.
      NASA’s Jet Propulsion Laboratory in Southern California provided the L-band radar, and ISRO’s Space Applications Centre in Ahmedabad developed the S-band radar. The NISAR mission marks the first time the two agencies have co-developed hardware for an Earth-observing mission.
      “We’re proud of the international team behind this remarkable satellite. The mission’s measurements will be global but its applications deeply local, as people everywhere will use its data to plan for a resilient future,” said Dave Gallagher, director, NASA JPL, which manages the U.S. portion of the mission for NASA. “At its core is synthetic aperture radar, a technology pioneered at NASA JPL that enables us to study Earth night and day, through all kinds of weather.”
      Including L-band and S-band radars on one satellite is an evolution in SAR airborne and space-based missions that, for NASA, started in 1978 with the launch of Seasat. In 2012, ISRO began launching SAR missions starting with Radar Imaging Satellite (RISAT-1), followed by RISAT-1A in 2022, to support a wide range of applications in India.
      In the coming weeks, the spacecraft will begin a roughly 90-day commissioning phase during which it will deploy its 39-foot (12-meter) radar antenna reflector. This reflector will direct and receive microwave signals from the two radars. By interpreting the differences between the two, researchers can discern characteristics about the surface below. As NISAR passes over the same locations twice every 12 days, scientists can evaluate how those characteristics have changed over time to reveal new insights about Earth’s dynamic surfaces.
      The NISAR mission is an equal collaboration between NASA and ISRO. Managed for the agency by Caltech, NASA JPL leads the U.S. component of the project and is providing the mission’s L-band SAR. NASA also is providing the radar reflector antenna, the deployable boom, a high-rate communication subsystem for science data, GPS receivers, a solid-state recorder, and payload data subsystem.
      Space Applications Centre Ahmedabad, ISRO’s lead center for payload development, is providing the mission’s S-band SAR instrument and is responsible for its calibration, data processing, and development of science algorithms to address the scientific goals of the mission. U R Rao Satellite Centre in Bengaluru, which leads the ISRO components of the mission, is providing the spacecraft bus. The launch vehicle is from ISRO’s Vikram Sarabhai Space Centre, launch services are through ISRO’s Satish Dhawan Space Centre, and satellite operations are by ISRO Telemetry Tracking and Command Network. National Remote Sensing Centre in Hyderabad is responsible for S-band data reception, operational products generation, and dissemination.
      To learn more about NISAR, visit:
      https://nisar.jpl.nasa.gov
      -end-
      Karen Fox / Elizabeth Vlock
      Headquarters, Washington
      202-358-1600
      karen.c.fox@nasa.gov / elizabeth.a.vlock@nasa.gov
      Andrew Wang / Jane J. Lee
      Jet Propulsion Laboratory, Pasadena, Calif.
      626-379-6874 / 818-354-0307
      andrew.wang@jpl.nasa.gov / jane.j.lee@jpl.nasa.gov
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      Last Updated Jul 30, 2025 EditorJessica TaveauLocationNASA Headquarters Related Terms
      NISAR (NASA-ISRO Synthetic Aperture Radar) Earth Science Earth Science Division Jet Propulsion Laboratory NASA Headquarters Science Mission Directorate View the full article
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