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Vega’s PRETTY CubeSat: unlocking satnav for Earth data
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By European Space Agency
Image: The varied landscape of England’s Lake District is featured in this image captured by the Copernicus Sentinel-2 mission. View the full article
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By NASA
The TRACERS (Tandem Reconnection and Cusp Electrodynamics Reconnaissance Satellites) mission will help scientists understand an explosive process called magnetic reconnection and its effects in Earth’s atmosphere. Credit: University of Iowa/Andy Kale NASA will hold a media teleconference at 11 a.m. EDT on Thursday, July 17, to share information about the agency’s upcoming Tandem Reconnection and Cusp Electrodynamics Reconnaissance Satellites, or TRACERS, mission, which is targeted to launch no earlier than late July.
The TRACERS mission is a pair of twin satellites that will study how Earth’s magnetic shield — the magnetosphere — protects our planet from the supersonic stream of material from the Sun called solar wind. As they fly pole to pole in a Sun-synchronous orbit, the two TRACERS spacecraft will measure how magnetic explosions send these solar wind particles zooming down into Earth’s atmosphere — and how these explosions shape the space weather that impacts our satellites, technology, and astronauts.
Also launching on this flight will be three additional NASA-funded payloads. The Athena EPIC (Economical Payload Integration Cost) SmallSat, led by NASA’s Langley Research Center in Hampton, Virginia, is designed to demonstrate an innovative, configurable way to put remote-sensing instruments into orbit faster and more affordably. The Polylingual Experimental Terminal technology demonstration, managed by the agency’s SCaN (Space Communications and Navigation) program, will showcase new technology that empowers missions to roam between communications networks in space, like cell phones roam between providers on Earth. Finally, the Relativistic Electron Atmospheric Loss (REAL) CubeSat, led by Dartmouth College in Hanover, New Hampshire, will use space as a laboratory to understand how high-energy particles within the bands of radiation that surround Earth are naturally scattered into the atmosphere, aiding the development of methods for removing these damaging particles to better protect satellites and the critical ground systems they support.
Audio of the teleconference will stream live on the agency’s website at:
nasa.gov/live
Participants include:
Joe Westlake, division director, Heliophysics, NASA Headquarters Kory Priestley, principal investigator, Athena EPIC, NASA Langley Greg Heckler, deputy program manager for capability development, SCaN, NASA Headquarters David Miles, principal investigator for TRACERS, University of Iowa Robyn Millan, REAL principal investigator, Dartmouth College To participate in the media teleconference, media must RSVP no later than 10 a.m. on July 17 to Sarah Frazier at: sarah.frazier@nasa.gov. NASA’s media accreditation policy is available online.
The TRACERS mission will launch on a SpaceX Falcon 9 rocket from Space Launch Complex 4 East at Vandenberg Space Force Base in California.
This mission is led by David Miles at the University of Iowa with support from the Southwest Research Institute in San Antonio. NASA’s Heliophysics Explorers Program Office at the agency’s Goddard Space Flight Center in Greenbelt, Maryland, manages the mission for the agency’s HeliophysicsDivision at NASA Headquarters in Washington. The University of Iowa, Southwest Research Institute, University of California, Los Angeles, and University of California, Berkeley, all lead instruments on TRACERS that will study changes in the Earth’s magnetic field and electric field. NASA’s Launch Services Program, based at the agency’s Kennedy Space Center in Florida, manages the Venture-class Acquisition of Dedicated and Rideshare contract.
To learn more about TRACERS, please visit:
nasa.gov/tracers
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Abbey Interrante / Karen Fox
Headquarters, Washington
301-201-0124 / 202-358-1600
abbey.a.interrante@nasa.gov / karen.c.fox@nasa.gov
Sarah Frazier
Goddard Space Flight Center, Greenbelt, Maryland
202-853-7191
sarah.frazier@nasa.gov
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Last Updated Jul 10, 2025 LocationNASA Headquarters Related Terms
Earth Heliophysics Science Mission Directorate Solar Wind TRACERS View the full article
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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
Science & Research Artificial Intelligence (AI) Explore More
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