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By NASA
NASA/Kim Shiflett NASA astronauts Christina Koch, Artemis II mission specialist, and Victor Glover, Artemis II pilot, walk on the crew access arm of the mobile launcher in the Vehicle Assembly Building at NASA’s Kennedy Space Center in Florida on Tuesday, Aug. 12, 2025.
On Aug. 11 and 12, teams with the agency’s Exploration Ground Systems Program along with NASA astronauts Reid Wiseman, Victor Glover, and Christina Koch, and CSA (Canadian Space Agency) astronaut Jeremy Hansen, practiced launch day operations if launch occurs at night. They simulated putting their spacesuits on and driving to the launch pad as well as emergency procedures they would use in the unlikely event of an emergency during the launch countdown requiring them to evacuate the launch pad.
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.
Image credit: NASA/Kim Shiflett
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By NASA
1 min read
NASA’s Black Marble: Stories from the Night Sky
Earth (ESD) Earth Explore Explore Earth Home Air Quality Climate Change Freshwater Life on Earth Severe Storms Snow and Ice The Global Ocean Science at Work Earth Science at Work Technology and Innovation Powering Business Multimedia Image Collections Videos Data For Researchers About Us Viewed from space, Earth at night tells endless stories. Using satellite data, we can track population growth, natural disaster damage, cultural celebrations, and even space weather. Studying these glowing patterns helps us understand human activity, respond to disasters, and witness a changing world.
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Last Updated Aug 04, 2025 Related Terms
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By NASA
As the Sun approaches the most active part of its eleven-year magnetic cycle this summer, NASA volunteers have been watching it closely. Now they’ve spotted a new trend in solar behavior that will have you reaching for your suntan lotion. It’s all about something called a “Type II” solar radio burst:
“Type II solar radio bursts are not commonly detected in the frequency range between 15 to 30 megahertz,” said Prof. Chuck Higgins, Co-founder of Radio JOVE. “Recently, we’re seeing many of them in that range.”
Let’s unpack that. Our Sun often sprays powerful blasts of radio waves into space. Heliophysicists classify these radio bursts into five different types depending on how the frequency of the radio waves drifts over time. “Type II” solar radio bursts seem to come from solar flares and enormous squirts of hot plasma called coronal mass ejections.
Now, Thomas Freeman, an undergraduate student at Middle Tennessee State University, and other volunteers working on NASA’s Radio JOVE project have observed something interesting about these Type II bursts: they are now showing up at lower frequencies—somewhere in between FM and AM radio.
What does it mean? It means our star is full of surprises! These Radio JOVE observations of the Sun’s radio emissions during solar maximum can be used to extend our knowledge of solar emissions to lower frequencies and, therefore, to distances farther from the Sun.
Radio JOVE is a NASA partner citizen science project in which participants assemble and operate radio astronomy telescopes to gather and contribute data to support scientific studies. Radio JOVE collaborated with SunRISE Ground Radio Lab, organized teams of high school students to observe the Sun, and recently published a paper on these Type II solar radio bursts. Learn more and get involved!
A Type II solar radio burst on April 23rd, 2024, seen as the gently sloping yellow band drifting from 17:49 to 18:02 UTC in the 15-30 MHz radio frequency-time spectrogram. Credit: Tom Ashcraft, Lamy, NM Share
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Last Updated Jul 23, 2025 Related Terms
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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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