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By NASA
NASA/Johns Hopkins University Applied Physics Laboratory/Southwest Research Institute/Alex Parker This image, taken by NASA’s New Horizons spacecraft on July 14, 2015, is the most accurate natural color image of Pluto. This natural-color image results from refined calibration of data gathered by New Horizons’ color Multispectral Visible Imaging Camera (MVIC). The processing creates images that would approximate the colors that the human eye would perceive, bringing them closer to “true color” than the images released near the encounter. This single color MVIC scan includes no data from other New Horizons imagers or instruments added. The striking features on Pluto are clearly visible, including the bright expanse of Pluto’s icy, nitrogen-and-methane rich “heart,” Sputnik Planitia.
Image credit: NASA/Johns Hopkins University Applied Physics Laboratory/Southwest Research Institute/Alex Parker
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By Amazing Space
Massive Solar Prominence "The Beast" Threatens Eruption? Space Weather Update July 14 2025 NASA SDO
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By NASA
The crew of NASA’s SpaceX Crew-11 mission sit inside a Dragon training spacecraft at SpaceX in Hawthorne, California. Pictured from left: Roscosmos cosmonaut Oleg Platonov, NASA astronauts Mike Fincke and Zena Cardman, and JAXA (Japan Aerospace Exploration Agency) astronaut Kimiya Yui (Credit: SpaceX). NASA’s SpaceX Crew-11 mission is set to launch a four-person crew to the International Space Station later this summer. Some of the crew have volunteered to participate in a series of experiments to address health challenges astronauts may face on deep space missions during NASA’s Artemis campaign and future human expeditions to Mars.
The research during Crew-11 includes simulated lunar landings, tactics to safeguard vision, and other human physiology studies led by NASA’s Human Research Program.
Select crew members will participate in a series of simulated Moon landings, before, during, and after their flight. Using a handheld controller and multiple screens, the astronauts will fly through simulated scenarios created to resemble the lunar South Pole region that Artemis crews plan to visit. This experiment allows researchers to evaluate how different gravitational forces may disorient astronauts and affect their ability to pilot a spacecraft, like a lunar lander.
“Even though many landing tasks are automated, astronauts must still know how to monitor the controls and know when to take over to ensure a safe landing,” said Scott Wood, a neuroscientist at NASA’s Johnson Space Center in Houston coordinating the scientific investigation. “Our study assesses exactly how changes in gravity affect spatial awareness and piloting skills that are important for navigating these scenarios.”
A ground control group completing the same tasks over a similar timeframe will help scientists better understand gravitational effects on human performance. The experiment’s results could inform the pilot training needed for future Artemis crews.
“Experiencing weightlessness for months and then feeling greater levels of gravity on a planet like Mars, for example, may increase the risk of disorientation,” said Wood. “Our goal is to help astronauts adapt to any gravitational change, whether it’s to the Moon, a new planet, or landing back on Earth.”
Other studies during the mission will explore possible ways to treat or prevent a group of eye and brain changes that can occur during long-duration space travel, called spaceflight associated neuro-ocular syndrome (SANS).
Some researchers suspect the redistribution of bodily fluids in constant weightlessness may increase pressure in the head and contribute to SANS. One study will investigate fluid pressure on the brain while another will examine how the body processes B vitamins and whether supplements can affect how astronauts respond to bodily fluid shifts. Participating crew members will test whether a daily B vitamin supplement can eliminate or ease symptoms of SANS. Specific crew members also will wear thigh cuffs to keep bodily fluids from traveling headward.
Crew members also will complete another set of experiments, called CIPHER (Complement of Integrated Protocols for Human Exploration Research), which measures how multiple systems within the human body change in space. The study includes vision assessments, MRI scans, and other medical exams to provide a complete overview of the whole body’s response to long-duration spaceflight.
Several other studies involving human health and performance are also a part of Crew-11’s science portfolio. Crew members will contribute to a core set of measurements called Spaceflight Standard Measures, which collects physical data and biological samples from astronauts and stores them for other comparative studies. Participants will supply biological samples, such as blood and urine, for a study characterizing how spaceflight alters astronauts’ genetic makeup. In addition, volunteers will test different exercise regimens to help scientists explore what activities remain essential for long-duration journeys.
After landing, participating crew members will complete surveys to track any discomfort, such as scrapes or bruises, acquired from re-entry. The data will help clarify whether mission length increases injury risks and could help NASA design landing systems on future spacecraft as NASA prepares to travel to the Moon, Mars, and beyond.
NASA’s Human Research Program pursues methods and technologies to support safe, productive human space travel. Through science conducted in laboratories, ground-based analogs, and aboard the International Space Station, the program investigates how spaceflight affects human bodies and behaviors. Such research drives NASA’s quest to innovate ways that keep astronauts healthy and mission-ready.
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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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