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20 Years After Landing: How NASA’s Twin Rovers Changed Mars Science
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By NASA
Curiosity Navigation Curiosity Home Mission Overview Where is Curiosity? Mission Updates Science Overview Instruments Highlights Exploration Goals News and Features Multimedia Curiosity Raw Images Images Videos Audio Mosaics More Resources Mars Missions Mars Sample Return Mars Perseverance Rover Mars Curiosity Rover MAVEN Mars Reconnaissance Orbiter Mars Odyssey More Mars Missions Mars Home 3 min read
Curiosity Blog, Sols 4595-4596: Just Another Beautiful Day on Mars
NASA’s Mars rover Curiosity acquired this image using its Left Navigation Camera on July 9, 2025 — Sol 4594, or Martian day 4,594 of the Mars Science Laboratory mission — at 11:03:48 UTC. NASA/JPL-Caltech Written by Ashley Stroupe, Mission Operations Engineer at NASA’s Jet Propulsion Laboratory
Earth planning date: Wednesday, July 9, 2025
In today’s plan, we have a little bit of everything. With it being winter still, we are taking advantage of the ability to let the rover sleep in, doing most of the activities in the afternoon when it is warmer and we need less heating. As the Systems Engineer (Engineering Uplink Lead) today, I sequenced the needed heating and some other engineering housekeeping activities.
We start off with an extensive remote science block with Mastcam imaging of a nearby trough to look for potential sand activity. There is color imaging of a displaced block, “Ouro,” near a circular depression — could this be a small crater? Mastcam also takes a look at a ridge “Volcán Peña Blanca” to look at the sedimentary structures, which may provide insights into its formation. ChemCam LIBS and Mastcam team up to look at the “Los Andes” target, which is the dark face of a nearby piece of exposed bedrock. ChemCam RMI and Mastcam check out a distant small outcrop to examine the geometry of the layers. We also throw in environmental observations, a Mastcam solar Tau and a Navcam line-of-site looking at dust in the atmosphere. After a nap, Curiosity will be doing some contact science activities on “Cataratas del Jardín” and “Rio Ivirizu” bedrock targets. Looking at two nearby targets for variability can help us understand the local geology. Cataratas del Jardín gets a brushing to clear away the dust before both targets are examined by MAHLI and APXS. Fortunately for the Arm Rover Planner, both of these targets are fairly flat and easy to reach. Before going to sleep for the night, Curiosity will stow the arm to be ready for driving on the next sol.On the second sol, there is more remote science. ChemCam LIBS and Mastcam will examine “Torotoro,” another piece of layered bedrock. ChemCam RMI will take a mosaic of “Paniri,” which is an interesting incision in the rock that is filled with another material. There are also environmental observations, a Navcam dust devil survey and a suprahorizon movie. After another nap, Curiosity is getting on the road. We’re heading southwest (direction shown in the image) about 50 meters (about 164 feet), but we need to sneak between sandy pits and skirt around some terrain that we can’t see behind. The terrain here provides pretty nice driving, though, without a lot of big boulders, steep slopes, or pointy rocks that can poke holes in our wheels. After the standard post-drive imaging for our next plan, there are some Navcam observations to look for clouds and our normal look under the rover with MARDI before Curiosity goes to sleep for the night.
For more Curiosity blog posts, visit MSL Mission Updates
Learn more about Curiosity’s science instruments
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Last Updated Jul 15, 2025 Related Terms
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Explore This Section Science Goddard Space Flight Center Linking Satellite Data and… Overview Learning Resources Science Activation Teams SME Map Opportunities More Science Activation Stories Citizen Science 4 min read
Linking Satellite Data and Community Knowledge to Advance Alaskan Snow Science
Seasonal snow plays a significant role in global water and energy cycles, and billions of people worldwide rely on snowmelt for water resources needs, including water supply, hydropower, agriculture, and more. Monitoring snow water equivalent (SWE) is critical for supporting these applications and for mitigating damages caused by snowmelt flooding, avalanches, and other snow-related disasters. However, our ability to measure SWE remains a challenge, particularly in northern latitudes where in situ SWE observations are sparse and satellite observations are impacted by the boreal forest and environmental conditions. Despite limited in situ SWE measurements, local residents in Arctic and sub-Arctic regions provide a vast and valuable body of place-based knowledge and observations that are essential for understanding snowpack behavior in northern regions.
As part of a joint NASA SnowEx, NASA’s Minority University Research and Education Project (MUREP) for American Indian and Alaska Native STEM (Science, Technology, Engineering, & Mathematics) Engagement (MAIANSE), and Global Learning & Observations to Benefit the Environment (GLOBE) Program partnership, a team of scientists including NASA intern Julia White (NASA Goddard Space Flight Center, University of Alaska Fairbanks), Carrie Vuyovich (NASA Goddard Space Flight Center), Alicia Joseph (NASA Goddard Space Flight Center), and Christi Buffington (University of Alaska Fairbanks, GLOBE Implementation Office) is studying snow water equivalent (SWE) across Interior Alaska. This project combines satellite-based interferometric synthetic aperture radar (InSAR) data, primarily from the Sentinel-1 satellite, with ground-based observations from the Snow Telemetry (SNOTEL) network and GLOBE (Global Learning Observations to Benefit the Environment). Together, these data sources help the team investigate how SWE varies across the landscape and how it affects local ecosystems and communities. The team is also preparing for future integration of data from NASA’s upcoming NISAR (NASA ISRO Synthetic Aperture Radar) mission, which is expected to enhance SWE retrieval capabilities.
After a collaborative visit to the classroom of Tammie Kovalenko in November 2024, Delta Junction junior and senior high school students in vocational agriculture (Vo Ag) classes, including members of Future Farmers of America (FFA), began collecting GLOBE data on a snowdrift located just outside their classroom. As the project progressed, students developed their own research questions. One student, Fianna Rooney, took the project even further — presenting research posters at both the GLOBE International Virtual Science Symposium (IVSS) and both the FFA Regional and National Conventions. Her work highlights the growing role of Alaskan youth in science, and how student-led inquiry can enrich both education and research outcomes. (This trip was funded by the NASA Science Activation Program’s Arctic and Earth SIGNs – STEM Integrating GLOBE & NASA – project at the University of Alaska Fairbanks.)
In February 2025, the team collaborated with Delta Junction Junior High and High School students, along with the Delta Junction Trails Association, to conduct a GLOBE Intensive Observation Period (IOP), “Delta Junction Snowdrifts,” to collect Landcover photos, snow depth, and snow water equivalent data. Thanks to aligned interests and research goals at the Alaska Satellite Facility (ASF), the project was further expanded into Spring 2025. Collaborators from ASF and the Alaska Center for Unmanned Aircraft Systems Integration (ACUASI) collected high resolution airborne data over the snowdrift at the Delta Junction Junior and Senior High School. This complementary dataset helped strengthen connections between satellite observations and ground-based student measurements.
This effort, led by a NASA intern, scientists, students, and Alaskan community members, highlights the power of collaboration in advancing science and education. Next steps will include collaboration with Native Alaskan communities near Delta Junction, including the Healy Lake Tribe, whose vast, generational knowledge will be of great value to deepening our understanding of Alaskan snow dynamics.
Learn more about how NASA’s Science Activation program connects NASA science experts, real content, and experiences with community leaders to do science in ways that activate minds and promote deeper understanding of our world and beyond: https://science.nasa.gov/learn/about-science-activation/
Julia White and Delta Junction student following GLOBE protocols for snow depth. Tori Brannan Share
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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 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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As Hubble marks three and a half decades of scientific breakthroughs and technical resilience, the “Hubble at 35 Years” symposium offers a platform to reflect on the mission’s historical, operational, and scientific legacy. Hubble’s trajectory—from early challenges to becoming a symbol of American scientific ingenuity—presents valuable lessons in innovation, collaboration, and crisis response. Bringing together scientists, engineers, and historians at NASA Headquarters ensures that this legacy informs current and future mission planning, including operations for the James Webb Space Telescope, Roman Space Telescope, and other next-generation observatories. The symposium not only honors Hubble’s transformative contributions but also reinforces NASA’s commitment to learning from the past to shape a more effective and ambitious future for space science.
Hubble at 35 Years
Lessons Learned in Scientific Discovery and NASA Flagship Mission Operations
October 16–17, 2025
James Webb Auditorium, NASA HQ, Washington, D.C.
The giant Hubble Space Telescope (HST) can be seen as it is suspended in space by Discovery’s Remote Manipulator System (RMS) following the deployment of part of its solar panels and antennae on April 25, 1990.NASA The story of the Hubble Space Telescope confirms its place as the most transformative and significant astronomical observatory in history. Once called “the eighth wonder of the world” by a former NASA administrator, Hubble’s development since its genesis in the early 1970s and its launch, repair, and ultimate impact since 1990 provide ample opportunity to apply insights from its legacy. Scientists and engineers associated with groundbreaking discoveries have always operated within contexts shaped by forces including the government, private industry, the military, and the public at large. The purpose of this symposium is to explore the insights from Hubble’s past and draw connections that can inform the development of mission work today and for the future.
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