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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
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Last Updated Jul 15, 2025 Related Terms
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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. 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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This artist’s concept animation shows the orbital dynamics of KOI-134 system which, in 2025, a paper revealed to have two planets: KOI-134 b and KOI-134 c. NASA/JPL-Caltech/K. Miller (Caltech/IPAC) The Planets
KOI-134 b and KOI-134 c
This artist’s concept shows the KOI-134 system which, in 2025, a paper revealed to have two planets: KOI-134 b and KOI-134 c. NASA/JPL-Caltech/K. Miller (Caltech/IPAC) The Discovery
A new investigation into old Kepler data has revealed that a planetary system once thought to house zero planets actually has two planets which orbit their star in a unique style, like an old-fashioned merry-go-round.
Key Facts
The KOI-134 system contains two planets which orbit their star in a peculiar fashion on two different orbital planes, with one planet exhibiting significant variation in transit times. This is the first-discovered system of its kind.
Details
Over a decade ago, scientists used NASA’s Kepler Space Telescope to observe the KOI-134 system and thought that it might have a planet orbiting, but they deemed this planet candidate to be a false positive, because its transits (or passes in front of its star) were not lining up as expected. These transits were so abnormal that the planet was actually weeded out through an automated system as a false positive before it could be analyzed further.
However, NASA’s commitment to openly sharing scientific data means that researchers can constantly revisit old observations to make new discoveries. In this new study, researchers re-analyzed this Kepler data on KOI-134 and confirmed that not only is the “false positive” actually a real planet, but the system has two planets and some really interesting orbital dynamics!
First, the “false positive” planet, named KOI-134 b, was confirmed to be a warm Jupiter (or a warm planet of a similar size to Jupiter). Through this analysis, researchers uncovered that the reason this planet eluded confirmation previously is because it experiences what are called transit timing variations (TTVs), or small differences in a planet’s transit across its star that can make its transit “early” or “late” because the planet is being pushed or pulled by the gravity from another planet which was also revealed in this study. Researchers estimate that KOI-134 b transits across its star as much as 20 hours “late” or “early,” which is a significant variation. In fact, it was so significant that it’s the reason why the planet wasn’t confirmed in initial observations.
As these TTVs are caused by the gravitational interaction with another planet, this discovery also revealed a planetary sibling: KOI-134 c. Through studying this system in simulations that include these TTVs, the team found that KOI-134 c is a planet slightly smaller than Saturn and closer to its star than KOI-134 b.
This artist’s concept shows the KOI-134 system which, in 2025, a paper revealed to have two planets: KOI-134 b and KOI-134 c. NASA/JPL-Caltech/K. Miller (Caltech/IPAC) KOI-134 c previously eluded observation because it orbits on a tilted orbital plane, a different plane from KOI-134 b, and this tilted orbit prevents the planet from transiting its star. The two orbital planes of these planets are about 15 degrees different from one another, also known as a mutual inclination of 15 degrees, which is significant. Due to the gravitational push and pull between these two planets, their orbital planes also tilt back and forth.
Another interesting feature of this planetary system is something called resonance. These two planets have a 2 to 1 resonance, meaning within the same time that one planet completes one orbit, the other completes two orbits. In this case, KOI-134 b has an orbital period (the time it takes a planet to complete one orbit) of about 67 days, which is twice the orbital period of KOI-134 c, which orbits every 33-34 days.
Between the separate orbital planes tilting back and forth, the TTVs, and the resonance, the two planets orbit their star in a pattern that resembles two wooden ponies bobbing up and down as they circle around on an old-fashioned merry go round.
Fun Facts
While this system started as a false positive with Kepler, this re-analysis of the data reveals a vibrant system with two planets. In fact, this is the first-ever discovered compact, multiplanetary system that isn’t flat, has such a significant TTV, and experiences orbital planes tilting back and forth.
Also, most planetary systems do not have high mutual inclinations between close planet pairs. In addition to being a rarity, mutual inclinations like this are also not often measured because of challenges within the observation process. So, having measurements like this of a significant mutual inclination in a system, as well as measurements of resonance and TTVs, provides a clear picture of dynamics within a planetary system which we are not always able to see.
The Discoverers
A team of scientists led by Emma Nabbie of the University of Southern Queensland published a paper on June 27 on their discovery, “A high mutual inclination system around KOI-134 revealed by transit timing variations,” in the journal “Nature Astronomy.” The observations described in this paper and used in simulations in this paper were made by NASA’s Kepler Space Telescope and the paper included collaboration and contributions from institutions including the University of Geneva, University of La Laguna, Purple Mountain Observatory, the Harvard-Smithsonian Center for Astrophysics, the Georgia Institute of Technology, the University of Southern Queensland, and NASA’s retired Kepler Space Telescope.
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Explore This Section Perseverance Home Mission Overview Rover Components Mars Rock Samples Where is Perseverance? Ingenuity Mars Helicopter Mission Updates Science Overview Objectives Instruments Highlights Exploration Goals News and Features Multimedia Perseverance Raw Images Images Videos Audio 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
An Update From the 2025 Mars 2020 Science Team Meeting
A behind-the-scenes look at the annual Mars 2020 Science Team Meeting
Members of the Mars 2020 Science Team examine post-impact sediments within the Gardnos impact structure, northwest of Oslo, Norway, as part of the June 2025 Science Team Meeting. NASA/Katie Stack Morgan Written by Katie Stack Morgan, Mars 2020 Acting Project Scientist
The Mars 2020 Science Team gathered for a week in June to discuss recent science results, synthesize earlier mission observations, and discuss future plans for continued exploration of Jezero’s crater rim. It was also an opportunity to celebrate what makes this mission so special: one of the most capable and sophisticated science missions ever sent to Mars, an experienced and expert Science Team, and the rover’s many science accomplishments this past year.
We kicked off the meeting, which was hosted by our colleagues on the RIMFAX team at the University of Oslo, with a focus on our most recent discoveries on the Jezero crater rim. A highlight was the team’s in-depth discussion of spherules observed at Witch Hazel Hill, features which likely provide us the best chance of determining the origin of the crater rim rock sequence.
On the second day, we heard status updates from each of the science instrument teams. We then transitioned to a session devoted to “traverse-scale” syntheses. After 4.5 years of Perseverance on Mars and more than 37 kilometers of driving (more than 23 miles), we’re now able to analyze and integrate science datasets across the entire surface mission, looking for trends through space and time within the Jezero rock record. Our team also held a poster session, which was a great opportunity for in-person and informal scientific discussion.
The team’s modern atmospheric and environmental investigations were front and center on Day 3. We then rewound the clock, hearing new and updated analyses of data acquired during Perseverance’s earlier campaigns in Jezero’s Margin unit, crater floor, and western fan. The last day of the meeting was focused entirely on future plans for the Perseverance rover, including a discussion of our exploration and sampling strategy during the Crater Rim Campaign. We also looked further afield, considering where the rover might explore over the next few years.
Following the meeting, the Science Team took a one-day field trip to visit Gardnos crater, a heavily eroded impact crater with excellent examples of impact melt breccia and post-impact sediment fill. The team’s visit to Gardnos offered a unique opportunity to see and study impact-generated rock units like those expected on the Jezero crater rim and to discuss the challenges we have recognizing similar units with the rover on Mars. Recapping our Perseverance team meetings has been one of my favorite yearly traditions (see summaries from our 2022, 2023, and 2024 meetings) and I look forward to reporting back a year from now. As the Perseverance team tackles challenges in the year to come, we can seek inspiration from one of Norway’s greatest polar explorers, Fridtjof Nansen, who said while delivering his Nobel lecture, “The difficult is that which can be done at once; the impossible is that which takes a little longer.”
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Last Updated Jul 01, 2025 Related Terms
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NASA-Assisted Scientists Get Bird’s-Eye View of Population Status
Through the eBird citizen scientist program, millions of birders have recorded their observations of different species and submitted checklists to the Cornell Lab of Ornithology. Through a partnership with NASA, the lab has now used this data to model and map bird population trends for nearly 500 North American species.
Led by Alison Johnston of the University of St. Andrews in Scotland, the researchers reported that 75% of bird species in the study are declining at wide-range scales. And yet this study has some good news for birds. The results, published in Science in May, offer insights and projections that could shape the future conservation of the places where birds make their homes.
“This project demonstrates the power of merging in situ data with NASA remote sensing to model biological phenomena that were previously impossible to document,” said Keith Gaddis, NASA’s Biological Diversity and Ecological Forecasting program manager at the agency’s headquarters in Washington, who was not involved in the study. “This data provides not just insight into the Earth system but also provides actionable guidance to land managers to mitigate biodiversity loss.”
Rock wren in Joshua Tree National Park. National Park Service / Jane Gamble A team from Cornell, the University of St. Andrews, and the American Bird Conservancy used land imaging data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instruments to distinguish among such specific bird habitats as open forests, dense shrublands, herbaceous croplands, and forest/cropland mosaics. They also drew on NASA weather information and water data that matched the dates and times when birders made their reports.
When combined with a 14-year set of eBird checklists — 36 million sets of species observations and counts, keyed directly to habitats — the satellite data gave researchers almost a strong foundation to produce a clear picture of the health of bird populations. But there was one missing piece.
Wrestling with Wren Data
While some eBird checklists come from expert birders who’ve hiked deep into wildlife preserves, others are sent in by novices watching bird feeders and doing the dishes. This creates what Cornell statistician Daniel Fink described as “an unstructured, very noisy data set,” complete with gaps in the landscape that birders did not reach and, ultimately, some missing birds.
To account for gaps where birds weren’t counted, the researchers trained machine learning models to fill in the maps based on the remote sensing data. “For every single species — say the rock wren — we’ve created a simulation that mimics the species and a variety of ways that it could respond to changes in the environment,” Johnston said. “Thousands of simulations underlie the results we showed.”
CornellLab eBird The researchers achieved unprecedented resolution, zeroing in on areas 12 miles by 12 miles (27 km by 27 km), the same area as Portland, Oregon. This new population counting method can also be applied to eBird data from other locations, Fink said. “Now we’re using modeling to track bird populations — not seasonally through the year, but acrossthe years — a major milestone,” he added.
“We’ve been able to take citizen science data and, through machine learning methodology, put it on the same footing as traditionally structured surveys, in terms of the type of signal we can find,” said Cornell science product manager Tom Auer. “It will increase the credibility and confidence of people who use this information for precise conservation all over the globe.”
The Up Side
Since 1970, North America has lost one-quarter of its breeding birds, following a global trend of declines across species. The causes range from increased pollution and land development to changing climate and decreased food resources. Efforts to reverse this loss depend on identifying the areas where birds live at highest risk, assessing their populations, and pinpointing locations where conservation could help most.
For 83% of the reported species in the new study, the decline was greatest in spots where populations had previously been most abundant — indicating problems with the habitat.
“Even in species where populations are declining a lot, there are still places of hope, where the populations are going up,” Johnston said. The team found population increases in the maps of 97% of the reported species. “That demonstrates that there’s opportunity for those species.”
“Birds face so many challenges,” said Cornell conservationist Amanda Rodewald. “This research will help us make strategic decisions about making changes that are precise, effective, and less costly. This is transformative. Now we can really drill in and know where specifically we’re going to be able to have the most positive impact in trying to stem bird declines.”
By Karen Romano Young
NASA Headquarters, Washington
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Last Updated Jun 25, 2025 Related Terms
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