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machine learningNASA SMD AI
The v1 dataset includes AIA/HMI observations 2010-2018 and v2 includes AIA/HMI observations 2010-2020 in all 10 wavebands (94A, 131A, 171A, 193A, 211A, 304A, 335A, 1600A, 1700A, 4500A), with 512x512 resolution and 6 minutes cadence; HMI vector magnetic field observations in Bx, By, and Bz components, with 512x512 resolution and 12 minutes cadence; The EVE observations in 39 wavelengths from 2010-05-01 to 2014-05-26, with 10 seconds cadence.
astronomymachine learningNASA SMD AI
The SOHO/LASCO data set (prepared for the challenge hosted in Topcoder) provided here comes from the instrument’s C2 telescope and comprises approximately 36,000 images spread across 2,950 comet observations. The human eye is a very sensitive tool and it is the only tool currently used to reliably detect new comets in SOHO data - particularly comets that are very faint and embedded in the instrument background noise. Bright comets can be easily detected in the LASCO data by relatively simple automated algorithms, but the majority of comets observed by the instrument are extremely faint, noise-...
astronomymachine learningNASA SMD AIsegmentation
Pan-STARSS imaging data and associated labels for galaxy segmentation into galactic centers, galactic bars, spiral arms and foreground stars derived from citizen scientist labels from the Galaxy Zoo: 3D project.
fluorescence imagingGeneLabgeneticgenetic mapsmicroscopyNASA SMD AI
Fluorescence microscopy images of individual nuclei from mouse fibroblast cells, irradiated with Fe particles or X-rays with fluorescent foci indicating 53BP1 positivity, a marker of DNA damage. These are maximum intensity projections of 9-layer microscopy Z-stacks.
gene expressionGeneLabgeneticgenetic mapsNASA SMD AIspace biology
RNA sequencing data from spaceflown and control mouse liver samples, sourced from NASA GeneLab and augmented with generative adversarial network.
anomaly detectionclassificationdisaster responseearth observationenvironmentalNASA SMD AIsatellite imagerysocioeconomicurban
Detection of nighttime combustion (fire and gas flaring) from daily top of atmosphere data from NASA's Black Marble VNP46A1 product using VIIRS Day/Night Band and VIIRS thermal bands.
astronomymachine learningNASA SMD AIsatellite imagery
Hubble Space Telescope imaging data and associated identification labels for galaxy morphology derived from citizen scientist labels from the Galaxy Zoo: Hubble project.
analyticsarchivesdeep learningmachine learningNASA SMD AIplanetary
NASA missions like the Curiosity and Perseverance rovers carry a rich array of instruments suited to collect data and build evidence towards answering if Mars ever had livable environmental conditions. These rovers can collect rock and soil samples and can take measurements that can be used to determine their chemical makeup.
Because communication between rovers and Earth is severely constrained, with limited transfer rates and short daily communication windows, scientists have a limited time to analyze the data and make difficult inferences about the chemistry in order to prioritize the next operations and send those instructions back to the rover.
This project aimed at building a model to automatically analyze gas chromatography mass spectrometry (GCMS) data collected for Mars exploration in order to help the scientists in their analysis of understanding the past habitability of Mars.
More information are available at https://mars.nasa.gov/msl/spacecraft/instruments/sam/ and the data from Mars are available and described at https://pds-geosciences.wustl.edu/missions/msl/sam.htm.
We request that you cite th...
analyticsarchivesdeep learningmachine learningNASA SMD AIplanetary
NASA missions like the Curiosity and Perseverance rovers carry a rich array of instruments suited to collect data and build evidence towards answering if Mars ever had livable environmental conditions. These rovers can collect rock and soil samples and can take measurements that can be used to determine their chemical makeup.
Because communication between rovers and Earth is severely constrained, with limited transfer rates and short daily communication windows, scientists have a limited time to analyze the data and make difficult inferences about the chemistry in order to prioritize the next operations and send those instructions back to the rover.
This project aimed at building a model to automatically analyze evolved gas analysis mass spectrometry (EGA-MS) data collected for Mars exploration in order to help the scientists in their analysis of understanding the past habitability of Mars.
More information are available at https://mars.nasa.gov/msl/spacecraft/instruments/sam/ and the data from Mars are available and described at https://pds-geosciences.wustl.edu/missions/msl/sam.htm.
We request that you ci...