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15 results for “Labeled Data Set”
Labelled magnetic reconnection simulation data set
<p>Numerical simulations have been performed on Marconi at CINECA (Italy) under the ISCRA initiative. The corresponding data can be found at: <a href="https://doi.org/10.5281/zenodo.3935887">https://doi.org/10.5281/zenodo.3935887</a></p>
Mars orbital image (HiRISE) labeled data set
<p>This data set contains 3820 landmarks that were extracted from 168 HiRISE images. The landmarks were detected in HiRISE browse images. For each landmark, we cropped a square bounding box the included the full extent of the landmark plus a 30-pixel margin to left, right, top, and bottom. Each cropped image was then resized to 227x227 pixels.</p> <p><strong>Contents</strong>:</p> <ul> <li>map-proj/: Directory containing individual cropped landmark images</li> <li>labels-map-proj.txt: Class labels (ids) for each landmark image</li> <li>landmark_mp.py: Python dictionary that maps class ids to semantic names</li> </ul> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI: 10.5281/zenodo.1048301</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</p> <p> </p>
Mars Target Encyclopedia - LPSC abstracts labeled data set
<p>This data set contains annotated text versions of 2-page abstracts published at the Lunar and Planetary Science Conference in 2015 and 2016.</p> <p>The original PDF abstracts are available at:</p> <ul> <li>https://www.hou.usra.edu/meetings/lpsc2015/programAbstracts/view/</li> <li>https://www.hou.usra.edu/meetings/lpsc2016/programAbstracts/view/</li> </ul> <p>The text files in this archive were extracted using the Apache Tika PDF parsing tool. The text is provided here so that the annotations can be viewed. The text content remains copyright of the original abstract authors.</p> <p>The annotations (entities and relations) are provided in the format used by the brat annotation tool. To view the annotations in a web-based graphical form, install the brat tool (http://brat.nlplab.org/). These annotations were generated using brat v1.3. The annotation files are also human-readable and can be parsed in to be used directly in code.</p> <p><strong>Contents</strong>:</p> <ul> <li>lpsc15/: 62 abstracts</li> <li>lpsc16/: 55 abstracts</li> </ul> <p>Each directory contains a .txt and .ann file for each abstract. The .ann file is in brat standoff format (http://brat.nlplab.org/standoff.html).</p> <p>Additional .conf files are provided to generate color highlighting and keyboard shortcuts. These are used by the brat tool.</p> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI:</p> <p>10.5281/zenodo.1048419</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, Raymond Francis, Thamme Gowda, You Lu, Ellen Riloff, Karanjeet Singh, and Nina Lanza. "Mars Target Encyclopedia: Rock and Soil Composition Extracted from the Literature." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</p>
Mars surface image (Curiosity rover) labeled data set
<p>This data set consists of 6691 images spanning 24 classes that were collected by the Mars Science Laboratory (MSL, Curosity) rover by three instruments (Mastcam Right eye, Mastcam Left eye, and MAHLI). These images are the "browse" version of each original data product, not full resolution. They are roughly 256x256 pixels each.</p> <p>We divided the MSL images into train, validation, and test data sets according to their sol (Martian day) of acquisition. This strategy was chosen to model how the system will be used operationally with an image archive that grows over time. The images were collected from sols 3 to 1060 (August 2012 to July 2015). The exact train/validation/test splits are given in individual files. Full-size images can be obtained from the PDS at https://pds-imaging.jpl.nasa.gov/search/ .</p> <p><strong>Contents</strong>:</p> <ul> <li>calibrated/: Directory containing calibrated MSL images</li> <li>train-calibrated-shuffled.txt: Training labels (images in shuffled order)</li> <li>val-calibrated-shuffled.txt: Validation labels</li> <li>test-calibrated-shuffled.txt: Test labels</li> <li>msl_synset_words-indexed.txt: Mapping from class IDs to class names</li> </ul> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI:</p> <p>10.5281/zenodo.1049137</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</p>
Tobii Pro Spectrum hand-labelled data set
<p>For recording this data set, the Tobii Pro Lab software and a Tobii Pro Spectrum eye tracking device with sampling frequencies up to 600 Hz were used. The provided monitor had a size of 23.8 inches and a 16:9 aspect ratio. The exact procedure for generating this data is described in (1).<br>Three recordings at 300 Hz and three recordings at 600 Hz are extracted from the data in (1). From this data, time spans of about 20 seconds are cut out and the fixations and saccades are labelled by hand. <br><br>(1) Timur Ezer, Matthias Greiner, Lisa Grabinger, Florian Hauser, and Jürgen Mottok. Eye tracking as technology in education: Data quality analysis and improvements. In ICERI2023 Proceedings, 16th annual International Conference of Education, Research and Innovation, pages 4500–4509, Valencia, Spain, 13-15 November, 2023 2023. IATED. ISBN 978-84-09-55942-8. doi: 10.21125/iceri.2023.1127. URL https://doi.org/10.21125/iceri.2023.1127 </p>
Original data sets of a DEER/PELDOR ring test of four doubly spin-labelled mutants of the protein YopO
<p>The dataset is discussed in manuscript "Benchmark test and guidelines for DEER/PELDOR experiments on nitroxide-labeled biomolecules" by Olav Schiemann, Caspar A. Heubach, Dinar Abdullin, Katrin Ackermann, Mykhailo Azarkh, Elena Bagryanskaya, Malte Drescher, Burkhard Endeward, Jack H. Freed, Laura Galazzo, Daniella Goldfarb, Tobias Hett, Laura Esteban Hofer, Luis Fábregas Ibáñez, Eric J. Hustedt, Svetlana Kucher, Ilya Kuprov, Janet E. Lovett, Andreas Meyer, Sharon Ruthstein, Sunil Saxena, Stefan Stoll, Christiane Timmel, Marilena Di Valentin, Hassane S. Mchaourab, Thomas F. Prisner, Bela E. Bode, Enrica Bordignon, Marina Bennati, Gunnar Jeschke. It was generated in a ring test by seven laboratories. The authors names of individual data are not assigned on purpose, rather data sets are referred to by laboratory identifiers A, B, C, D, E, F, G. The Supplementary Information for the above mentioned manuscript, which contains a link to this dataset, describes how the samples were prepared and how the measurements were performed.</p>
Data set of labeled scenes in a barn in front of automatic milking system
<p>This data set includes supplementary files to our article \emph{"Deep learning image recognition of cow behavior near an automatic milking robot with an open data set"} (Leonardo Santiago Benitez Pereira, Olli Koskela, Ilpo Pölönen, Ilmo Aronen and Iivari Kunttu, in review, 2020). We acquired continuous video data over two-month period of cows in front of Automatic Milking Station (AMS). Each frame of the video is labeled as a single image belonging to one of classes described below.</p> <p>This data set includes 253 files of video data having in total \num{1526473} labeled frames. The data set used in the article consisted of 280 videos, but for privacy reasons, 27 video files including persons were removed from this data set, but their Java Script Object Notation (JSON) label files are left for, e.g., temporal analyses.</p>
Prioritising GitHub Priority Labels - Data Set and Software
<p>This is the data set and software produced for the paper <em>Prioritising GitHub Priority Labels</em>, J. Caddy and C. Treude.</p> <p>The CSV file contains a manually categorised set of GitHub issue labels that are priority-related. They have been ranked and normalised into three values; "High", "Medium", and "Low" priorities. These labels have been gathered from the 5000 most-starred repositories on GitHub as of 2022-06-01.</p> <p>The Python script makes use of this data set as an example, and will retrieve the highest priority issues from all of the repositories contributed to by the author specified.</p> <p>Run the python script from the same directory as the CSV file, providing the username you wish to see the highest priority issues for as the first command line argument. Supply your GitHub Personal Access Token either at the prompt so it's not displayed, or as the second command line argument.</p>
Micro-CT tomographic data set of 38 mummy labels from the BNU in Strasbourg (2/2)
<p><strong>Summary</strong></p> <p>This submission contains a tomographic dataset of 38 mummy labels from the BNU in Strasbourg used to perceive the anatomical identification possibilities of the woods used for mummy labels and to carry out ring width measurements. The data will be made available as part of [Blondel et al., 2024].</p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the EasyTom 150/160 X-ray tomograph (RX Solutions). This tomograph is equipped with a sealed X-ray generator with a compact tube and an interchangeable-plane sensor fitted with a CsI scintillator. The CT scanner parameters for the session carried out on the mummy labels were set at 90 Kv with an intensity of 195 mA for an acquisition resolution varying between 11 and 42 µm with 2016 projections (that is about 20 images on average per projection) with a frame rate of 12,5 and a temperature of 28°C. Each image was then reconstructed by filtered retroprojection using the XAct software (RX Solutions).</p> <p><strong>Information on placing mummy labels in the tomograph</strong></p> <p>The installation of the mummy labels was the same for all the different labels, some of which varied in size. They were attached to a plastic clamping vice-type support covered in expanded foam to prevent the labels from being marked during clamping, before being placed on the tomograph's rotating platform.</p> <p><strong>Issues relating to the data collected</strong></p> <p>The data collected for this study were carried out to perceive the possibilities of anatomical identification from tomographic images in the transverse plane. The tangential and radial planes were not of sufficiently high resolution due to the dimensions of the mummy labels, see details in [Blondel et al., 2024]. The other objective was to use tomographic imagery to facilitate the acquisition of ring widths in the transverse plane of mummy labels. The mummy labels were not tomographed in their entirety. Only the central part, a few centimetres high, was tomographed to maximise resolution. The number of projections and the resolution per label are specified in table form in [Blondel et al., 2024], as they vary according to the width and thickness of the mummy labels. All raw tomography image data (i.e. without corrections) are available in .tif format. The post-processing steps are described in the methodology of [Blondel et al., 2024].</p> <p><strong>List of Contents</strong></p> <p>The content of the submission is divided into 38 data sets corresponding to the 38 mummy labels. Each set is labelled with the inventory number of the BNU mummy label and its resolution. Each set contains:<br>- all the images of the transverse plane in .tif format, the number of projections of which varies from one label to another depending on the resolution of the acquisitions, see details in [Blondel et al., 2024].<br>- The .xls file containing a summary of the scanner metadata for each of the mummy labels.<br>- The three images processed in the transverse plane for each label, including those used to measure ring width for the 7 labels for which ring width measurement was possible, as presented in [Blondel et al., 2024].<br>- Colour photographs of the front and back of each tomographed mummy label including those on which ring width measurements were taken on their surface, unless otherwise stated<a title="" href="#_ftn1" name="_ftnref1">[1]</a>. All these photographs are marked: Coll._et_photogr._BNU_Strasbourg_OpenLicence, accompanied by the inventory number.</p> <p><strong>Acknowledgments</strong></p> <p>We would also like to thank engineers Damien Favier and Antoine Egele from the Charles Sadron Institute for their work on the tomographic acquisitions carried out on the 38 mummy labels.</p> <div><br> <div> <p><a title="" href="#_ftnref1" name="_ftn1">[1]</a> The photographs of the front and back of mummy label HO255 are not available, as they are currently being studied.</p> </div> </div>
Mars orbital image (HiRISE) labeled data set version 3.2
<p> </p> <p><strong>Please note that the file hirise-map-proj-v3_2.zip below contains the latest images and labels associated with this data set. </strong></p> <p> </p> <p>This dataset contains a total of 64,947 landmark images that were detected and extracted from HiRISE browse images, spanning 232 separate source images.</p> <p>This set was formed from 10,815 original landmarks. Each original landmark was cropped to a square bounding box that included the full extent of the landmark plus a 30-pixel margin to the left, right, top, and bottom. Each landmark was then resized to 227x227 pixels. 9,022 of these images were then augmented to generate 6 additional landmarks using the following methods:</p> <p>1. 90 degrees clockwise rotation<br> 2. 180 degrees clockwise rotation<br> 3. 270 degrees clockwise rotation<br> 4. Horizontal flip<br> 5. Vertical flip<br> 6. Random brightness adjustment</p> <p>The remaining 1,793 images were not augmented. Combining these with the 7*9,022 images, gives a total of 64,947 separate images.</p> <p><br> <strong>Contents:</strong><br> - map-proj-v3_2/: Directory containing individual cropped landmark images<br> - labels-map-proj-v3_2.txt: Class labels (ids) for each landmark image. File includes two columns separated by a space: filename, class_id</p> <p>- labels-map-proj-v3_2_train_val_test.txt: Includes train/test/val labels and upsampling used for trained model. File includes three columns separated by a space: filename, class_id, set<br> - landmarks_map-proj-v3_2_classmap.csv: Dictionary that maps class ids to semantic names</p> <p><strong>Class Discussion:</strong></p> <p>We give a discussion of the various landmarks that make up our classes.<strong> </strong></p> <p>Bright dune and dark dune are two sand dune classes found on Mars. Dark dunes are completely defrosted, whereas bright dunes are not. Bright dunes are generally bright due to overlying frost and can exhibit black spots where parts of the dune are defrosting.</p> <p>The crater class consists of crater images in which the diameter of the crater is greater than or equal to 1/5 the width of the image and the circular rim is visible for at least half the crater's circumference.</p> <p>The slope streak class consists of images of dark flow-like features on slopes. These features are believed to be formed by a dry process in which overlying (bright) dust slides down a slope and reveals a darker sub-surface.</p> <p>Impact ejecta refers to material that is blasted out from the impact of a meteorite or the eruption of a volcano. We also include cases in which the impact cleared away overlying dust, exposing the underlying surface. In some cases, the associated crater may be too small to see. Impact ejecta can also include lava that spilled out from the impact (blobby ("lobate") instead of blast-like), more like an eruption (triggered by the impact). Impact ejecta can be isolated, or they can form in clusters when the impactor breaks up into multiple fragments.</p> <p>Spiders and Swiss cheese are phenomena that occur in the south polar region of Mars. Spiders have a central pit with radial troughs, and they are believed to form as a result of sublimation of carbon dioxide ice. This process can produce mineral deposits on top, which look like dark or light dust that highlights cracks in the CO2 ice. Spiders can resemble impact ejecta due to their radial troughs, but impact ejecta tends to have straight radial jets that fade as they get farther from the center. The spider class also includes fan-like features that form when a geyser erupts through the CO2 layer and the material is blown by the wind away from the cracks. Fans are typically unidirectional (following the wind direction), whereas impact ejecta often extends in multiple directions. Swiss cheese is a terrain type that consists of pits that are formed when the sun heats the ice making it sublimate (change solid to gas).</p> <p>Other is a catch-all class that contains images that fit none of the defined classes of interest. This class makes up the majority of our data set.</p>
MER Opportunity and Spirit Rovers Pancam Images Labeled Data Set
<p><strong>Introduction</strong></p> <p>The data set is based on 3,004 images collected by the Pancam instruments mounted on the Opportunity and Spirit rovers from NASA's Mars Exploration Rovers (MER) mission. We used rotation, skewing, and shearing augmentation methods to increase the total collection to 70,864 (see Image Augmentation section for more information). Based on the <a href="https://merdatacatalog.com/survey">MER Data Catalog User Survey</a> [1], we identified 25 classes of both scientific (e.g. soil trench, float rocks, etc.) and engineering (e.g. rover deck, Pancam calibration target, etc.) interests (see Classes section for more information). The 3,004 images were labeled on <a href="https://www.zooniverse.org/">Zooniverse platform</a>, and each image is allowed to be assigned with multiple labels. The images are either 512 x 512 or 1024 x 1024 pixels in size (see Image Sampling section for more information).</p> <p><strong>Classes</strong></p> <p>There is a total of 25 classes for this data set. See the list below for class names, counts, and percentages (the percentages are computed as count divided by 3,004). Note that the total counts don't sum up to 3,004 and the percentages don't sum up to 1.0 because each image may be assigned with more than one class. </p> <ul> <li>Class name, count, percentage of dataset</li> <li>Rover Deck, 222, 7.39%</li> <li>Pancam Calibration Target, 14, 0.47%</li> <li>Arm Hardware, 4, 0.13%</li> <li>Other Hardware, 116, 3.86%</li> <li>Rover Tracks, 301, 10.02%</li> <li>Soil Trench, 34, 1.13%</li> <li>RAT Brushed Target, 17, 0.57%</li> <li>RAT Hole, 30, 1.00%</li> <li>Rock Outcrop, 1915, 63.75%</li> <li>Float Rocks, 860, 28.63%</li> <li>Clasts, 1676, 55.79%</li> <li>Rocks (misc), 249, 8.29%</li> <li>Bright Soil, 122, 4.06%</li> <li>Dunes/Ripples, 1000, 33.29%</li> <li>Rock (Linear Features), 943, 31.39%</li> <li>Rock (Round Features), 219, 7.29%</li> <li>Soil, 2891, 96.24%</li> <li>Astronomy, 12, 0.40%</li> <li>Spherules, 868, 28.89%</li> <li>Distant Vista, 903, 30.23%</li> <li>Sky, 954, 31.76%</li> <li>Close-up Rock, 23, 0.77%</li> <li>Nearby Surface, 2006, 66.78%</li> <li>Rover Parts, 301, 10.02%</li> <li>Artifacts, 28, 0.93%</li> </ul> <p><strong>Image Sampling</strong></p> <p>Images in the MER rover Pancam archive are of sizes ranging from 64x64 to 1024x1024 pixels. The largest size, 1024x1024, was by far the most common size in the archive. For the deep learning dataset, we elected to sample only 1024x1024 and 512x512 images as the higher resolution would be beneficial to feature extraction.</p> <p>In order to ensure that the data set is representative of the total image archive of 4.3 million images, we elected to sample via "site code". Each Pancam image has a corresponding two-digit alphanumeric "site code" which is used to track location throughout its mission. Since each "site code" corresponds to a different general location, sampling a fixed proportion of images taken from each site ensure that the data set contained some images from each location. In this way, we could ensure that a model performing well on this dataset would generalize well to the unlabeled archive data as a whole. We randomly sampled 20% of the images at each site within the subset of Pancam data fitting all other image criteria, applying a floor function to non-whole number sample sizes, resulting in a dataset of 3,004 images.</p> <p><strong>Train/validation/test sets split</strong></p> <p>The 3,004 images were split into train, validation, and test data sets. The split was done so that roughly 60, 15, and 25 percent of the 3,004 images would end up as train, validation, and test data sets respectively, while ensuing that images from a given site are not split between train/validaiton/test data sets. This resulted in 1,806 train images, 456 validation images, and 742 test images. </p> <p><strong>Augmentation</strong></p> <p>To augment the images in train and validation data sets (note that images in the test data set were not augmented), three augmentation methods were chosen that best represent transformations that could be realistically seen in Pancam images. The three augmentations methods are rotation, skew, and shear. The augmentation methods were applied with random magnitude, followed by a random horizontal flipping, to create 30 augmented images for each image. Since each transformation is followed by a square crop in order to keep input shape consistent, we had to constrict the magnitude limits of each augmentation to avoid cropping out important features at the edges of input images. Thus, rotations were limited to 15 degrees in either direction, the 3-dimensional skew was limited to 45 degrees in any direction, and shearing was limited to 10 degrees in either direction. Note that augmentation was done only on training and validation images. </p> <p><strong>Directory Contents</strong></p> <ul> <li>images: contains all 70,864 images</li> <li>train-set-v1.1.0.txt: label file for the training data set</li> <li>val-set-v1.1.0.txt: label file for the validation data set</li> <li>test-set-v1.1.0.txt: label file for the testing data set</li> </ul> <p>Images with relatively short file names (e.g., 1p128287181mrd0000p2303l2m1.img.jpg) are original images, and images with long file names (e.g., 1p128287181mrd0000p2303l2m1.img.jpg_04140167-5781-49bd-a913-6d4d0a61dab1.jpg) are augmented images. The label files are formatted as "Image name, Class1, Class2, ..., ClassN".</p> <p> </p> <p><strong>Reference</strong></p> <p>[1] S.B. Cole, J.C. Aubele, B.A. Cohen, S.M. Milkovich, and S.A. Shields, Identifying Community Needs for a Mars Exploration Rovers (MER), Daata Catalog, 51st Lunar and Planetary Science Conference (LPSC), 2020.</p>
Micro-CT tomographic data set of 38 mummy labels from the BNU in Strasbourg (1/2)
<p><strong>Summary</strong></p> <p>This submission contains a tomographic dataset of 38 mummy labels from the BNU in Strasbourg used to perceive the anatomical identification possibilities of the woods used for mummy labels and to carry out ring width measurements. The data will be made available as part of [Blondel et al., 2024].</p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the EasyTom 150/160 X-ray tomograph (RX Solutions). This tomograph is equipped with a sealed X-ray generator with a compact tube and an interchangeable-plane sensor fitted with a CsI scintillator. The CT scanner parameters for the session carried out on the mummy labels were set at 90 Kv with an intensity of 195 mA for an acquisition resolution varying between 11 and 42 µm with 2016 projections (that is about 20 images on average per projection) with a frame rate of 12,5 and a temperature of 28°C. Each image was then reconstructed by filtered retroprojection using the XAct software (RX Solutions).</p> <p><strong>Information on placing mummy labels in the tomograph</strong></p> <p>The installation of the mummy labels was the same for all the different labels, some of which varied in size. They were attached to a plastic clamping vice-type support covered in expanded foam to prevent the labels from being marked during clamping, before being placed on the tomograph's rotating platform.</p> <p><strong>Issues relating to the data collected</strong></p> <p>The data collected for this study were carried out to perceive the possibilities of anatomical identification from tomographic images in the transverse plane. The tangential and radial planes were not of sufficiently high resolution due to the dimensions of the mummy labels, see details in [Blondel et al., 2024]. The other objective was to use tomographic imagery to facilitate the acquisition of ring widths in the transverse plane of mummy labels. The mummy labels were not tomographed in their entirety. Only the central part, a few centimetres high, was tomographed to maximise resolution. The number of projections and the resolution per label are specified in table form in [Blondel et al., 2024], as they vary according to the width and thickness of the mummy labels. All raw tomography image data (i.e. without corrections) are available in .tif format. The post-processing steps are described in the methodology of [Blondel et al., 2024].</p> <p><strong>List of Contents</strong></p> <p>The content of the submission is divided into 38 data sets corresponding to the 38 mummy labels. Each set is labelled with the inventory number of the BNU mummy label and its resolution. Each set contains:<br>- All the images of the transverse plane in .tif format, the number of projections of which varies from one label to another depending on the resolution of the acquisitions, see details in [Blondel et al., 2024].<br>- The .xls file containing a summary of the scanner metadata for each of the mummy labels.<br>- The three images processed in the transverse plane for each label, including those used to measure ring width for the 7 labels for which ring width measurement was possible, as presented in [Blondel et al., 2024].<br>- Colour photographs of the front and back of each tomographed mummy label including those on which ring width measurements were taken on their surface, unless otherwise stated<a title="" href="#_ftn1" name="_ftnref1">[1]</a>. All these photographs are marked: Coll._et_photogr._BNU_Strasbourg_OpenLicence, accompanied by the inventory number.</p> <p><strong>Acknowledgments</strong></p> <p>We would also like to thank engineers Damien Favier and Antoine Egele from the Charles Sadron Institute for their work on the tomographic acquisitions carried out on the 38 mummy labels.</p> <div><br> <div> <p><a title="" href="#_ftnref1" name="_ftn1">[1]</a> The photographs of the front and back of mummy label HO255 are not available, as they are currently being studied.</p> </div> </div>
Tracing low-CO2 fluxes in incubation and 13C labeling experiments; data set
<p>Data set containing data from feature tests (1-3) as well as photosynthesis and respiration measurements.</p> <p> </p>
Mars surface image (Curiosity rover) labeled data set version 1
This data set consists of 6691 images spanning 24 classes that were collected by the Mars Science Laboratory (MSL, Curosity) rover by three instruments (Mastcam Right eye, Mastcam Left eye, and MAHLI). These images are the "browse" version of each original data product, not full resolution. They are roughly 256x256 pixels each. We divided the MSL images into train, validation, and test data sets according to their sol (Martian day) of acquisition. This strategy was chosen to model how the system will be used operationally with an image archive that grows over time. The images were collected from sols 3 to 1060 (August 2012 to July 2015). The exact train/validation/test splits are given in individual files. Full-size images can be obtained from the PDS at https://pds-imaging.jpl.nasa.gov/search/ .
Mars orbital image (HiRISE) labeled data set version 3
This data set contains a total of 73,031 landmarks. 10,433 landmarks were detected and extracted from 180 HiRISE browse images, and 62,598 landmarks were augmented from 10,433 original landmarks. For each original landmark, we cropped a square bounding box that includes the full extent of the landmark plus a 30-pixel margin to left, right, top and bottom. Each cropped landmark was resized to 227x227 pixels, and then was augmented to generate 6 additional landmarks using the following methods: 1. 90 degrees clockwise rotation 2. 180 degrees clockwise rotation 3. 270 degrees clockwise rotation 4. Horizontal flip 5. Vertical flip 6. Random brightness adjustment
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.