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138 results for “Boulders”
Pre-processed (in Detectron2 and YOLO format) planetary images and boulder labels collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 4976 planetary images of boulder fields located on Earth, Mars and Moon. The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024. The data was already splitted into train, validation and test datasets, but feel free to re-organize the labels at your convenience. </p> <p>For each image, all of the boulder outlines within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). In addition, the boulder outlines were also pre-processed so that it can be ingested directly in YOLOv8.</p> <p>A description of what is what is given in the README.txt file (in addition in how to load the custom datasets in Detectron2 and YOLO). Most of the other files are mostly self-explanatory. Please see previous dataset or manuscript for more information. If you want to have more information about specific lunar and martian planetary images, the IDs of the images are still available in the name of the file. Use this ID to find more information (e.g., M121118602_00875_image.png, ID M121118602 ca be used on https://pilot.wr.usgs.gov/). I will also upload the raw data from which this pre-processed dataset was generated (see <a href="https://zenodo.org/records/14250970">https://zenodo.org/records/14250970</a>).</p> <p>Thanks to this database, you can easily train a Detectron2 Mask R-CNN or YOLO instance segmentation models to automatically detect boulders. </p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── boulder2024/ ├── jupyter-notebooks/ │ └── REGISTERING_BOULDER_DATASET_IN_DETECTRON2.ipynb ├── test/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── train/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── validation/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── detectron2_inst_seg_boulder_dataset.json ├── README.txt ├── yolo_inst_seg_boulder_dataset.yaml</code></pre> <p> </p> <pre><code>detectron2_inst_seg_boulder_dataset.json</code></pre> <p>is a json file containing the masks as expected by Detectron2 (see <a href="https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html">https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html</a> for more information on the format). In order to use this custom dataset, you need to register the dataset before using it in the training. There is an example how to do that in the jupyter-notebooks folder. You need to have detectron2, and all of its depedencies installed. </p> <pre><code>yolo_inst_seg_boulder_dataset.yaml</code></pre> <p>can be used as it is, however you need to update the paths in the .yaml file, to the test, train and validation folders. More information about the YOLO format can be found here (<a href="https://docs.ultralytics.com/datasets/segment/">https://docs.ultralytics.com/datasets/segment/</a>).</p>
Raw planetary images and boulder labels data (as shapefiles) collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 64 large images of craters on the lunar and martian surfaces and 3 images of boulder fields on Earth (see manuscript <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a> for more information on those terrestrial locations). The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024.</p> <p>For each image, the boulder outlines within specific tiles within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). </p> <p>For each location, you will find a raster with a .tif format, and three shapefiles:</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a tiles-completely-mapped file, which depicts the patches/tiles/windows on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches/tiles/windows (pick the term you are the most familiar with) within a raster.</p> </li> </ul> <p>In addition you will find .pkl (which stands for pickle), which contains some information about the patches/tiles/windows if you would need to clip those windows out from the original raster. You can find more information in the way we process this raw data into a format which can be ingested in a deep learning model (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>) in the two following github repositories (<a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth</a> and <a href="https://github.com/astroNils/MLtools/tree/main" target="_blank" rel="noopener">https://github.com/astroNils/MLtools</a>). If you don't plan in adding more training data, you can directly used the pre-processed database (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>).</p> <p>There are multiple locations/images per planetary body. Cold spots are located on the Moon, but they are saved in a folder of their own. </p> <p>Note that the cold spots boulder mapping shapefiles are partially manually mapped, and partially originating from predictions made from a deep learning model (which explains the outline of boulders are predicted within one pixel).</p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── raw_data/ ├── coldspots/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</code></pre>
Fractures on Bennu's boulders
<p>This is the data set corresponding to the publication entitled Alignment of fractures on Bennu’s boulders indicative of rapid asteroid surface evolution, by the same authors, which is to be published by Nature Geoscience. </p>
Indicative distribution map for Ecosystem Functional Group MT1.4 Boulder and cobble shores
<p>This archive contains indicative distribution maps and profiles for <strong>MT1.4 Boulder and cobble shores</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Chickadee breeding and parasite survey from Boulder, Sugarloaf, and MRS nest boxes, 2019 - 2021.
Between the years 2019 and 2021 chickadees were sampled at the NWT LTER as part of a broader study encompassing multiple projects (The Boulder Chickadee Study). Here we provide data on the presence of infections with haemosporidian parasites for Black-capped Chickadees (Poecile atricapillus) and Mountain Chickadees (P. gambeli). Haemosporidian parasites are a diverse group of apicomplexans that infect vertebrate hosts during a portion of their life cycle (Valkiunas, G. 2004). We screened chickadees for infections with three haemosporidian genera: Leucocytozoon, Plasmodium, and Haemoproteus using a nested PCR protocol (Hellgren et al. 2004). Both nestlings and adults were screened for parasites in 2019 and 2020 but we only screened adults in 2021. Chickadees sampled at NWT LTER did not have detectable infections with Haemoproteus or Plasmodium, however, we found multiple infections with Leucocytozoon. In conjunction with findings at other field sites, infections with Leucocytozoon parasites appear more common for adult chickadees sampled at higher elevation, while Plasmodium parasites were almost exclusively found in the city of Boulder (Theodosopoulos et al. 2023).
LAPSE-RATE ground-based Doppler lidar datasets from Univ Colorado Boulder
<p>This dataset includes measurements obtained using the University of Colorado Windcube v1 lidars during the 2018 LAPSE-RATE (Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment) field campaign. This campaign took place in the San Luis Valley of Colorado between 14-21 July, 2018. </p> <p>On 14 July, both lidars were co-located at the Saguache site for intercomparison. WC49 (here named DPLR2) was moved to the Moffat School site late in the day on the 14th. Moffat School data starts on the 15th.</p> <p>Bad or missing data is set to -9999.0 for all fields.</p> <p>The a1 and the a2 datafiles are identical except that the a2 files include beam information (0, 90, 180, or 270) that was omitted from the a1 files.</p>
DOORS: Dataset fOr bOuldeRs Segmentation
<p>The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as hazard detection during critical operations and navigation. This task is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. Moreover, the lack of publicly available labeled datasets for these applications damps the research about data-driven algorithms. To tackle these challenges, the Dataset fOr bOuldeRs Segmentation (DOORS) has been designed. The dataset is thought to be useful for (but not limited to) boulders recognition, centroid regression, segmentation, and navigation applications. The dataset is divided into two sets:</p> <ul> <li><strong>Regression: </strong>Contains images, masks, and labels for 4 splits of single boulders positioned on the surface of a spherical mesh. It can be used to perform navigation, boulder recognition, segmentation, and centroid regression.</li> <li><strong>Segmentation: </strong>Contain images, masks, and labels of 2 datasets: DS1 and DS2. DS1 is made of the same images of the Regression dataset but is specifically designed for segmentation. DS2 is made of images with multiple instances of boulders appearing on the surface of the Didymos asteroid model</li> </ul> <p>A detailed characterization of the statistical properties of the DOORS dataset and the description of the Blender setup used to generate it is visible in "DOORS: Dataset fOr bOuldeRs Segmentation. Statistical properties and Blender setup", by Mattia Pugliatti and Francesco Topputo, arXiv pre-print, Oct 2022. </p> <p> </p>
The bearing capacity of asteroid (65803) Didymos estimated from boulder tracks
<p>This material constitutes the source data and codes used for the the computations and plots of the paper 'The bearing capacity of asteroid (65803) Didymos estimated from boulder tracks' by Bigot, Lombardo et al. This article has been published in Nature Communications on 30 July, 2024.</p> <p>The folder "Source Data" contains an Excel document that provides the raw data used to make the figures and supplementary figures. </p> <p>The folder "Codes" contains the Matlab codes used for the computations of the results, including comments on the figures produced by each code. It also contains a .mat file that consitutes the topographic data of Didymos from Barnouin et al. (2024), used in the code 'TopographyDidymos.m'.</p> <p>The folder "Images" contains the three DART images (DRACO) and the Moon image from LROC used in this article.</p> <p> </p>
Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"
<p><strong>File 1:</strong> raw_data_BOULDERING.zip</p> <p><strong>Size:</strong> 8.8 GB</p> <p><strong>Summary: </strong>It contains all of the rasters (planetary images) and labeled boulders (raw data):</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches within a raster.</p> </li> </ul> <p>There are multiple locations/images per planetary body.</p> <p><strong>Structure:</strong></p> <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ ├── <image_name>-ROM.shp │ ├── <image_name>-boulder-mapping.shp │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</pre> <p> </p> <p><strong>File 2:</strong> best_model.zip</p> <p><strong>Size:</strong> 624.7 MB</p> <p><strong>Summary:</strong></p> <p>This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics):</p> <ul> <li> <p>augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information.</p> </li> </ul> <ul> <li> <p>Base-RCNN-FPN.yaml (base model setup file).</p> </li> <li> <p>config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file).</p> </li> <li> <p>Mars-MoonEarth-v050...yaml (model setup file).</p> </li> <li> <p>log.txt (log during training of the algorithm).</p> </li> <li> <p>model_0055999.pth (model weights at second last saving step)</p> </li> <li> <p>model_0063999.pth (model weights at last saving step)</p> </li> </ul> <p>We advice the use of model weights model_0055999.pth (to avoid slight overfitting).</p> <p><strong>File 3:</strong> Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images)</p> <p><strong>Size:</strong> 252.8 MB</p> <p><strong>Summary:</strong></p> <p>This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files.</p> <ul> <li> <p>The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json".</p> </li> <li> <p>The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset.</p> </li> <li> <p>The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles.</p> </li> <li> <p>The shapefile folder is actually empty (it should not be there!).</p> </li> </ul> <p><strong>Structure:</strong></p> <pre>. └── preprocessed_inputs/ ├── json ├── pkl ├── preprocessing/ │ ├── train/ │ │ ├── images │ │ └── labels │ ├── validation/ │ │ ├── images │ │ └── labels │ └── test/ │ ├── images │ └── labels └── shp</pre> <p> </p>
A multi-scale labeled dataset for boulder segmentation and navigation on small bodies
<p>The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as hazard detection during critical operations, safety quantification, autonomous planning of scientific operations, and autonomous navigation. This task, however, is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. Moreover, the lack of publicly available labeled datasets damps the research about data-driven algorithms. The following dataset has been designed and made publicly available to tackle these challenges. Its purpose is twofold. First, from the lessons learned from previous datasets, to develop a multi-purpose, high-fidelity dataset with boulders scattered across the surface of a small body. Second, to exploit domain randomization, artificial noise addition, scaling, and post-processing, enabling the design of data-driven pipelines. </p> <p>The methodology used to generate the dataset is illustrated in the work "A multi-scale labeled dataset for boulder segmentation and navigation on small bodies" by Mattia Pugliatti and Michele Maestrini, presented at the 74th IAC (International Astronautical Congress), 2024, Baku, Azerbaijan.</p> <p>The dataset contains the image-label pairs of 47502 samples, organized with the following structure: </p> <p>Dataset_PugliattiMaestrini_2023IAC<br> --img<br> --labels<br> --masks</p> <p>The dataset is comprised of 47502 samples. The "img" folder contains the input, 512x 512 grayscale images. The "labels" folder includes the .txt segmentation labels of the 15 most prominent boulders for each image detected with the methodology illustrated in the IAC paper. The "masks" dataset contains the segmentation masks for all image layers, with the values being encoded between 0 and 17 as uint8. The samples are named as XXXXXX_YYY. XXXXXX stands for the image's original ID during rendering. YYY corresponds to the sub-splits of the original image obtained at rendering: </p> <p> 001 - Top-Left crop<br> 002 - Top-Right crop<br> 003 - Bottom-Left crop<br> 004 - Bottom-right crop<br> 005 - Whole, resized</p> <p>The file "10000_ub_2023-01-18 00.09.43.txt" contains all the values of the rendering inputs detailed in the IAC paper.</p>
Boulder Chickadee Study nest temperature data, Boulder County, Colorado, 2024.
Incubation is a crucial part of the avian life cycle; eggs must be kept warm enough for embryos to develop. As egg temperatures drop rapidly when not being actively incubated, birds must balance the thermal requirements of their developing offspring with their own energy requirements when deciding to make a feeding trip. Incubation behavior can vary with ambient temperature, and across the breeding cycle. Here, we examine the incubation behavior of black-capped (Poecile atricapillus) and mountain (P. gambeli) chickadees where they live together in the Front Range of the Colorado Rocky Mountains. We deployed iButton temperature monitors in and around chickadee nests to record nest and ambient temperatures during the incubation period. For each deployment, we visually inspected temperature data to determine whether incubation was detected. For each deployment in which incubation was detected we quantified incubation behavior using the incR R package. Across the four nests for which incubation was detected, females (the incubating sex) spent about 85% of the day incubating, and left the nest about 32 times per day for an average of 6.7 minutes. On average, nest temperatures were maintained around 35˚C.
Mechanical properties of rubble pile asteroids (Dimorphos, Itokawa, Ryugu, and Bennu) through surface boulder morphological analysis
<p>This material constitutes the dataset used for the computation of the results of the paper Mechanical properties of rubble pile asteroids (Dimorphos, Itokawa, Ryugu, and Bennu) through surface boulder morphological analysis by Robin & al. </p> <p>The folders "Bennu", "Dimorphos", "Itokawa" and "Ryugu" contains the raw images from PDS used for this study as well as the images with the outlined boulders made with segmentanygrains (https://github.com/zsylvester/segmenteverygrain), and the associated morphological descriptor values. The folder "ExtremeCases" contains the raw and outlined images for extreme roundness cases.</p> <p>This material also contains the main figures of the paper as well as the code to remake most the figures.</p> <p>Last version of this manuscript has been submitted to Nature Communications on May 14th, 2024 and accepted on May 17th, 2024.</p> <p><strong>Abstract</strong>: </p> <table> <tbody> <tr> <td>Planetary defense efforts rely on estimates of the mechanical properties of asteroids, which are difficult to constrain accurately from Earth. The mechanical properties of asteroid material are also important in the interpretation of the Double Asteroid Redirection Test (DART) impact. Here we perform a detailed morphological analysis of the surface boulders on Dimorphos using images, the primary data set available from the DART mission. We estimate the bulk angle of internal friction of the boulders to be 32.7 ± 2.5° from our measurements of the roundness of the 34 best-resolved boulders ranging in size from 1.67 to 6.64 m. The elongated nature of the boulders around the DART impact site implies that they were likely formed through impact processing. Finally, we find striking similarities in the morphology of the boulders on Dimorphos with those on other rubble pile asteroids (Itokawa, Ryugu and Bennu). This leads to very similar internal friction angles across the four bodies and suggests that a common formation mechanism has shaped the boulders. Our results provide key inputs for understanding the DART impact and for improving our knowledge about the physical properties, the formation and the evolution of both near-Earth rubble-pile and binary asteroids.</td> </tr> <tr></tr> </tbody> </table>
Dataset accompanying the journal article: Bouldering psychotherapy is not inferior to cognitive behavioural therapy in the group treatment of depression: A randomized controlled trial
<p>SPSS-Dataset containing the (not-imputed) raw data for the non-inferiority trial on BPT vs. CBT. All personal data removed. Only data of participants of the CBT or BPT group included.</p>
EXPLORE Expert Data Challenges 2022 - Boulders dataset
<p>This dataset contains Lunar Craters images and labels in COCO json format that will be used for the EXPLORE Expert Data Challenge 2022.</p> <p> </p> <p>More information at: https://exploredatachallenges.space/</p> <p> </p> <p>Source dataset is derived from Watkins, Ryan (2019)</p> <p>Images were processed from NASA PDS raw data and labels extracted using python scripts. </p>
Data from: Geomorphological signatures of known hurricanes and validation of theoretical emplacement formulations: coastal boulder deposits on Cuban low-lying marine terraces
<p>Here we show the models made from SfM photogrammetry for 5 boulders found on the Cuban island emplaced by three hurricanes on the low marine terrace. To generate a 3D-scaled SfM point cloud of each studied boulder, we adapted an easy SfM workflow using Agisoft Metashape software (version 1.7.2). We surveyed each CBD obtaining pictures between 0.5 m - 3 m distance from the boulder and also climbing on the boulder to maximise the diversity of picture angles. The survey was carried over a time lapse of ~30 minutes and under cloudy conditions to avoid shadows created by direct sunlight. For scaling SfM results and assessing the quality of measurements, we used six wooden balls as photogrammetric targets. The diameter of the balls is 30 ± 0.2 x 10-3 m, as verified by repeated caliper measurements. We complemented these targets with two 70 x 10 cm wooden scales. All these targets were used as reference scale bars to correct the spherical distortion inherent to the camera optics and perform an auto-calibration. We further used these targets to check possible distortion after post processing and the generation of the resulting 3D model. In order to create a point cloud and a solid volume, we followed the software workflow: we first aligned the images to identify tiepoints, which were then used in bundle adjustment to compute the 3D dense clouds in a second step.</p>
Fig. 2. Oepikellid ostracods from Palaeozoic erratic boulders. A in Sexual dimorphism and pore systems in Ordovician ostracodes
Fig. 2. Oepikellid ostracods from Palaeozoic erratic boulders. A. Holotype of the type species of the oepikellid ostracod Levisulculus, Levisulculus lineatus Jaanusson, 1957 (UM T89), female left valve, length (L) 0.89 mm. B. Holotype of Primitia extraria Öpik, 1937 (TUG 1120−1; Kukruse Stage, Estonia), tecnomorphic right valve, L 0.88 mm. C. Holotype of Primitia troedssoni Thorslund, 1940 (UM T10), tecnomorphic right valve, L 0.79 mm. D. Holotype of Primitia granulosa Thorslund, 1940 (UM T11), tecnomorphic right valve, L 0.86 mm (Jaanusson 1957: pl. 8: 12, Öpik 1937: pl. 10: 19, Thorslund 1940: pl. 1: 16, 13). E–H. Primitia elongata obliqua Steusloff, 1895: type series, all tecnomorphic valves embedded in rock. Geschiebe (glacial erratic boulder) from Neubrandenburg. E. Lectotype GG 114−27, left valve, L 1.16 mm (without velum). F. GG 114−26, right valve, L 1.07 mm. G. GG 114−28, right valve, L 0.99 mm (without velum). H. GG 114−29, right valve, L 0.82 mm. I. Primitia canaliculata Steusloff, 1895, holotype GG 114−25, steinkern of a juvenile right valve embedded in rock, L 0.70 mm, same erratic boulder.
Text-fig. 3. Textures of main volcaniclastic deposits exposed in abandoned Ludvíkovice quarry. a: radial cracks surrounding some boulders (see arrows) in hot lahar deposit. b: jig-saw fit of fractures (see arrows) within a mega-block of debris-avalanche deposit. c: pseudo-fiamme texture of compacted argillized pumice-fall deposit. d: trachybasaltic lapilli-stone of phreato-magmatic eruption. e: palaeo-relief developed and buried within the pyroclastic unit. f: diagonal bedding in fluvial volcanigenic sandstones. g: diluted and fine-grained lahars embedded in volcanigenic sandstones. in A New Oligocene Flora From Ludvíkovice Near Děčín (České Středohoří Mts., The Czech Republic)
Text-fig. 3. Textures of main volcaniclastic deposits exposed in abandoned Ludvíkovice quarry. a: radial cracks surrounding some boulders (see arrows) in hot lahar deposit. b: jig-saw fit of fractures (see arrows) within a mega-block of debris-avalanche deposit. c: pseudo-fiamme texture of compacted argillized pumice-fall deposit. d: trachybasaltic lapilli-stone of phreato-magmatic eruption. e: palaeo-relief developed and buried within the pyroclastic unit. f: diagonal bedding in fluvial volcanigenic sandstones. g: diluted and fine-grained lahars embedded in volcanigenic sandstones.
Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 28. Spinograptus spinosus Wood, 1900, L. nilssoni Biozone, EEP, Poland. 29. Spinograptus latespinosus Kozłowska−Dawidziuk, 1997. 30. Spinograptus munchi Eisenack, 1951, C. praedubeli/C. deubeli Biozone, EEP, Poland. 31. Papiliograptus papilio Lenz and Kozłowska−Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 32. Plectograptus? karlsteinensis Kozłowska−Dawidziuk, Lenz, and Štorch, 2001, C. praedubeli/C. deubeli Biozone, Barrandian. 33. Neogothograptus thorsteinssoni Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 34. Neogothograptus alatiformis Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 35. Neogothograptus purus Kozłowska−Dawidziuk, 1995, EEP, Poland. 36. Holoretiolites mancki (Münch, 1931). 37. Holoretiolites helenaewitoldi sp. nov., L. progenitor Biozone, EEP, Poland. 38. Plectograptus wimani Eisenack, 1951, N. nilssoni Biozone, Baltic erratic boulder, Poland. 39. Plectograptus robustus Obut and Zaslavskaya, 1983, L. nilssoni Biozone, EEP, Kaliningrad. 40. Plectograptus macilentus Törnquist, 1887, L. scanicus Biozone, Baltic erratic boulder, Poland. 41. Semiplectograptus urbaneki Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. 42. Plectodinemagraptus gracilis Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. Figures adapted from: 1, Melchin (1999); 2, 4–5, Bouček and Münch (1944); 3, holotype photographed by A. Lenz; 6, 9, Bates and Kirk (1992); 7, Bates and Kirk (1997); 8, Štorch (1994); 10–15, 38, 40–42, Kozłowska−Dawidziuk (1995); 16, Kozłowska−Dawidziuk (2001); 17–21, Lenz and Kozłowska−Dawidziuk (2001); 22, 25, Kozłowska−Dawidziuk (1990); 23, 35, photo taken by author; 24, 32, Kozłowska−Dawidziuk et al. (2001); 26, 27, 31, Lenz and Kozłowska−Dawidziuk (2002a); 28, 35, photo taken by author; 29, Kozłowska−Dawidziuk (1997); 30, Kozłowska−Dawidziuk 2002; 36, Kozłowska−Dawidziuk and Lenz (2001); 37, this paper; 39, Obut and Zaslavskaya (1983). Not to scale. Abbreviations: RD, Rhuddanian; SHEIN, Sheinwoodian; GORST, Gorstian; LUDF, Ludfordian. Biozonal scheme after Koren' et al. 1996; Geological time scale by International Commission on Stratigraphy, International Union of Geological Sciences 2004 (www.stratigraphy.org) in Evolution of retiolitid graptolites-a synopsis
Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 28. Spinograptus spinosus Wood, 1900, L. nilssoni Biozone, EEP, Poland. 29. Spinograptus latespinosus Kozłowska−Dawidziuk, 1997. 30. Spinograptus munchi Eisenack, 1951, C. praedubeli/C. deubeli Biozone, EEP, Poland. 31. Papiliograptus papilio Lenz and Kozłowska−Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 32. Plectograptus? karlsteinensis Kozłowska−Dawidziuk, Lenz, and Štorch, 2001, C. praedubeli/C. deubeli Biozone, Barrandian. 33. Neogothograptus thorsteinssoni Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 34. Neogothograptus alatiformis Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 35. Neogothograptus purus Kozłowska−Dawidziuk, 1995, EEP, Poland. 36. Holoretiolites mancki (Münch, 1931). 37. Holoretiolites helenaewitoldi sp. nov., L. progenitor Biozone, EEP, Poland. 38. Plectograptus wimani Eisenack, 1951, N. nilssoni Biozone, Baltic erratic boulder, Poland. 39. Plectograptus robustus Obut and Zaslavskaya, 1983, L. nilssoni Biozone, EEP, Kaliningrad. 40. Plectograptus macilentus Törnquist, 1887, L. scanicus Biozone, Baltic erratic boulder, Poland. 41. Semiplectograptus urbaneki Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. 42. Plectodinemagraptus gracilis Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. Figures adapted from: 1, Melchin (1999); 2, 4–5, Bouček and Münch (1944); 3, holotype photographed by A. Lenz; 6, 9, Bates and Kirk (1992); 7, Bates and Kirk (1997); 8, Štorch (1994); 10–15, 38, 40–42, Kozłowska−Dawidziuk (1995); 16, Kozłowska−Dawidziuk (2001); 17–21, Lenz and Kozłowska−Dawidziuk (2001); 22, 25, Kozłowska−Dawidziuk (1990); 23, 35, photo taken by author; 24, 32, Kozłowska−Dawidziuk et al. (2001); 26, 27, 31, Lenz and Kozłowska−Dawidziuk (2002a); 28, 35, photo taken by author; 29, Kozłowska−Dawidziuk (1997); 30, Kozłowska−Dawidziuk 2002; 36, Kozłowska−Dawidziuk and Lenz (2001); 37, this paper; 39, Obut and Zaslavskaya (1983). Not to scale. Abbreviations: RD, Rhuddanian; SHEIN, Sheinwoodian; GORST, Gorstian; LUDF, Ludfordian. Biozonal scheme after Koren' et al. 1996; Geological time scale by International Commission on Stratigraphy, International Union of Geological Sciences 2004 (www.stratigraphy.org)
Black-capped and mountain chickadee hybridization and breeding ecology in Boulder County, CO
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The boulder population of asteroid 4 Vesta
<p>These are data described in the paper "The boulder population of asteroid 4 Vesta: Size-frequency distribution and survival time" by Schröder et al., submitted to Earth & Space Science (Wiley). The data concern boulder counts for craters on the asteroid Vesta.</p> <p>Boulder counts for all 69 craters in Table 1 of the paper are provided as text files with 3 columns: (1) longitude and (2) latitude, both in degrees, and (3) boulder diameter in meters. Generally, boulder identifications are only reliable above 3 image pixels (60 m), but the data files contain all boulders that we (tentatively) identified, including those smaller than 60 m.</p> <p>We also provide figures in PDF format, one for each crater, of the spatial boulder distribution. The figures have the format of Fig. 6 in the paper: Green, small dots represent boulders with a size between 3 and 4 pixels (60 m < d < 80 m). Red, large dots represent boulders larger than 4 pixels (d > 80 m). Boulders smaller than 60 m are not shown.</p>
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Allen Brain Atlas
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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
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International Brain Laboratory public data
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OpenNeuro
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