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Dataset results
339 results for “raw images”
Raw Synthetic Particle Image Dataset (RSPID)
<p>Synthetic Particle Image Velocimetry (PIV) data generated by PIV Image Generator Software. Which is a tool that generates synthetic Particle Imaging Velocimetry (PIV) images with the purpose of validating and benchmarking PIV and Optical Flow methods in tracer based imaging for fluid mechanics (Mendes et al., 2020). </p><p>This data was generated with the following parameters:</p><ul><li>image width: 665 pixels;</li><li>image height: 630 pixels;</li><li>bit depth: 8 bits;</li><li>particle radius: 1, 2, 3, 4 pixels;</li><li>particle density: 15, 17, 20, 23, 25, 32 particles;</li><li>delta x factor: 0.05, 0.1, 0.15, 0.2, 0.25 %;</li><li>noise level: 1, 5, 10, 15;</li><li>out-of-plane standard deviation: 0.01, 0.025, 0.05;</li><li>flows: rankine uniform, rankine vortex, parabolic, stagnation, shear, decaying vortex.</li></ul>
Drone raw images of cattle in french grazing areas
<p><strong>The updated data are part of the Horizon Europe project ICAERUS</strong> regarding the livestock monitoring use case in a task where the objective is to test, optimize and scale up models regarding animal counting (cattle and sheep). More information here : <a href="https://icaerus.eu/">https://icaerus.eu/</a></p> <p>The dataset encompasses around <strong>900 raw .jpeg drone images </strong><strong>of grazing areas where cattle graze</strong> collected between June and August 2023. Drone used were Mavic 3 Enterprise and Thermal. Data collection is underway, with the aim of collecting images throughout the year and on several farms, to capture variability in animal and background colors and brightness conditions. Image tags (“cattle” vs “no cattle”) are not available for the moment but will be in the next months in next versions of this dataset. There is a strong imbalance between images with “cattle” and image with “no cattle” representative of areas to monitor. </p> <p><br> The nadir images were collected during flight planned with DJI Pilot 2 at a constant altitude regarding the take-off position (30 m, 60 m, 100 m). </p> <p>The data are organized by a first directory by farm where the images were collected and then with one directory by flight planned. <br> A summary is available in the Table_summary.xls. Name of the directory of each planned flight is defined such as DJI_YYYYMMDDHHMM_XX with the date (YYYYMMDD), the hour in UTC+2 (HHMM), and XX representing a mission number. </p> <p><br> .exif data of each images provide many information regarding the drone (GPS position, absolute and relative altitude, gimble information, speed etc.). Further details will be added in the next versions of the dataset.</p> <p><strong>The authors of the dataset are opened to any collaboration regarding animal counting models.</strong></p> <p><br> For more information, please contact: adrien.lebreton@idele.fr </p>
Raw diffraction images of the first bromodomain of human BRD4 in complex with (+)-JD1
<p>Raw diffraction images of the first bromodomain of human BRD4 in complex with (+)-JD1, an Organometallic BET Bromodomain Inhibitor. The final structure is deposited in the Protein Data Bank under accession code <a href="http://www.rcsb.org/structure/6SE4">6SE4</a>.</p> <p>The structure is part of the following publication:</p> <p>Hassell-Hart, S., Runcie, A., Krojer, T., Doyle, J., Lineham, E., Ocasio, C.A., Neto, B.A.D., Fedorov, O., Marsh, G., Maple, H., et al. (2019). Synthesis and Biological Investigation of (+)-JD1, an Organometallic BET Bromodomain Inhibitor. Organometallics. doi: 10.1021/acs.organomet.9b00750.</p> <p> </p> <p>Additional information:</p> <p>dataset: BRD4A-JD1_i03<br> beamline: Diamond Light Source I03<br> visit: mx19301-7<br> date: 25-11-2018<br> Flux: 2.03e+11<br> Ω Start: 0.0°<br> Ω Osc: 0.15°<br> Ω Overlap: 0°<br> No. Images: 1200<br> Resolution: 1.50Å<br> Wavelength: 0.9762Å<br> Exposure: 0.030s<br> Transmission: 100.00%<br> Beamsize: 80x20μm</p> <p>datasets: BRD4A-JD1_i04<br> beamline: Diamond Light Source I04<br> visit: mx19301-9<br> date: 08-12-2018<br> Flux: 6.62e+11<br> Ω Start: 0.0°<br> Ω Osc: 0.50°<br> Ω Overlap: 0°<br> No. Images: 720<br> Resolution: 2.30Å<br> Wavelength: 1.7384Å<br> Exposure: 0.050s<br> Transmission: 100.00%<br> Beamsize: 32x20μm</p> <p>Additionally, a cif file containing refinement restraints and a png file for the ligand JD1 is included.</p>
High-throughput in-situ plankton imaging from the East China Sea: raw images and acantharian ROIs
<p>Vertical imaging profiles were performed at four stations (3, 10, 15, 17; closed circles on the map) during the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) MR17-03C cruise from May 29 to June 13, 2017 with an ISIIS small-imager (<a href="https://www.planktonimaging.com/smaller-imagers">https://www.planktonimaging.com/smaller-imagers</a>) attached to the JAMSTEC DEEP TOW 6KCTD (<a href="https://www.jamstec.go.jp/e/about/equipment/ships/deeptow.html">https://www.jamstec.go.jp/e/about/equipment/ships/deeptow.html</a>). The ISIIS camera was programmed to take 1 photo per second coinciding with an LED flash. Each photo imaged 0.39 L (st. 3 and 10) or 0.35 L (st. 15 and 17) parcels of water in 2448 x 2050 pixel resolution, with each pixel being 22.5 µm. A Sea-Bird SBE 9 CTD was deployed with the DEEP TOW and the ISIIS internal clock was calibrated to match the CTD’s so that CTD data could be used to determine the depth at which each image was taken. Raw images are labeled with the time stamp. Acantharian ROIs are labeled with the timestamp for the raw image from which they were cropped. If more than one acantharian ROI was found in a single raw image, a letter was appended to the ROI file name. </p> <p>Accompanying data (CTD, sequencing) and analyses are available from the GitHub repository: <a href="https://github.com/maggimars/Acanth_ImageSeq">https://github.com/maggimars/Acanth_ImageSeq</a>.</p> <p> </p>
Raw diffraction images of endothelin ETB receptor in complex with sarafotoxin S6b
<p>Diffraction images of endothelin ET<sub>B</sub> receptor in complex with sarafotoxin S6b (PDB code: <a href="https://www.rcsb.org/structure/6LRY">6LRY</a>).</p> <p>4 datasets were collected with helical method (60-120°/crystal), and 28 small-wedge (10°/crystal) datasets were collected manually. The diffraction images were collected from loop-harvested microcrystals using an <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md">EIGER</a> X 9M detector at a wavelength of 1 Å on BL32XU, SPring-8. 16 datasets were merged at 3.0 Å resolution in the published result (<a href="https://doi.org/10.1016/j.bbrc.2019.12.091">Izume et al. BBRC 2020</a>) using XDS with <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/kamo-en.md">KAMO</a> pipeline.</p> <p>NOTE</p> <ul> <li>Most frames have (relatively weak) lipid rings.</li> </ul>
Raw diffraction images of Drosophila Piwi
<p>Crystal structure of Drosophila Piwi (PDB code: <a href="https://www.rcsb.org/structure/6KR6">6KR6</a>).</p> <p>28 mercury-bound data and 4 native data were included (see file_list.txt for details). Each dataset consists of 180° (except two 90° datasets) from single crystal and was collected using <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md">EIGER</a> X 9M detector at a wavelength of 1 Å with helical data collection scheme using 15×10 μm beam on BL32XU, SPring-8. </p> <p>All diffraction images were processed using DIALS 1.10.2 through <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/kamo-en.md">KAMO</a> pipeline, and merged using XSCALE from XDS package with kamo.multi_merge. The crystals belong to space group P2<sub>1</sub>2<sub>1</sub>2<sub>1</sub> with a=62.1, b=115.6, c=119.9 Å. Finally 23 mercury-bound datasets were merged at 2.9 Å resolution in the published result (<a href="https://doi.org/10.1038/s41467-020-14687-1">Yamaguchi et al. Nature Communications, 2020</a>). Merging improved resolution and electron density of PAZ domain that was difficult to interpret with a single dataset.</p>
Diagnostic electron microscopy of viruses with low-voltage electron microscopes. Raw image files with brief description.
<p>The zipped data container contains the raw (unprocessed) images that we have used for the preparation of our manuscript entiteled:</p> <p>"Diagnostic electron microscopy of viruses with low-voltage electron microscopes" <a href="https://doi.org/10.1369%2F0022155420929438">https://doi.org/10.1369/0022155420929438</a></p> <p>Lars Möller, Gudrun Holland, Michael Laue</p> <p>Advanced Light and Electron Microscopy (ZBS 4), Centre for Biological Threats and Special Pathogens, Robert Koch Institute, D-13353 Berlin, Germany</p> <p>The brief description of the data set comprises the abstract of the manuscript, the figures (including captions) and a description of the materials and methods used for their generation.</p>
Raw images used for colony classification
<p>Raw images of plates with yeast colonies used in Figure 2 of "Carl et al. A fully automated deep learning pipeline for high-throughput colony segmentation and classification" (Biology Open 2020 : bio.052936 doi: 10.1242/bio.052936 Published 2 June 2020). The paper describes the development of a novel computational pipeline for colony segmentation and classification that achieves accuracy comparable to human performance.</p> <p>The actual data was generated in a project that was published earlier (Duempelmann, L. e<em>t al. </em>Inheritance of a Phenotypically Neutral Epimutation Evokes Gene Silencing in Later Generations. <em>Molecular Cell</em> <strong>74, 3</strong> (2019).) The experiments were testing trans-generational inheritance of <em>ade6<sup>+</sup> </em>silencing in <em>Schizosaccharomyces pombe</em>. <em>ade6<sup>+</sup></em> silencing was first induced by expression of small interfering RNAs (siRNAs) that are complementary to the <em>ade6<sup>+</sup></em> gene in a <em>paf1-Q264Stop</em> nonsense-mutant background, leading to red colonies. Paf1 is a subunit of the Paf1 complex (Paf1C), which represses siRNA-induced heterochromatin formation in <em>S. pombe</em>.</p>
Raw diffraction images of heliorhodopsin E108D mutant
<p>Crystal structure of <em>Thermoplasmatales archaeon</em> heliorhodopsin E108D mutant<em> </em>(PDB code: <a href="https://www.rcsb.org/structure/7CLJ">7CLJ</a>).</p> <p>79 small-wedge (10°/crystal) datasets collected from loop-harvested microcrystals using <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md">EIGER</a> X 9M detector at a wavelength of 1 Å on BL32XU, SPring-8. Beam size was around 5×5 µm<sup>2</sup> and oscillation step was 0.1°. The crystals belonged to space group P4<sub>2</sub>2<sub>1</sub>2 with unit cell parameters a~72.9, c~115 Å.</p> <p>52 datasets were indexed and integrated using XDS with <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/kamo-en.md">KAMO</a> pipeline. Finally 12 integrated results were merged at 2.6 Å resolution after CC-based clustering and outlier rejection by KAMO in the published result (<a href="https://doi.org/10.1016/j.bbrc.2020.06.124">Tanaka et al. BBRC, 2020</a>).</p>
Raw diffraction images of linker-PDE and TMD-linker of RhPDE
<p>Crystal structures of linker-PDE (PDB code: 7D7P) and TMD-linker (PDB code: 7D7Q) of Rhodopsin phosphodiesterase (Rh-PDE). For TMD (PDB code: 7CJ3) please refer to <a href="https://cxidb.org/id-171.html">CXIDB ID 171</a>.</p> <p>TMD-linker: 91 small-wedge (10 or 5°/crystal) datasets collected from loop-harvested microcrystals using <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md">EIGER</a> X 9M detector at a wavelength of 1 Å on BL32XU, SPring-8. Beam size was around 15×10 or 5×5 µm<sup>2</sup> and oscillation step was 0.1°. The crystals belonged to space group I222 with unit cell parameters a~76, b~137, c~207 Å. 74 datasets were indexed and integrated using XDS with <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/kamo-en.md">KAMO</a> pipeline. Finally 58 integrated results were merged at 3.5 Å resolution after CC-based clustering and outlier rejection by KAMO in the published result.</p> <p>Linker-PDE: 128 small-wedge (10°/crystal) datasets collected from different potions of a few large crystals broken in cryoloops using <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md">EIGER</a> X 9M detector at a wavelength of 1 Å on BL32XU, SPring-8. Beam size was around 15×10 µm<sup>2</sup> and oscillation step was 0.1°. The crystals belonged to space group C2 with unit cell parameters a~117, b~68, c~57 Å, β~111°. 109 datasets were indexed and integrated using XDS with <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/kamo-en.md">KAMO</a> pipeline. Finally 64 integrated results were merged at 2.1 Å resolution after CC-based clustering and outlier rejection by KAMO in the published result.</p>
Raw data accompanying the manuscript "Multiscale and multimodal optical imaging of the human liver"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Multiscale and multimodal optical imaging of the human liver". The file CARS_SRS.zip contains folders with all raw CARS and SRS data (TIFF format). The file CLSM.zip contains confocal laser scanning microscopy data using the manufacturers data format (Zeiss). The file LSFM.zip contains light sheet fluorescence microscopy data files using the manufacturers data format (LaVision Biotec). The file OPT.zip contains raw optical projection tomography data at different excitation wavelengths (TIFF format). The file SRSIM.zip contains reconstructed structured illumination microscopy data files (TIFF format).</p>
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 9 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 9 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_566-DJI_570 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in the Vaigat Strait (NW Greenland) on 3 August 2019
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 3 August 2019. The UAV survey commenced at 15:06 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in the Vaigat Strait (NW Greenland) on 6 August 2019
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 6 August 2019. The UAV survey commenced at 11:10 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p> <p> </p>
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 22 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_659-662 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 17 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_528, 530, 531-533 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 11 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 11 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_330, 332, 334, 335, 336, 337, 338, 339 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
Raw data used for COI delineation of the Eupolybothrus species: Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
<p>Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar</p>
Raw diffraction images of polyhedra in-vivo crystals
<p>Diffraction images of wild type cypovirus polyhedra in-vivo crystals (WTPhC) and the mutant (Δ3-PhC) related to PDB codes 5GQM and 5GQN, respectively.</p> <p>Small-wedge (5°/crystal) datasets were collected from loop-harvested microcrystals using EIGER X 9M detector at a wavelength of 1 Å on BL32XU, SPring-8. The datasets for 5GQN were collected automatically using ZOO system.</p> <p>The crystals belonged to space group <em>I</em>23 with unit cell parameter a~103 Å. 14 and 41 datasets were merged at 1.68 and 1.55 Å resolution in the published result (Abe <em>et al</em>. <em>ACS Nano</em> 2017; PDB codes: 5GQM & 5GQN, respectively) using KAMO; see https://github.com/keitaroyam/yamtbx/wiki/Processing-Polyhedra-data-(5GQM-&-5GQN)</p> <p>NOTE</p> <ul> <li> <p>flatfield correction was not applied to the images and you need to apply it using the correction table saved in master.h5 files.</p> </li> <li> <p>master.h5 files were modified; see https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md</p> </li> <li> <p>Most frames have ice (rings).</p> </li> </ul>
RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment
<p>This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level.. <br><br>The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops.</p> <p>The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number><br><strong>_1</strong>: Blue<br><strong>_2</strong>: Green<br><strong>_3</strong>: Red<br><strong>_4</strong>: Near Infrared<br><strong>_5</strong>: RedEdge<br><br>Example: An image name <strong>IMG_0200_3 </strong>represents the scene number<strong> 200</strong> in <strong>Red channel</strong></p> <p>This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format.</p> <p>The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article. <br><br>Field experimentation roles:</p> <p>The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.</p> <p>Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. </p> <p>Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.</p>
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.