Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
15
datasets available to search
ShareScore release 0.9.0
Dataset results
15 results for “re-identification”
Animal Re-Identification from Video
<p>Repository of annotated videos, images and extracted features of multiple animals</p> <p> </p> <p><strong>1. Videos</strong></p> <p>The videos are available in the file "videos.zip".</p> <p>The original videos included in this repository have been sourced from <a href="https://pixabay.com/">Pixabay</a> under Pixabay License</p> <ul> <li>Free for commercial use</li> <li>No attribution required</li> </ul> <p>The video data is summarised below:</p> <table> <thead> <tr> <th><em>Short Name</em></th> <th><em>Video Name</em></th> <th><em># Frames</em></th> <th><em>Size</em></th> <th><em># Bounding boxes</em></th> <th><em># Identities</em></th> </tr> </thead> <tbody> <tr> <td>Pigs</td> <td>Pigs_49651_960_540_500f.mp4</td> <td>500</td> <td>( 960, 540)</td> <td>6184</td> <td>26</td> </tr> <tr> <td>Koi fish</td> <td>Koi_5652_952_540.mp4</td> <td>536</td> <td>( 952, 540)</td> <td>1635</td> <td>9</td> </tr> <tr> <td>Pigeons (curb)</td> <td>Pigeons_8234_1280_720.mp4</td> <td>443</td> <td>(1280, 720)</td> <td>4700</td> <td>16</td> </tr> <tr> <td>Pigeons (ground)</td> <td>Pigeons_4927_960_540_600f.mp4</td> <td>600</td> <td>( 960, 540)</td> <td>3079</td> <td>17</td> </tr> <tr> <td>Pigeons (square)</td> <td>Pigeons_29033_960_540_300f.mp4</td> <td>300</td> <td>( 960, 540)</td> <td>4892</td> <td>28</td> </tr> </tbody> </table> <p> </p> <p><strong>2. Annotated videos</strong></p> <p>The annotated videos are available in the file "annotated_videos.zip":</p> <ul> <li>Annotated_Pigs_49651_960_540_500f.mp4. Annotation contributed by <a href="https://github.com/LucyKuncheva/Animal-Identification-from-Video/blob/main/mas00a@bangor.ac.uk">Lucy Kuncheva</a></li> <li>Annotated_Koi_5652_952_540.mp4. Annotation contributed by <a href="https://github.com/LucyKuncheva/Animal-Identification-from-Video/blob/main/mas00a@bangor.ac.uk">Lucy Kuncheva</a></li> <li>Annotated_Pigeons_8234_1270_720.mp4. Annotation contributed by <a href="https://github.com/LucyKuncheva/Animal-Identification-from-Video/blob/main/wll19pkk@bangor.ac.uk">Wilf Langdon</a></li> <li>Annotated_Pigeons_4927_960_540_600f.mp4. Annotation contributed by <a href="https://github.com/LucyKuncheva/Animal-Identification-from-Video/blob/main/eeub05@bangor.ac.uk">Frank Krzyzowski</a></li> <li>Annotated_Pigeons_29033_960_540_300f.mp4. Annotation contributed by <a href="https://github.com/LucyKuncheva/Animal-Identification-from-Video/blob/main/wnw19njx@bangor.ac.uk">Owen West</a></li> </ul> <p> </p> <p><strong>3. Images</strong></p> <p>The individual images are in the file "images.zip".</p> <p>For each video, all the images are in the corresponding folder. Inside, there is a folder for each individual with all the images. The filename of each image includes the frame number.</p> <p> </p> <p><strong>4. Frames information</strong></p> <p>The correspondence between images and frames in the videos are in the file "frames.zip"</p> <p>The prefixes "h1_" and "h2_" denote, respectively, the first and second halves of the videos.</p> <p>The columns on these files are:</p> <ul> <li>x, y: coordinates in pixels of the top left corner of the bounding box.</li> <li>width, height: of the bounding box in pixels.</li> <li>frame: frame number.</li> <li>max_w, max_h.</li> <li>label: the label (class) number.</li> <li>image: file name.</li> </ul> <p> </p> <p><strong>5. Extracted features</strong></p> <p>Files with the extracted features are in "features.zip".</p> <p>The prefixes "h1_" and "h2_" denote, respectively, the data corresponding to the first and second halves of the videos.</p> <p>Five representations are used:</p> <ul> <li>"RGB" moments.</li> <li>"HOG": Histogram of Oriented Gradients</li> <li>"LBP": Local Binary Patterns.</li> <li>"AE": AutoEncoders.</li> <li>"MN2": extracted from a Keras MobileNetV2 model pre-trained on Imagenet</li> </ul> <p>The representation appears as a postfix in the file names.</p> <p>In each csv file, each image appears as a row. The feature values followed by the label (class) number.</p> <p> </p> <p><strong>6. Source code</strong></p> <p>Sample code (matlab & python) is available at <a href="https://github.com/admirable-ubu/animal-recognition">https://github.com/admirable-ubu/animal-recognition</a></p> <p> </p>
pallet-block-502 – A chipwood re-identification dataset
<p>The dataset "pallet-block-502" contains images of 502 chipwood pallet blocks, of which 10 pictures each were taken from different, labeled perspectives, amounting to a total of 5020 images. This dataset is an integral part of our publication <a href="https://ieeexplore.ieee.org/abstract/document/9613250">"Towards Re-Identification for Warehousing Entities – A Work-in-Progress Study"</a>, in which it was used for the purpose of re-identification.<strong> Therefore, if you use this dataset for research, please cite this publication.</strong></p> <p>The specifications of the camera models that were used can be found on <a href="https://github.com/ChrsPi/Towards-Re-Identification-for-Warehousing-Entities">github</a>. Further information on how to use the dataset can be taken from the above mentioned publication. The sucessor of this dataset is <a href="https://zenodo.org/record/6358607">"pallet-block-32965"</a>. Both datasets are part of a collaboration between TU Dortmund University and Fraunhofer IML. Another related re-identification dataset is <a href="https://zenodo.org/record/7386956">"galvanized-636"</a>.</p> <p>If you have any questions concerning this dataset, feel free to contact the corresponding author, <a href="https://www.linkedin.com/in/jeromerutinowski/">Jérôme Rutinowski</a>.</p> <p>This work is part of the project "Silicon Economy Logistics Ecosystem" which is funded by the German Federal Ministry of Transport and Digital Infrastructure.</p>
pallet-block-32965 – A chipwood re-identification dataset
<p>The dataset "<strong>pallet-block-32965</strong>" contains images of 32965 chipwood pallet blocks, of which 4 pictures each were taken from 2 different perspectives, with 2 different cameras, amounting to a total of 131860 images. This dataset is the successor of the dataset <a href="https://zenodo.org/record/6353714">"pallet- block-502"</a>. This dataset is an integral part of our publication "<a href="https://ieeexplore.ieee.org/abstract/document/10068869">Deep Learning Based Re-Identification of Wooden Euro-pallets</a>", in which it was used for the purpose of re-identification. <strong>Therefore, if you use this dataset for research, please cite this publication.</strong></p> <p>This dataset is part of a collaboration between TU Dortmund University and Fraunhofer IML. Another related re-identification dataset is <a href="https://zenodo.org/record/7386956">"galvanized-636"</a>. If you have any questions concerning this dataset, feel free to contact <a href="https://www.linkedin.com/in/jeromerutinowski/">Jérôme Rutinowski</a>.</p> <p>This work is part of the project "Silicon Economy Logistics Ecosystem" which is funded by the German Federal Ministry of Transport and Digital Infrastructure.</p>
galvanized-636 – A galvanized steel re-identification dataset
<p>The dataset "galvanized-636" contains images of 636 sheets of galvanized steel, of which four pictures each were taken per side from different, labeled perspectives, amounting to a total of 5,088 images. This dataset can be used for the purpose of re-identification.<strong> Therefore, if you use this dataset for research, please cite us using the Zenodo DOI.</strong></p> <p>The recording specifications are the following:</p> <ul> <li>Camera: Canon EOS 6D (with ambient lighting: ISO 1600; white balance; 1/50 shutter; F22 aperture. With photography lighting: ISO 1600; 1/30 shutter; F22 aperture)</li> <li>Naming convention: first integer = number of metal sheet (ms); f = front; b = back; AL = ambient lighting; PL = photography lighting; 75/90 = recording angle</li> <li>Images per sheet: Combination of AL/PL + 75°/90° --> 4 images per sheet</li> </ul> <p>The predecessors of this dataset are <a href="https://zenodo.org/record/6353714">"pallet- block-502"</a> and <a href="https://zenodo.org/record/6358607">"pallet-block-32965"</a>. If you have any questions concerning these datasets, feel free to contact the corresponding author, <a href="https://www.linkedin.com/in/jeromerutinowski/">Jérôme Rutinowski</a>.</p> <p>This work is part of the project "Silicon Economy Logistics Ecosystem" which is funded by the German Federal Ministry of Transport and Digital Infrastructure.</p>
PolarBearVidID: A Video-based Re-Identification Benchmark Dataset for Polar Bears
<p><em><strong>The peer-reviewed publication for this dataset has now been published in Animals, an MDPI journal, and can be accessed here: <a href="https://doi.org/10.3390/ani13050801">https://doi.org/10.3390/ani13050801</a>. Please cite this when using the dataset.</strong></em></p> <p><em>PolarBearVidID</em> includes 13 individual polar bears housed in six institutions. Each identity has 110 sequences on average. The maximum length of the sequences is 8 seconds, respectively 100 frames at a frame rate of 12.5 frames per second. The average length of the sequences is 96.69 images. In total, the dataset includes 1431 sequences. The resolution of the images is set to 256 x 128 pixels. Finally, <em>PolarBearVidID</em> is the first dataset to enable utilizing the movement of a non-human species as a feature for the task of re-identification.</p>
FIGURE 6 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 6. Sergio guassutinga (Rodrigues, 1971): male (cl 20.5 mm) from Icapuí, Ceará, Brazil (MZUSP 32610), A, dorsal view; B, lateral view; C, left (major) first cheliped, lateral view. Scale bars: 8 mm.
FIGURE 2. Neocallichirus maryae Karasawa, 2004 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 2. Neocallichirus maryae Karasawa, 2004: male (cl 17.8 mm) from Icapuí, Ceará, Brazil (MZUSP 34939), A, telson, dorsal view; B, same, detail of posterolateral margin; C, right uropod, dorsal view. Scale bars as indicated.
FIGURE 9 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 9. Sergio guara (Rodrigues, 1971): male (cl 10.5 mm) from Icapuí, Ceará, Brazil (MZUSP 35380), A, dorsal view; B, lateral view; C, first chelipeds, right lateral view; D, major first cheliped, left lateral view. Scale bars: 10 mm.
FIGURE 1. Neocallichirus maryae Karasawa, 2004 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 1. Neocallichirus maryae Karasawa, 2004: male (cl 8.0 mm) from Paracuru, Ceará, Brazil (LIMCE-UFC 515), A, dorsal view; B, lateral view; C, left (major) first cheliped, lateral view. Scale bars: 4 mm.
FIGURE 7 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 7. Sergio guassutinga (Rodrigues, 1971) (C, D): male (cl 20.5 mm) from Icapuí, Ceará, Brazil (MZUSP 32610), A, telson, dorsal view; B, same, detail of posterolateral margin; C, right uropod, dorsal view. Scale bars as indicated.
FIGURE 5 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 5. Neocallichirus cacahuate Felder & Manning, 1995: male (cl 8.4 mm) from Icapuí, Ceará, Brazil (MZUSP 32615), A, major first cheliped, lateral view, in situ; B, minor first cheliped, lateral view; C, same, detail of fingers, mesial view; D, first pleopod, ventral view; E, second pleopod, ventral view. Scale bars as indicated.
FIGURE 4 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 4. Neocallichirus cacahuate Felder & Manning, 1995: male (cl 8.4 mm) from Icapuí, Ceará, Brazil (MZUSP 32615), A, frontal margin and frontal appendages, dorsal view; B, same, lateral view; C, left third maxilliped, distal four articles, lateral view; D, telson, dorsal view; E, same, detail of posterolateral margin; F, right uropod, dorsal view. Scale bars as indicated.
FIGURE 3 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 3. Neocallichirus cacahuate Felder & Manning, 1995: male (cl 8.4 mm) from Icapuí, Ceará, Brazil (MZUSP 32615), A, dorsal view; B, C, lateral views. Scale bars: 6 mm.
FIGURE 8 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 8. Neocallichirus grandimana (Gibbes, 1850): male (cl 24.2 mm) from Meireles, Ceará, Brazil (NHMW 25556), A, dorsal view; B, left lateral view; C, first chelipeds, right lateral view; D, frontal region, dorsal view. Scale bars: 20 mm [A–C], 4 mm [D].
Re-identification of Individuals in Genomic Datasets Using Public Face Images
<p>Image-genome pairs in these synthetic datasets were created by combining a subset of the publicly available face image dataset, CelebA, and genotypes from OpenSNP. The genome in a given pair does not correspond to the individual in the image (taken from CelebA), but comes instead from an individual with the same set of phenotypes (taken from OpenSNP). Artificial genotypes were created for each image (genotype refers only to the small subset of SNPs we are interested in) using all available data from OpenSNP where self-reported phenotypes are present.</p> <p>In the Synthetic-Ideal dataset, to each image, we assigned a genotype from OpenSNP that corresponds to an individual with the same phenotypes, such that the probability of the selected phenotypes is maximized, given the genotype. In other words, we picked the genotype from the OpenSNP data that is most representative of an individual with a given set of phenotypes.</p> <p>In the Synthetic-Realistic dataset, to each image, we assigned a genotype from OpenSNP that corresponds to an individual with the same phenotypes, but at random according to the empirical distribution of phenotypes for particular SNPs in our data.</p> <p>Since CelebA does not have labels for all considered phenotypes, 1000 images from this dataset were manually labeled by one of the authors. After cleaning and removing ambiguous cases, the resulting datasets consist of 456 records.</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.