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.
92
datasets available to search
ShareScore release 0.9.0
Dataset results
92 results for “Histological image”
Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the English Channel (ICES area 27.7.d) stock
<p><strong>Contents: </strong></p> <p>This dataset can be completed with the : <strong>Macroscopic, histological and stereological image dataset of the Striped red mullet (<em>Mullus surmuletus</em>) ovaries from the Bay of Biscay (ICES area 27.7.g,j & 27.8.a-c) stock</strong>, which can also be found on the Zenodo repository.</p> <p>This dataset contains the macroscopic and histological images of the ovaries of 214 Striped red mullet (female, <em>Mullus surmuletus</em>, Linnaeus 1758) collected from the English Channel stock (ICES area 27.7.d) in February 2021 (n=20), March 2021 (n=13), April 2021 (n=12), May 2021 (n=15), August 2021 (n=15), September 2021 (n=15), October 2021 (n=41), November 2021 (n=10), December 2021 (n=14), January 2022 (n=30), February 2022 (n=15) and August 2022 (n=14).</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li><strong>Macroscopic_pictures.zip: </strong>archive in zip format of 621 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 211 female Striped red mullets dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder <em>et al.</em>, 2022) with : <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish’s ID number.</li> </ul> </li> </ul> <ul> <li><strong>Histology_slides.zip :</strong> archive in zip format containing the ovarian histological slides digitized using an Olympus V120 slide scanner, x20 lens. The pictures (.vsi from the OlympusVSI format) are of the 484 histological slides acquired during this study.</li> <li>Data was split for smaller size downloads : <ul> <li><strong>Histology_slides_1of5 :</strong> histological sections for individuals numbered 001 to 045</li> <li><strong>Histology_slides_2of5 :</strong> histological sections for individuals numbered 046 to 138</li> <li><strong>Histology_slides_3of5 :</strong> histological sections for individuals numbered 154 to 180</li> <li><strong>Histology_slides_4of5 :</strong> histological sections for individuals numbered 196 to 270</li> <li><strong>Histology_slides_5of5 :</strong> histological sections for individuals numbered 271 to 334</li> </ul> </li> </ul> <p> </p> <p><strong>Data:</strong></p> <ul> <li><strong>Readings.zip :</strong> archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li><strong>Calibration</strong> : Reading results of 4 different agents, with the first and last readings, as well as the Qupath scripts used.</li> <li><strong>Homogeneity</strong> : Reading results for 96 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 96 slides belong to 16 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li><strong>Total </strong>: Reading results for 214 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> </ul> <ul> <li><strong>Macro_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macro_MULL.xlsx</strong> file, as well as their meaning.</li> <li><strong>Macro_MULL.xlsx</strong> : Excel file (.xlsx) containing measurements of macroscopic parameters for all 214 fish sampled during this study. The information contained in this table is as follows: <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (<strong>Macroscopic_pictures_Data</strong>)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode <em>et al.</em>, 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish’s otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish’s gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </li> </ul> <ul> <li><strong>Stereo_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Stereo_MULL.csv</strong> file, as well as their meaning.</li> <li><strong>Stereo_MULL.csv</strong> : a text data file (.csv) of the stereology count results of 294 slides read during this study. Among these slides, 96 were read to test the homogeneity distribution of different cell types found throughout each ovary (16 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see <strong>Calibration</strong> folder for reading results of the 4 agents). Finally, 214 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin <em>et al.</em> 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with <strong>Fish_id</strong></li> </ul> </li> </ul>
Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the Bay of Biscay (ICES area 27.7.g,j & 27.8.a-c) stock
<p><strong>Contents: </strong></p> <p>This dataset can be completed with the : <strong>Macroscopic, histological and stereological image dataset of the Striped red mullet (<em>Mullus surmuletus</em>) ovaries from the English Channel (ICES area 27.7.d) stock</strong>, which can also be found on the Zenodo repository.</p> <p>This dataset contains the macroscopic and histological images of the ovaries of 103 Striped red mullet (female, <em>Mullus surmuletus</em>, Linnaeus 1758) collected from the Bay of Biscay stock (ICES areas 27.7.j,g & 27.8.a-c) in November 2020 (n=9), May 2021 (n=11), June 2021(n=7), July 2021 (n=15), September (n=15), October 2021 (n=3), November 2021 (n=27) and February 2022 (n=15).</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li><strong>Macroscopic_pictures.zip: </strong>archive in zip format of 290 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 103 female Striped red mullets dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder <em>et al.</em>, 2022) with : <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish’s ID number.</li> </ul> </li> </ul> <ul> <li><strong>Histology_slides.zip:</strong> archive in zip format containing the ovarian histological slides digitized using an Olympus V120 slide scanner, x20 lens. The pictures (.vsi from the OlympusVSI format) are of the 264 histological slides acquired during this study. Data was split for smaller size downloads : <ul> <li><strong>Histology_slides_1of3 :</strong> histological sections for individuals numbered 062 to 094</li> <li><strong>Histology_slides_2of3 :</strong> histological sections for individuals numbered 100 to 250</li> <li><strong>Histology_slides_3of3 :</strong> histological sections for individuals numbered 290 to 304</li> </ul> </li> </ul> <p> </p> <p><strong>Data:</strong></p> <ul> <li><strong>Readings.zip:</strong> archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li><strong>Calibration </strong>: Reading results of 4 different agents, with the first and last readings, as well as the Qupath scripts used<strong>.</strong></li> <li><strong>Homogeneity</strong> : Reading results for 84 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 84 slides belong to 14 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li><strong>Total</strong> : Reading results for 103 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> </ul> <ul> <li><strong>Macro_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macro_MULL.xlsx</strong> file, as well as their meaning.</li> <li><strong>Macro_MULL.xlsx</strong> : Excel file (.xlsx) containing measurements of macroscopic parameters for all 103 fish sampled during this study. The information contained in this table is as follows: <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (<strong>Macroscopic_pictures_Data</strong>)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode <em>et al.</em>, 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish’s otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish’s gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </li> </ul> <ul> <li><strong>Stereo_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Stereo_MULL.csv</strong> file, as well as their meaning.</li> <li><strong>Stereo_MULL.csv</strong> : a text data file (.csv) of the stereology count results of 173 slides read during this study. Among these slides, 84 were read to test the homogeneity distribution of different cell types found throughout each ovary (14 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see <strong>Calibration</strong> folder for reading results of the 4 agents). Finally, 103 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin <em>et al.</em> 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with <strong>Fish_id</strong></li> </ul> </li> </ul>
2 million histological images of breast cancer tumors with her2 labels
<p><strong>Data Description</strong><br> This is a 2 million set of non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human breast cancer tumor tissue.</p> <p>The anonymized dataset comes from a cohort of BC patients from the A. C. Camargo Cancer Center (ACCCC, N = 504). All patients were treated for breast cancer at the ACCCC between 2019 and 2021. As part of their diagnosis, in HER2 IHC score 2+ cases, patients' HER2 status was determined following the ASCO guidelines updated in 2018, with visual evaluation of IHC assay and either a FISH or DDISH test. All cases with metastasis or neoadjuvant treatment were excluded.</p> <p>A total of 426 H&E stained high resolution images (40x magnification) were scanned from biopsy and resection tissue samples with a Leica Aperio AT2 scanner. Ethical approval of the ACCCC study was given by the ethics committee of the Fundação Antônio Prudente. We divided the cases into the following 3 groups according to the results of the IHC and ISH tests: HER2-negative, HER2-low and HER2-high.</p> <p>The slides were divided into 256 px x 256 px tiles at 0.5 um/pixel magnification. Then, we used a custom trained ConvNext-tiny neural network to only include tiles from the tumor region and its environment, generating a total of 2051877 image patches.</p> <p>A sample is considered her2-negative with an IHC score of 0; her2-low with an IHC score of 1+ or an IHC score of 2+ with a negative ISH-based test result, and her2-high with an IHC score of 2+ with a positive ISH-based test or an IHC score of 3+.</p> <p>The accompanying code used for training the models is available at https://github.com/tojallab/wsi-mil</p>
Macroscopic, histological and stereological image dataset of Megrim (Lepidorhombus whiffiagonis) ovaries from the ICES Celtic Seas, south of Greater North sea or Bay of Biscay Ecoregions
<p><strong>Contents: </strong></p> <p>This dataset contains the macroscopic and histological images of the ovaries of 202 Megrim (female, <em>Lepidorhombus whiffiagonis</em>, Walbaum, 1792) collected from the ICES Celtic Seas, south Greater North sea or Bay of Biscay Ecoregions (Eco) in November 2019 (n=25; Eco=7h & 7j), November 2020 (n=14, Eco=7h & 7j), December 2020 (n=1, Eco=7h), May 2021 (n=15, Eco=7h & 7g), June 2021 (n=15, Eco=7h), July 2021 (n=15, Eco=7h & 7e), October 2021 (n=15, Eco=7g & 7f), October 2021 (n=6, Eco=8a & 8b & 8c), November 2021 (n=6, Eco=8a & 8b), November 2021 (n=15, Eco=7j), December 2021 (n=15, Eco=7e & 7g), January 2022 (n=15, Eco=7f), February 2022 (n=15, Eco=7g), March 2022 (n=15, Eco=7g) and May 2022 (n=15, Eco=7g).</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li><strong>Macroscopic_pictures.zip: </strong>archive in zip format of 549 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 202 female megrim dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder <em>et al.</em>, 2022) with : <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish's ID number.</li> </ul> </li> <li><strong>Histology_slides.zip :</strong> archive in zip format containing the ovarian histological slides digitized using an Aperio CS (Scan Scope Console software, v.10.2.0.2352), x20 lens. The whole slide images (.svs) are of the 461 histological slides acquired during this study. </li> </ul> <p> </p> <p><strong>Data:</strong></p> <ul> <li><strong>Readings.zip :</strong> archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li><strong>Calibration</strong> : Reading results of 3 different agents, with the first and last readings, as well as the QuPath scripts used.</li> <li><strong>Homogeneity</strong> : Reading results for 102 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 102 slides belong to 17 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li><strong>Total </strong>: Reading results for 202 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> <li><strong>Macro_WHI_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macro_WHI.xlsx</strong> file, as well as their meaning.</li> <li><strong>Macro_WHI.xlsx</strong> : Excel file (.xlsx) containing measurements of macroscopic parameters for all 202 fish sampled during this study. The information contained in this table is as follows: <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (<strong>Macroscopic_pictures_Data</strong>)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode <em>et al.</em>, 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish's otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish's gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </li> <li><strong>Stereo_WHI_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Stereo_WHI.csv</strong> file, as well as their meaning.</li> <li><strong>Stereo_WHI.csv</strong> : a text data file (.csv) of the stereology count results of 287 slides read during this study. Among these slides, 102 were read to test the homogeneity distribution of different cell types found throughout each ovary (17 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see <strong>Calibration</strong> folder for reading results of the 3 agents). Finally, 202 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin <em>et al.</em> 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with <strong>Fish_id</strong></li> </ul> </li> </ul> <p> </p> <p><strong>Contact :</strong></p> <p>This dataset was established under the MATO (MATurité Objectif des poissons par l'histologie quantitative) project, during the PhD of Carine Sauger (October 2021-2023), financed by France Fillière Pêche (FFP/2020/AM/MF/109), under the supervision of IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) and BOREA (Biologie des Organismes et Ecosystèmes Aquatiques), and with the collaboration of a research facility from the University of Caen-Normandie : CMABIO3 (Centre de Microscopie Appliquée à la Biologie). For any enquiries, please contact: carine.sauger@gmail.com or laurent.dubroca@ifremer.fr</p>
CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
<p>This is the dataset from the preprint "Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images" by Raufeisen et al. (2024). It contains 6,683 annotations (3,991 nuclei and 2,607 whole cells) of head and neck squamous cell carcinoma cells in hematoxylin and eosin stained histological images. The annotations are in COCO format and distributed over 83 PNG images. Cyto R-CNN was trained on this dataset and compared with other state-of-the-art methods. The CytoNuke dataset is released under the CC BY 4.0 license.</p> <p>The histological images are from the CPTAC dataset:<br>National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2018). The Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma Collection (CPTAC-HNSCC) (Version 15) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.UW45NH81</p> <p>Funding: Behrus Puladi was funded by the Medical Faculty of RWTH Aachen University as part of the Clinician Scientist Program. We acknowledge FWF enFaced 2.0 [KLI 1044, https://enfaced2.ikim.nrw/] and KITE (Plattform für KI-Translation Essen) from the REACT-EU initiative [https://kite.ikim.nrw/, EFRE-0801977]. Fabian Hörst, Jianning Li, Jens Kleesiek and Jan Egger received funding from the Cancer Research Center Cologne Essen (CCCE).</p>
Nissl_4, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains some images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Nissl_3, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Nissl_2, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Nissl_1, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p> <p> </p>
Nissl_6, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p> <p> </p> <p> </p>
Nissl_5, Raw images for Machine learning for histological annotation and quantification of cortical layers
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Fig. 1. Histological image depicting a in Host-parasite interactions during a biological invasion: The fate of lungworms (Rhabdias spp.) inside native and novel anuran hosts
Fig. 1. Histological image depicting a transverse section of (a) R. hylae larva in the connective tissue of the head of a cane toad and (b) the inflammatory response composed primarily of macrophages and multinucleated giant cells surrounding the parasite. Haematoxylin and eosin stain, 400× magnification, scale bar equals 30 μm.
100,000 histological images of human colorectal cancer and healthy tissue
<p><strong>Data Description "NCT-CRC-HE-100K"</strong></p> <ul> <li>This is a set of 100,000 non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue.</li> <li>All images are 224x224 pixels (px) at 0.5 microns per pixel (MPP). All images are color-normalized using Macenko's method (http://ieeexplore.ieee.org/abstract/document/5193250/, DOI <a href="https://doi.org/10.1109/ISBI.2009.5193250">10.1109/ISBI.2009.5193250</a>).</li> <li>Tissue classes are: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM).</li> <li>These images were manually extracted from N=86 H&E stained human cancer tissue slides from formalin-fixed paraffin-embedded (FFPE) samples from the NCT Biobank (National Center for Tumor Diseases, Heidelberg, Germany) and the UMM pathology archive (University Medical Center Mannheim, Mannheim, Germany). Tissue samples contained CRC primary tumor slides and tumor tissue from CRC liver metastases; normal tissue classes were augmented with non-tumorous regions from gastrectomy specimen to increase variability.</li> </ul> <p><strong>Ethics statement "NCT-CRC-HE-100K"</strong></p> <p>All experiments were conducted in accordance with the Declaration of Helsinki, the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS), the Belmont Report and the U.S. Common Rule. Anonymized archival tissue samples were retrieved from the tissue bank of the National Center for Tumor diseases (NCT, Heidelberg, Germany) in accordance with the regulations of the tissue bank and the approval of the ethics committee of Heidelberg University (tissue bank decision numbers 2152 and 2154, granted to Niels Halama and Jakob Nikolas Kather; informed consent was obtained from all patients as part of the NCT tissue bank protocol, ethics board approval S-207/2005, renewed on 20 Dec 2017). Another set of tissue samples was provided by the pathology archive at UMM (University Medical Center Mannheim, Heidelberg University, Mannheim, Germany) after approval by the institutional ethics board (Ethics Board II at University Medical Center Mannheim, decision number 2017-806R-MA, granted to Alexander Marx and waiving the need for informed consent for this retrospective and fully anonymized analysis of archival samples).</p> <p><strong>Data set "CRC-VAL-HE-7K"</strong></p> <p>This is a set of 7180 image patches from N=50 patients with colorectal adenocarcinoma (no overlap with patients in NCT-CRC-HE-100K). It can be used as a validation set for models trained on the larger data set. Like in the larger data set, images are 224x224 px at 0.5 MPP. All tissue samples were provided by the NCT tissue bank, see above for further details and ethics statement.</p> <p><strong>Data set "NCT-CRC-HE-100K-NONORM"</strong></p> <p>This is a slightly different version of the "NCT-CRC-HE-100K" image set: This set contains 100,000 images in 9 tissue classes at 0.5 MPP and was created from the same raw data as "NCT-CRC-HE-100K". However, no color normalization was applied to these images. Consequently, staining intensity and color slightly varies between the images. Please note that although this image set was created from the same data as "NCT-CRC-HE-100K", the image regions are not completely identical because the selection of non-overlapping tiles from raw images was a stochastic process.</p> <p><strong>General comments</strong></p> <p>Please note that the classes are only roughly balanced. Classifiers should never be evaluated based on accuracy in the full set alone. Also, if a high risk of training bias is excepted, balancing the number of cases per class is recommended.</p>
Macroscopic, histological and stereological image dataset of Four-Spot megrim (Lepidorhombus boscii) ovaries from the ICES Celtic Seas and Bay of Biscay Ecoregions
<p><strong>Contents: </strong></p> <p>This dataset contains the macroscopic and histological images of the ovaries of 68 Four-spot megrim (female, <em>Lepidorhombus boscii</em> (Risso, 1810)) collected from the ICES Celtic Seas or Bay of Biscay Ecoregions (Eco) in November 2019 (n=25; Eco=7j), November 2020 (n=16, Eco=7h & 7j), October 2021 (n=12, Eco=8a,b,c) and November 2021 (n=15, Eco=7j) during the annual scientific campaign EVHOE (Evaluation Halieutique Ouest de l'Europe).</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li><strong>Macroscopic_pictures.zip: </strong>archive in zip format of 139 pictures (.JPG; 2Mo-6Mo; JPG; 350pp) from 51 female Four-spot megrim dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder <em>et al.</em>, 2022) with : </li> <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish's ID number.</li> </ul> <li><strong>Histology_slides.zip :</strong> archive in zip format containing the ovarian histological slides digitized using an Aperio CS (Scan Scope Console software, v.10.2.0.2352), x20 lens. The whole slide images (.svs) are of the 218 histological slides acquired during this study. </li> </ul> <p> </p> <p><strong>Data:</strong></p> <ul> <li><strong>QuPath.zip :</strong> archive in zip format containing the reading grids for the stereology readings of the histological cross sections of the ovaries under QuPath. For more information on QuPath stereology readings, see Dubroca <em>et al.</em> (2023) in <strong>References</strong>.</li> <ul> <li><strong>Stereo_BOS_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the stereology reading grids under QuPath, as well as their meaning</li> </ul> <li><strong>Macro_BOS_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macro_BOS.xlsx</strong> file, as well as their meaning.</li> <li><strong>Macro_BOS.csv</strong> : Data file (.csv) containing measurements of macroscopic parameters for all 68 fish sampled during this study. The information contained in this table is as follows: </li> <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (<strong>Macroscopic_pictures_Data</strong>)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode <em>et al.</em>, 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish's otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish's gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </ul> <p> </p> <p><strong>Contact :</strong></p> <p>This dataset was established under the MATO (MATurité Objectif des poissons par l'histologie quantitative) project, during the PhD of Carine Sauger (October 2021-2023), financed by France Fillière Pêche (FFP/2020/AM/MF/109), under the supervision of IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) and BOREA (Biologie des Organismes et Ecosystèmes Aquatiques), and with the collaboration of a research facility from the University of Caen-Normandie : CMABIO3 (Centre de Microscopie Appliquée à la Biologie). For any enquiries, please contact: carine.sauger@gmail.com or laurent.dubroca@ifremer.fr</p>
Azcorra2023 - Histological images for fiber placement localization
<p>Microscopy images of coronal mouse brain slices used for localization of fiber placement in striatum after acute fiber photometry experiments, as used in Azcorra et al. Nat Neuro 2023.</p>
Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes - Supplementary Figure 4 representative histology images
<p>This includes the original full-size representative histology images presented in Supplementary Fig. 4 for the paper "Zhang*, Jonsson*, Nathan*, Millard*, et al, Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes, Nature, 2023<strong>"</strong>.</p> <p>Representative fragments from patients in each CTAP, showing composite and individual staining of each marker in the lymphocyte panel or stromal cell panel. A total of 150 fragments from 36 individuals (mean 4.2 fragments per individual, range 2-9 fragments per individual) were stained in batches and analyzed as a single cohort.</p>
Image tiles of TCGA-CRC-DX histological whole slide images, non-normalized, tumor only
<p>These are image tiles of tumor tissue of N=604 colorectal cancer (CRC) histological whole slide images in the TCGA database. The tumor tissue was manually outlined and cut into non-overlapping tiles of 512x512 px at 0.5 µm/px. No further preprocessing was applied. No color normalization was applied. Tiles are compressed with JPEG. Each ZIP file corresponds to one whole slide image (original format: SVS).</p> <p>Please cite our previous publication related to this dataset: https://www.nature.com/articles/s41591-019-0462-y</p> <p>Please observe the original TCGA licenses if you use the data https://portal.gdc.cancer.gov/</p> <p>Original image credit to TCGA: https://portal.gdc.cancer.gov/</p> <p>Image tiles were created with QuPath v0.1.2 https://qupath.github.io</p> <p>Genetic data matching these images are available at https://cbioportal.org</p> <p>More information on the procedures: https://zenodo.org/record/3694994</p>
Breast carcinoma histological images from the Department of Pathology, "Agios Pavlos" General Hospital of Thessaloniki, Greece
<p>This dataset contains cases of breast carcinoma histological specimens received at the Department of Pathology, “Agios Pavlos” General Hospital of Thessaloniki, Greece. The dataset consists of 300 annotated images of resolution 1280x960 corresponding to 21 different patients with invasive ductal carcinoma of the breast of grades 1-3 (grouped in the corresponding folders).</p> <p>More details about the dataset are presented in the paper: </p> <p>K. Dimitropoulos, P. Barmpoutis, C. Zioga, A. Kamas, K. Patsiaoura and N. Grammalidis, "Grading of invasive breast carcinoma through Grassmannian VLAD encoding", submitted for publication to PLOS ONE, 2017.</p>
Example image and Model for virtual histological staining of unlabeled autopsy tissue
<p>Example images can be found at "Example_images.zip". Here's what you will find:</p><ul><li><strong>Input</strong>: Autofluorescence images of unstained autopsy tissue sections; each FOV corresponds to a .mat file, with the 1st channel representing the DAPI channel and the 2nd channel representing the TxRed channel.</li><li><strong>Output</strong>: Virtually stained H&E images generated by the network model.</li><li><strong>Ground Truth</strong>: Histochemically stained H&E images for reference/comparison.</li></ul><p>10 individual FOVs are provided as example images. Each FOV measures 2048 x 2048 pixels, corresponding to roughly 333 x 333 µm². The 10 FOVs are labeled from "001" to "010".</p><ul><li>"001"-"005": These correspond to tissue regions suffering from severe autolysis, rendering staining artifacts in the histochemically stained images (ground truth).</li><li>"006"-"010": These correspond to well-preserved tissue regions with high histochemical staining quality.</li></ul><p>How to use the model file and the corresponding codes can be found at: https://github.com/liyuzhu1998/Autopsy-Virtual-Staining/tree/main</p>
Cell Graph data for predicting MSI vs. MSS from histological images
<p><strong>Abstract: </strong>The cell graph data extracted from histological images for predicting microstatellite status. This is a patch-level (patch refers to the sub-image tessellated from the whole-slide image) binary classification task: the two datasets classify images patches to either MSS (microsatellite stable) or MSIMUT (microsatellite instable or highly mutated). The original histology patches are available from [1]. </p> <p><strong>Cell Graph Contraction:</strong> In this dataset, we establish a cell graph for each patch. The cell graph can represent the cell-cell interaction and the collection of cell graphs for all patches provide a precise characterization of tumor microenvironment. With only the availability of raw image patches, we leverage the nuclei regions segmented by a well-tuned CA<sup>2.5</sup>-Net [2] to extract the node features of each single nuclei node. And then we extract a total number of 94 pre-defined pathomics features for each nuclei region as the corresponding graph node feature. As the morphological signals are believed relative to cell-cell interplay, the cell-specific features which include the nuclei coordination, optical, and representations, then characterize the cell-level morphological behavior. We then calculate the pair-wise Euclidean distance between nuclei centroids to establish edges of a cell graph</p> <p><strong>Reference</strong></p> <p>For more details about the construction of cell-graph, please visit our paper: https://arxiv.org/abs/2206.07599.</p> <p>The associated codes and usage for the datasets are available at: https://github.com/yiqings/HEGnnEnhanceCnn.</p> <p>[1] Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples. https://doi.org/10.5281/zenodo.2530835</p> <p>[2] Huang, J., Shen, Y., Shen, D., & Ke, J. (2021, September). CA 2.5-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection. In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em> (pp. 445-454). Springer, Cham.</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.