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CLDF dataset derived from Valenzuela, Pilar and Roberto Zariquiey's " Language classification in Western Amazonia: advances in favor of the Pano-Takana Hypothesis" from 2023
<p>Cite the source of the dataset as:</p> <blockquote> <p>Valenzuela, Pilar and Zariquiey, Roberto (2023). Language classification in Western Amazonia: advances in favor of the Pano-Takana Hypothesis. LIAMES: Línguas Indígenas Americanas, Campinas, SP. https://doi.org/10.20396/liames.v23i00.8670150</p> </blockquote>
[MedMNIST+] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification with Multiple Size Options: 28 (MNIST-Like), 64, 128, and 224
<h2><strong>Code</strong> [<a href="https://github.com/MedMNIST/MedMNIST" target="_blank" rel="noopener">GitHub</a>] | <strong>Publication</strong> [<a href="https://doi.org/10.1038/s41597-022-01721-8" target="_blank" rel="noopener">Nature Scientific Data'23</a> / <a href="https://doi.org/10.1109/ISBI48211.2021.9434062" target="_blank" rel="noopener">ISBI'21</a>] | <strong>Preprint</strong> [<a href="https://arxiv.org/abs/2110.14795" target="_blank" rel="noopener">arXiv</a>]</h2> <p> </p> <p><strong>Abstract</strong></p> <p>We introduce MedMNIST, a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST is designed to perform classification on lightweight 2D and 3D images with various data scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression and multi-label). The resulting dataset, consisting of approximately 708K 2D images and 10K 3D images in total, could support numerous research and educational purposes in biomedical image analysis, computer vision and machine learning. We benchmark several baseline methods on MedMNIST, including 2D / 3D neural networks and open-source / commercial AutoML tools. The data and code are publicly available at <a href="https://medmnist.com/">https://medmnist.com/</a>.</p> <p><em><strong>Disclaimer</strong></em>: The only official distribution link for the MedMNIST dataset is <a href="https://doi.org/10.5281/zenodo.10519652">Zenodo</a>. We kindly request users to refer to this original dataset link for accurate and up-to-date data.</p> <p><strong><em>Update</em>:</strong> We are thrilled to release <a href="https://github.com/MedMNIST/MedMNIST/blob/main/on_medmnist_plus.md">MedMNIST+</a> with larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D. As a complement to the previous 28-size MedMNIST, the large-size version could serve as a standardized benchmark for medical foundation models. Install the latest API to try it out!</p> <p> </p> <p><strong>Python Usage</strong></p> <p>We recommend our official <a href="https://github.com/MedMNIST/MedMNIST">code</a> to download, parse and use the MedMNIST dataset:</p> <blockquote> <pre>% pip install medmnist<br>% python</pre> <div> <div>To use the standard 28-size (MNIST-like) version utilizing the downloaded files:</div> <br> <div>>>> from medmnist import PathMNIST</div> <div>>>> train_dataset = PathMNIST(split="train")</div> <br> <div>To enable automatic downloading by setting `download=True`:</div> <br> <div>>>> from medmnist import NoduleMNIST3D</div> <div>>>> val_dataset = NoduleMNIST3D(split="val", download=True)</div> <br> <div>Alternatively, you can access MedMNIST+ with larger image sizes by specifying the `size` parameter:</div> <br> <div>>>> from medmnist import ChestMNIST</div> <div>>>> test_dataset = ChestMNIST(split="test", download=True, size=224)</div> </div> </blockquote> <p> </p> <p><strong>Citation</strong></p> <p>If you find this project useful, please cite both v1 and v2 paper as:</p> <blockquote> <p>Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni. Yang, Jiancheng, et al. "MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification." Scientific Data, 2023.</p> <p>Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis". IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.</p> </blockquote> <p>or using bibtex:</p> <blockquote> <pre>@article{medmnistv2, title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification}, author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing}, journal={Scientific Data}, volume={10}, number={1}, pages={41}, year={2023}, publisher={Nature Publishing Group UK London} } @inproceedings{medmnistv1, title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis}, author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing}, booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, pages={191--195}, year={2021} }</pre> </blockquote> <p>Please also cite the corresponding paper(s) of source data if you use any subset of MedMNIST as per the description on the <a href="https://medmnist.github.io/">project website</a>.</p> <p> </p> <p><strong>License</strong></p> <p>The MedMNIST dataset is licensed under <em>Creative Commons Attribution 4.0 International</em> (<a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>), except DermaMNIST under <em>Creative Commons Attribution-NonCommercial 4.0 International</em> (<a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a>).</p> <p>The code is under <a href="https://github.com/MedMNIST/MedMNIST/blob/main/LICENSE">Apache-2.0 License</a>.</p> <p> </p> <p><strong>Changelog</strong></p> <p><a href="https://doi.org/10.5281/zenodo.10519652">v3.0</a> (this repository): Released MedMNIST+ featuring larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D.</p> <p><a href="https://doi.org/10.5281/zenodo.10519195">v2.2</a>: Removed a small number of mistakenly included blank samples in OrganAMNIST, OrganCMNIST, OrganSMNIST, OrganMNIST3D, and VesselMNIST3D. </p> <p><a href="https://doi.org/10.5281/zenodo.6496656">v2.1</a>: Addressed an issue in the NoduleMNIST3D file (i.e., nodulemnist3d.npz). Further details can be found in this <a href="https://github.com/MedMNIST/MedMNIST/issues/22#issuecomment-1103438191">issue</a>.</p> <p><a href="https://doi.org/10.5281/zenodo.5208230">v2.0</a>: Launched the initial repository of MedMNIST v2, adding 6 datasets for 3D and 2 for 2D.</p> <p><a href="https://doi.org/10.5281/zenodo.4269852">v1.0</a>: Established the initial repository (in a separate repository) of MedMNIST v1, featuring 10 datasets for 2D.</p> <p> </p> <p><strong>Note</strong>: This dataset is <strong>NOT</strong> intended for clinical use.</p>
A Comprehensive Dataset for Webpage Classification (Part 3: Benign 2)
<p>This dataset, split across three parts due to Zenodo's size constraints, serves as a fundamental resource for enhancing webpage classification techniques. It encompasses 1,069,715 URLs, each annotated with labels to signify their categorization into Malicious, Benign, or Adult content, and further into 20 detailed sublabels for granular analysis. The dataset is designed to facilitate the evaluation and benchmarking of machine learning models, notably Stochastic Gradient Descent (SGD) and Support Vector Classifier (SVC), across a variety of tokenization methods and input types, including URLs, raw HTML, and parsed HTML content.</p> <p>The primary objective of assembling this dataset is to support research into effective webpage classification, thereby improving content prioritization and filtering in web crawling applications. It has been meticulously curated to provide a robust framework for studying the impact of different feature representation techniques on classification accuracy.</p> <p>The dataset is structured as JSON lines (jsonl) files, with each entry detailing a URL's label, sublabel, source, status code, and HTML content. This comprehensive dataset is divided into three parts due to size constraints on Zenodo, each targeting specific content categories to ensure ease of use and accessibility for researchers:</p> <ul> <li><strong>Part 1: Adult & Malicious</strong> encompasses URLs classified under Adult and Malicious categories, offering insights into content that requires stringent filtering.</li> <li><strong>Part 2: Benign 1</strong> and <strong>Part 3: Benign 2</strong> cover benign URLs, facilitating the study of safe web content and its classification nuances.</li> </ul> <p>We also created a .csv file without the HTML content so it is easier to work with URLs only, this .csv file contains the next columns `['uid', 'url', 'label', 'sublabel']`</p> <p>By providing this dataset, we aim to contribute significantly to the field of webpage classification, offering a valuable asset for researchers and practitioners looking to advance the state of web crawling technology and its applications.</p> <p> </p> <p>JSON line format for each line:</p> <pre><code>{"url": "<URL>", "label": "<Label>", "sublabel": "<Sublabel>", "source": "<Source of this URL>", "status_code": <Status Code>, "html": "<HTML Textual Content>"}</code><br><br></pre> <p><br><strong>Other parts of this dataset:</strong></p> <ul> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 1: Adult & Malicious)" href="../records/10775260">A Comprehensive Dataset for Webpage Classification (Part 1: Adult & Malicious)</a></li> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 2: Benign 1)" href="../records/10795435">A Comprehensive Dataset for Webpage Classification (Part 2: Benign 1)</a></li> </ul> <p> </p> <p><strong>Citation</strong></p> <p>if you use this dataset, please cite us:</p> <p><code>Al-Maamari, M., Istaiti, M., Zerhoudi, S., Dinzinger, M., Granitzer, M. and Mitrovic, J., A COMPREHENSIVE DATASET FOR WEBPAGE CLASSIFICATION.</code></p> <p>https://ca-roll.github.io/downloads/A_Comprehensive_Dataset_for_Webpage_Classification.pdf</p> <p> </p> <p><code>Granitzer, M., Voigt, S., Fathima, N.A., Golasowski, M., Guetl, C., Hecking, T., Hendriksen, G., Hiemstra, D., Martinovič, J., Mitrović, J. and Mlakar, I., 2023. Impact and development of an Open Web Index for open web search. <em>Journal of the Association for Information Science and Technology.</em></code></p> <p>https://doi.org/10.1002/asi.24818</p>
A Comprehensive Dataset for Webpage Classification (Part 2: Benign 1)
<p>This dataset, split across three parts due to Zenodo's size constraints, serves as a fundamental resource for enhancing webpage classification techniques. It encompasses 1,069,715 URLs, each annotated with labels to signify their categorization into Malicious, Benign, or Adult content, and further into 20 detailed sublabels for granular analysis. The dataset is designed to facilitate the evaluation and benchmarking of machine learning models, notably Stochastic Gradient Descent (SGD) and Support Vector Classifier (SVC), across a variety of tokenization methods and input types, including URLs, raw HTML, and parsed HTML content.</p> <p>The primary objective of assembling this dataset is to support research into effective webpage classification, thereby improving content prioritization and filtering in web crawling applications. It has been meticulously curated to provide a robust framework for studying the impact of different feature representation techniques on classification accuracy.</p> <p>The dataset is structured as JSON lines (jsonl) files, with each entry detailing a URL's label, sublabel, source, status code, and HTML content. This comprehensive dataset is divided into three parts due to size constraints on Zenodo, each targeting specific content categories to ensure ease of use and accessibility for researchers:</p> <ul> <li><strong>Part 1: Adult & Malicious</strong> encompasses URLs classified under Adult and Malicious categories, offering insights into content that requires stringent filtering.</li> <li><strong>Part 2: Benign 1</strong> and <strong>Part 3: Benign 2</strong> cover benign URLs, facilitating the study of safe web content and its classification nuances.</li> </ul> <p>We also created a .csv file without the HTML content so it is easier to work with URLs only, this .csv file contains the next columns `['uid', 'url', 'label', 'sublabel']`</p> <p>By providing this dataset, we aim to contribute significantly to the field of webpage classification, offering a valuable asset for researchers and practitioners looking to advance the state of web crawling technology and its applications.</p> <p> </p> <p>JSON line format for each line:</p> <pre><code>{"url": "<URL>", "label": "<Label>", "sublabel": "<Sublabel>", "source": "<Source of this URL>", "status_code": <Status Code>, "html": "<HTML Textual Content>"}</code><br><br></pre> <p><br><strong>Other parts of this dataset:</strong></p> <ul> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 1: Adult & Malicious)" href="../records/10775260">A Comprehensive Dataset for Webpage Classification (Part 1: Adult & Malicious)</a></li> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 3: Benign 2)" href="../records/10795437">A Comprehensive Dataset for Webpage Classification (Part 3: Benign 2)</a></li> </ul> <p> </p> <p><strong>Citation</strong></p> <p>if you use this dataset, please cite us:</p> <p><code>Al-Maamari, M., Istaiti, M., Zerhoudi, S., Dinzinger, M., Granitzer, M. and Mitrovic, J., A COMPREHENSIVE DATASET FOR WEBPAGE CLASSIFICATION.</code></p> <p>https://ca-roll.github.io/downloads/A_Comprehensive_Dataset_for_Webpage_Classification.pdf</p> <p> </p> <p><code>Granitzer, M., Voigt, S., Fathima, N.A., Golasowski, M., Guetl, C., Hecking, T., Hendriksen, G., Hiemstra, D., Martinovič, J., Mitrović, J. and Mlakar, I., 2023. Impact and development of an Open Web Index for open web search. <em>Journal of the Association for Information Science and Technology.</em></code></p> <p>https://doi.org/10.1002/asi.24818</p>
A Comprehensive Dataset for Webpage Classification (Part 1: Adult & Malicious)
<p>This dataset, split across three parts due to Zenodo's size constraints, serves as a fundamental resource for enhancing webpage classification techniques. It encompasses 1,069,715 URLs, each annotated with labels to signify their categorization into Malicious, Benign, or Adult content, and further into 20 detailed sublabels for granular analysis. The dataset is designed to facilitate the evaluation and benchmarking of machine learning models, notably Stochastic Gradient Descent (SGD) and Support Vector Classifier (SVC), across a variety of tokenization methods and input types, including URLs, raw HTML, and parsed HTML content.</p> <p>The primary objective of assembling this dataset is to support research into effective webpage classification, thereby improving content prioritization and filtering in web crawling applications. It has been meticulously curated to provide a robust framework for studying the impact of different feature representation techniques on classification accuracy.</p> <p>The dataset is structured as JSON lines (jsonl) files, with each entry detailing a URL's label, sublabel, source, status code, and HTML content. This comprehensive dataset is divided into three parts due to size constraints on Zenodo, each targeting specific content categories to ensure ease of use and accessibility for researchers:</p> <ul> <li><strong>Part 1: Adult & Malicious</strong> encompasses URLs classified under Adult and Malicious categories, offering insights into content that requires stringent filtering.</li> <li><strong>Part 2: Benign 1</strong> and <strong>Part 3: Benign 2</strong> cover benign URLs, facilitating the study of safe web content and its classification nuances.</li> </ul> <p>We also created a .csv file without the HTML content so it is easier to work with URLs only, this .csv file contains the next columns `['uid', 'url', 'label', 'sublabel']`</p> <p>By providing this dataset, we aim to contribute significantly to the field of webpage classification, offering a valuable asset for researchers and practitioners looking to advance the state of web crawling technology and its applications.</p> <p>JSON line format for each line:</p> <p><code>{"url": "<URL>", "label": "<Label>", "sublabel": "<Sublabel>", "source": "<Source of this URL>", "status_code": <Status Code>, "html": "<HTML Textual Content>"}</code><br><br><strong>Other parts of this dataset:</strong></p> <ul> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 2: Benign 1)" href="../records/10795435">A Comprehensive Dataset for Webpage Classification (Part 2: Benign 1)</a></li> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 3: Benign 2)" href="../records/10795437">A Comprehensive Dataset for Webpage Classification (Part 3: Benign 2)</a></li> </ul> <p> </p> <p><strong>Citation</strong></p> <p>if you use this dataset, please cite us:</p> <p><code>Al-Maamari, M., Istaiti, M., Zerhoudi, S., Dinzinger, M., Granitzer, M. and Mitrovic, J., A COMPREHENSIVE DATASET FOR WEBPAGE CLASSIFICATION.</code></p> <p>https://ca-roll.github.io/downloads/A_Comprehensive_Dataset_for_Webpage_Classification.pdf</p> <p> </p> <p><code>Granitzer, M., Voigt, S., Fathima, N.A., Golasowski, M., Guetl, C., Hecking, T., Hendriksen, G., Hiemstra, D., Martinovič, J., Mitrović, J. and Mlakar, I., 2023. Impact and development of an Open Web Index for open web search. <em>Journal of the Association for Information Science and Technology.</em></code></p> <p>https://doi.org/10.1002/asi.24818</p>
A Real-Time Eye-Tracking Dataset for Autism Severity Classification Using Deep Learning
<p>Eye-Tracking (ET) technologies have shown significant potential in autism research, providing critical insights into gaze patterns and their correlation with autism severity. However, a persistent challenge in developing Deep Learning (DL) models for ET analysis is the lack of publicly available, annotated datasets tailored for specific tasks. In order to close this gap, we present a novel, meticulously annotated resource designed to classify autism severity based on ET data. This dataset consists of 4,000 high-resolution (416×416 pixels) eye images derived from video recordings of 40 participants, evenly distributed across four autism severity groups: low, mild, medium, and high.</p> <p>Each participant's video was processed to extract 50 frames per session, capturing diverse gaze behaviors such as fixations, saccades, and smooth pursuits. Both left and right eye images were segmented from these frames, yielding 100 images per participant and ensuring balanced representation across severity categories (1,000 images per group). The dataset is annotated with detailed metadata, including subject ID, frame number, autism severity level, and eye type (left or right), providing a robust foundation for precise feature extraction and analysis.</p> <p><span>Facilitating its application in DL model development, this dataset addresses a critical gap in the limited availability of ET datasets. It provides a robust benchmark for autism severity classification, establishing a foundational resource for advancing Machine Learning(ML) research in the domain of autism</span><span>. This dataset serves as a critical resource for advancing ET-based classification models, fostering accurate and efficient assessment of autism severity, and supporting broader autism research.</span></p>
Version 4.2 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI)
<p><em>(Version </em>20230306<em>, </em>btmsp files modified May 31, 2023, additional taxonomic information added Dec 27, 2024<em>) </em></p> <p>Version 4.2 (20230306) of the RKI MALDI-ToF mass spectra database represents the third update of the original database (version 20161027, <a href="http://doi.org/10.5281/zenodo.163517">https://doi.org/10.5281/zenodo.163517</a>). The RKI Database v.4.2 now contains a total of 11055 MALDI-ToF mass spectra from 1601 microbial strains of highly pathogenic (i.e. biosafety level 3, BSL-3) bacteria such as <em>Bacillus anthracis</em>, <em>Brucella melitensis</em>, <em>Yersinia pestis</em>, <em>Burkholderia mallei / pseudomallei</em> and <em>Francisella tularensis</em> as well as a selection of spectra of their close and distant relatives. The database can be used as a reference for the diagnosis of BSL-3 bacteria using proprietary and free software packages for MALDI-ToF MS-based microbial identification. The spectral data are provided as a zip archive (<a href="https://zenodo.org/records/14562231/files/zenodo%20db%20230306.zip?download=1&preview=1">zenodo db 230306.zip</a>) containing the original mass spectra in their native data format (Bruker Daltonics). Please refer to the pdf file (<a href="https://zenodo.org/records/14562231/files/230306-ZENODO-Metadata.pdf?download=1&preview=1">230306-ZENODO-Metadata.pdf</a>) for information on cultivation conditions, sample preparation and details of the spectra acquisition. Please do not try to print this document (>1600 pages!).</p> <p>Version 20230306 of the RKI database contains for the first time files in the <em>btmsp</em> format (e.g. <a href="https://zenodo.org/records/14562231/files/2023-May-23-Bacillus-RKI-Database-568.btmsp?download=1&preview=1">2023-May-23-Bacillus-RKI-Database-568.btmsp </a> <a href="https://zenodo.org/api/files/35e90a0c-653d-4ba4-bf93-50b2bd80d073/2023-May-23-Bacillus-RKI-Database-570.btmsp"> </a>and others). These files were generated using the MALDI Biotyper software (Bruker Daltonics) and contain a total of 1601 main spectra (msp) from the BSL-3 database in the proprietary data format of the MALDI Biotyper software. *.<em>btmsp </em>files can be imported and used for identification with this software solution. Please refer to the manufacturer's manual for details on importing <em>btmsp </em>files. Note that the btmsp file available in database version 4 is broken and cannot be imported.</p> <p>The pkf files (<a href="https://zenodo.org/records/14562231/files/230306_ZENODO_30Peaks_0.75.pkf?download=1&preview=1">230306_ZENODO_30Peaks_0.75.pkf</a>, <a href="https://zenodo.org/records/14562231/files/230306_ZENODO_45Peaks_0.75.pkf?download=1&preview=1">230306_ZENODO_45Peaks_0.75.pkf</a>) represent two versions of the MS peak list data in a Matlab compatible format. The latter data can be imported into MicrobeMS, a free Matlab-based software solution developed at the RKI. MicrobeMS can be used for the identification of microorganisms by MALDI-ToF MS and is available at <a href="https://wiki-ms.microbe-ms.com">https://wiki-ms.microbe-ms.com</a>.</p> <p>The Excel file <a href="https://zenodo.org/records/14562231/files/Taxonomy%20information%20-%20RKI%20MALDI-ToF%20MS%20database%20of%20HPB%20at%20ZENODO%20v.4.xlsx?download=1&preview=1">Taxonomy information - RKI MALDI-ToF MS database of HPB at ZENODO v.4.xlsx</a> contains additional taxonomic information such as a detailed list of bacterial MALDI-ToF mass spectra (sheet #1), overviews on the number of spectra per strain, species or bacterial genus (sheet #2), numbers of strains per species, or genus (sheet #3), etc.</p> <p>The RKI mass spectrometry database is updated regularly.</p> <p>The author would like to thank the following individuals for providing microbial strains and species or mass spectra thereof. Without their help, this work would not have been possible.</p> <ul> <li><strong>Wolfgang Beyer</strong> - University of Hohenheim, Faculty of Agricultural Sciences, Stuttgart, Germany</li> <li><strong>Guido Werner</strong> - Robert Koch-Institute, Nosocomial Pathogens and Antibiotic Resistances (FG13), Wernigerode, Germany</li> <li><strong>Alejandra Bosch</strong> - CINDEFI, CONICET-CCT La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina</li> <li><strong>Michal Drevinek</strong> - National Institute for Nuclear, Biological and Chemical Protection, Milin, Czech Republic</li> <li><strong>Roland Grunow, Daniela Jacob, Silke Klee, Susann Dupke </strong>and <strong>Holger Scholz</strong> - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany</li> <li><strong>Jörg Rau </strong>- Chemisches und Veterinäruntersuchungsamt Stuttgart, Fellbach, Germany</li> <li><strong>Jens Jacob</strong> - Robert Koch-Institute, Hospital Hygiene, Infection Prevention and Control (FG14), Berlin, Germany</li> <li><strong>Martin Mielke</strong> - Robert Koch-Institute, Department 1 - Infectious Diseases, Berlin, Germany</li> <li><strong>Monika Ehling-Schulz</strong> - Functional Microbiology, Institute of Microbiology, University of Veterinary Medicine, Vienna, Austria</li> <li><strong>Armand Paauw</strong> - Department of Medical Microbiology, CBRN protection, Universitair Medisch Centrum Utrecht, TNO, Rijswijk, The Netherlands</li> <li><strong>Herbert Tomaso</strong><strong> </strong>– Friedrich-Löffler-Institut (FLI), Federal Research Institute for Animal Health, Jena, Germany</li> <li><strong>Gabriel Karner</strong><strong> </strong>- Karner Düngerproduktion GmbH, Research & Development, Neulengbach, Austria</li> <li><strong>Rainer </strong><strong>Borriss</strong><strong> </strong>- Institute of Marine Biotechnology e.V. (IMaB), Greifswald, Germany</li> <li><strong>Le Thi Thanh Tam</strong><strong> </strong>- Division of Plant Pathology and Phyto-Immunology, Plant Protection Research Institute, Hanoi, Socialist Republic of Vietnam</li> <li><strong>Xuewen</strong><strong> Gao</strong><strong> </strong>- College of Plant Protection, Nanjing Agricultural University, Key Laboratory of Integrated Management of Crop Diseases and Pests, Nanjing, People’s Republic of China</li> </ul> <p>For a detailed description of the database see: Lasch, P., Beyer, W., Bosch, A. <em>et al.</em> A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria. <em>Sci Data</em> <strong>12</strong>, 187 (2025). <a href="https://doi.org/10.1038/s41597-025-04504-z">https://doi.org/10.1038/s41597-025-04504-z</a></p>
Coastal and Marine Ecological Classification Standard (CMECS) Catalog
<p>The <strong>Coastal and Marine Ecological Classification Standard (CMECS) Catalog</strong> is the authoritative collection of ecological units (terms + definitions) and unit relationships (the CMECS classification framework).</p> <p>The CMECS Catalog is the complete representation of the CMECS classification. It contains all units that are or have been members of the CMECS classification throughout its lifecycle, as well as various annotations that provide metadata for each unit that enable Findability, Accessibility, Interoperability, and Reuse (FAIR, <a href="https://www.go-fair.org/fair-principles/" rel="nofollow">https://www.go-fair.org/fair-principles/</a>). The CMECS Catalog (cmecs.owl) file is stored and managed in a Git repository; authoritative versions are publicly released via <a href="https://github.com/NOAA-OCM/cmecs" target="_blank" rel="noopener">the NOAA-OCM/cmecs GitHub</a> as changes are made. Version releases also include the CMECS Catalog in CSV and XLSX formats. A browsable text output of the CMECS Catalog ecological units and implementation guidance, the <a href="https://github.com/NOAA-OCM/cmecs/wiki/CMECS-Thesaurus-Quick-Link"><strong>CMECS Thesaurus</strong></a>, is also available in PDF and MD formats.</p> <p>This release includes changes to the Substrate Component Unit Codes and fixes to Biotic Component typographical errors. Details are available on the <a href="https://github.com/NOAA-OCM/cmecs/releases/tag/v1.1.1" target="_blank" rel="noopener">CMECS GitHub v1.1.1 Release Page.</a></p> <p><strong>Questions? Please contact the CMECS Implementation Group at ocm.cmecs-ig@noaa.gov</strong></p> <p>For more information about the CMECS Catalog, see the <a href="https://github.com/NOAA-OCM/cmecs/wiki">https://github.com/NOAA-OCM/cmecs/wiki.</a></p> <p>For more information about CMECS, including technical guidance and classification examples, visit the <a href="https://iocm.noaa.gov/standards/cmecs-home.html" rel="nofollow">NOAA Integrated Ocean and Coastal Mapping (IOCM) team's CMECS webpage</a>.</p> <p>CMECS follows a Dynamic Standard Process to review and adopt changes that are proposed by the CMECS user community when necessary. More information about CMECS maintenance can be found on the <a href="https://www.ncei.noaa.gov/products/coastal-marine-ecological-classification-standard" rel="nofollow">NOAA National Centers for Environmental Information (NCEI) CMECS webpage</a> under the <strong>Vocabulary Maintenance</strong> section, along with instructions for proposing revisions to CMECS and a form for submitting proposals.</p>
Kobalt: Extension Corpus and Annotation Guidelines for Verb Classification and Dependency Adjustments
<p>Kobalt (Zinsmeister et al. 2012) is a task-based corpus of essays written by learners and native speakers of German. This repository contains data that was not included in the original corpus and new layers of annotation to the original and the extended corpus, specifically morphological and syntactic classification of verbs and corrections and changes to dependency parses. Please refer to the annotation guidelines included in this repository for further information.<br> </p>
BirdVox-296h: a large-scale dataset for detection and classification of flight calls
<p>BirdVox 296 hours dataset (BirdVox-296h)<br> ====================================</p> <p>Version 2.1, May 2022.</p> <p><br> Created By<br> ----------</p> <p>Andrew Farnsworth (1), Benjamin Mark Van Doren (1), Steve Kelling (1), Vincent Lostanlen (2), Justin Salamon (3), Aurora Cramer (4), Juan Pablo Bello (4)</p> <p>(1): Cornell Lab of Ornithology (CLO)<br> (2): Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS<br> (3): Adobe Research<br> (4): New York University</p> <p>https://wp.nyu.edu/birdvox<br> <br> </p> <p>Description<br> ---------------</p> <p>The BirdVox-296h dataset contains 148 audio recordings, each two hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured by nine different sensors, originally numbered 1, 2, 3, 4, 5, 6, 7, 8, and 10.<br> <br> Ornithologist Andrew Farnsworth used the Raven software to pinpoint and label every avian flight call in time and frequency. He found 26138 sound events, of which 21546 are flight calls from Passeriformes. Of those, 13385 are identifiable in terms of family, and 8669 are identifiable in terms of both family and species. The annotation process took over 600 hours.</p> <p>The dataset can be used, among other things, for the research, development and testing of machine listening models for bird migration monitoring.</p> <p> </p> <p>Data Files<br> ------------</p> <p>The BirdVox-296h_wav folder contains 148 recordings as WAV files, sampled at 24 kHz, with a single channel (mono). Each recording lasts exactly two hours and is named according to the following format:</p> <p>YYYY-MM-DD_hh-mm-ss_unitUU.wav</p> <p>Where Y means Year, M means Month, D means Day, h means hour, m means minute, and s means second. This date format corresponds to the start time of the recording file, expressed in Coordinated Universal Time (UTC).</p> <p>The field UU contains two digits corresponding to the identifier of the autonomous recording unit (i.e., bioacoustic sensor). UU is either equal to 01, 02, 03, 04, 05, 06, 07, 08, or 10. Note that 09 is absent from the list because sensor 09 failed during the acquisition campaign.</p> <p> </p> <p>Metadata Files<br> -------------------</p> <p>The BirdVox-296h_csv-annotations folder contains CSV files, one for each audio file. The columns of each CSV file are:</p> <p>ID,Time (s),Frequency (Hz),Taxonomy Code,Fine Label,Medium Label,Coarse Label</p> <p><br> "Taxonomy Code" is compliant with the BirdVoxClassify software: github.com/BirdVox/BirdVoxClassify</p> <p>"Fine Label", "Medium Label", and "Coarse Label" most often correspond to species, family and order respectively.</p> <p> </p> <p>The BirdVox-296h_gps-coordinates.csv file contains the approximate GPS coordinates of the sensors (latitudes and longitudes rounded to 2 decimal points) of all nine sensors.</p> <p> </p> <p> </p> <p>Conditions of Use<br> -----------------</p> <p>Dataset created by Andrew Farnsworth, Steve Kelling, Vincent Lostanlen, Justin Salamon, Aurora Cramer, and Juan Pablo Bello.</p> <p>The BirdVox-full-night dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, Cornell Lab of Ornithology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-full-night dataset or any part of it.</p> <p> </p> <p>Feedback<br> -------------</p> <p>Please help us improve BirdVox-296h by sending your feedback to:<br> vincent.lostanlen@ls2n.fr and af27@cornell.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p>Acknowledgements<br> --------------------------</p> <p>Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes.</p> <p>We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.</p>
BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification
<p>BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification<br> ===============================================================<br> Version 2.0, February 2022.</p> <p>https://wp.nyu.edu/birdvox</p> <p><br> Description<br> ---------------</p> <p>BirdVox-ANAFCC is a dataset of short audio waveforms, each of them containing a flight call from one of 14 birds of North America: four American sparrows, one cardinal, two thrushes, and seven New World warblers.<br> * American Tree Sparrow (ATSP)<br> * Chipping Sparrow (CHSP)<br> * Savannah Sparrow (SAVS)<br> * White-throated Sparrow (WTSP)<br> * Red-breasted Grosbeak (RBGR)<br> * Gray-cheeked Thrush (GCTH)<br> * Swainson's Thrush (SWTH)<br> * American Redstart (AMRE)<br> * Bay-breasted Warbler (BBWA)<br> * Black-throated Blue Warbler (BTBW)<br> * Canada Warbler (CAWA)<br> * Common Yellowthroat (COYE)<br> * Mourning Warbler (MOWA)<br> * Ovenbird (OVEN)</p> <p>It also contains other sounds which are often confused for one of the species above. These "confounding factors" encompass flight calls from other species of birds, vocalizations from non-avian animals, as well as some machine beeps.</p> <p>BirdVox-ANAFCC results from an aggregation of various smaller datasets, integrated under a common taxonomy. For more details on this taxonomy, we refer the reader to [1]:</p> <p>[1] Cramer, Lostanlen, Salamon, Farnsworth, Bello. Chirping up the right tree: Incorporating biological taxonomies into deep bioacoustic classifiers. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.</p> <p>The second version of the BirdVox-ANAFCC dataset (v2.0) contains flight calls from the BirdVox-full-night dataset. These flight calls were present in the ICASSP 2020 benchmark but did not appear in the initial release of BirdVox-ANAFCC.</p> <p><br> Data Files<br> ------------<br> BirdVox-ANAFCC contains the recordings as HDF5 files, sampled at 22,050 Hz, with a single channel (mono). Each HDF5 file contains flight call vocalizations of a particular species. The name of each HDF5 file follows the format: `<data-source>_<taxonomy-code>_original.h5`. The name of the HDF5 dataset in each file is "waveforms", with the corresponding key for each audio recording varying in format depending on the data source.</p> <p> </p> <p>Metadata Files<br> ---------------<br> `taxonomy.yaml` details the three-level taxonomy structure used in this dataset, reflected in three-number-codes which largely follow "<family>.<order>.<species>". Additionally, at any level of the taxonomy, the numeric code "0" is reserved for "other" and the code "X" refers to unknown. For example, 1.1.0 corresponds to an American Sparrow with a species outside of our scope of interest, and 1.1.X corresponds to an American Sparrow of unknown species. At the top level (family), the "other" codes (0.\*.\*) deviate from the family-order-species in order to capture a variety of other out-of-scope sounds, including anthropophony, non-avian biophony, and biophony of avians outside of the scope of interest.</p> <p><br> Please acknowledge BirdVox-ANAFCC in academic research<br> --------------------------------------------------------------------------</p> <p>When BirdVox-ANAFCC is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>Cramer, Lostanlen, Salamon, Farnsworth, Bello. Chirping up the right tree: Incorporating biological taxonomies into deep bioacoustic classifiers. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.</p> <p>The creation of this dataset was supported by NSF grants 1125098 (BIRDCAST) and 1633259 (BIRDVOX), a Google Faculty Award, the Leon Levy Foundation, and two anonymous donors.</p> <p> </p> <p>Conditions of Use<br> ----------------------</p> <p>Dataset created by Aurora Cramer, Vincent Lostanlen, Bill Evans, Andrew Farnsworth, Justin Salamon, and Juan Pablo Bello.<br> <br> The BirdVox-ANAFCC dataset is offered free of charge under the terms of the Creative Commons Attribution International License:<br> https://creativecommons.org/licenses/by/4.0/<br> <br> The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, the authors are not liable for, and expressly exclude all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-ANAFCC dataset or any part of it.</p> <p><br> Feedback<br> -------------</p> <p>Please help us improve BirdVox-full-night by sending your feedback to:<br> vincent.lostanlen@gmail.com and auroracramer@nyu.edu</p> <p>In case of a problem, please include as many details as possible.<br> <br> <br> Versions<br> ------------<br> 1.0, May 2020: initial version, paired with ICASSP 2020 publication.<br> 2.0, February 2022: added a missing dataset file (BirdVox-70k), updated name of first author (Aurora Cramer).<br> </p> <p><br> Acknowledgement<br> --------------------------<br> Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes.</p> <p>We thank contributors and maintainers of the Macaulay Library and the Xeno-Canto website.</p> <p>We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.</p>
QuerTCI: A Tool Integrating GitHub Issue Querying with Comment Classification
<p>Issue tracking systems enable users and developers to comment on problems plaguing a software system. Empirical Software Engineering (ESE) researchers study (open-source) project issues and the comments and threads within to discover---among others---challenges developers face when, e.g., incorporating new technologies, platforms, and programming language constructs. However, issue discussion threads accumulate over time and thus can become unwieldy, hindering any insight that researchers may gain. While existing approaches alleviate this burden by classifying issue thread comments, there is a gap between searching popular open-source software repositories (e.g., those on GitHub) for issues containing particular keywords and feeding the results into a classification model. In this paper, we demonstrate a research infrastructure tool called QuerTCI that bridges this gap by integrating the GitHub issue comment search API with the classification models found in existing approaches. Using queries, ESE researchers can retrieve GitHub issues containing particular keywords, e.g., those related to a certain programming language construct, and subsequently classify the kinds of discussions occurring in those issues. Using our tool, our hope is that ESE researchers can uncover challenges related to particular technologies using certain keywords through popular open-source repositories more seamlessly than previously possible. A tool demonstration video may be found at: https://youtu.be/fADKSxn0QUk.</p>
Periodic vegetation pattern classification in Sudan
<p>This archive contains features computed from satellites images in Kordofan State in Sudan. SPOT (Systeme Probatoire d’Observation de la Terre) images with a 10-m ground resolution and preprocessing level 2A were divided into non-overlapping square windows of 410 by 410 m. We calculated for each of these windows:</p> <ul> <li>skewness of the grayscale distribution of each window</li> <li>index of vegetation pattern anisotropy</li> <li>azimuthal angle in the first PCA plane, which directly correlates with the dominant frequency in the windows</li> <li>distance from PCA origin, which expresses the degree of scale dominance</li> <li>mean annual rainfall computed from gridded monthly estimates from the Tropical Rainfall Measuring Mission (TRMM, NASA/JAXA) 3B43 V6 product acquired from 1 January 1998 to 31 December 2007 and resampled to 410 by 410 m.</li> <li>slope computed from the Shuttle Radar Topography Mission (SRTM) digital elevation model with three arc seconds<br> horizontal (ca 92 m in this area) spatial resolution.</li> </ul> <p>The resulting pattern classification:</p> <ul> <li>1, spots</li> <li>2, labyrinthine</li> <li>3, gaps</li> <li>4, bands</li> <li>5, non-periodic</li> <li>Nodata, area not covered by SPOT images</li> </ul> <p>Data is provided as rasters in Arc/Info ASCII grid format (also known as Esri grid). The projection and datum for all datasets are UTM zone 35 N, WGS 1984.</p> <p>Details on the methods are availble in the following publication: Deblauwe, V., Couteron, P., Lejeune, O., Bogaert, J. & Barbier, N. (2011) Environmental modulation of self-organized periodic vegetation patterns in Sudan. Ecography, 34, 990-1001. <a href="https://doi.org/10.1111/j.1600-0587.2010.06694.x">https://doi.org/10.1111/j.1600-0587.2010.06694.x</a></p>
Language intensity classification and Neuronal Networks
<p>Kohonen Self-Organizing Maps (SOM) are a particular type of artificial neural network created by Teuvo Kohonen. Their unsupervised learning makes them suitable for application, among other things, to grouping tasks. Occupations have been grouped into this analysis considering the similarity in value of the variables that define this occupation in terms of language proficiency requirements. The 8 variables used were: v1-speaking skills, v2-writing skills, v3-speech clarity, v4-speech recognition, v5-English knowledge, v6-speaking ability, v7-communication with people outsiders, v8-communication with superiors, equals or subordinates. After applying this authomatic classification technique, the SOC occupations are classified according to five distinc groups with decreasing linguistic intensity:</p> <p>Class 1: High linguistic intenisty requirements<br> Class 2: Medium-high linguistic intenisty requirements<br> Class 3: Medium linguistic intenisty requirements<br> Class 4: Medium-low linguistic intenisty requirements<br> Class 5: Low linguistic intenisty requirements</p>
Crop classification dataset for testing domain adaptation or distributional shift methods
<p>In this upload we share processed crop type datasets from both France and Kenya. These datasets can be helpful for testing and comparing various domain adaptation methods. The datasets are processed, used, and described in this paper: <a href="https://doi.org/10.1016/j.rse.2021.112488">https://doi.org/10.1016/j.rse.2021.112488</a> (arXiv version: <a href="https://arxiv.org/pdf/2109.01246.pdf">https://arxiv.org/pdf/2109.01246.pdf</a>). </p> <p>In summary, each point in the uploaded datasets corresponds to a particular location. The label is the crop type grown at that location in 2017. The 70 processed features are based on Sentinel-2 satellite measurements at that location in 2017. The points in the France dataset come from 11 different departments (regions) in Occitanie, France, and the points in the Kenya dataset come from 3 different regions in Western Province, Kenya. Within each dataset there are notable shifts in the distribution of the labels and in the distribution of the features between regions. Therefore, these datasets can be helpful for testing for testing and comparing methods that are designed to address such distributional shifts.</p> <p>More details on the dataset and processing steps can be found in <a href="https://doi.org/10.1016/j.rse.2021.112488">Kluger et. al. (2021)</a>. Much of the processing steps were taken to deal with Sentinel-2 measurements that were corrupted by cloud cover. For users interested in the raw multi-spectral time series data and dealing with cloud cover issues on their own (rather than using the 70 processed features provided here), the raw dataset from Kenya can be found in <a href="https://openreview.net/forum?id=5HR3vCylqD">Yeh et. al. (2021)</a>, and the raw dataset from France can be made available upon request from the authors of this Zenodo upload.</p> <p>All of the data uploaded here can be found in "CropTypeDatasetProcessed.RData". We also post the dataframes and tables within that .RData file as separate .csv files for users who do not have R. The contents of each R object (or .csv file) is described in the file "Metadata.rtf".</p> <p><strong>Preferred Citation:</strong></p> <p>-Kluger, D.M., Wang, S., Lobell, D.B., 2021. Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sens. Environ. 262, 112488. https://doi.org/10.1016/j.rse.2021.112488.</p> <p>-URL to this Zenodo post https://zenodo.org/record/6376160</p>
Linguistic-symbolic classification of occupations
<p>As a result, it has been considered the following occupational categories, based on the degree of symbolic analysis and language intensity: (A) high symbolic analysts, (B) low symbolic analysts, (C) high intensity oral interaction with public (high public service), (D) low intensity oral interaction with public (low public service), and (E) manual labor occupations with limited symbolic and oral demands. Further, and within (B) category -low symbolic analysts-, it is distinguished between (B1) those whose work is generally inside the organization (such as file clerks) and (B2) those whose work includes interacting with the public (such as receptionists). In a similar way, (C) category is also divided into (C1) category of nurses and (C2-C5) which group the remainder of the high public service occupations.</p> <p>The result of the categorization of occupations by language use is summarized in the table below:</p> <table> <thead> <tr> <th>Major occupational classification</th> <th>Linguistic characteristics of occupation</th> <th>Sub classification</th> <th>Example of occupation</th> </tr> </thead> <tbody> <tr> <td>A: High symbolic analysts</td> <td>Produce/consume long or complex written communications, with variable but often important oral communication</td> <td>A1. Upper management</td> <td>Chief executive, human resource executive</td> </tr> <tr> <td> </td> <td> </td> <td>A2. Professionals</td> <td>Lawyer, doctor</td> </tr> <tr> <td> </td> <td> </td> <td>A3. Lower management</td> <td>First line manager/supervisor</td> </tr> <tr> <td> </td> <td> </td> <td>A4. High symbolic analysts, not managers</td> <td>Public relations specialists, computer systems specialists</td> </tr> <tr> <td>B: Low symbolic analysts</td> <td>Produce/consume short or simple written communications, with variable but often important oral communication</td> <td>B1. Low symbolic analysts with low likelihood of public interaction</td> <td>File clerks</td> </tr> <tr> <td> </td> <td> </td> <td>B2. Low symbolic analysts with high likelihood of public interaction</td> <td>Receptionists, billing/appointment clerks</td> </tr> <tr> <td>C: In-person service workers with high communicative demands</td> <td>Important oral communication, limited but present written skills, and high public interaction</td> <td>C1. Nurses</td> <td>Nurses</td> </tr> <tr> <td> </td> <td> </td> <td>C2. Assistants and technicians in public service settings</td> <td>Medical technicians</td> </tr> <tr> <td> </td> <td> </td> <td>C3. Police, etc.</td> <td>Police, detectives, investigators</td> </tr> <tr> <td> </td> <td> </td> <td>C4. Firefighters, emergency medical technicians</td> <td>Firefighters, emergency medical technicians</td> </tr> <tr> <td> </td> <td> </td> <td>C5. Miscellaneous</td> <td>Counselors, dispatchers</td> </tr> <tr> <td>D. In-person service workers with low communicative demands</td> <td>Simple oral communication and public interaction, very limited or no writing</td> <td>(no subcategories in our study)</td> <td>home health care aides, security guards</td> </tr> <tr> <td>E. Manual work</td> <td>Limited oral and written consumption and production</td> <td>E1. Skilled manual work</td> <td>Plumber</td> </tr> <tr> <td> </td> <td> </td> <td>E2. Unskilled manual work</td> <td>Janitor</td> </tr> </tbody> </table> <p> </p>
Taxnonomic classifications for all structure in the QM9 dataset
<p>The classification of molecules according to ClassyFire [1] for the QM9 dataset [2].</p> <p>The QM9 dataset is a set of nearly 140k organic molecules with no more than 9 C, N, O, and F atoms optimized to a stable structure with DFT. </p> <p>ClassyFire is a tool and taxonomic library for the labeling of molecules.</p> <p>1. Djoumbou Feunang, Y. <em>et al.</em> ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. <em>J. Cheminform.</em> <strong>8</strong>, 1–20 (2016).</p> <p>2. Ramakrishnan, R., Dral, P. O., Rupp, M. & Von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. <em>Sci. Data</em> <strong>1</strong>, 1–7 (2014).</p> <p> </p> <p>The data directory ('QM9_jsons_classified.tar.gz') contains a `json` file for each structure in the QM9 dataset. The name of the file is the same identifier as from QM9. Data fields include:</p> <p>- `cf_alternative_parents` : classifications describing the compound that do not fall in the given ancestry</p> <p>- `cf_ancestors` : classes along the taxonomic branch for the structure </p> <p>- `cf_class` : ClassyFire given class</p> <p>- `cf_superclass` : ClassyFire given super class</p> <p>- `cf_subclass` : ClassyFire given subclass</p> <p>- `cf_direct_parent` : Class one level above this structure on the taxonomic branch</p> <p>- `cf_description` : Exposition on the given class</p> <p>- `cf_identifier` : identifier for the structure in the ClassyFire database</p> <p>- `cf_intermediate_nodes` : classes connecting branches on taxonomic tree</p> <p>- `cf_kingdom` : ClassyFire given kingdom</p> <p>- `cf_molecular_framework` : describes aromaticity and number of cycles</p> <p>- `cf_predicted_chebi_terms` : terms describing the molecule in the ChEBI framework </p> <p>- `cf_predicted_lipidmaps_terms` : terms describing the molecule in LIPID MAPS framework</p> <p>- `cf_smiles` : smiles string given by ClassyFire</p> <p>- `cf_substituents` : substituent groups in the structure </p> <p> </p> <p>Many fields contain subfields, seen in the example below for molecule with QM9 id 000123:</p> <p>{"cf_alternative_parents":[{"name":"Dialkylamines","description":"Organic compounds containing a dialkylamine group, characterized by two alkyl groups bonded to the amino nitrogen.","chemont_id":"CHEMONTID:0002228","url":"http:\/\/classyfire.wishartlab.com\/tax_nodes\/C0002228"},{"name":"Organopnictogen compounds","description":"Compounds containing a bond between carbon a pnictogen atom. Pnictogens are p-block element atoms that are in the group 15 of the periodic table.","chemont_id":"CHEMONTID:0004557","url":"http:\/\/classyfire.wishartlab.com\/tax_nodes\/C0004557"},{"name":"Hydrocarbon derivatives","description":"Derivatives of hydrocarbons obtained by substituting one or more carbon atoms by an heteroatom. They contain at least one carbon atom and heteroatom.","chemont_id":"CHEMONTID:0004150","url":"http:\/\/classyfire.wishartlab.com\/tax_nodes\/C0004150"}],"cf_ancestors":["Alpha-aminonitriles","Amines","Chemical entities","Dialkylamines","Hydrocarbon derivatives","Nitriles","Organic compounds","Organic cyanides","Organic nitrogen compounds","Organonitrogen compounds","Organopnictogen compounds","Secondary amines"],"cf_class":"Organonitrogen compounds","cf_classification_version":"2.1","cf_description":"This compound belongs to the class of organic compounds known as alpha-aminonitriles. These are organonitrogen compounds that contain an amino group located on the carbon at the position alpha to a carbonitrile group. They have the general formula RC(NH2)C#N, where the amine group can be substituted.","cf_direct_parent":{"name":"Alpha-aminonitriles","description":"Organonitrogen compounds that contain an amino group located on the carbon at the position alpha to a carbonitrile group. They have the general formula RC(NH2)C#N, where the amine group can be substituted.","chemont_id":"CHEMONTID:0004453","url":"http:\/\/classyfire.wishartlab.com\/tax_nodes\/C0004453"},"cf_external_descriptors":[],"cf_identifier":"Q5198051-1","cf_inchikey":"InChIKey=PVVRRUUMHFWFQV-UHFFFAOYSA-N","cf_intermediate_nodes":[{"name":"Nitriles","description":"Compounds having the structure RC#N; thus C-substituted derivatives of hydrocyanic acid, HC#N.","chemont_id":"CHEMONTID:0000362","url":"http:\/\/classyfire.wishartlab.com\/tax_nodes\/C0000362"}],"cf_kingdom":"Organic compounds","cf_molecular_framework":"Aliphatic acyclic compounds","cf_predicted_chebi_terms":["chemical entity (CHEBI:24431)","organic molecular entity (CHEBI:50860)","organonitrogen compound (CHEBI:35352)","secondary amino compound (CHEBI:50995)","nitrile (CHEBI:18379)","amine (CHEBI:32952)","secondary amine (CHEBI:32863)","cyanides (CHEBI:23424)","organic molecule (CHEBI:72695)","pnictogen molecular entity (CHEBI:33302)","nitrogen molecular entity (CHEBI:51143)"],"cf_predicted_lipidmaps_terms":[],"cf_smiles":"CNCC#N","cf_subclass":"Organic cyanides","cf_substituents":["Alpha-aminonitrile","Secondary amine","Secondary aliphatic amine","Organopnictogen compound","Hydrocarbon derivative","Amine","Aliphatic acyclic compound"],"cf_superclass":"Organic nitrogen compounds"}</p> <p> </p> <p>A visualization ''qm9_pie_labeled.png" is given of a fracturization of superclasses within qm9 down to subclass.</p>
Evaluation of Machine learning algortihms for classification
<p>The purpose of this report is to compare three different classifiers through supervised machine learning on two diverse datasets. The whole machine learning process was applied and conducted in different experiments. The exploration of the datasets as well as the preprocessing strategies are outlined in the following. Furthermore, the modelling processes and the performance measures on which their results are evaluated will be explained. Finally, different parameter adjustments and settings are compared and discussed which leads to a conclusion.</p>
zbMATH Open Mathematics Subject Classification Dataset (springer)
<p>zbMATH Open Mathematics Subject Classification Dataset consisting of the following fields</p> <p><em>doi</em>: digital object identifier</p> <p><em>msc</em>: primary MSC of the article</p> <p><em>zbl_id</em>: zbMATH Open identier</p>
From 20,000 years ago to near present climate classification of North America
<p>For North America and most of Central America during 20, 14, 13, 11, 10, 7, 5, and 1 thousand years ago (ka) and recent years, I applied a Köppen-Trewartha classification system. I slightly modified the system by keeping the thermal classes for the dry classes, subdividing boreal and polar classes based on an ecological threshold of the number of months at 5 °C, and condensing the number of rare subclasses.</p> <p>Climate data were from:</p> <p>Lorenz, DJ, Nieto-Lugilde, D, Blois, JL, Fitzpatrick, MC and Williams, JW. 2016a. Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100 AD. <em>Scientific Data</em>, 3, 1-19.</p> <p>Lorenz, DJ, Nieto-Lugilde, D, Blois, JL, Fitzpatrick, MC and Williams, JW. 2016b. Data from: Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD. Dryad Dataset. Available at https://doi.org/10.5061/dryad.1597g [last accessed 01 December 2020].</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.