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7,523 results for “Annotation”
Semantic annotation of PLoS journal citation contexts
<p>Dataset </p>
Rana sierrae annotated aquatic soundscapes (2022)
<p>This dataset is associated with the following manuscript, which contains details in the methodology of data collection and annotation: </p> <p>Lapp, S., Smith, T. C., Wilhelm, A, Knapp, R., Kitzes, J. In press. Aquatic soundscape recordings reveal diverse vocalizations and nocturnal activity of an endangered frog. The American Naturalist.</p> <p><em>Rana</em> <em>sierrae</em> (the Sierra Nevada yellow-legged frog) is an endangered species residing in high-elevation lakes in the Sierra Nevada mountains. The species is highly aquatic and, unlike most amphibians, primarily vocalizes while underwater. As a result, its vocalizations have rarely been recorded and its vocal repertoire is not well studied.</p> <p>This dataset contains an annotated set of underwater soundscape recordings containing <span>1236</span> annotations of <em>R. sierrae</em> vocalizations. We annotated five distinct vocalization types of<em> R. sierrae</em>, only two of which have been previously documented for this species. Besides the calls of <em>R.</em> <em>sierrae</em>, these audio recordings also contain stridulation sounds (not annotated), which were most likely produced by members of the family Corixidae or other aquatic invertebrates that stridulate underwater. </p>
Rare Diseases hand-annotated news articles and research articles
<p>This dataset was produced in 2023 from the data collected throughout 2022 from MEDLINE (scientific articles) and from Event Registry (news) for the development of the Rare Diseases Mining project (https://idefine-europe.org/medline)</p><p>The data is distributed across 16 diseases supporting the research paper "Automatic text classification and interactive data visualization of published scientific and news articles on Rare Diseases"</p><p>The available data comes in 2 kinds and file formats:<br>CSV - the hand annotation of the news articles in TXT with 5 to 10 MeSH headings<br>JSON - the input file for the evaluation of the classifier, including the title, news article body and MeSH heading IDs (available from https://www.ncbi.nlm.nih.gov/mesh/)</p><p>The CSV files with name starting in "f1_", "pr_", "re_" are the results of the F1/Precision/Recall evaluation for each of the cases.</p><p>This work was prepared by Joao Pita Costa (researcher) and curated by Tanja Zdolšek Draksler (domain expert) </p>
tFoodL: Larger Semantic Table Annotations Benchmark for Food Domain
<p><strong>tFoodL</strong> is the successor work of <a href="https://zenodo.org/records/10048187">tFood</a> that is generated by <a href="https://github.com/fusion-jena/KG2Tables">KG2Tables </a>using 10 levels of a recursive hierarchy of related concepts in Wikidata.</p><p>Similar to tFood, it is a dataset for tabular data to knowledge graph matching. It is derived for the Food domain and has two types of tables. On the one hand, <strong>Horizontal Relational Tables</strong> are where each table represents a collection of entities. On the other hand, <strong>Entity Tables</strong> represent a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).</p><p><strong>tFoodL</strong> contains 43,255 entity and horizontal tables, while this repository contains only the validation fold (10%) of the entire benchmark with its ground truth data (gt). </p><p>The supported tasks for semantic table annotations are: </p><ol><li>Topic Detection (<strong>TD</strong>) links the entire table to an entity or a class from the target KG.</li><li>Cell Entity Annotation (<strong>CEA</strong>) maps individual table cells to entities from the target KG.</li><li>Column Type Annotation (<strong>CTA</strong>) links individual table columns to classes from the target KG.</li><li>Column Property Annotation (<strong>CPA</strong>) detects the relations between column pairs from the target knowledge graph.</li><li>Row Annotation (<strong>RA) </strong>annotates the entire row to a KG entity or property.</li></ol>
tBiodivL: Larger Semantic Table Annotations Benchmark for Biodiversity Domain
<p><strong>tBiodivL</strong> is a dataset for tabular data to knowledge graph matching. It is derived from the Biodiversity domain and has two types of tables. On the one hand, <strong>Horizontal Relational Tables</strong> are where each table represents a collection of entities. On the other hand, <strong>Entity Tables</strong> represent a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).</p><p><strong>tBiodivL</strong> is generated by <a href="https://github.com/fusion-jena/KG2Tables">KG2Tables </a>using 10 levels of a recursive hierarchy of related concepts in Wikidata. It is the successor work of <a href="https://doi.org/10.5281/zenodo.10283015">tBiodiv</a></p><p><strong>tBiodivL </strong>contains <strong>222,353</strong> entity and horizontal tables, while this repository contains only a sample of <strong>1% of the total generated tables</strong> of the entire benchmark with its ground truth data (gt). The Full size of this dataset is <strong>312 GB</strong>. We will update this repository with the full dataset in the Future.</p><p>Please get in touch if you are interested in the full dataset, </p><p>The supported tasks for semantic table annotations are: </p><ol><li>Topic Detection (<strong>TD</strong>) links the entire table to an entity or a class from the target KG.</li><li>Cell Entity Annotation (<strong>CEA</strong>) maps individual table cells to entities from the target KG.</li><li>Column Type Annotation (<strong>CTA</strong>) links individual table columns to classes from the target KG.</li><li>Column Property Annotation (<strong>CPA</strong>) detects the relations between column pairs from the target knowledge graph.</li><li>Row Annotation (<strong>RA) </strong>annotates the entire row to a KG entity or property.</li></ol>
tBiomedL: Larger Semantic Table Annotations Benchmark for Biomedical Domain
<p><strong>tBiomedL </strong>is a dataset for tabular data to knowledge graph matching. It is derived for the Biodiversity domain and has two types of tables. On the one hand, <strong>Horizontal Relational Tables</strong> are where each table represents a collection of entities. On the other hand, <strong>Entity Tables</strong> represent a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).</p><p><strong>tBiomedL </strong>is generated by <a href="https://github.com/fusion-jena/KG2Tables">KG2Tables </a>using five levels of a recursive hierarchy of related concepts in Wikidata. It is the successor work of <a href="https://doi.org/10.5281/zenodo.10283103">tBiomed</a></p><p><strong>tBiomedL </strong>contains <strong>860,479</strong> entity and horizontal tables, while this repository contains only <strong>a sample of 1%</strong> of the total of the entire benchmark with its ground truth data (gt). The Full size of this dataset is <strong>27</strong> <strong>GB</strong>. We will update this repository with the full dataset, including the test fold with its ground truth data in the Future.</p><p>Please get in touch if you are interested in the full dataset, </p><p>The supported tasks for semantic table annotations are: </p><ol><li>Topic Detection (<strong>TD</strong>) links the entire table to an entity or a class from the target KG.</li><li>Cell Entity Annotation (<strong>CEA</strong>) maps individual table cells to entities from the target KG.</li><li>Column Type Annotation (<strong>CTA</strong>) links individual table columns to classes from the target KG.</li><li>Column Property Annotation (<strong>CPA</strong>) detects the relations between column pairs from the target knowledge graph.</li><li>Row Annotation (<strong>RA) </strong>annotates the entire row to a KG entity or property.</li></ol>
Genbank annotation and sequence of the genome of a Rickettsiales symbiont of Reticulomyxa filosa
<p>The genome sequence of a Rickettsiales symbiont was selectively from the genome sequencing reads of its host, Reticulomyxa filosa. Those sequences were obtained from a previous third-party study (doi: 10.1016/j.cub.2013.11.027). The selective assembly procedure was based on GC content and coverage of contigs obtained from the total read sets with SPAdes. Full details are provided in the manuscript file.<br>The obtained symbiont sequence was then annotated with Prokka, and the gbk output file selected.</p><p>This genome was obtained and analysed in the context of a larger genome comparative studies on the Rickettsiales, aimed to investigate the evolutionary patterns within the whole lineage.</p>
Dataset and Analysis Scripts for Survey "Understanding Security Tactics in Microservice APIs using Annotated Software Architecture Decomposition Models -- A Controlled Experiment"
<pre>Dataset, R-Scripts and questionnaire templates for our survey <em>Understanding Security Tactics in Microservice APIs using Annotated Software Architecture Decomposition Models -- A Controlled Experiment.</em></pre>
Globe230k: A Benchmark Dense-Pixel Annotation Dataset for Global Land Cover Mapping
<p>We (Intelligent Mining and Analysis of Remote Sensing big data, IMARS) create a large-scale annotated dataset (Globe230k) for land use/land cover (LULC) mapping, which is annotated on Google Earth image of 1 m spatial resolution. Globe230k is annotated by numerous experts and students major in survey and mapping after necessary training, through visual interpretation on very high-resolution images, as well as in-situ field survey, under the guidance of the organized annotation pipeline. Globe230k has three superiorities:</p> <p>1) Large scale: the Globe230k includes 232,819 annotated images with the size of 512x512 and spatial resolution of 1 m, with more than 3x1010 annotated pixels, and it includes 10 first-level categories. </p> <p>2) Rich diversity: the annotated images are sampled from worldwide regions, with coverage area of over 60,000 km2, indicating a high variability and diversity. Besides, in order to ensure the category balance, we intentionally give more chance to the rare categories to be sampled, such as wetland, ice/snow, etc.</p> <p>3) Multi-modal: Globe230k not only contains RGB bands, but also include other important features for Earth system research, such as Normalized differential vegetation index (NDVI), digital elevation model (DEM), vertical-vertical polarization (VV) bands, vertical-horizontal polarization (VH) bands, which can facilitate the multi-modal data fusion research. Due to the large size of the multi-modal dataset (DEM 1.91G, NDVI 164G, VVVH 372G), these dataset are stored on Baidu Yunpan, the download link is :https://pan.baidu.com/s/12AKbiqOXSf4fnm7mYkCE0g?pwd=230k, the extraction code is 230k.</p> <p>The image patches and their corresponding annotated patches are respectively stored in "image_patch.zip" and "label_patch.zip" file. The RGB image is in forms of ".jpg", with size of 512x512, the pixel value is ranged from 0-255. The annotated patches is in forms of ".png", also with size of 512x512, the pixel value is ranged from 1-10, which respectively represent 1#cropland, 2#forest, 3#grass, 4#shrubland, 5#wetland, 6#water, 7#tundra, 8#impervious, 9#bareland, 10#ice/snow. The corresponding DEM, NDVI and VVVH patches are all in form of ".tif", with size of 512x512 (due to the different resolution of DEM, NDVI and VVVH patches, they are all uniformly resized to the same scale as the image patch). </p> <p>The total 232,819 pairs are officially divided into training set, validation set, and test set, based on ratio of 7:1:2, which can be find in "train_num.txt","val_num.txt","test_num.txt" file. Based on this division, the official baseline accuracy of several state-of-the-art semantic segmentation can be found in the related arcticle (https://spj.science.org/doi/10.34133/remotesensing.0078).</p> <p>We hope it can be used as a benchmark to promote further development of global land cover mapping and semantic segmentation algorithm development.</p>
Large-scale annotated dataset for cochlear hair cell detection and classification
<p>Our sense of hearing is mediated by cochlear hair cells, of which there are two types organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains 5 - 15 thousand terminally differentiated hair cells, and their survival is essential for hearing as they do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. Machine learning can be used to automate the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, rat, guinea pig, pig, primate, and human cochlear tissue, from normal conditions and following <i>in-vivo</i> and <i>in-vitro</i>ototoxic drug application. The dataset includes over 107,000 hair cells which have been manually identified and annotated as either inner or outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair-cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to give other hearing research groups the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease. </p><p>Associated code is provided here: https://github.com/indzhykulianlab/hcat-data</p>
Public metagenome datasets annotated using SingleM, using a supplemented reference package.
<p>The SingleM package used for supplementing is available at 10.5281/zenodo.10360136</p>
RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment
<p>This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level.. <br><br>The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops.</p> <p>The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number><br><strong>_1</strong>: Blue<br><strong>_2</strong>: Green<br><strong>_3</strong>: Red<br><strong>_4</strong>: Near Infrared<br><strong>_5</strong>: RedEdge<br><br>Example: An image name <strong>IMG_0200_3 </strong>represents the scene number<strong> 200</strong> in <strong>Red channel</strong></p> <p>This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format.</p> <p>The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article. <br><br>Field experimentation roles:</p> <p>The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.</p> <p>Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. </p> <p>Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.</p>
Breviloquia Italica annotations
<div> <p>This resource contains the annotations produced by the <a href="https://github.com/breviloquia-italica">Breviloquia Italica</a> project.</p> <p>Please note that the <code>occurrences.csv</code> file is documented, but absent due to Twitter's restrictions.</p> </div>
263 MAG annotations for three nested metagenomic studies describe crop-shrub-microbe interactions in an agroecology system in the Sahel
<p>The Sahel region of West Africa is a vulnerable eco-region, where climate change induced drought and a rapidly growing population pose serious threats to food security and contribute to soil degradation. Local and biologically based systems are necessary to maintain crop yields and soil health, and intercropping with native woody shrubs Guiera senegalensis has been discovered as a solution. We have previously shown that soil microbial communities are significantly altered by the presence of shrubs, and that these organisms may have plant growth promoting properties. Here, we augment those data with metagenomic and metatranscriptomic data across three nested experiments: a landscape scale experiment across a rainfall and soil type gradient, a long-term experimental site, and a growth chamber simulated drought experiment. We recovered 263 95% ANI dereplicated metagenome-assembled genomes (MAGs) of medium and high quality to evaluate their relative enrichment and what their encoded metabolisms reveal about mechanisms of microbiome millet support. These data contribute to our understanding of the role of the microbial community crop drought resilience in the Sahel and in semi-arid cropping systems globally. Here we present the DRAM annotations of each MAG, all associated metadata, viral genes and vOTUs from the Optimized Shrub Intercropping Study (OSS), and eukaryotic contigs from the OSS</p>
Deep Learning Annotation Dataset and Images of Pea Aphids
<p><span>The small size and extensive polymorphisms of aphids make it difficult to identify larvae and adults solely based on their morphology. Here, we present an identification tool for the developmental stages of <em>Acyrthosiphon</em> <em>pisum</em> (Hemiptera: Aphididae) based on deep learning as a proof of concept. You Only Look Once (YOLO) algorithm is one of the most effective deep learning techniques for object detection. Although several studies have been conducted using deep learning technology for the detection and counting of tiny pests, the type of light source and size of the images were the limiting factors, as training was highly focused on uniform datasets and small insects. One way to overcome this problem is to introduce many types of datasets obtained from various light sources and microscopic magnifications. This strategy minimizes errors and omissions in aphid detection across all developmental stages in aphid individuals to the greatest extent possible. The experimental results showed that our modified YOLOv8 model could obtain over 95.9% and 99% accuracy for mean average precision (mAP) and Recall, respectively, under various light sources, such as yellow, white, and natural light, and stereomicroscope magnifications. This study showed an improved accuracy of aphid recognition at all developmental stages.</span><span> </span><span>The study presents a novel deep learning model utilizing the YOLO algorithm to identify developmental stages of </span><em><span>A</span></em><span>. </span><em><span>pisum</span></em><span>. This model achieves high accuracy across various light sources and magnifications, thereby enhancing aphid biology studies.</span></p>
DICOM WG-26 Whole Slide Imaging Annotations Connectathon: Imaging Data Commons entry
<p>This data descriptor contains DICOM Slide Microscopy (SM modality) images and DICOM 2D point and polygon Bulk Annotations (ANN modality) submitted by the Imaging Data Commons team as part of the participation in the 2024 DICOM Working Group 26 Annotations Connectathon (<a href="https://dicom-wg26-connectathons.github.io/2024-annotations/" target="_blank" rel="noopener">https://dicom-wg26-connectathons.github.io/2024-annotations/</a>).</p> <p>Detailed content of the descriptor is as follows:</p> <ol> <li>Images correspond to 3 series selected from the DICOM-converted slides incuded in the TCGA-READ collection (originally shared in vendor-specific format in <a href="https://portal.gdc.cancer.gov/projects/TCGA-READ" target="_blank" rel="noopener">https://portal.gdc.cancer.gov/projects/TCGA-READ</a>) and available from NCI Imaging Data Commons [1] at <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_read" target="_blank" rel="noopener">https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_read</a>. Specifically, the following series are included, as defined by their `PatientID`, `StudyInstanceUID` and `SeriesInstanceUID` values. Each individual series contains multiple files corresponding to different resolution layers, shared as zip files. <ol> <li>`TCGA-AF-2687.zip`: series `1.3.6.1.4.1.5962.99.1.2251401802.152239158.1638633974346.2.0` <a href="https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.213661963103110408605329613498871186883/series/1.3.6.1.4.1.5962.99.1.2251401802.152239158.1638633974346.2.0" target="_blank" rel="noopener">https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.213661963103110408605329613498871186883/series/1.3.6.1.4.1.5962.99.1.2251401802.152239158.1638633974346.2.0</a></li> <li>`TCGA-AF-2689.zip`: series `1.3.6.1.4.1.5962.99.1.2259090539.712983657.1638641663083.2.0` <a href="https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.312916405820155829215771528638931942827/series/1.3.6.1.4.1.5962.99.1.2259090539.712983657.1638641663083.2.0" target="_blank" rel="noopener">https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.312916405820155829215771528638931942827/series/1.3.6.1.4.1.5962.99.1.2259090539.712983657.1638641663083.2.0</a></li> <li>`TCGA-AF-2690.zip`: series `1.3.6.1.4.1.5962.99.1.2247972296.1080138101.1638630544840.2.0` <a href="https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.158926540358526295486644564130790202309/series/1.3.6.1.4.1.5962.99.1.2247972296.1080138101.1638630544840.2.0" target="_blank" rel="noopener">https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.158926540358526295486644564130790202309/series/1.3.6.1.4.1.5962.99.1.2247972296.1080138101.1638630544840.2.0</a></li> </ol> </li> <li>`polygon_annotations.zip`: 2D polygon annotations of the boundary of the cell nuclei. Conversion into DICOM ANN was performed from the original content shared in [2].</li> <li>`point_annotations.zip`: 2D point annotations corresponding to the centroids of the cell nuclei defined in the prior polygon annotations.</li> <li>`rectangle_annotations.zip`: 2D rectangle annotations corresponding to the axis-aligned bounding box of the cell nuclei defined in the prior polygon annotations.</li> <li>`ellipse_annotations.zip`: 2D ellipse annotations corresponding to the ellipse fit to the cell nuclei defined in the prior polygon annotations.</li> <li>`screenshots.pdf`: screenshots demonstrating visualization of the included annotations in the open source Slim viewer (https://github.com/ImagingDataCommons/slim) (note these visualizations were produced using a development branch of the software)</li> <li>`run_verify.sh`: script that was used to run `dciodvfy` validator and save validation results.</li> </ol> <p>Zip files with the annotations also include the output of the `dciodvfy` validator (https://dclunie.com/dicom3tools/dciodvfy.html) version `20240227102104` for each of the files.</p> <p> </p>
Figs 127-134 in Annotated type catalogue of the Orthalicoidea (Mollusca, Gastropoda, Stylommatophora) in the Muséum d'histoire naturelle, Geneva
Figs 127-134. Orthalicidae. (127) Kara viriata (Morelet, 1863), syntype, MHNG-INVE-78772 (H = 58.7). (128) Kara yanamensis (Morelet, 1863), syntype, MHNG-INVE-60202 (H = 55.4). (129) Scholvienia jaspidea (Morelet, 1863), syntype, MHNG-INVE-60211 (H = 47.2). (130) Orthalicus phlogerus (d'Orbigny, 1835), syntype, MHNG-INVE-64982 (H = 47.2). (131-132) Orthalicus zigzag (Lamarck, 1822), syntype, MHNG-INVE-51144 (H = 50.1). (133-134) Liguus fasciatus (Müller, 1774), (133) paratype of Liguus fasciatus viridis Clench, 1934, MHNG-INVE-64933 (H = 55.8), (134) paratype of Liguus crenatus barbouri Clench, 1929, MHNG-INVE-64938 (H = 43.2).
Figs 113-126 in Annotated type catalogue of the Orthalicoidea (Mollusca, Gastropoda, Stylommatophora) in the Muséum d'histoire naturelle, Geneva
Figs 113-126. Odontostomidae. (113-114) Bahiensis bahiensis (S. Moricand, 1834), syntype, MHNG-INVE-64638 (H = 18.5). (115-117) Tomigerus clausus (Spix, 1827), syntype of Helix (Cochlodonta) tomigera S. Moricand, 1836, MHNG- INVE-64717 (H = 10.1). (118-121) Biotocus turbinatus (Pfeiffer, 1845), syntype of Helix tomigeroides S. Moricand, 1846, MHNG-INVE-64718 (H = 11.4). (122) Spixia striata (Spix, 1827), paralectotype of Pupa spixii major d'Orbigny, 1837, MHNG-INVE-64662 (H = 34.6). (123-124) Burringtonia pantagruelina (S. Moricand, 1834), (123) syntype, MHNG-INVE-64695 (H = 52.9), (124) syntype of Helix (Cochlodina) pantagruelina minor S. Moricand, 1836, MHNG-INVE-64698 (H = 45.1). (125) Cyclodontina inflata (Wagner in Spix, 1827), specimen from original series of Bulimus scabrellus 'Anthony' Dohrn, 1882, MHNG-INVE-64686 (H = 19.7). (126) Plagiodontes patagonicus (d'Orbigny, 1835), paralectotype, MHNG-INVE-64708 (H = 22.2).
Fig. 135 in Annotated type catalogue of the Orthalicoidea (Mollusca, Gastropoda, Stylommatophora) in the Muséum d'histoire naturelle, Geneva
Fig. 135. Bothriembryontidae. Placostylus porphyrostomus monackensis (Crosse, 1888), possible syntype of Bulimus duplex major Gassies, 1871, MHNG-INVE-64837 (H = 88.0). Figs 136-137. Bulimulidae. (136-137) Drymaeus (Mesembrinus) polygrammus (S. Moricand, 1836), syntype, MHNG-INVE-64561 (H = 14.0). Fig. 138. Amphibulimidae. Dryptus pardalis (Férussac, 1821), syntype, MHNG-INVE-60142 (H = 70.2). Fig. 139. Orthalicidae. Liguus fasciatus (Müller, 1774), paratype of Liguus fasciatus archeri Clench, 1934, MHNG-INVE-64921 (H = 54.5).
Figs 84-96 in Annotated type catalogue of the Orthalicoidea (Mollusca, Gastropoda, Stylommatophora) in the Muséum d'histoire naturelle, Geneva
Figs 84-96. Bulimulidae. (84-85) Protoglyptus heterogrammus (S. Moricand, 1836), syntype, MHNG-INVE-64598 (H = 12.5), scale 0.5 mm. (86-87) Protoglyptus longisetus (S. Moricand, 1846), syntype, MHNG-INVE-64605 (H = 6.91), scale 0.5 mm. (88) Auris melastoma (Swainson, 1820), syntype of Helix (Cochlogena) rhodospira chrysostoma S. Moricand, 1836, MHNG-INVE-60161 (H = 56.4). (89) Auris illheocolus (S. Moricand, 1836), syntype, MHNG-INVE-60171 (H = 66.7). (90) Auris egregia (Jay, 1836), syntype of Helix (Cochlogena) maximiliana minor S. Moricand, 1836, MHNG- INVE-60152 (H = 38.2). (91) Bostryx cuspidatus (Morelet, 1863), syntype, MHNG-INVE-60377 (H = 30.2). (92) Bostryx acromelas (Morelet, 1863), syntype, MHNG-INVE-60378 (H = 19.8). (93) Bostryx spiculatus spiculatus (Morelet, 1860), paralectotype, MHNG-INVE-60411 (H = 23.3). (94) Bostryx veruculum (Morelet, 1860), syntype, MHNG-INVE-60384 (H = 25.2). (95-96) Bostryx virgultorum (Morelet, 1863), paralectotype respectively lectotype, MHNG-INVE-60341 (H = 30.5). ►
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.