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Figure 7. Serrulate setae. A, typical serrulate setae from maxilliped 1 in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 7. Serrulate setae. A, typical serrulate setae from maxilliped 1 of Pagurus bernhardus. Setules are small and only present on the distal half of the seta. B, middle part of serrulate seta with setules in three rows. C, setules from serrulate seta arranged randomly along the shaft. Note strong serration. D, small setules with weak articulations (arrows). E, scalelike setules from serrulate seta of Palaemon adspersus. Note serration on distal rim (arrows). F, terminal pore (arrow) from serrulate seta. G, serrulate setae on the coxa of maxilla 1 of Penaeus monodon. Abbreviation: Su, serrulate setae.
Figure 9. Papposerrate setae. A, typical papposerrate seta from maxilliped 1 in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 9. Papposerrate setae. A, typical papposerrate seta from maxilliped 1 of Cherax quadricarinatus, with long, randomly arranged setules on proximal part and denticles in two rows on distal part. B, transition region between long setules and denticles. Abbreviations: D, denticles; LS, long setules; SS, short setules.
Figure 8. Serrate setae. A in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 8. Serrate setae. A, typical serrate setae from the endopod of maxilla 1 of Cherax quadricarinatus. Denticles in two strict rows on the distal half. B, serrate seta with setules (arrow). Arrowhead indicates denticles. C, tip of serrate seta with terminal pore (arrow). No denticles, only scale-like setules near the tip (arrowhead). D, partial (arrows) and complete fusion of denticles on serrate seta from Penaeus monodon. E, serrate setae on the dactylus of maxilliped 3 of Palaemon adspersus. F, serrate setae on the dactylus of maxilliped 2 of Pe. monodon. Abbreviation: Se, serrate setae.
Figure 2 in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 2. Types of projections found on the general cuticle. A, type I projection, a seta, is an elongate circular projection, which is articulated with the general cuticle (arrow). It is the most common type of projection. B, type II projection, a seta, from maxilla 1 of Pagurus bernhardus with a more or less direct transition into the general cuticle. In the other species articulated setae are situated in the same place (compare with Fig. 11A). They may have small outgrowths (arrows). C, type III projections, denticles, from maxilliped 1 of Panulirus argus. Arrows indicate direct transition into general cuticle without an articulation. D, type IV projections, setules, from the paragnath of Stenopus hispidus. Arrows indicate serration and arrowheads indicate articulation with the general cuticle. Note the flattened shape at the base.
Figure 11. Cuspidate setae. A in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 11. Cuspidate setae. A, Typical cuspidate setae from the basis of maxilla 1 of Cherax quadricarinatus. Note clear articulation with general cuticle (arrowheads) and compare with Figure 2B. B, cuspidate seta with teeth-like outgrowths in two rows (arrows). C, subterminal pore (arrow) from cuspidate seta with debris in pore. D, cuspidate setae on the dactylus of maxilliped 2 of Carcinus maenas. One is lacking articulation (arrow). E, cuspidate setae on the endopod of maxilla 2 of Penaeus monodon. Abbreviation: Cu, cuspidate setae.
Figure 6. Plumose setae. A in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 6. Plumose setae. A, Typical plumose setae from the exopod of maxilliped 2 of Panulirus argus. Arrows indicate supracuticular articulations. B, basal part of setule. No articulation is seen (arrows). C, setule (inserted in a groove) from plumose seta. Note absence of serration. D, plumose seta with pseudo articulations (arrows) from an exopod flagellum. E, plumose setae on the exopod flagellum of maxilliped 2 of Pan. argus. Abbreviations: Endo, endopod; Exo fla, flagellum of exopod; Pl, plumose setae.
Figs 1–8 in New taxa of pygmy grasshoppers from Australia with notes on classification of the subfamily Batrachideinae (Orthoptera: Tetrigidae)
Figs 1–8. Batrachideinae, female: 1–3, Vingselina crassa; 4–6, Paraselina multifora; 7, P. trituberculata; 8, P. brunneri. Head and pronotum, lateral (1, 4) and dorsal (2, 5) views; pronotum, lateral view (7, 8); head and dorsal part of pronotum, frontal view (3, 6). [1–3, after photos of Tumbrinck (Cigliano et al., 2018); 4–6, after photos of Rehn (1952); 7, 8, after Sjöstedt (1932)].
Figs 21–23 in New taxa of pygmy grasshoppers from Australia with notes on classification of the subfamily Batrachideinae (Orthoptera: Tetrigidae)
Figs 21–23. Selivinga tribulata sp. nov., male: 21, 22, body, lateral (21) and dorsal (22) views; 23, apex of abdo- men, lateral view.
Figs 9–15 in New taxa of pygmy grasshoppers from Australia with notes on classification of the subfamily Batrachideinae (Orthoptera: Tetrigidae)
Figs 9–15. Anaselina minor: 9–11, female; 12–15, male. Body, lateral (9, 12) and dorsal (10, 13) views; apex of abdomen, ventral (11) and lateral (15) views; head, frontal view (14).
Crayfish classification
<p>This dataset is composed 2486 images. The labels are Crayfish, Crayfish Adult and Crayfish Juvenile</p>
Joint representation of molecular networks from multiple species improves gene classification - Data
<p>This is the data the accompanies the manuscript <em>Joint representation of molecular networks from multiple species improves gene classification</em></p> <p>Below is the license agreement for each of the publicly available datasets </p> <ul> <li><a href="https://wiki.thebiogrid.org/doku.php/terms_and_conditions">BioGRID</a></li> <li><a href="http://geneontology.org/docs/go-citation-policy/">GO</a></li> <li><a href="https://www.disgenet.org/legal">DisGeNet</a></li> <li><a href="https://monarchinitiative.org/about/licensing">Monarch</a></li> <li><a href="http://eggnog-mapper.embl.de">eggNOG</a></li> </ul> <p>No license agreement was available on <a href="http://imp.princeton.edu">IMP web site</a>, however we have obtained permission from the owner of the material to redistribute the network.</p>
Data from: Supervised classification of plant communities with artificial neural networks
<p>This dataset was used to test the performance of artificial neural networks for supervised classification of plant communities, published in:</p><p>Černá L. & Chytrý M. (2005) Supervised classification of plant communities with artificial neural networks. <i>Journal of Vegetation Science</i> 16, 407-414. https://doi.org/10.1111/j.1654-1103.2005.tb02380.x</p><p>The meaning of the individual columns (separated by semicolons) in the file is as follows (for details see the above-mentioned article):</p><ul><li>Plot no - unique number of the vegetation plot</li><li>Group expert - plot membership in classes 1-11 of the expert classification</li><li>Subset expert random B - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for the expert classification</li><li>Subset expert dg species B - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for the expert classification</li><li>Assignment expert random - a class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification</li><li>Assignment expert dg-sp - a class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification</li><li>Group cluster - plot membership in classes 1-11 of the numerical classification</li><li>Subset cluster random - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for numerical classification</li><li>Subset cluster dg species - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for expert classification, for numerical classification</li><li>Assignment cluster random - class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification, for numerical classification</li><li>Assignment cluster dg-sp - class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification, for numerical classification </li><li>598 species, with cover/abundance estimates on an ordinal scale of 1-9</li></ul>
Classification of obesity levels based on eating habits and physical condition
<p>This dataset encompasses information intended for the assessment of obesity levels among individuals in the nations of Mexico, Peru, and Colombia.</p><p>The main dataset is prepared by other authors in the article (https://doi.org/10.1016/j.dib.2019.104344) I have only used this dataset to perform my final project related to the Homework Assignment 6: Machine Learning Application in Project Dataset. <br>Here is some detaied explanation about the dataset:<br> </p><p><strong>The attributes related with eating habits are:</strong></p><ol><li>Frequent consumption of high caloric food (FAVC)</li><li>Frequency of consumption of vegetables (FCVC)</li><li>Number of main meals (NCP)</li><li>Consumption of food between meals (CAEC)</li><li>Consumption of water daily (CH20)</li><li>Consumption of alcohol (CALC)</li></ol><p><strong>The attributes related with the physical condition are:</strong></p><ol><li>Calories consumption monitoring (SCC)</li><li>Physical activity frequency (FAF)</li><li>Time using technology devices (TUE)</li><li>Transportation used (MTRANS)</li></ol><p><strong>other variables obtained were:</strong></p><ol><li>Gender</li><li>Age</li><li>Height</li><li>Weight</li><li>family history with overweight</li><li>SMOKE activity</li></ol><p>Finally, all data was labeled and the class variable NObesity was created with the values of:</p><p>a) Insufficient Weight</p><p>b) Normal Weight</p><p>c) Overweight Level I</p><p>d) Overweight Level II</p><p>e) Obesity Type I</p><p>f) Obesity Type II</p><p>g) Obesity Type III</p>
Neural Field Arena - Classification
<p>Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities, including images, shapes, and scenes. Subsequently, many works have explored the use of NeFs as representations for downstream tasks, e.g. classifying an image based on the parameters of a NeF that has been fit to it. However, the impact of the NeF hyperparameters on their quality as downstream representation is scarcely understood and remains largely unexplored. This is partly caused by the large amount of time required to fit datasets of neural fields.</p> <p>Thanks to <a href="https://github.com/samuelepapa/fit-a-nef" target="_blank" rel="noopener">fit-a-nef</a>, a JAX-based library that leverages parallelization to enable fast optimization of large-scale NeF datasets, we performed a comprehensive study that investigates the effects of different hyperparameters --including initialization, network architecture, and optimization strategies-- on fitting NeFs for downstream tasks.<br>Based on the proposed library and our analysis, we propose <strong>Neural Field Arena</strong>, a benchmark consisting of neural field variants of popular vision datasets, including MNIST, CIFAR, variants of ImageNet, and ShapeNetv2.<br>Our library and the Neural Field Arena will be open-sourced to introduce standardized benchmarking and promote further research on neural fields.</p> <p>The datasets that are currently available are the following:</p> <ol> <li>MNIST, SIREN.</li> <li>CIFAR10, SIREN,</li> <li>MicroImageNet, SIREN.</li> <li>ShapeNet, SIREN.</li> </ol> <p>More datasets will be added in the future.</p>
Metazoa-level USCOs as markers in species delimitation and classification
<p><span>Metazoa-level<strong> </strong>Universal Single-Copy Orthologs (USCOs) are universally applicable markers for DNA taxonomy in animals which can replace or supplement single-gene barcoding. While Metazoa-level USCOs from target enrichment data were shown to reliably distinguish species, it remains to be tested whether USCOs are an evenly distributed, representative sample of a given metazoan genome, and hence can facilitate detection of past hybridization events. Besides, unlinked loci are a principal assumption in coalescent-based species delimitation approaches. 239 chromosome-level genomes were analyzed to show that Metazoa-level<strong> </strong>USCOs are a representative sample of a genome: in terms of distances to each other on a chromosome, but also over the chromosomes, they are almost as evenly distributed as protein-coding genes in general are. We tested the suitability of Metazoa-level USCOs extracted from genomes for species delimitation and phylogeny in four case studies: <em>Anopheles</em> mosquitos, <em>Drosophila</em> fruit flies, <em>Heliconius </em>butterflies, and Darwin's finches. In almost all instances USCOs allowed delineating species and yielded phylogenies that correspond to those generated from whole genome data.<strong> </strong>Our results show<strong> </strong>that USCO genes can be considered as genetically unlinked for practical purposes and representative for an entire metazoan genome. Our phylogenetic analyses demonstrate that USCOs may complement single-gene barcoding and provide more accurate taxonomic inferences. Combining USCOs from sources that used different versions of ortholog reference libraries to infer marker orthology may be challenging and at times impact taxonomic conclusions. However, we expect this problem to become less severe as the size of genome reference libraries and their sampling of organismic lineages is rapidly increasing.</span></p>
Probabilistic classification of Fermi LAT gamma-ray sources (effect of covariate shift)
<p>Version 1:</p> <p>These are data products connected to <a href="https://arxiv.org/abs/2307.09584">https://arxiv.org/abs/2307.09584</a>, where an analysis of the effect of covariate shift on the probabilistic classification of the Fermi LAT gamma-ray sources from the 4FGL-DR3 catalog is performed.</p> <p>The files </p> <p>4FGL-DR3_6class_GMM_nmin100_prob_cat.csv<br>4FGL-DR3_6class_GMM_nmin100_weighted_prob_cat.csv</p> <p>contain probabilistic classification into 6 classes (determined in <a href="https://arxiv.org/abs/2307.09584">https://arxiv.org/abs/2301.07412</a>) with random forest and neural networks methods. The catalog in "4FGL-DR3_6class_GMM_nmin100_weighted_prob_cat.csv" is constructed including weights for associated sources used in training in order to account for the difference in the distribution of associated (training dataset) and unassociated (target dataset) sources. The catalog in "4FGL-DR3_6class_GMM_nmin100_prob_cat.csv" is constructed with unweighted training samples.</p> <p>The files</p> <p>4FGL-DR3_6class_GMM_nmin100_summary.csv<br>4FGL-DR3_6class_GMM_nmin100_weighted_summary.csv</p> <p>contain the corresponding summaries of the definition of classes and predicted numbers of sources for the RF and NN algorithms for associated sources (averaged over cases when the sources are in the testing samples) and unassociated sources.</p> <p>Detailed description of the construction of the catalogs can be found in <a href="https://arxiv.org/abs/2307.09584">https://arxiv.org/abs/2307.09584</a>.</p> <p>Version 2: update for the Fermi LAT 4FGL-DR4 catalog.</p> <p>The filenames slightly change.<br>Probabilistic catalogs with unweighted and weighted training respectively:<br>4FGL-DR4_6classes_GMM_prob_cat.csv<br>4FGL-DR4_6classes_GMM_weighted_prob_cat.csv<br><br>The corresponding summary files:<br>4FGL-DR4_6classes_GMM_summary.csv<br>4FGL-DR4_6classes_GMM_weighted_summary.csv</p> <p>Version 3: catalogs corresponding to the published version of the paper. The filenames and the format are the same as in Version 2.</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>
RISIS-KNOWMAK NUTS adapted classification
<p>This file provides the correspondence table between EUROSTAT NUTS3 classification and the adapted regional classification used by the RISIS-KNOWMAK project. This regional classification fits the structure of knowledge production in Europe and addresses some knowm problems of the NUTS3 classification, such as the treatment of large agglomerations, while remaining fully compatible with the EUROSTAT NUTS regional classification. This compatibility allows combining all KNOWMAK data with regional statistics (at NUTS3 level, 2021 edition) from EUROSTAT.</p> <p>More precisely, the classification includes EUROSTAT metropolitan regions (based on the aggregation of NUTS3-level regions) and NUTS2 regions for the remaining areas; further, a few additional centers for knowledge production, like Oxford and Leuven, have been singled out at NUTS3 level. The resulting classification is therefore more fine-grained than NUTS2 in the areas with sizeable knowledge production, but at the same time recognizes the central role of metropolitan areas in knowledge production. While remaining compatible with NUTS, the classification allows addressing two well-known shortcomings: a) the fact that some large cities are split between NUTS regions (London) and b) the fact that NUTS3 classification in some countries includes many very small regions, as in the case of Germany</p>
CESNET-TLS22: A large dataset for fine-grained classification of TLS services
<p><strong>Please refer to the original article for further data description:</strong> Jan Luxemburk et al. Fine-grained TLS services classification with reject option, Computer Networks, 2023, 109467, ISSN 1389-1286, <a href="https://doi.org/10.1016/j.comnet.2022.109467">https://doi.org/10.1016/j.comnet.2022.109467</a></p> <p><strong>We recommend using the</strong> <strong>CESNET DataZoo python library, which facilitates the work with large network traffic datasets. </strong>More information about the DataZoo project can be found in the GitHub repository <a href="https://github.com/CESNET/cesnet-datazoo">https://github.com/CESNET/cesnet-datazoo</a>.</p> <p>The recent success and proliferation of machine learning and deep learning have provided powerful tools, which are also utilized for encrypted traffic analysis, classification, and threat detection. These methods, neural networks in particular, are often complex and require a huge corpus of training data. Moreover, because most of the network traffic is being encrypted, the traditional deep-packet-inspecting (DPI) solutions are becoming obsolete, and there is an urgent need for modern classification methods capable of analyzing encrypted traffic. These methods have to forgo the packet's opaque payload and focus on flow statistics and packet metadata sequences like packet sizes, directions, and inter-arrival times. The classification can be further extended with the task of "rejecting" unknown traffic, i.e., the traffic not seen during the training phase. This makes the problem more challenging, and neural networks offer superior performance for tackling this problem.<strong> When the factors of (1) the hardness of classification of encrypted traffic with unknown traffic detection and (2) the neural networks' inherent need for large datasets are combined, the requirement for a rich, large, and up-to-date dataset is even stronger.</strong></p> <p>Therefore, we created a large dataset spanning two weeks, consisting of 141 million network flows, and having 191 fine-grained service labels. The dataset is intended as a benchmark for the task of identification of services in encrypted traffic with the detection of unknown services.</p> <p><strong>Data capture</strong> The data was captured in the flow monitoring infrastructure of the <a href="https://www.cesnet.cz">CESNET2</a> network. The capturing was done for two weeks between 4.10.2021 and 17.10.2021. The following table provides per-week flow count, capture period, and uncompressed size:</p> <ul> <li><strong>W-2021-40</strong> <ul> <li>Uncompressed Size: 22 GB</li> <li>Capture Period: 4.10.2021 - 10.10.2021</li> <li>Flows: 73.2M</li> </ul> </li> <li><strong>W-2021-41</strong> <ul> <li>Uncompressed Size: 20 GB</li> <li>Capture Period: 11.10.2021 - 17.10.2021</li> <li>Flows: 68.5M</li> </ul> </li> <li><strong>CESNET-TLS22</strong> <ul> <li>Uncompressed Size: 42 GB</li> <li>Capture Period: 4.10.2021 - 17.10.2021</li> <li>Flows: 141.7M</li> </ul> </li> </ul> <p><strong>Dataset structure</strong> The dataset flows are delivered in compressed CSV files, which contain one flow per row. For each flow data file, there is a JSON file with the number of saved flows per service. There is also the <em>stats-week.json</em> file aggregating flow counts of a whole week and the <em>stats-dataset.json</em> file aggregating flow counts for the entire dataset. The mapping between services and service providers is provided in the <em>servicemap.csv</em> file, which also includes SNI domains used for ground truth labeling. The following table describes flow data fields in CSV files:</p> <ul> <li><strong>ID:</strong> Unique identifier</li> <li><strong>BYTES:</strong> Number of transmitted bytes from client to server</li> <li><strong>BYTES_REV:</strong> Number of transmitted bytes from server to client</li> <li><strong>PACKETS:</strong> Number of packets transmitted from client to server</li> <li><strong>PACKETS_REV:</strong> Number of packets transmitted from server to client</li> <li><strong>DURATION:</strong> Duration of the flow in seconds</li> <li><strong>PPI:</strong> Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]]</li> <li><strong>PPI_LEN:</strong> Number of packets in the PPI sequence</li> <li><strong>PPI_DURATION:</strong> Duration of the PPI sequence in seconds</li> <li><strong>PPI_ROUNDTRIPS:</strong> Number of roundtrips in the PPI sequence</li> <li><strong>APP:</strong> Web service label</li> <li><strong>CATEGORY:</strong> Service category</li> <li><strong>TCP_FLAGS:</strong> TCP flags sent from client to server</li> <li><strong>TCP_FLAGS_REV:</strong> TCP flags sent from server to client</li> <li><strong>FLAG_CWR:</strong> Presence of the CWR flag</li> <li><strong>FLAG_CWR_REV:</strong> Presence of the CWR flag in the reverse direction</li> <li><strong>FLAG_ECE:</strong> Presence of the ECE flag</li> <li><strong>FLAG_ECE_REV:</strong> Presence of the ECE flag in the reverse direction</li> <li><strong>FLAG_URG:</strong> Presence of the URG flag</li> <li><strong>FLAG_URG_REV:</strong> Presence of the URG flag in the reverse direction</li> <li><strong>FLAG_ACK:</strong> Presence of the ACK flag</li> <li><strong>FLAG_ACK_REV:</strong> Presence of the ACK flag in the reverse direction</li> <li><strong>FLAG_PSH:</strong> Presence of the PSH flag</li> <li><strong>FLAG_PSH_REV:</strong> Presence of the PSH flag in the reverse direction</li> <li><strong>FLAG_RST:</strong> Presence of the RST flag</li> <li><strong>FLAG_RST_REV:</strong> Presence of the RST flag in the reverse direction</li> <li><strong>FLAG_SYN:</strong> Presence of the SYN flag</li> <li><strong>FLAG_SYN_REV:</strong> Presence of the SYN flag in the reverse direction</li> <li><strong>FLAG_FIN:</strong> Presence of the FIN flag</li> <li><strong>FLAG_FIN_REV:</strong> Presence of the FIN flag in the reverse direction</li> </ul> <p><strong>Link to other CESNET datasets</strong></p> <ul> <li><a href="https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/">https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/</a></li> <li><a href="https://github.com/CESNET/cesnet-datazoo">https://github.com/CESNET/cesnet-datazoo</a></li> </ul> <p><strong>Please cite the original article:</strong></p> <blockquote> <p>@article{luxemburk_fine-grained-tls_2023, author = {Jan Luxemburk and Tomáš Čejka}, title = {Fine-grained TLS services classification with reject option}, journal = {Computer Networks}, volume = {220}, pages = {109467}, year = {2023}, issn = {1389-1286}, doi = {https://doi.org/10.1016/j.comnet.2022.109467}, url = {https://www.sciencedirect.com/science/article/pii/S1389128622005011} }</p> </blockquote>
Figure 2 Sphecodini male genital capsule. A in A revised genus-level classification for the Neotropical groups of the cleptoparasitic bee tribe Sphecodini Schenck (Hymenoptera, Apidae, Halictinae)
Figure 2 Sphecodini male genital capsule. A) Austrosphecodes sp., B) Microsphecodes sp., C) Melissocleptis capriciosa, D) Ptilocleptis tomentosa. Abbreviations: gs = gonocoxite striations, isp = gonostylus internal setose patch, vp = ventral prong. All images under the same scale.
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.