Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,250
datasets available to search
ShareScore release 0.7.1
Dataset results
6,250 results for “classification”
Fig. 2 in Morphological diagnoses of higher taxa in Ophiuroidea (Echinodermata) in support of a new classification
Fig. 2. Examples of skeletal structures of brittlestars. A. Dorsal disc with scales and plates in Amphiura Forbes, 1843 (P). B. Oral frame with teeth (T) and apical tooth cluster (APC) in Ophiocoma L. Agassiz, 1836. C. Oral frame with infradental oral papillae (IP) in Ophioplax Lyman, 1875. D. Disc spines in Ophiacantha Müller & Troschel, 1842. E. Disc granules in Ophiocoma L. Agassiz, 1836. F. Disc tubercles in Acrocnida Gislén, 1926. G. Dental plate (DP) in Ophiura Lamarck, 1801, with teeth attached. H. Dental plate with sockets for regular teeth (TS) and for apical tooth cluster (APC) in Ophiocoma L. Agassiz, 1836. I–J. Lateral arm plate in Amphiura Forbes, 1843. I. External view with arm spine articulations (ASA). J. Internal view. Scale bars in millimetres.
Fig. 1 in Morphological diagnoses of higher taxa in Ophiuroidea (Echinodermata) in support of a new classification
Fig. 1. Summary phylogenetic tree of the ophiuroid higher taxonomy. Modified from O'Hara et al. (2017: fig. 1), with Ophiobyrsidae included based on O'Hara et al. (2017: fig. S3) sample Ophiuroidea_ sp_IE.2009.1713 (= Ophiophrixus_confinis) and additional unpublished exon-capture data on samples Ophiobyrsa_rudis_F222711 and Ophiosmilax_sp_IE.2207.6967. All-sites PLRS/RAxML tree with node support and age confidence intervals (coloured bars). Node support shows all-sites RAxML BS followed by the proportion of the data subset trees in agreement. For all other nodes support was 100/1. The root was fixed according to O'Hara et al. (2014) at 270 Ma.
Classification of Matching Molecular Series on the Basis of SAR Phenotypes and Structural Relationships
<p>A database comprising a total of 13,236 pairs of MMS with different SAR characteristics is provided. For each pair the corresponding MMS-cores are provided as SMILES. In addition, for each MMS-core the number of compounds and the SAR phenotype are given. ChEMBL target IDs (CHEMBLID_Target) designate target sets from which the MMS pairs originate. </p>
Training CNNs with Low-Rank Filters for Efficient Image Classification: Trained Models
<p>Models from experiments referenced in the paper "Training CNNs with Low-Rank Filters for Efficient Image Classification", https://arxiv.org/abs/1511.06744</p> <p>Model names differ from those in the paper, but the csv files for each set of experiments relates the paper's name for the model and the real name of the model here:</p> <ul> <li>cifarma.csv: Network-in-Network CIFAR10 Models</li> <li>mitma.csv: MIT Places Models</li> <li>googlenetma.csv: GoogLeNet ILSVRC2012 Models</li> <li>vggma.csv: VGG-11 ILSVRC2012 Models</li> </ul> <p> </p> <p> </p>
FIGURE 9 in A taxonomic review of the anilline genus Zeanillus Jeannel (Coleoptera: Carabidae: Bembidiini) of New Zealand, with descriptions of seven new species, re-classification of the species, and notes on their biogeography and evolution
FIGURE 9. SEM illustrations of pygidium and apex of elytra, dorsal aspect, of Zeanillus species. A— Z. phyllobius; B— Z. punctigerus; C— Z. pellucidus; D— Z. lescheni. Legend: ea—apex of right elytron; ed 8 — apical seta; pgd—pygidium; suelytral suture. Scale bars = 0.05 mm.
FIGURE 10 in A taxonomic review of the anilline genus Zeanillus Jeannel (Coleoptera: Carabidae: Bembidiini) of New Zealand, with descriptions of seven new species, re-classification of the species, and notes on their biogeography and evolution
FIGURE 10. SEM illustrations of structural features of legs of Zeanillus species, various aspects. A – E right protarsus and protibia, ventral aspect: A, D – E—females; B – C—males. A— Z. phyllobius; B— Z. punctigerus; C— Z. pellucidus; D— Z. pallidus; E— Z. brouni. F – G, H, J right and I, K left mesotibia: F— Z. phyllobius, ventral aspect; G— Z. punctigerus, ventral aspect; H— Z. pellucidus, medial aspect; I— Z. pallidus, ventral aspect; J— Z. lescheni, ventral aspect; K— Z. brouni, ventral aspect. L – M, O right and N, P – Q left metatibia: L— Z. phyllobius, ventral aspect; M— Z. punctigerus, ventral aspect; N— Z. pellucidus, medial aspect; O— Z. pallidus, ventral aspect; P— Z. lescheni, ventral aspect; Q— Z. brouni, medial aspect. Legend: ac—antenna cleaner; as—adhesive seta; asp—anterior spur; asr—anterior setal row; cls—clip seta; msb—mesotibial brush; msms—mesotibial modified seta; mss—mesotibial spur; mtb—metatibial brush; mtms—metatibial modified seta; mtsmetatibial spur; psp—posterior spur; psr—posterior setal row; sb—setal band; ta 1 – ta 4 — tarsomeres 1 – 4. Scale bars = 0.05 mm.
FIGURE 8 in A taxonomic review of the anilline genus Zeanillus Jeannel (Coleoptera: Carabidae: Bembidiini) of New Zealand, with descriptions of seven new species, re-classification of the species, and notes on their biogeography and evolution
FIGURE 8. SEM illustrations of chaetotaxy of elytra, dorso-lateral aspect, of Zeanillus species. A— Z. punctigerus; B— Z. pallidus; C— Z. pellucidus; D— Z. lescheni. Legend: ed 2 — scutellar seta; ed 3 — 1 st discal seta; ed 4 – 5 — 2 nd discal seta; ed 6 – 7 — 3 d discal seta; ed 8 — apical seta; eo 1 – 9 — setae 1 – 9 from the umbilical series; smes—subapical marginal elytral seta; sssubapical sinuation. Scale bars = 0.2 mm.
FIGURE 6 in A taxonomic review of the anilline genus Zeanillus Jeannel (Coleoptera: Carabidae: Bembidiini) of New Zealand, with descriptions of seven new species, re-classification of the species, and notes on their biogeography and evolution
FIGURE 6. SEM illustrations of prothorax, ventral aspect, of Zeanillus species. A— Z. phyllobius; B— Z. punctigerus; C— Z. pallidus; D— Z. pellucidus; E— Z. lescheni; F— Z. brouni. Legend: pas—prosternal ambulatory seta; pep—proepipleuron; pes—proepisternum; prcx—procoxa; ps—prosternum; psp—prosternal intercoxal process. Scale bars = 0.1 mm.
FIGURE 5 in A taxonomic review of the anilline genus Zeanillus Jeannel (Coleoptera: Carabidae: Bembidiini) of New Zealand, with descriptions of seven new species, re-classification of the species, and notes on their biogeography and evolution
FIGURE 5. SEM illustrations of pronotum, dorsal aspect, of Zeanillus species. A— Z. phyllobius; B— Z. punctigerus; C— Z. pallidus; D— Z. pellucidus; E— Z. lescheni; F— Z. nanus. Legend: aps—apicolateral pronotal seta; bdt—basilateral pronotal denticle; bs—basilateral pronotal seta; bsn—basilateral pronotal sinuation; ed 2 — scutellar seta; ls—midlateral pronotal seta; mg—marginal pronotal gutter; sct—scutellum. Scale bars = 0.1 mm.
FIGURE 2 in A taxonomic review of the anilline genus Zeanillus Jeannel (Coleoptera: Carabidae: Bembidiini) of New Zealand, with descriptions of seven new species, re-classification of the species, and notes on their biogeography and evolution
FIGURE 2. SEM illustrations of chaetotaxy of head and pronotum, dorso-lateral aspect, of Zeanillus species. A— Z. phyllobius; B— Z. pallidus; C— Z. pellucidus; D— Z. lescheni. Legend: aps—apicolateral pronotal seta; cs—clypeal seta; fsfrontal seta; ls—midlateral pronotal seta; pos—postorbital seta; ssa—anterior supraorbital seta; ssp—posterior supraorbital seta. Scale bars = 0.1 mm.
FIGURE 1 in A taxonomic review of the anilline genus Zeanillus Jeannel (Coleoptera: Carabidae: Bembidiini) of New Zealand, with descriptions of seven new species, re-classification of the species, and notes on their biogeography and evolution
FIGURE 1. SEM illustrations of head, dorsal aspect, of Zeanillus species. A— Z. phyllobius; B— Z. punctigerus; C— Z. pallidus; D— Z. pellucidus; E— Z. lescheni; F— Z. nanus. Legend: cl—clypeus; cs—clypeal seta; fcc—fronto-clypeal carina; fs—frontal seta; lb—labrum; mp 3 — maxillary palpomere 3; mp 4 — maxillary palpomere 4; pos—postorbital seta; ssa—anterior supraorbital seta; ssp—posterior supraorbital seta. Scale bars = 0.1 mm.
FIGURE 4 in A taxonomic review of the anilline genus Zeanillus Jeannel (Coleoptera: Carabidae: Bembidiini) of New Zealand, with descriptions of seven new species, re-classification of the species, and notes on their biogeography and evolution
FIGURE 4. SEM illustrations of labial complex, ventral aspect, of Zeanillus species. A— Z. phyllobius; B— Z. punctigerus; C— Z. pallidus; D— Z. pellucidus; E— Z. lescheni; F— Z. brouni. Legend: alms—anterolateral mental seta; gsc—glossal sclerite; lms—lateral mental seta; lss—lateral submental seta; m—mentum; mp 3 — maxillary palpomere 3; mp 4 — maxillary palpomere 4; mt—mental tooth; mss—mental-submental suture; pms—paramedial mental seta; prss—primary basal submental seta; sm—submentum. Scale bars = 0.1 mm.
The System for Classification of Low-Pressure Systems (SyCLoPS) Dataset (Based on ERA5)
<p>This is the ERA5 System for Classification of Low-Pressure Systems (SyCLoPS) dataset<strong> version 6</strong>. Details of SyCLoPS algorithms are described in the paper titled <strong>The System for Classification of Low-Pressure Systems (SyCLoPS): An All-in-One Objective Framework for Large-scale Data sets </strong>published on <em>J. Geophys. Res. Atm.</em>:<strong> [<a href="https://doi.org/10.1029/2024JD041287">https://doi.org/10.1029/2024JD041287]</a></strong></p> <p><strong>Important: The most up-to-date SyCLoPS codes are now kept on GitHub: [<a href="https://github.com/yepkids/SyCLoPS">https://github.com/yepkids/SyCLoPS</a>] <br></strong>*Updates on GitHub: SyCLoPS can now run entirely in Python. See the GitHub README page for details.*</p> <p>SyCLoPS user manual: [<a href="https://climate.ucdavis.edu/syclops.php">https://climate.ucdavis.edu/syclops.php</a>]</p> <p><strong>Known issues</strong></p> <ol> <li>The master TE branch now lacks the ability to deal with large missing values in datasets (e.g. 1e20), this will result in unreasonable values in the classification process for some datasets. NaNs as missing values are safe to proceed with. We are working on this issue and users can expect a newer TE version with fixes in the near future. For now, users can install this fork of TempestExtremes via CMAKE, which can be found here: [<a href="https://github.com/yepkids/tempestextremes"><strong>https://github.com/yepkids/tempestextremes</strong></a>], to work around this problem. This fork provides a temporary solution that adds missing value support for operators used by SyCLoPS and has been tested. Note that this is not a stable release, and please report any problems with this fork to Yushan Han (yshhan@ucdavis.edu). You can also choose to convert all missing values in your input files to NaNs.</li> </ol> <p>TempestExtremes software (master branch): [<a href="https://github.com/ClimateGlobalChange/tempestextremes">https://github.com/ClimateGlobalChange/tempestextremes</a>] </p> <p><strong>Major updates and bug fixes in this version:</strong></p> <ol> <li><strong>The classified and input LPS dataset is now extended from 1979-2022 to 1970-2024 </strong>(See Chapter 3 of the manual on how to load and use the output classified catalog).</li> <li>Note: This release will have a slight difference in the number of nodes found and some tracks compared to previous releases for the overlapped period. This is partly due to the extension of the tracks at the beginning of 1979 and the end of 2022, and also to the use of the newly developed "--mergeequal" argument for ERA5 MSLP nodes (this is to avoid some rare cases where two nodes with exactly the same MSLP values are close to each other but are not merged; see the manual section 2.2 for more details). </li> </ol> <p><strong>The following files can be obtained from version 4:</strong></p> <ol> <li>The labeled size blobs of each year: "<strong>size_blobs_1979_2022.tar.gz</strong>"</li> <li>The labeled precipitation blobs of each year: "<strong>preci_blobs_1979_2022.tar.gz</strong>"</li> </ol> <p>Please contact Yushan Han (yshhan@ucdavis.edu) if you have questions about the SyCLoPS framework. Please contact Paul Ullrich (paullrich@ucdavis.edu) if you have any questions about the TE software.</p> <p>See below for a table of atmospheric variables required for SyCLoPS and a flowchart of the classification process. See the SyCLoPS manual for more details.</p>
Data for methylome sequencing: Enriching and Profiling Methylomes for Tumor Classification and Liquid Biopsies
<p>We benchmarked and demonstrated the versatility of FLEXseq (Fragment Ligation EXclusive methylation sequencing) across different sample types: genomic DNA from the K562 (leukemia) cell line, DNA mix-in titrations of four immune cell types (B cells, T cells, monocytes, and neutrophils), DNA titrations of three cancer cell lines (breast invasive carcinoma [BRCA], colon adenocarcinoma [COAD], and glioblastoma [GBM]) mixed with those four immune cell mixtures separately, input titrations of cell-free (cf) DNA from one plasma sample and DNA from formalin-fixed paraffin-embedded (FFPE) tissues, cfDNA from 106 cerebrospinal fluids (CSF) and 42 other body fluids, and DNA from 37 FFPE tissues.</p> <p>We sequenced all the samples mentioned above using FLEXseq. Paired-end reads were quality and length trimmed with cutadapt version 3.5, and all high-quality sequencing reads were then aligned to the hg38 reference genome using Bismark v0.23.0. We then filtered out reads with unmethylated cytosine in the non-CpG context with filter_non_conversion function. Next, we used the bismark_methylation_extractor function to extract the methylation calls (removing single-nucleotide polymorphisms [SNP]).</p> <p>We also used the bam2pat function from wgbs_tools, to convert bam files into .pat files for deconvolution, keeping reads covering at least three CpG sites. The .pat files preserve fragment-level data and were de-identified by removing SNPs using the mask_pat function. </p> <p>We used CNVkit (v0.9.10) to analyze and visualize genome-wide copy numbers. Our inputs into CNVkit were Bismark/Bowtie 2 aligned BAM files deduplicated by Bismark based on end positions and fragment lengths. We then generated log2copy ratio plots for all body fluid and FFPE samples based on the pooled reference and visualized them across all bins using the DNAcopy R package.</p> <p> </p>
Monthly data on caesarean practice using the Ten Group Classification System (2020-2025)
<p>This dataset present summary statistics (numbers of vaginal deliveries and number of caesarean sections according to the Ten groups) that were collected daily from January 2020 to June 2025 in the 32 hospitals participating in the Quali-Dec project. The data were extracted from the hospitals’ birth registers and medical records by a trained health care provider and controlled quarterly by the country data manager. Monthly summary statistics were collected and managed by using REDCap electronic data capture tools hosted at the Karolinska Institute and controlled at a monthly frequency by the principal data manager for pending data and duplicated records. The dataset is composed with 1984 monthly records that have been uploaded to Redcap: 400 from Argentina, 392 from Burkina Faso, 404 from Thailand and 401 from Vietnam. The data collected covered 728,586 deliveries: 392,194 vaginal births and 336,392 caesarean sections.</p>
MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters
<p><strong>The dataset</strong></p> <p>MagicBathyNet is a benchmark dataset made up of image patches of Sentinel-2, SPOT-6 and aerial imagery, bathymetry in raster format and seabed classes annotations. MagicBathyNet has been designed to be geographically well distributed. It’s coverage includes two very different coastal areas (in terms of water column characteristics and bottom type): i) Agia Napa area in Cyprus, covering a wide range of typical Mediterranean waters and seabed types, and ii) Puck Lagoon area in Poland, representing in a great degree Baltic Sea waters and bottom.</p> <p>MagicBathyNet contains 3355 RGB co-registered triplets of Sentinel-2 (S2), SPOT-6, and aerial image patches, complemented by 1244 RGB co-registered S2 and SPOT-6 doublets, 3354 DSM (Digital Surface Model) raster patches for the aerial patches and 3396 DSM raster patches for S2 and SPOT-6. Additionally, it contains 533 annotated raster patches for seabed habitat and type, facilitating supervised pixel-based classification. Each patch covers 180x180m, represented by 18x18 pixels in S2 imagery, 30x30 pixels in SPOT-6 imagery and 720x720 pixels in airborne imagery. </p> <p>For the implementation code and pre-trained models visit our project page: <a href="https://www.magicbathy.eu/magicbathynet.html">https://www.magicbathy.eu/magicbathynet.html</a> </p> <p><strong>MagicBathyNet.zip </strong>file contains the original dataset presented in the respective paper.</p> <p><strong>MagicBathyNet_extension_for_Swin-BathyUNet.zip</strong> file is added in the new version to support the experiments and the results presented in "Agrafiotis, P., & Demir, B. (2025). Deep learning-based bathymetry retrieval without in-situ depths using remote sensing imagery and SfM-MVS DSMs with data gaps. <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>, <em>225</em>, 341-361. <a href="https://doi.org/10.1016/j.isprsjprs.2025.04.020">https://doi.org/10.1016/j.isprsjprs.2025.04.020</a> "</p> <p> </p> <p> </p> <p><strong>Citation</strong></p> <p>If you use the code in this repository or the dataset please cite our paper:</p> <p>P. Agrafiotis, L. Janowski, D. Skarlatos, and B. Demir, <a href="https://arxiv.org/abs/2405.15477" target="_blank" rel="noopener noreferrer">"MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters"</a>, arXiv:2405.15477, 2024.</p> <p>or </p> <p>P. Agrafiotis, Ł. Janowski, D. Skarlatos and B. Demir, "MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-Based Classification in Shallow Waters," <em>IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium</em>, Athens, Greece, 2024, pp. 249-253, doi: 10.1109/IGARSS53475.2024.10641355.</p> <p><strong>Folder structure</strong></p> <p>┗ 📂 magicbathynet/<br> ┣ 📂 agia_napa/<br> ┃ ┣ 📂 img/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 depth/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 gts/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📜 [modality]_split_bathymetry.txt<br> ┃ ┣ 📜 [modality]_split_pixel_class.txt<br> ┃ ┣ 📜 norm_param_[modality]_an.txt<br> ┃<br> ┣ 📂 puck_lagoon/<br> ┃ ┣ 📂 img/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 depth/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 gts/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📜 [modality]_split_bathymetry.txt<br> ┃ ┣ 📜 [modality]_split_pixel_class.txt<br> ┃ ┣ 📜 norm_param_[modality]_pl.txt</p> <p> </p> <p><strong>Package for benchmarking MagicBathyNet dataset</strong></p> <p>Donwload the package for benchmarking MagicBathyNet dataset in learning-based bathymetry and pixel-based classification here:</p> <p><a href="https://github.com/pagraf/MagicBathyNet">https://github.com/pagraf/MagicBathyNet</a></p> <p> </p> <p><strong>Version history</strong></p> <p>v1.0.0 - First release</p> <p> </p> <p><strong>License</strong></p> <p>Dataset: Creative Commons Attribution Non Commercial 4.0 International</p> <p>Code: Attribution-NonCommercial-ShareAlike 4.0 International License</p> <p>Copyright (c) 2024 The MagicBathyNet Authors</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This work was part of the project MagicBathy which is a research project funded by the European Commission for the period 2023-2025. It is funded under the HORIZON Europe MSCA Postdoctoral Fellowships - European Fellowships (GA 101063294).</p> <p>European Space Agency (ESA) is also acknowledged for providing the SPOT-6 images within its TPM programme in the frame of proposal PP0092443 and Airbus for being the provider of the original SPOT-6 images. The Dep. of Land and Surveys of Cyprus is acknowledged for providing the LiDAR reference data for Cyprus.</p>
Figure 2A–J in Phylogeny and classification of tribe Aedini (Diptera: Culicidae)
Figure 2A–J. Single most parsimonious cladogram (Fit = 187.51606) obtained from analysis of the data (Appendix 1) under implied weights (K = 9). Numbers on the branches correspond to the characters listed in the data set (Appendix 1). Darkened circles indicate 'unique' character states that can be placed onto the cladogram in only a single position, although they may be interpreted as undergoing subsequent transformation or secondary reversal. Open circles represent homoplastic character states that are placed on more than one branch of the cladogram. Numbers in circles refer to the numbered taxa listed in the legend to Figure 1.
Figure 10. Simple 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 10. Simple setae. A, typical simple setae from the mandibular palp of Panulirus argus. No outgrowths are seen. B, terminal pore (arrow) from simple seta. C, simple setae situated on the basis of maxilla 2 of Carcinus maenas. Abbreviation: Si, simple setae.
Figure 5. Pappose 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 5. Pappose setae. A, overview of two typical pappose setae from Cherax quadricarinatus. Note random arrangement of setules. B, tips of pappose setae from Stenopus hispidus. Setules get smaller closer to the tip (arrow). C, serration on the setules (arrows) from pappose seta. D, pappose setae on the exopod of maxilliped 1 of Carcinus maenas. E, pappose setae on the mandibular palp of Ca. maenas. F, pappose setae on the coxa of maxilliped 1 of Pagurus bernhardus. Abbreviation: Pa, pappose setae.
Figure 4 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 4. Substructures of setae. A, infracuticular articulation with the general cuticle. Arrow indicates deep socket. B, supracuticular articulation (arrows) with the general cuticle. C, annulus seen as a ring in the cuticle (arrow). D, two rows of denticles arranged distally on a seta. E, large setule displaying articulation (arrow) with setal shaft. F, small setule with weak articulation (arrows). G, stitched picture showing gradual change from setule (arrow) to denticle (arrowhead) on the same seta. H, subterminal pore (arrow) from seta with denticles. I, terminal pore (arrow) from seta with denticles.
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.