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30,817 results for “type”
FIGURES 61–72. Fig. 61 in Additions and corrections to the type list of Heteroptera (Insecta: Hemiptera) of the Costa Lima Collection, Oswaldo Cruz Foundation
FIGURES 61–72. Fig. 61, Spiniger neivai Costa Lima, 1940b [= Neivacoris neivai (Costa Lima, 1940b)]; holotype n° 4397. Fig. 62, S.ochrinotatus Costa Lima, 1940b[= Zelurus ochrinotatus (Costa Lima, 1940b)]; holotype n° 4398. Fig. 63, S. osorioi Costa Lima, 1939 [= Zelurus truculentus (Stål, 1860)]; holotype n° 4004. Fig. 64, S. penidoi Costa Lima, 1940b [= Zelurus penidoi (Costa Lima, 1940b)]; holotype n° 2073. Fig. 65, S. pintoi Costa Lima, 1940b [= Zelurus pintoi (Costa Lima, 1940b)]; holotype n° 2855. Fig. 66, S. seabrai Costa Lima & Leite, 1950 [= Zelurus seabrai (Costa Lima & Leite, 1950)]; syntype n° 5408. Fig. 67, S. spitzi Costa Lima, 1940b [= Zelurus spitzi (Costa Lima, 1940b)]; paratype n° 4187. Fig. 68. S. travassosi Costa Lima, 1940b [= Zelurus travassosi (Costa Lima, 1940b)]; holotype n° 2848. Fig. 69, S. variegatus Costa Lima, 1940b [=Zelurus variegatus (Costa Lima, 1940b)]; holotype n° 2089. Fig. 70, S. zikani Costa Lima, 1940b [=Zelurus zikani (Costa Lima, 1940b)]; holotype n° 2847. Fig. 71, Zelurus fluminensis Lent & Wygodzinsky, 1946; n° 2086. Fig. 72, Z. itatiaiensis Lent & Wygodzinsky, 1946; alotype n° 4396. Bars = 2 mm.
FIGURES 85–92. Fig. 85 in Additions and corrections to the type list of Heteroptera (Insecta: Hemiptera) of the Costa Lima Collection, Oswaldo Cruz Foundation
FIGURES 85–92. Fig. 85, Pessoaia maculata Wygodzinsky, 1943; alotype n° 4441. Fig. 86, P. limai Usinger, 1942; paratype unnumbered. Fig. 87, P. piratoides Costa Lima, 1940a; paratype n° 4438. Fig. 88, Vescia angrensis Seabra & Hathaway, 1942; holotype n° 4437. Fig. 89, Acanthocheila nigrescens Drake & Bondar, 1932; paratype n° 3092. Fig. 90, A. costalimai Monte; n° 2968. Fig. 91, Gargaphia brunfelsiae Monte, 1938; paratype n° 2970. Fig. 92, G. costalimai Monte, 1938; paratype n° 4141. Bars: 85–88 = 2 mm; 88–92 = 1 mm.
FIGURES 73–84. Fig. 73 in Additions and corrections to the type list of Heteroptera (Insecta: Hemiptera) of the Costa Lima Collection, Oswaldo Cruz Foundation
FIGURES 73–84. Fig. 73, Zelurus neglectus Lent & Wygodzinsky, 1947a; paratype n° 3616. Fig. 74, Salyavata nigrofasciata Costa Lima, 1935b; holotype n° 1873. Fig. 75, Ocrioessa barberi Costa Lima, 1941; holotype n° 4522. Fig. 76, Oncocephalus mazzai Costa Lima, 1941 [= O. validispinis Reuter, 1882]; holotype n° 4523. Fig. 77, Pygolampis duckei Costa Lima & Seabra, 1945a; holotype n° 4570. Fig. 78, Rutuba buenoi Costa Lima & Seabra, 1945a [= Pnirontis buenoi (Costa Lima & Seabra, 1945a)]; holotype n° 4565. Fig. 79, Seridentus havilandi Costa Lima & Seabra, 1945b; holotype n° 4599. Fig. 80, Stenopodessa lanei Costa Lima & Seabra, 1945b; holotype n° 4589. Fig. 81, S. piligera Barber, 1930; holotype n° 4588. Fig. 82, Zylobus parkoi Costa Lima & Seabra, 1945b [= Nitornus parkoi (Costa Lima & Seabra, 1945b); holotype n° 4607. Fig. 83, Triatoma costalimai Verano & Galvão, 1958; syntype n° 5938. Fig. 84, T. matogrossensis Leite & Barbosa, 1953; holotype n° 5721. Bars = 2 mm.
FIGURES 37–48. Fig. 37 in Additions and corrections to the type list of Heteroptera (Insecta: Hemiptera) of the Costa Lima Collection, Oswaldo Cruz Foundation
FIGURES 37–48. Fig. 37, Apiomerus pinheiroi Costa Lima, Seabra & Hathaway, 1951; holotype n° 4764.Fig. 38, A.signatus Costa Lima, Seabra & Hathaway, 1951; holotype n° 4802. Fig. 39, A. spitzi Costa Lima; n° 4657. Fig. 40, A. zikani Costa Lima, Seabra & Hathaway, 1951; holotype n° 4819. Fig. 41, Heniartes zikani Wygodzinsky, 1947; paratype n° 4819. Fig. 42, Paramanicocoris rubroniger Costa Lima, Hathaway & Seabra, 1947 [= Manicocoris rubroniger (Costa Lima, Hathaway & Seabra, 1947)]; holotype n° 4699. Fig. 43, Heza simulans Costa Lima; n°2972. Fig. 44, Rocconota bruchi Costa Lima, 1941; holotype n° 4534. Fig. 45, Phymata melloi Costa Lima, 1935 [= P. communis Handlirsch, 1897]; syntype n° 1874. Fig. 46, Aradomorpha championi Lent & Wygodzinsky, 1944; paratype n° 4639. Fig. 47, A. chinai Costa Lima, 1940c; holotype n° 4416. Fig. 48, Spiniger almeidai Costa Lima, 1940b [= Zelurus lineatus (Le Peletier & Serville, 1825)]; holotype n° 2838. Bars = 2 mm.
FIGURES 49–60. Fig. 49 in Additions and corrections to the type list of Heteroptera (Insecta: Hemiptera) of the Costa Lima Collection, Oswaldo Cruz Foundation
FIGURES 49–60. Fig. 49, Spiniger coralinus Costa Lima, 1940b [= Zelurus coralinus (Costa Lima, 1940b)]; holotype n° 2097.Fig. 50, S. diasi Costa Lima, 1940b [= Zelurus diasi (Costa Lima, 1940b)]; holotype n° 4166. Fig. 51, S. domesticus Pinto, 1927 [= Opisthacidius pertinax (Breddin, 1903)]; syntype n° 2099. Fig. 52, S. flavofasciatus espiritosantensis Seabra & Hathaway, 1943 [= Zelurus flavofasciatus (Stål, 1859)]; holotype n° 4540. Fig. 53, S. fulvomaculatus nigrolineatus Costa Lima, 1940b [= Zelurus nigrolineatus (Costa Lima, 1940b)]; holotype n° 2531. Fig. 54, S. genumaculatus Costa Lima, 1940b [= Zelurus genumaculatus (Costa Lima, 1940b)]; holotype n° 4167. Fig. 55, S. lopesi Costa Lima, 1940b [= Zelurus lopesi (Costa Lima, 1940b)]; holotype n°4186. Fig. 56, S. luctuosus Costa Lima, 1940b [= Zelurus luctuosus (Costa Lima, 1940b)]; paratype n° 4165. Fig. 57, S. lugubris Costa Lima, 1940b [= Zelurus lugubris (Costa Lima, 1940b)]; holotype n° 2074. Fig. 58, S. lutzi Costa Lima, 1940b [= Opisthacidius lutzi (Costa Lima, 1940b)]; holotype n° 2100. Fig. 59, S. martinsi Costa Lima, 1940b [= Zelurus martinsi (Costa Lima, 1940b)]; holotype n° 4157. Fig. 60, S. mazzai Costa Lima, 1941 [= Zelurus mazzai (Costa Lima, 1941)]; holotype n° 4527. Bars = 2 mm.
Expert and AI-generated annotations of the tissue types for the RMS-Mutation-Prediction microscopy images
<div> <p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations</a>.. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> </div> <div> <div> <p>This dataset contains 2 components:</p> <ol> <li>Annotations of multiple regions of interest performed by an expert pathologist with eight years of experience for a subset of hematoxylin and eosin (H&E) stained images from the RMS-Mutation-Prediction image collection [1,2]. Annotations were generated manually, using the Aperio ImageScope tool, to delineate regions of alveolar rhabdomyosarcoma (ARMS), embryonal rhabdomyosarcoma (ERMS), stroma, and necrosis [3]. The resulting planar contour annotations were originally stored in ImageScope-specific XML format, and subsequently converted into Digital Imaging and Communications in Medicine (DICOM) Structured Report (SR) representation using the open source conversion tool [4].</li> <li>AI-generated annotations stored as probabilistic segmentations.</li> </ol> <p><strong>WARNING</strong>: After the release of IDC v20 (v2 of this data record), it was discovered that a mistake had been made during data conversion that affected the newly-released segmentations accompanying the "RMS-Mutation-Prediction" collection. Segmentations released in v20 for this collection have the segment labels for alveolar rhabdomyosarcoma (ARMS) and embryonal rhabdomyosarcoma (ERMS) switched in the metadata relative to the correct labels. Thus segment 3 in the released files is labelled in the metadata (the SegmentSequence) as ARMS but should correctly be interpreted as ERMS, and conversely segment 4 in the released files is labelled as ERMS but should be correctly interpreted as ARMS. This mistake was fixed in the version v3 of this record (IDC data release v21).</p> <p>Many pixels from the whole slide images annotated by this dataset are not contained inside any annotation contours and are considered to belong to the background class. Other pixels are contained inside only one annotation contour and are assigned to a single class. However, cases also exist in this dataset where annotation contours overlap. In these cases, the pixels contained in multiple contours could be assigned membership in multiple classes. One example is a necrotic tissue contour overlapping an internal subregion of an area designated by a larger ARMS or ERMS annotation. The ordering of annotations in this DICOM dataset preserves the order in the original XML generated using ImageScope. These annotations were converted, in sequence, into segmentation masks and used in the training of several machine learning models. Details on the training methods and model results are presented in [1]. In the case of overlapping contours, the order in which annotations are processed may affect the generated segmentation mask if prior contours are overwritten by later contours in the sequence. It is up to the application consuming this data to decide how to interpret tissues regions annotated with multiple classes. The annotations included in this dataset are available for visualization and exploration from the National Cancer Institute Imaging Data Commons (IDC) [5] (also see IDC Portal at <a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov</a>) as of data release v18. Direct link to open the collection in IDC Portal: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations</a>.</p> </div> <div> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>pan_cancer_nuclei_seg_dicom-collection_id-idc_v19-aws.s5cmd</code> corresponds to the annotations for th eimages in the <code>collection_id</code> collection introduced in IDC data release v19. DICOM Binary segmentations were introduced in IDC v20. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <p>For each of the collections, the following manifest files are provided:</p> <ol> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <p>If you use the files referenced in the attached manifests, we ask you to cite this dataset, as well as the publication describing the original dataset <a href="https://paperpile.com/c/NHiBXI/njdR">[2]</a> and publication acknowledging IDC <a href="https://paperpile.com/c/NHiBXI/uJJZ">[5]</a>.</p> <h3>References</h3> </div> </div> <div> <p>[1] D. Milewski et al., "Predicting molecular subtype and survival of rhabdomyosarcoma patients using deep learning of H&E images: A report from the Children's Oncology Group," Clin. Cancer Res., vol. 29, no. 2, pp. 364–378, Jan. 2023, doi: 10.1158/1078-0432.CCR-22-1663.</p> <p>[2] Clunie, D., Khan, J., Milewski, D., Jung, H., Bowen, J., Lisle, C., Brown, T., Liu, Y., Collins, J., Linardic, C. M., Hawkins, D. S., Venkatramani, R., Clifford, W., Pot, D., Wagner, U., Farahani, K., Kim, E., & Fedorov, A. (2023). DICOM converted whole slide hematoxylin and eosin images of rhabdomyosarcoma from Children's Oncology Group trials [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8225132" rel="noopener">https://doi.org/10.5281/zenodo.8225132</a></p> <p>[3] Agaram NP. Evolving classification of rhabdomyosarcoma. Histopathology. 2022 Jan;80(1):98-108. doi: 10.1111/his.14449. PMID: 34958505; PMCID: PMC9425116,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9425116/</p> <p>[4] Chris Bridge. (2024). ImagingDataCommons/idc-sm-annotations-conversion: v1.0.0 (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.10632182" rel="noopener">https://doi.org/10.5281/zenodo.10632182</a></p> <p>[5] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. Radiographics 43, (2023).</p> </div>
Summary statistics of eQTLs obtained from single-nuclei RNA-seq in 8 major brain cell-types for mendelian randomisation
<p>This dataset contains <em>cis</em>-eQTL summary statistics for 8 brain cell-types, generated on a snRNA-seq dataset on post-mortem brains from 391 individuals (full), as well as a controls-only (subset of full, 183 individuals).</p> <p>Genotype dosage matrices were obtained with <code>SeqArray</code>, where 0 = homozygous alt, 1 = heterozygous, 2 = homozygous ref https://bioconductor.org/packages/release/bioc/manuals/SeqArray/man/SeqArray.pdf . The eQTL models as applied by <code>MatrixEQTL</code> therefore use "ref" as the effect allele (as implemented in their additive model).</p> <p>The eQTL summary statistics for within the "full" and "controls-only" dataset have been packed into <code>.tar.gz</code> files for each cell-type, where unpacking will yield summary statistics by chromosome. Each file contains the following columns;</p> <p>1. <code>SNP</code> (in rsid format)</p> <p>2. <code>gene</code> (in symbol format)</p> <p>3. <code>t.stat</code> (t-statistic as determined by MatrixEQTL)</p> <p>4. <code>p.value</code> (linear model association p-value)</p> <p>5. <code>FDR</code> (false discovery rate as determined by MatrixEQTL)</p> <p>6. <code>beta</code> (effect size / slope of the linear model)</p> <p>7. <code>chrom</code> (chromosome in "chrN" format)</p> <p>8. <code>position</code> (SNP position, hg38 build)</p> <p>9. <code>effect_allele</code> (this is the "ref" allele as described above)</p> <p>10. <code>other_allele</code> (alternate allele)</p> <p>11. <code>maf</code> (minor allele frequency, as determined by SeqArray on this dataset)</p> <p> </p> <p>In addition, single-cell expression matrices in count format are available in the <code>single-cell_data.tar</code> archive for the full 391 individuals (2,348,438 cells). This archive contains processed single-cell counts as described in our manuscript for the 4 datasets included; "BRYOIS_192" (separated into "MS" and "AD" as per their publication), "MATTHEWS", "ROCHE_MPD92" and "MRC_60". In addition, a cell-level metadata file containing covariates and cell-type labels across all datasets is included (<code>cell_level_metadata.rds</code>). Cell barcodes and individual IDs have been renamed to preserve anonymity.</p> <p> </p> <p><strong>January 2025 update: </strong>Now published at <strong><em>Nature Genetics</em></strong>. <strong>https://www.nature.com/articles/s41588-024-02050-9</strong></p> <p><strong>May 2025 update: </strong>Added the aggregated pseudo "Bulk" eQTLs as seen in Fig 1. d. </p>
Artifact: Quick Theory Exploration for Algebraic Data Types via Program Transformations
<p>This is the repeatability package, including the tool (called LemmaCalc) in the paper, the benchmark files, a TheSy binary, Z3 binaries for Linux, and scripts to repeat the experiments.</p> <p>Parts of the paper supported by this artifact: Main results in Fig 1, implementation of Algs 1--3.</p>
GBIF data summaries: GBIF national node type records: Sweden
GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. <p></p>https://www.gbif.org
GBIF data summaries: GBIF national node type records: Brazil
GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. <p></p>https://www.gbif.org
GBIF data summaries: GBIF national node type records: UK
GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. <p></p>https://www.gbif.org
GBIF data summaries: GBIF national node type records: Netherlands
GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. <p></p>https://www.gbif.org
GBIF data summaries: GBIF national node type records: Germany
GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. <p></p>https://www.gbif.org
The effects of grass silage additive type and barley grain preservation method on rumen function, microbial ecology, and energy metabolism of dairy cows; supplemental material
<p>Supplementary material related to "The effects of grass silage additive type and barley grain preservation method on rumen function, microbial ecology, and energy metabolism of dairy cows"</p>
Supplemental data for: Sex differences in branched-chain amino acid and tryptophan metabolism and pathogenesis of youth-onset type 2 diabetes
<p>Supplemental data for: Sex differences in branched-chain amino acid and tryptophan metabolism and pathogenesis of youth-onset type 2 diabetes. </p><p>Metabolites measured from spot urine samples of youth and adolescents. </p>
FIGURES 82–85 in An annotated and illustrated catalog of the primary type material of Hemiptera deposited in the Florida State Collection of Arthropods
FIGURES 82–85. Dorsal habitus and labels of holotypes of 82). Gypona (Obtusana) ileota Freytag, 2005; 83). Gypona (Obtusana) toxum Freytag, 2005; 84). Gypona (Marganalana) woodruffi Freytag, 2005; 85). Ladoffa suttoni Freytag & Lozada, 2013. Scale bars = 1 mm.
FIGURES 43–45 in An annotated and illustrated catalog of the primary type material of Hemiptera deposited in the Florida State Collection of Arthropods
FIGURES 43–45. Dorsal habitus and labels of holotypes of 43). Parnisa santacruzensis Sanborn, 2019; 44). Pomponia daklakensis Sanborn, 2009; 45). Proarna gianucai Sanborn, 2008. Scale bars = 5 mm.
FIGURES 34–36 in An annotated and illustrated catalog of the primary type material of Hemiptera deposited in the Florida State Collection of Arthropods
FIGURES 34–36. Dorsal habitus and labels of holotypes of 34). Herrera nigratorquata Sanborn, 2018; 35). Herrera nigropercula Sanborn, 2020; 36). Herrera polygramma Sanborn, 2020. Scale bars = 5 mm.
FIGURES 40–42 in An annotated and illustrated catalog of the primary type material of Hemiptera deposited in the Florida State Collection of Arthropods
FIGURES 40–42. Dorsal habitus and labels of holotypes of 40). Herrera signifera Sanborn, 2019; 41). Neocicada centramericana Sanborn, 2005. Dorsal habitus and labels of neotype of 42). Neotibicen superbus (Fitch, 1855). Scale bars = 5 mm.
FIGURES 7–9 in An annotated and illustrated catalog of the primary type material of Hemiptera deposited in the Florida State Collection of Arthropods
FIGURES 7–9. Dorsal habitus and labels of holotypes of 7). Carineta acommosis Sanborn, 2020; 8). Carineta apicoinfuscata Sanborn, 2011; 9). Carineta digitata Sanborn, 2020. Scale bars = 5 mm.
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.