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12,753 results for “Corrections”
Fig. 1 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 1. Part of the Bayesian phylogenetic tree of Buccinoidea Rafinesque, 1815 obtained with the cox- 1, 16S and 28S concatenated dataset representing family Buccinidae Rafinesque, 1815. The full tree is available as supplementary material (Supplementary file 1). Subfamily Siphonaliinae Finlay, 1928 is collapsed. Vertical numbered lines on the right mark the subfamilies of Buccinidae: 1 = Beringiinae Golikov & Starobogatov, 1975; 2 = Neptuneinae Stimpson, 1865; 3 = Volutopsinae Habe & Sato, 1973; 4 = Parancistrolepidinae Habe, 1972. Posterior probabilities and bootstrap values are shown for each medium supported node. Highly supported nodes are marked with black dots. The colors of the text refer to valid genus (orange = Volutharpa P. Fischer, 1856) and species referred to or morphologically attributable to formerly recognized (sub)genera of Buccininae: red = Thysanobuccinum Golikov & Gulbin in Golikov, 1980; blue = Ovulatibuccinum Golikov & Sirenko, 1988; green = Bathybuccinum Golikov & Sirenko, 1988.
Fig. 4 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 4. Buccinum hasegawai sp. nov. A–B. Holotype (ZIN 62775 (Buc274)), SL 18.5 mm. C. Paratype 1 (ZIN 62902 (Buc276)) from the type locality, SL 14.8 mm. D. Paratype 2 (MIMB 42300 (Buc273)), Urup I., 450–460 m, SL 14.7 mm. E. Paratype (ZIN 26228), Iturup I., 414 m, SL 16.1 mm. F. Paratype (ZIN 26228), Iturup I., 414 m, SL 16.2 mm, lateral view to show the penis. G. Buccinum bombycinum Dall, 1907 (ZIN 48100) Iturup I., 910–920 m, SL 15.3 mm. H–I. Syntype (USNM 1105311), Japan, Honshu I., Suruga Bay, 536 m, SL 23.9 mm. I = enlarged upper part of the shell to show the sculpture. Scale bars: A–H = same scale; I = 5 mm. Photos H–I: courtesy of USNM.
Fig. 2 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 2. Sequenced species of Buccinum Linnaeus, 1758 previously attributed to Thysanobuccinum Golikov & Gulbin in Golikov, 1980, Bathybuccinum Golikov & Sirenko, 1988 and Ovulatibuccinum Golikov & Sirenko, 1988. A–B. Buccinum tunicatum Golikov & Gulbin, 1977 (Buc265), Okhotsk Sea, off Urup I., depth 142 m, R/V Akademik Oparin, 56 cr., sta 7, SL 20.4 mm. C. Buccinum sp. 2 (Buc175), off Onagawa, Miyagi, Honshu I., Japan, depth 3302–3311 m, SL 20.3 mm. D–D'. Buccinum cf. tunicatum (Buc269), Okhotsk Sea coast of Urup I., 2 m, R/V Akademik Oparin, 56 cr., sta 26, SL 8.7 mm. D = at the same scale as others; D' = enlarged. E–E'. Buccinum bicordatum (Golikov & Sirenko, 1988) (Buc188), off Onagawa, Miyagi, Honshu I., Japan, depth 342–343 m, SL 8.4 mm. E = at the same scale as others; E' = enlarged. F. Buccinum unicordatum (Golikov & Sirenko, 1988) (Buc266), Kurile Is, Iturup I., Prostor Bay, depth 264–270 m, SL 15.9 mm. G. Buccinum chinoi nom. nov. (Buc190), off Onagawa, Miyagi, Honshu I., Japan, depth 342–343 m, SL 9.0 mm. H. Buccinum fimbriatum (Golikov & Sirenko, 1988) (Buc275), Kurile Is, Simushir I., depth 436 m, R/V Akademik Oparin, 56 cr., stn 19, SL 16.6 mm. I. Buccinum sp. 1 (Buc189), off Onagawa, Miyagi, Honshu I., Japan, depth 342–343 m, SL 22.4 mm. All shells (except D', E') at the same scale.
Fig. 3 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 3. Sequenced species of Buccinum Linnaeus, 1758, Volutharpa P. Fischer, 1856, and Plicibuccinum Golikov & Gulbin, 1977. A. Buccinum tenuisulcatum Golikov & Gulbin, 1977 (Buc264), Okhotsk Sea, Kurile Is, Onekotan I., depth 571–580 m, R/V Akademik Oparin, 56 cr., stn 68, SL 29.4 mm. B. Buccinum cyaneum Bruguière, 1789 (Buc281), Barents Sea, Teriberka Inlet, depth 10–17 m, SL 18.1 mm. C–D. Volutharpa ampullacea (Middendorff, 1848) (Buc277), Kurile Is, off Chirpoi I., depth 148–147 m, SL 14.1 mm. E. Buccinum nipponense Dall, 1907 (Buc187), off Otsuchi, Iwate, Honshu I., Japan, depth 479–484 m, SL 40.8 mm. F. Buccinum percrassum Dall, 1883 (Buc283), Kurile Is, Simushir I., 1–6 m, SL 33.5 mm. G–H. Buccinum cf. kobjakovae Golikov & Gulbin, 1977 (Buc278), Kurile Is, Simushir I., R/V Akademik Oparin, 56 cr., stn 19, 46°40.6′ N, 151°58.4′ E, depth 436 m, SL 12.1 mm. I. Plicibuccinum declivis (Habe & Ito, 1976) (Buc289), Japan Sea, Primorje, R/V Akademik Oparin, 64 cr., stn 73, 43°43.5′ N, 135°22.9′ E, depth 45–49 m, SL 36.2 mm. Shells not to scale.
Downscaled and bias corrected 10 km water withdrawal of China, 1981-2010
<p>This dataset contains</p> <p>(1) MonthlyDomIndcon_CN_0.1deg_1981-2010.nc -- the 0.1° gridded monthly domestic & industrial water consumption of China</p> <p>(2) MonthlyDomInduse_CN_0.1deg_1981-2010.nc -- the 0.1° gridded monthly domestic & industrial water withdrawal of China</p> <p>(3) MonthlyIrrigation_CN_0.1deg_1981-2010.nc -- the 0.1° gridded monthly irrigation water withdrawal of China</p> <p>These data are first spatially downscaled from Huang et al. (2018) (https://doi.org/10.5281/zenodo.1209296) based on population density, GDP and irrigation area, and then bias corrected against provincial-level statistics published by local water agencies. Refer to Huang et al. (2018) and Dong et al. (2022) for more details.</p> <p>Dong, N., Wei, J., Yang, M., Yan, D., Yang, C., Gao, H., et al. (2022). Model estimates of China's terrestrial water storage variation due to reservoir operation. Water Resources Research, 58, e2021WR031787. <a href="https://doi.org/10.1029/2021WR031787">https://doi.org/10.1029/2021WR031787</a></p>
Specimen displacement correction for powder x-ray diffraction in Debye-Scherrer geometry with a flat area detector
<p>This is a repository of synchrotron, powder XRD data including area detector images (.tiff) and integrated intensity vs 2theta files (.xye) for an experiment determining a sample displacement correction equation for powder x-ray diffraction in Debye-Scherrer geometry with a flat area detector. The accuracy of this equation and the corresponding corrections were verified by comparing it with corrections based on finding new integration parameters from an internal standard reference material.</p> <p>This work was published in the Journal of Applied Crystallography, the citation is shown below:</p> <p>Hulbert, B. S. & Kriven, W. M. (2023). J. Appl. Cryst. 56.</p> <p><a href="https://doi.org/10.1107/S1600576722011360">https://doi.org/10.1107/S1600576722011360</a></p>
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
<p>Dataset for the paper "Predicting Patch Correctness Based on the Similarity of Failing Test Cases"</p> <p>Tool name: BATS</p>
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
<p>Change the absolute path of the files in 'experiment/config' of the github repository to refer to the two downloaded files below.</p> <p>1. ASE2022withTextUnique.zip: the original dataset with patches text and commit messages text.</p> <p>2. ASE_features2_bert.pickle: the feature from Tian et al.'s ASE2020 paper.</p>
Data (corrected spectra per pixels) from "Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery"
<p>open access data related to the paper "<strong>Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery</strong>" Riera E., Ungerman M., Pey A., Rigot G., Hubas C., Rossi F. (under revision to Restoration Ecology)</p> <ul> <li>pixels_corrected_spectra.npy: data matrix of the corrected spectra for each pixels</li> <li>wavelenghts.npy: the vector of the wavelengths </li> <li>pixels_metada.csv: metadata related the corrected spectra for each pixels</li> </ul> <p>To be processed on python for further statistical analyses. script available on github: <a href="https://github.com/ELI-RIERA/HYPER3D">https://github.com/ELI-RIERA/HYPER3D</a></p>
Figure 2 Bar chart represents the percentage of the correct diagnosis using fuzzy diagnosis, K- nearest neighbor and Naïve Bayes classifiers.-Comparison of Fuzzy Diagnosis with K-Nearest Neighbor and Naïve Bayes Classifiers in Disease Diagnosis
<p>Figure 2 Bar chart represents the percentage of the correct diagnosis using fuzzy<br> diagnosis, K- nearest neighbor and Naïve Bayes classifiers.</p>
Correcting for population stratification reduces false positive and false negative results in joint analyses of host and pathogen genomes [G2G-Simulator: Simulated dataset]
<p>Data associated with the paper 'Correcting for population stratification reduces false positive and false negative results in joint analyses of host and pathogen genomes'.</p> <p>It contains the raw simulated data from the 'G2G-Simulator' program. </p> <p>Those data need to be loaded in a R environment .</p> <p>You can reproduce plots present in the paper by parsing the R object using the script 'parse_paper_data.R' present in the G2G-Simulator GitHub repository (https://github.com/onaret/G2G-Simulator/paper/parse_paper_dataset.R). </p>
FIGURE 4. Rollo H in Rollo H. Beck's Visits to Isla Guadalupe, Mexico, with Additions and Corrections to the Island's Avifauna
FIGURE 4. Rollo H. Beck's field notes from Isla Guadalupe, 3 August 1912 (CAS). See Appendix A for transcription.
FIGURE 1 in Rollo H. Beck's Visits to Isla Guadalupe, Mexico, with Additions and Corrections to the Island's Avifauna
FIGURE 1. Labels on the type specimens of Oceanodroma macrodactyla, the only bird specimens saved from the fire following the 1906 San Francisco earthquake. Photo courtesy of CAS.
FIGURE 2 in Rollo H. Beck's Visits to Isla Guadalupe, Mexico, with Additions and Corrections to the Island's Avifauna
FIGURE 2. Specimens collected on 3 August 1912 by Rollo H. Beck labeled Oceanodroma macrodactyla. Photos courtesy AMNH.
Data for Stochastically accelerated perturbative triples correction in coupled cluster calculations
<p>This files contains all the data used to perform the plots in the "Stochastically accelerated perturbative triples correction in coupled cluster calculations" article.</p>
FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing data -- Preprocessed datasets
<div> <div> <div> <div> <p>This dataset accompanies the publication "FedscGen: Privacy-Aware Federated Batch Effect Correction of Single-Cell RNA Sequencing Data" and includes eight single-cell RNA sequencing (scRNA-seq) datasets used to benchmark the FedscGen and scGen methods. The datasets are provided in <code>.h5ad</code> format and include comprehensive metadata necessary for replication and further analysis.</p> <h3>Datasets</h3> <p>We analyze various datasets to compare FedscGen against scGen (centralized) in terms of batch correction. For simplicity, we refer to the dataset by abbreviations:</p> <ol> <li> <p><strong>Cell Line (CL)</strong>:</p> <ul> <li>Derived from the 293t_jurkat experiment with three batches: Zheng et al., 2017.</li> </ul> </li> <li> <p><strong>Human Dendritic Cells (HDC)</strong>:</p> <ul> <li>scRNA-seq data of human dendritic cells across two batches: Villani et al., 2017.</li> </ul> </li> <li> <p><strong>Human Pancreas (HP)</strong>:</p> <ul> <li>Consolidated data from five sources with 14,767 cells each: Baron et al., 2016; Muraro et al., 2016; Segerstolpe et al., 2016; Wang et al., 2016; Xin et al., 2016.</li> </ul> </li> <li> <p><strong>Mouse Brain (MB)</strong>:</p> <ul> <li>Merged datasets with 691,600 and 141,606 cells: Saunders et al., 2018; Rosenberg et al., 2018.</li> </ul> </li> <li> <p><strong>Mouse Cell Atlas (MCA)</strong>:</p> <ul> <li>Data focusing on 11 cell types from various organs: Han et al., 2018; The Tabula Muris Consortium, 2018.</li> </ul> </li> <li> <p><strong>Mouse Hematopoietic Stem and Progenitor Cells (MHSPC)</strong>:</p> <ul> <li>Data from SMART-seq2 and MARS-seq protocols: Nestorowa et al., 2016; Paul et al., 2015.</li> </ul> </li> <li> <p><strong>Mouse Retina (MR)</strong>:</p> <ul> <li>Data from two unassociated laboratories with 26,830 and 44,808 cells: Macosko et al., 2015; Shekhar et al., 2016.</li> </ul> </li> <li> <p><strong>PBMC (human Peripheral Blood Mononuclear Cell)</strong>:</p> <ul> <li>scRNA-seq data with two batches: Zheng et al., 2017.</li> </ul> </li> </ol> <p><strong>Usage Notes</strong>: Each dataset is provided in <code>.h5ad</code> format, compatible with common single-cell analysis tools such as Scanpy. Detailed metadata is included within each file.</p> <p><strong>Keywords</strong>: Single-cell RNA sequencing, scRNA-seq, Batch effect correction, Privacy-aware, Federated learning, scGen, FedscGen, Clinical multi-center studies, Genomics, Bioinformatics</p> <p><strong>Contact</strong>: For questions or further information, please contact Mohammad Bakhtiari at <a href="mailto:mohammad.bakhtiari@uni-hamburg.de.">mohammad.bakhtiari@uni-hamburg.de.</a></p> <p><strong>License</strong>: Creative Commons Attribution 4.0 International (CC BY 4.0)</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Fig. 3 in A List of Pselaphine Species (Insecta, Coleoptera, Staphylinidae) Collected from Mt. Phia Oac, North Vietnam in 2017 with a Corrected Checklist of the Pselaphine Species Known from Vietnam
Fig. 3. Photos of pselaphine species collected in the survey. A. Pyxidicerina, gen. and sp. undet 1; B. Pseudoplectus? sp. 1, male; C. ditto, female; D. Trimium? sp. 1; E. Intestinarius sp. 1; F. I. sp. 2; G. Trisinus sp. 1; H. T. sp. 2; I. T. sp. 3; J. Sathytes sp. 1; K. Reichenbachia sp. 1; L. Trissemus sp. 1; M. Batraxis sp. 1; N. Triomicrus sp. 1, male; O. ditto, female; P. Plagiophorus sp. 1; Q. P. sp. 2; R. Pseudophanias sp. 1; S. Pselaphodes sp. 1; T. Nomuraius piaoacus Yin et Lee, 2013, male; U. ditto, female.
Fig. 4 in A List of Pselaphine Species (Insecta, Coleoptera, Staphylinidae) Collected from Mt. Phia Oac, North Vietnam in 2017 with a Corrected Checklist of the Pselaphine Species Known from Vietnam
Fig. 4. SEM photos of Pseudoplectus? sp. 1. A–C. male; D–F. female. A, D. habitus in dorsal view; B, E. head and pronotum; C, F. head enlarged.
Fig. 6 in A List of Pselaphine Species (Insecta, Coleoptera, Staphylinidae) Collected from Mt. Phia Oac, North Vietnam in 2017 with a Corrected Checklist of the Pselaphine Species Known from Vietnam
Fig. 6. SEM photos of Nomuraius piaoacus Yin et Li. A–C. male; D–F. female. A, D. Head and pronotum in dorsal view; B, E. Ditto, enlarged; C, F. antennal club.
Fig. 2 in A List of Pselaphine Species (Insecta, Coleoptera, Staphylinidae) Collected from Mt. Phia Oac, North Vietnam in 2017 with a Corrected Checklist of the Pselaphine Species Known from Vietnam
Fig. 2. Habitats and conditions of the traps in the survey at N Vietnam in 2017. A. A view of Mt. Phia Oac; B. A view of the summit of Mt. Phia Oac; C. A forest view around the summit; D. A view of W (tungusten) mine on the halfway up to Mt. Phia Oac; E. The portable light trap set in Mt. Phia Oac.; F. Simplified Winkler apparatuses used in the survey.
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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.