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11,177 results for “identify”
FIG. 9. — Cryptovalsa halosarceiicola K.D in Modern taxonomic approaches to identifying diatrypaceous fungi from marine habitats, with a novel genus Halocryptovalsa Dayarathne & K.D.Hyde, gen. nov.
FIG. 9. — Cryptovalsa halosarceiicola K.D.Hyde (BRIP 20340 – holotype): A, B, herbarium material.
FIG. 2 in Modern taxonomic approaches to identifying diatrypaceous fungi from marine habitats, with a novel genus Halocryptovalsa Dayarathne & K.D.Hyde, gen. nov.
FIG. 2. — Continuation.
FIG. 1 in Modern taxonomic approaches to identifying diatrypaceous fungi from marine habitats, with a novel genus Halocryptovalsa Dayarathne & K.D.Hyde, gen. nov.
FIG. 1. — Continuation.
Replication Package for Identifying Self-Admitted Technical Debt in Issue Tracking Systems using Machine Learning
<p>This dataset includes pre-trained word embeddings and a weighted file that can be used to identify self-admitted technical debt (SATD) from issue tracking systems.</p>
Identifying Chern numbers of superconductors from local measurements
<p>This repository contains the following:</p> <p>1. Data for Fig.1b</p> <ul> <li>phase_diagram_gap_and_kf_in_0.1_12_e0_in_-0.8_0.8.txt</li> </ul> <p>2. Data for Fig.6a</p> <ul> <li>Chern_marker_scaling_vs_N_x_abs_C.1_kf_3.67900_e0_0.35400_gap0_0.30374_bandwidth_1.70100.txt</li> <li>Chern_marker_scaling_vs_N_x_abs_C.2_kf_2.87600_e0_0.28800_gap0_0.13449_bandwidth_1.56362.txt</li> <li>Chern_marker_scaling_vs_N_x_abs_C.3_kf_3.48700_e0_0.16200_gap0_0.15893_bandwidth_1.34522.txt</li> </ul> <p>3. Data for Fig.6b</p> <ul> <li>Chern_marker_scaling_vs_V_0_abs_C.1_gap_balanced_aver.0.2192_kf_3.67900_e0_0.35400_gap0_0.30374_bandwidth_1.70100.txt</li> <li>Chern_marker_scaling_vs_V_0_abs_C.2_gap_balanced_aver.0.2192_kf_2.87600_e0_0.28800_gap0_0.13449_bandwidth_1.56362.txt</li> <li>Chern_marker_scaling_vs_V_0_abs_C.3_gap_balanced_aver.0.2192_kf_3.48700_e0_0.16200_gap0_0.15893_bandwidth_1.34522.txt</li> </ul> <p>4. Shiba model test dataset</p> <ul> <li>Shiba_lattice_dataset.json</li> </ul>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 1
<p>This deposit contains the supporting records of images and image analysis presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218</p> <p>Associated Zenodo repositories:</p> <table> <thead> <tr> <th scope="col">Description</th> <th scope="col">DOI</th> </tr> </thead> <tbody> <tr> <td>Main figures PART 1, Figure 1,2,3,5</td> <td>10.5281/zenodo.7653239</td> </tr> <tr> <td>Main figures PART 2, Figure 6</td> <td>10.5281/zenodo.7900973</td> </tr> <tr> <td>Supplemental 3DTC figures: S1, S4, S5, S7, S8, S9</td> <td>10.5281/zenodo.7894632</td> </tr> </tbody> </table> <p>Contents:1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses for figures 2, 3 and 5. Figure 6 analyses are included in a compansion repository: 10.5281/zenodo.7900973. Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/). Additional files may include gate files (.vtg) or max projections (.tif).</p> <p>2) a collection of zip files containing the RNAScope image files shown in: Figure 1 P,Q. The supplemental figure data for RNAScope. Figures S1,S4 and S5 are found in: 10.5281/zenodo.7894633.</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p>
Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex (Capra ibex) as tools for conserving migration
<p># GPS locations of Alpine ibex</p> <p>This dataset contains migratory tracks of Alpine ibex identified using the application Migration Mapper (https://migrationinitiative.org/content/migration-mapper) and used in the work <strong>Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex (<em>Capra ibex</em>) as tools for conserving migration</strong></p> <p># Dataset structure</p> <p>Each row of the dataset represents a GPS location with its coordinates contained in the x (longitude) and y(latitude) columns. Coordinates are given in wgs84 (epsg 4326).<br> The column t1_ informs on the date and time the location was recorded.<br> The id and pop columns provide information about the identity of the animal and the population to which it belongs.</p> <p> </p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", Supplemental 3DTC figures
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218 found in supplemental figures.</p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses by figures in the supplemental figure data for 3D tissue cytometry. The main figure data is found at: 10.5281/zenodo.7653239 and 10.5281/zenodo.7900973.</p> <p>2) a collection of zip files containing the RNAScope image files shown in: Figures S1,S4 and S5. The main RNAScope figure data is found at: 10.5281/zenodo.7653239</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p>
Dataset for Optimal reactive nitrogen control pathways identified for cost-effective PM2.5 mitigation in Europe
<p>This folder includes six folders:</p> <p>The "Nr anthropogenic emissions" folder includes the anthropogenic Nr emissions used in the baseline simulations for January, April, July, and October 2015.</p> <p>The "Concentration" folder includes daily surface ammonia (NH<sub>3</sub>), nitric acid (HNO<sub>3</sub>), nitrate (NO<sub>3</sub><sup>-</sup>), ammonium (NH<sub>4</sub><sup>+</sup>), sulfate (SO<sub>4</sub><sup>2-</sup>), and fine particulate matter (PM<sub>2.5</sub>) concentrations for January, April, July, and October 2015 from WRF-Chem simulations (one BASE simulation and twelve sensitivity simulations as stated in Method).</p> <p>The "N-share" folder includes the N-share caused by anthropogenic Nr (NH<sub>3</sub> and NO<sub>x</sub>)/ NH<sub>3</sub>/NO<sub>x</sub> emissions in 2015.</p> <p>The "Health" folder includes the PM2.5-related premature deaths from WRF-Chem combined with GEMM simulations in 2015. </p> <p>The "Instant efficiency" folder includes the instant efficiency of Nr/ NH<sub>3</sub>/NO<sub>x</sub> emission controls from WRF-Chem simulations in 2015. </p> <p>The "G ratio" folder includes the G ratio from WRF-Chem simulations for the year 2015 and the indicator months (January, April, July, and October). </p> <p>For any questions, please contact Zehui Liu<br> Email: liuzh18@pku.edu.cn<br> </p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Lisa Wallace, <a href="https://orcid.org/0000-0001-6665-5454">https://orcid.org/0000-0001-6665-5454</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Windsor Aguirre, <a href="https://orcid.org/0000-0003-3641-5120">https://orcid.org/0000-0003-3641-5120</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Supplemetary Data for the article: Machine-learning identified molecular fragments responsible for infrared emission features of polycyclic aromatic hydrocarbons
<p>This is a set of Supplementary materials for the article 'Machine-learning identified molecular fragments responsible for infrared emission features of polycyclic aromatic hydrocarbons', by Meng et al.</p> <p>Supplementary_Data_I.pdf contains an extensive table spanning 36 pages that lists the top-10 molecular fragments accountable for the spectral bands between 2.761 and 1172.745 μm. To access this table, hyperlinks within the document can be used for navigation.</p> <p>Supplementary_Data_II.pdf comprises a large table that encompasses 10,691 pages, including the top-100 molecular fragments responsible for the spectral bands between 2.761 and 1172.745 μm. Navigation through the hyperlinks enables access to this table.</p> <p>Supplementary_Data_III.csv encompasses the chemical formulas, number of unpaired valence electrons, spin multiplicities, xyz data, and SMILES strings of the PAHs carrying the additional spectra.</p> <p>Supplementary_data_IV.zip includes the input and output datasets along with the ML code. The code script is written in Python 3.7, and is supported by the following libraries: sklearn, json, numpy, and pandas.</p> <p>Supplementary_Information.pdf contains the evidence supporting the choice of the cutoff radius, as well as the figures of the count of the molecules in the dataset, the FI with changing datasets and hyperparameters, of cross-validation, and of UIE bands and emission features of four SH PAHs. Importance of three carbon skeleton fragments for bands in different intervals is also demonstrated.</p>
Dataset for 'Identifying Pauli spin blockade using deep learning'
<p>This data set contains the raw and processed data used in the publication 'Identifying Pauli spin blockade using deep learning' (<a href="https://arxiv.org/pdf/2202.00574.pdf">https://arxiv.org/pdf/2202.00574.pdf</a>). This also includes trained neural networks, and evaluation results from training neural networks. </p> <p>For code related to this publication, see <a href="https://github.com/oxquantum-repo/identifying_psb/">https://github.com/oxquantum-repo/identifying_psb/</a>.</p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 2
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218</p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses for figure 6 analyses.</p> <p>Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/). Additional files may include gate files (.vtg) or max projections (.tif).</p> <p> </p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p> <p> </p>
Supplementary Material to "Identifying major research themes in Sport Science" (Interactive Science Maps)
<p><strong>Links to interactive maps</strong></p> <p>(1) Link: <strong><a href="https://app.vosviewer.com/?map=https://drive.google.com/uc?id=182n1HEZygAoS_9Caxj83hi27y41N6SWm&item_size=4">World Map</a></strong> (<a href="https://tinyurl.com/2zufzslg">https://tinyurl.com/2zufzslg</a>)</p> <p>(2) Link: <strong><a href="https://app.vosviewer.com/?map=https://drive.google.com/uc?id=182n1HEZygAoS_9Caxj83hi27y41N6SWm&item_size=5&item_size_variation=0.4&scale=0.9">Sport Science Map</a></strong> (<a href="https://tinyurl.com/2fztyjhf">https://tinyurl.com/2fztyjhf</a>)</p>
Code. Midlife proteome-wide analysis identifies plasma biomarkers for 25-year dementia risk linked to diverse pathophysiology
<p>The code used for the analyses for the paper entitled "Midlife proteome-wide analysis identifies plasma biomarkers for 25-year dementia risk linked to diverse pathophysiology". </p>
Data release for "Identifying LISA verification binaries among the Galactic population of double white dwarfs"
<p>Posterior samples and SNR calculations associated with <em>Identifying LISA verification binaries among the Galactic population of double white dwarfs</em> (<a href="https://arxiv.org/abs/2210.10812">arxiv:2210.10812</a>, <a href="https://doi.org/10.1093/mnras/stad1288">MNRAS, 522, 5358 (2023)</a>).</p> <p>The <code>data</code> folder contains the following:</p> <ul> <li><code>vb_table</code>: Verification binary (VB) candidate properties obtained from electromagnetic (EM) observations. This data was compiled by Kupfer et al. and is available at <a href="https://gitlab.in2p3.fr/LISA/lisa-verification-binaries">https://gitlab.in2p3.fr/LISA/lisa-verification-binaries</a> (accessed on 2022 September 2). Used in Table 1.</li> <li><code>snr</code>: SNR calculations for each VB candidate as a function of mission duration. Used in Figure 2.</li> <li><code>basic</code>: Gravitational-wave (GW) parameter estimation study of each VB candidate. Used in Table 2 and Figure 3.</li> <li><code>detection_time</code>: VB SNR calculations with source property (and LISA launch time) uncertainty taken into account. Used in Figure 1 and Figure 4.</li> <li><code>em_priors</code>: GW parameter estimation study with EM information included in the priors. Used in Figure 5.</li> <li><code>unknown_noise</code>: GW parameter estimation study with the inclusion of noise parameters to vary the PSD. Used in Figure 6.</li> <li><code>filtered_populations</code>: Source properties of DWDs nearby in frequency to each VB candidate. See the <code>confused_sources.ipynb</code> notebook. Used in Figure 7.</li> <li><code>confusion_snr</code>: SNR calculations of the above confusion sources. Used in Figure 7.</li> <li><code>confusion</code>: A analysis of V803Cen with a realistic Galactic population of DWDs included in the data. Used in Figure 8.</li> </ul> <p>Code to reproduce every figure from the paper is available in <code>plots</code>.</p> <p>Supplementary notebooks associated with the generation of a realistic data instance (i.e. including a simulated Galactic population of DWDs nearby in frequency to V803Cen) can be found in <code>notebooks</code>.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by António Carmo Gouveia, <a href="https://orcid.org/0000-0002-7175-0365">https://orcid.org/0000-0002-7175-0365</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by David Maceira, <a href="https://orcid.org/0000-0002-4879-2268">https://orcid.org/0000-0002-4879-2268</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Peri Coleman, <a href="https://orcid.org/0000-0001-6218-7489">https://orcid.org/0000-0001-6218-7489</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
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.