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709 results for “Coverage”
GIS22 GIS Coverage Defining Soils (SSURGO) on Konza Prairie (1982-present)
The Konza Prairie soils dataset is derived from the USDA NRCS SSURGO soils definitions for Riley and Geary Counties (variant ca. 2012; soildatamart.nrcs.usda.gov/). The coverage contains MUSYM and Soil Names that correspond to the code. Additional and current SSURGO data is available from (soildatamart.nrcs.usda.gov/SSURGOMetadata.aspx) Associated metadata derived from NRCS SSURGO Metadata for: Riley County SSURGO Data - soildatamart.nrcs.usda.gov/Metadata.aspx?Survey=KS161&UseState=KS Geary County SSURGO Data -soildatamart.nrcs.usda.gov/Metadata.aspx?Survey=KS061&UseState=KS. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).
GIS30 GIS Coverages Defining Sample Locations for Abiotic Datasets on Konza Prairie (1972-present)
These data show sample locations for various abiotic data collected on Konza Prairie (rain gauges, soil moisture, and stream data). Included in these data are the locations for 12 rain gauges (GIS300) on Konza Prairie. The Konza headquarters weather station formerly consisted of two gauges which were operated year-round. The Konza headquarters weather station currently consists of one Otto-Pluvio2 gauge which is operated year-round. The remaining Konza-operated gauges run from April 1 to November 1. These data are to be used in conjunction with the APT01 (precipitation) dataset. GIS305 defines the locations where measurements of soil moisture (%volume) are taken on Konza Prairie. These data are to be used in conjunction with the ASM01 (soil moisture) dataset. GIS309 defines the locations within watershed N4D of soil sampler nests. In Jan 2020, we separated the original GIS310 file 'Wells in N4D' into GIS310 'Wells in N4D' and GIS309 'Soil Sampler Nests'. Prior to then, soil sampler nests and wells were combined in GIS310. GIS310 defines the locations within watershed N4D where samples are taken for analyzing the belowground water chemistry of the watershed. These data are to be used in conjunction with the AGW01 dataset. GIS311 defines the locations of 14 wells at two sites along Kings Creek. Depth and nutrient content of groundwater is measured at these sites. These data are to be used in conjunction with the AGW02 dataset. GIS315 defines the locations of stream sampling stations within multiple Konza watersheds. These data are to be used in conjunction with the NWC, ASS, ASD, and ASW datasets. GIS320 defines the locations of the rainfall collectors used to collect the samples analyzed as a part of the National Atmospheric Deposition Program. These data are to be used in conjunction with the ANA01 dataset. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).
GIS35 GIS Coverages Defining Sample Locations for Belowground Datasets on Konza Prairie (1982-present)
These data show the locations of research conducted at the below ground plots near Konza Headquarters. Record type 1 (GIS350) describes the 64 belowground plots receiving a variety of nutrient, burn, and mowing treatments. Data for BMS01, BMS02, and BNS01 are collected on these plots. Record type 6 (GIS355) describes the locations of the Micro-Rhizotrons. Two spatial datasets lie on the belowground plots, but are classified separately. These are the Lysimeters on belowground plots (GIS455) and Aboveground biomass on belowground plots (GIS505) datasets. GIS505 may be used alongside the BGPVC dataset, because it shares sample locations with PBB01. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).
GIS40 GIS Coverages Defining the Sample Locations of Konza Consumer Data (1982-present)
These data show the sampling locations for the consumer datasets at Konza Prairie. GIS400 defines the starting points for sweep samples of grasshoppers across Konza Prairie. These data may be used in conjunction with the sweep sample datasets (CGR02). GIS401 defines the starting points for sweep samples of grasshoppers across Konza Prairie, focusing on grazing impact. These data may be used in conjunction with the sweep sample datasets (CGR02Z). GIS405 defines the trap locations for small mammal sampling across Konza Prairie. These data may be used in conjunction with CSM0X. GIS 406 defines the locations of small mammal host parasite sampling at Konza Prairie. These data may be used in conjunction with CSM08. GIS410 defines the stream stretches for fish sampling across Konza Prairie. These data may be used in conjunction with CFC01. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).
GIS45 GIS Coverages Defining the Konza Nutrient Data Sample Locations (1982-present)
These data show the sample locations for soil bulk density and chemical characteristics along LTER vegetation plots. This dataset contains the transect lines (GIS450) and sample locations(GIS451) at which the soil cores are sampled. These data may be used in conjunction with the Soil Chemistry and Bulk Density (NSC01) datasets. GIS455 contains the locations of the lysimeters used to measure soil water chemistry on the belowground plots. These data may be used in conjunction with the NBS01 dataset. GIS460 contains the locations of the bulk precipitation collectors on Konza Prairie. These data may be used in conjunction with the NBP01 dataset. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).
GIS50 Coverages Defining the Konza Producer Data Sample Locations (1982-present)
These data show the sample locations for datasets pertaining to primary production at Konza Prairie. These data reference various treatments across Konza including varying burn frequencies, belowground plots, patch burn, exclosures, etc.Record type one (GIS500) contains sample locations for estimated standing crop biomass in various burning-grazing treatments (PABXX). Record type six (GIS505) contains sample locations for peak foliage biomass measured at the belowground plot experiments (PBBXX).Record type 11 and 12 contain the transect (GIS510) and plot (GIS511) locations for plots in the patch-burn experiments (PBGXX). Record type 16 (GIS515) contains the locations of exclosures used to sample primary productivity in bison grazed watersheds (PEB01). Record type 21 (GIS520) contains the locations of exclosures used to sample primary productivity in cattle grazed watersheds (PEB01X). Record type 26 (GIS525) contains the locations of sample sites for litterfall (PGLXX) collectors in the gallery forest. Record type 31 (GIS530) contains species composition transects, and (GIS531) provides locations for species transect plots in the patch-burn experiments for Konza Prairie. These data may be used in conjunction species composition (PVC01 and PVC02), primary production in grazing exclosures (PEB01, PEB01_X), soil chemistry and bulk density (NSC01) and primary production (PAB01). These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).
GIS55 GIS Coverages Defining the Konza HQ Irrigation System (1982-present)
These data show the components of the irrigation system near Konza Prairie HQ. Record types 1, 2, 3 and 4 demarcate the locations of the study plots heads (GIS550), transect lines (GIS551), irrigation lines (GIS552), and irrigation line joints (GIS553). Record types 4 and 5 describe the location of the storage piles (GIS554) and the irrigation reservoir (GIS555). This data may be used in conjunction with the Irrigation Transect Studies (WATXX) data. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).
GIS60 GIS Coverages Defining Other Konza Sample and Research Areas (1982-present)
These data show locations of samples and research areas at Konza that do not fit under our standard classifications. GIS 600 contains the locations of the Hulbert plots on Konza Prairie. GIS605 contains locations for rainfall shelters, ramps, experimental streams, restoration plots, the weather station, grasshopper cages, the climate extremes project. Currently no associated LTER datasets exist for these locations. GIS 610 provides a record of the historic Konza gridded location system. Older datasets may reference these locations with a column letter and row number. GIS615 contains the location for the Clean Air Status and Trends Network (CASTNET) site on Konza Prairie. For more information, visit the following link: http://www.epa.gov/castnet/javaweb/site_pages/KNZ184.html. GIS620 contains the location for the USGS gauging station. These data may be used in conjunction with the Stream Discharge for Kings Creek Measured at USGS Gauging Station (ASD01) dataset. For more information, visit the following link: http://waterdata.usgs.gov/nwis/nwisman/?site_no=06879650. GIS630) and GIS635 contain the location and treatment information for two bison grant grazing experiments. Currently, no associated LTER datasets exist for these data. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).
Supplemental artifacts of the paper: Efficient Binary-Level Coverage Analysis
<p>NOTE: the official repository of bcov is: <a href="https://github.com/abenkhadra/bcov">https://github.com/abenkhadra/bcov</a></p> <p>This repository contains the artifacts accompanying our paper: "Efficient Binary-Level Coverage Analysis", which appeared in ESEC/FSE'20. The artifacts consists of two packages, namely, bcov-benchmarks.tar.gz and bcov-artifacts.tar.gz. The former package contains the complete list of binaries described in our experiments. The artifacts of the latter package are organized as follows:</p> <p> - <strong>sample-binaries</strong>. Folder that contains sample binaries patched with bcov.</p> <p> - <strong>dataset.tar.gz</strong>. Package containing experimental data in csv format.</p> <p> - <strong>figures</strong>. Folder that contains the python script used to generate the figures<br> of our paper. It assumes that the dataset was first extracted to the folder `dataset`.</p> <p> - <strong>install.sh</strong>. This script builds and installs bcov together with its dependencies.</p> <p> - <strong>experiment-01.sh</strong>. This script patches our sample binaries and shows how coverage<br> data can be collected. It assumes that bcov was installed using the previous script.</p> <p> - <strong>bcov.tar.gz</strong>. Source code of the first public version of `bcov`. The tool is distributed under an MIT license.<br> </p>
Data from: The effect of probe density coverage on the detection of oenological tannins in quartz crystal microbalance with dissipation monitoring (QCM-D) experiments
<p>Polyphenols, crucial compounds in grapes, musts, and wines, influence grape ripening, must fermentation, and final wine quality. Current detection methods for polyphenols are expensive, time-consuming, and reliant on specialized laboratories and personnel. This study proposes the use of a functionalized acoustic sensor to address these limitations and efficiently detect oenological polyphenols.</p> <p>The method employs a quartz crystal microbalance with dissipation monitoring (QCM-D) combined with a gelatin-based probe layer to detect the target analyte. The sensor is functionalized by optimizing probe coverage density, accomplished through the use of 12-mercaptododecanoic acid (12-MCA) for probe immobilization onto the gold sensor surface, along with dithiothreitol (DTT) as a reducing and competitive binding agent. Varying concentrations of 12-MCA and DTT allow for control over probe density, with QCM-D measurements demonstrating effective adjustment, ranging from 0.2 × 10^13 to 2 × 10^13 molecules cm^−2. The study also explores the interaction between the probe and tannins, confirming the ability of the sensor to detect them. Notably, lower probe coverage yields higher detection signals when normalized to probe immobilization signals. Additionally, significant alterations in the mechanical properties of the functionalization layer occur after interaction with samples.</p> <p>Combining QCM-D with gelatin functionalization presents promising applications in the wine industry. This approach enables real-time monitoring, requires minimal sample preparation, and offers high sensitivity for quality control purposes.</p>
DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region
<p>This dataset has been prepared as part of the Interreg Alpine Space project Eco-AlpsWater (ASP569) - <em>Innovative Ecological Assessment and Water Management Strategy for the Protection of Ecosystem Services in Alpine Lakes and Rivers</em>, <a href="https://www.alpine-space.eu/projects/eco-alpswater/en/home">https://www.alpine-space.eu/projects/eco-alpswater/en/home</a></p> <p>Individual archives include 16S rRNA (cyanobacteria) and 18S rRNA (microalgae) FASTA sequences and associated blastn results obtained from the high throughput sequencing of plankton and biofilm bulk/eDNA samples collected in 2019 in 37 lakes and 22 rivers across the Alpine region. These are supporting files for the paper by Salmaso et al., 2022. DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region. Science of the Total Environment, in press.</p>
Twist Whole-Exome Sequencing Dataset - High Coverage - WGGC SIG4 Benchmarking
<p>GIAB Reference Genome for Benchmarking Initiatives in the West German Genome Center (WGGC) - SIG4. </p> <p>Twist Whole-Exome Sequencing Dataset - High Coverage - 200M Reads.</p> <p> </p> <p> </p>
Coverage and quality of open metadata for Dutch research output - dataset
<p>Record level data underlying the figures and tables in the report:<strong><br><br>Coverage and quality of open metadata for Dutch research output - report<br></strong></p> <p><a href="https://doi.org/10.5281/zenodo.10629457" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10629457</a><br><br>The current dataset contains 3 csv files:</p> <ul> <li><em>rpo_nl_list_long_20240201.csv</em> - list of identifiers (ROR ID, OpenAlex ID, OpenAIRE ID) of Dutch research performing organisations. <p>Identifiers were collected for the following groups of Dutch RPOs (see Appendix A):</p> <ul> <li> <p>Universities, organised in Universities of the Netherlands (UNL, n=14);</p> </li> <li> <p>University Medical Centres, organised in the Dutch Federation of University Medical Centres (NFU, n=9); </p> </li> <li> <p>National research institutes under the umbrella organisation of the Foundation for Dutch Scientific Research Institutes (NWO-i, n= 9);</p> </li> <li> <p>Research institutes of the Royal Netherlands Academy of Arts and Sciences (KNAW, n=10);</p> </li> <li> <p>Universities of Applied Sciences affiliated to the Netherlands Association of Universities of Applied Sciences (Vereniging Hogescholen) (VH, n=35 of 37)<br><br></p> </li> </ul> </li> <li><em>openalex_works_20231223_rpo_nl_2022 </em>- record-level data of OpenAlex records retrieved for all Dutch RPOs in scope of the pilot (UNL/NFU, NWO-i, KNAW, VH) for publication year 2022<br><br></li> <li><em>openaire_products_20240116_rpo_nl_2022 - </em>record-level data of OpenAlex records retrieved for all Dutch RPOs in scope of the pilot (UNL/NFU, NWO-i, KNAW, VH) for publication year 2022</li> </ul>
The Coverage of Basic and Applied Research in Press Releases in EurekAlert!
<p>This dataset contains data from our research into the coverage of basic and applied research in EurekAlert! press releases. The data covers the years 2015 to 2022 and includes a detailed record of press releases and their associated academic research.</p> <p>The following fields are described.</p> <p>EurekAlert_basic_applied_dataset:<br>EUID: unique identifier for each press release.<br>DOI: digital identifier for the associated research paper.<br>post time: The publish year of press release<br>RL: research level of research paper<br>LR_main_field: The academic field or discipline of the research.<br>Press Release Title:Title of the press release.<br>Paper Title: Title of the associated research paper. Abstract<br>Abstract:Abstract of the related research paper.<br>cosine_similarity:similarity score between press release title and paper title.<br>eu_flesch:Ease of reading of the full press release score<br>paper_flesch: Ease of reading score for abstracts of papers</p> <p>Institutional origins of press releases:<br>EUID: unique identifier for each press release.<br>DOI: digital identifier for the associated research paper.<br>RL: research level of research paper<br>LR_main_field: The academic field or discipline of the research.<br>Institution: The institutions issuing press releases<br>Affiliation of authos: The affiliations of paper author<br>Journal: The journal of paper</p> <p>Source:EurekAlert! and OpenAlex.</p> <p>Purpose: This dataset can be used to analyse the coverage of basic and applied research in press releases, as well as the types and fields of scientific research disseminated.</p>
Oral cholera vaccine coverage survey, Goma, August 2022
<p>Dataset collected during Vaccination Coverage Survey for Oral Cholera Vaccine in Goma, August 2022</p>
Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data 2010-2023
<p>This dataset contains surveillance study estimates for population size, HIV prevalence, and ART coverage among female sex workers (FSW), men who have sex with men (MSM), people who inject drugs (PWID), and transgender men and women (TGM/W) from 2010-2023. It was created to support the UNAIDS Estimates Key Population Workbook for use by HIV estimates teams in sub-Saharan Africa. Key population surveillance reports, including Ministry of Health-led biobehavioural surveys, mapping studies, and academic studies were used to populate the database.</p> <p>The dataset was populated using existing key population size estimate databases including:</p> <ul> <li>UNAIDS Key Population Atlas</li> <li>US Centers for Disease Control and Prevention surveillance database</li> <li>Global Fund against HIV/AIDS, TB, and Malaria surveillance database</li> <li>Global.HIV database</li> <li>Systematic review databases among MSM (<a href="https://pubmed.ncbi.nlm.nih.gov/31601542/" target="_blank" rel="noopener">Stannah et al, 2019</a> and <a href="https://pubmed.ncbi.nlm.nih.gov/37453439/" target="_blank" rel="noopener">Stannah et al., 2023</a>) and PWID (<a href="https://pubmed.ncbi.nlm.nih.gov/36996857/" target="_blank" rel="noopener">Degenhardt et al., 2023</a>)</li> </ul> <p><br>and was additionally supplemented by a literature review of peer-reviewed and grey literature sources.</p> <p>The data can be <a href="https://shiny.dide.ic.ac.uk/kp-data/" target="_blank" rel="noopener">explored in this web application</a> and the <a href="https://www.medrxiv.org/content/10.1101/2022.07.27.22278071v3" target="_blank" rel="noopener">accompanying manuscript can be found here</a></p>
Evaluating data-flow coverage in spectrum-based fault localization
<p>This release contains files with the results of the experiment comparing the use of data- and control-flow spectra for Spectrum-based Fault Localization. It also has instructions to run Jaguar to perform experiments. The subject programs used in the experiment are public available in our GitHub repository.</p>
Data for: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models"
<p>This repository contains the raw data to reproduce the paper: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models". Within the .tar.gz file, you will find the directory structure described above.</p> <h2>Directory Structure</h2> <h3>`data`</h3> <p>Contains the data to reproduce all figures in the manuscript. Used primarily by the Jupyter Notebooks that plot the data from the paper.</p> <h3>`eval`</h3> <p>Contains the predicted energies according to a MACE model for the following systems and facets:<br>- covsplit (100, 111, 211, 331, 410, 711): The NN model is trained on low-coverage structures and tested on high-coverage structures for a single facet<br>- evencov (100, 111, 211, 331, 410, 711): The NN is trained on even coverages and tested on odd coverages for a single facet<br>- facet (100, 111, 211, 331, 410, 711): the NN is trained on the facet indicated by the folder name (e.g., facet-100 means that the model was trained on Cu(100)) and tested on all of the other facets.<br>- full: the model was trained on all facets and all coverages<br>- slopes (various versions and configurations): the models were trained with different body-order correlation (v) for the Cu(711) facet and tested only on the Cu(711) facet<br>- Rh111: Energies for the Rh(111) + CHOH + CO systems.</p> <h3>`mcmc`</h3> <p>Contains the data for MCMC (Markov Chain Monte Carlo) evaluations for two systems: Cu and Rh<br>- copper-mcmc-public.tar.gz<br>- rhodium-mcmc-public.tar.gz</p> <h3>`models`</h3> <p>Contains the weights and parameters of the best-performing MACE models trained in this work, as selected by the validation loss:</p> <p>File formats: `.model` and `_swa.model` relate to the first-stage of training and the second-stage of training.</p> <h3>`pyscripts`</h3> <p>Python scripts to perform the MCMC sampling given the custom configuration file `sample_cfg.json`.</p> <h3>`scripts`</h3> <p>Shell scripts for evaluation and training the MACE models, along with the hyperparameters used in doing so.</p> <p>- Evaluation scripts (eval-*.sh)<br>- Training scripts (train-*.sh)</p> <h3>`train`</h3> <p>Training, validation, and testing data for all Cu and Rh facets in this work, according to the naming scheme described above.</p> <p>- Rh111<br>- covsplit<br>- evencov<br>- facet<br>- full<br>- slopes</p>
Deep Submergence Dive location dataset from Bell et al. Sci Advances: How Little We've Seen: A Visual Coverage Estimate of the Deep Seafloor
<p><span>How Little We’ve Seen: A Visual Coverage Estimate of the Deep Seafloor </span></p> <p><span>Katherine L.C. Bell,</span><sup><span>1</span></sup><em><sup><span>∗</span></sup></em><em><sup><span> </span></sup></em><span>Kristen N. Johannes,</span><sup><span>1<em>,</em>2</span></sup><span> </span></p> <p><span>Brian R.C. Kennedy,</span><sup><span>1<em>,</em>3 </span></sup><span>Susan E. Poulton</span><sup><span>1</span></sup><span> </span></p> <p><sup><span>1</span></sup><span>Ocean Discovery League, Saunderstown, RI 02874, USA, </span></p> <p><sup><span>2</span></sup><span>Integrative Oceanography Division, Scripps Institution of Oceanography, University of California San Diego, San Diego, CA 92037, USA </span></p> <p><sup><span>3</span></sup><span>Biology Department, Boston University, Boston, MA 02215 USA </span></p> <p><em><sup><span>∗</span></sup></em><span>To whom correspondence should be addressed: croff@alum.mit.edu. </span></p> <p><span><br>Despite the importance of visual observation in the ocean, we have imaged a minuscule fraction of the deep seafloor. Sixty-six percent of the entire planet is deep ocean (≥200 m), and our data show we have visually observed less than 0.001%, a total area approximately a tenth of the size of Belgium. Data gathered from over 44 thousand deep-sea dives indicate we have also seen an incredibly biased sample. Sixty-five percent of all in situ visual seafloor observations in our dataset were within 200 nm of only three countries: the United States, Japan, and New Zealand. Ninety-seven percent of all dives we compiled have been conducted by just five countries: the United States, Japan, New Zealand, France, and Germany. This small and biased sample is problematic when attempting to characterize, understand, and manage a global ocean.</span></p>
CoVerage Results
<p>Datasets generated from GISAID metadata for the figures in the CoVerage manuscript. Data includes the results for the months of November 2021 to January 2021 and January 2023 to March 2023.</p> <p>11_2021</p> <ul> <li>11-2021_fig3_lineage_dynamics_summary_statistics.csv - summary statistics or frequency data for selected pVOIs for the month of 11-2021</li> <li>11-2021_fig4_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 11-2021 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>11-2021_fig4_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 11-2021 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>11-2021_fig4_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 11-2021 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> <li>11-2021_fig4_antigenic_scores_map_visualization_all_sites.csv - calculated country antigenic scores for the month of 11-2021 using the scoring method of applying weights at all sites across the spike protein</li> </ul> <p>12_2021</p> <ul> <li>12-2021_fig3_lineage_dynamics_summary_statistics.csv - summary statistics or frequency data for selected pVOIs for the month of 12-2021</li> <li>12-2021_fig4_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 12-2021 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>12-2021_fig4_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 12-2021 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>12-2021_fig4_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 12-2021 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> <li>12-2021_fig4_antigenic_scores_map_visualization_all_sites.csv - calculated country antigenic scores for the month of 12-2021 using the scoring method of applying weights at all sites across the spike protein</li> </ul> <p>01_2022</p> <ul> <li>01-2022_fig4_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 01-2022 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>01-2022_fig4_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 01-2022 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>01-2022_fig4_antigenic_scores_map_visualization_all_sites.csv - </li> <li>calculated country antigenic scores for the month of 01-2022 using the scoring method of applying weights at all sites across the spike protein</li> <li>01-2022_fig4_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 01-2022 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> </ul> <p>01_2023</p> <ul> <li>01-2023_fig5_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 01-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>01-2023_fig5_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 01-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>01-2023_fig5_antigenic_scores_map_visualization_all_sites.csv - calculated country antigenic scores for the month of 01-2023 using the scoring method of applying weights at all sites across the spike protein</li> <li>01-2023_fig5_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 01-2023 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> </ul> <p>02_2023</p> <ul> <li>02-2023_fig5_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 02-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>02-2023_fig5_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 02-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>02-2023_fig5_antigenic_scores_map_visualization_all_sites.csv - calculated country antigenic scores for the month of 02-2023 using the scoring method of applying weights at all sites across the spike protein</li> <li>02-2023_fig5_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 02-2023 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> </ul> <p>03_2023</p> <ul> <li>03-2023_fig5_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 03-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>03-2023_fig5_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 03-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>03-2023_fig5_antigenic_scores_map_visualization_all_sites.csv - calculated country antigenic scores for the month of 03-2023 using the scoring method of applying weights at all sites across the spike protein</li> <li>03-2023_fig5_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 03-2023 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> </ul>
ScienceDex guides
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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.