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ALLENMINER data files
<p>This dataset accompanies the ALLENMINER software package ( http://fredpdavis.com/allenminer ) that searches gene expression in the adult and developing mouse brain and spinal cord atlases published by the Allen Institute for Brain Science.</p> <p>* fastsearch files are used to accelerate searches within named brain regions</p> <p>* allenminer_manuscript_data.tar.gz contains the results of the searches described in the original publication (Davis and Eddy, 2009).</p>
Binaural room scanning files for sound field synthesis localization experiment
<p>Binaural room scanning files that were used together with the SoundScape Renderer to perform the localization experiments described in Wierstorf [1].</p> <p>The results of the corresponding listening experiments are summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Genomic vcf file for D.melanogaster, Sussex LHM population
<p>Data, logs and code for genomic vcf genotypes file for the Morrow lab, D.melanogaster LHM sequencing and genotyping project.</p>
Input files for SICOPOLIS v25
<p>Archives containing the input files (and corresponding READMEs including references) for <a href="https://sicopolis.github.io/sicopolis/" target="_blank" rel="noopener">SICOPOLIS</a>: <br>'ant.tgz' - Antarctica, <br>'grl.tgz' - Greenland, <br>'nhem.tgz' - Entire northern hemisphere, <br>'scand.tgz' - Fennoscandia/Eurasia, <br>'tibet.tgz' - Tibet, <br>'asf.tgz' - Austfonna, <br>'mocho.tgz' - Mocho-Choshuenco ice cap, <br>'eismint.tgz' - EISMINT (Phase 2 SGE and modifications), <br>'heino.tgz' - ISMIP HEINO, <br>'nmars.tgz' - North polar cap of Mars, <br>'smars.tgz' - South polar cap of Mars.</p> <p>Manual download of these archives is not required! This will happen automatically when <a href="https://sicopolis.github.io/sicopolis/" target="_blank" rel="noopener">SICOPOLIS</a> is installed.</p>
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</p>
Wind Value Literature Selection for End-of-Life Valuation Excel File WP 5.1
<p>Authors from Wind Value, the Re-Wind Network and IEA Wind Task 45 carried out research on methods of processing end-of-life wind turbine blades. This included a structured literature review which selected the literature in the Excel file. Part of this work helps to estimate the value of an end-of-life wind farm contributing to Work Package 5.1 of the Wind Value project. The paper was pubished as Deeney et al. (2025) <a href="https://www.sciencedirect.com/science/article/pii/S1364032125000917?via%3Dihub">End-of-life wind turbine blades and paths to a circular economy</a>, <em>Renewable and Sustainable Energy Reviews. </em></p>
GENEActiv accelerometer file related to the #120 OxWearables / stepcount issue
<p>An example of .bin file that have an IndexError when processing.</p> <p>Consider <a title="#120 OxWearables / stepcount issue" href="https://github.com/OxWearables/stepcount/issues/120" target="_blank" rel="noopener">#120 OxWearables / stepcount issue</a> for more details.</p> <p>The .csv files are 1-second epoch conversions from the .bin file and contain <em>time</em>, <em>x</em>, <em>y</em>, <em>z</em> columns. The conversion was done by: </p> <ol> <li>reading the .bin with the <a title="GENEAread R package" href="https://www.rdocumentation.org/packages/GENEAread/" target="_blank" rel="noopener">GENEAread R package</a>.</li> <li>keeping only the time, x, y and z columns.</li> <li>saving the data.frame into a .csv file.</li> </ol> <p>The only difference between the .csv files is the column format used for the time column before saving:</p> <ul> <li>time column in XXXXXX_....csv had a string class</li> <li>time column in XXXXXT....csv had a "POSIXct" "POSIXt" class</li> </ul>
Data Files for Climate-based Maize Loss Rate Simulations
<p>This archive contains data files from an <a href="../records/13356711">open source pipeline</a> looking at how crop insurance rates may change in the future within the US Corn Belt using <a href="https://www.sciencedirect.com/science/article/pii/S0034425715001637">SCYM</a> and <a href="https://www.chc.ucsb.edu/data/chc-cmip6">CHC-CMIP6</a>. These are available under a Creative Commons license. Unless otherwise specified, these report on SSP245.</p> <p>See README for more details including column-level description of each resource. Funded by the <a href="https://dse.berkeley.edu/">Eric and Wendy Schmidt Center for Data Science and Environment</a> at the University of California, Berkeley.</p>
PubChem OECD PFAS Larger PFAS Parts file for MetFrag
<p>This is a <a href="https://msbi.ipb-halle.de/MetFrag/">MetFrag</a> database file constructed from the "Molecule contains PFAS parts larger than CF<sub>2</sub>/CF<sub>3</sub>" subnode of the OECD PFAS Definition node in the <a href="https://pubchem.ncbi.nlm.nih.gov/classification/#hid=120"> PFAS and Fluorinated Organic Compounds in PubChem Tree</a> on the Classification Browser in PubChem.</p> <p>This file was constructed by downloading the node contents, selecting the columns of interest, changing the headers to MetFrag-compatible headers. Entries containing Xe, Pr, Po, Ru and W were removed; charges were also removed from formulas to avoid issues with MetFragCL.</p> <p>The construction of the tree is documented <a href="https://gitlab.com/uniluxembourg/lcsb/eci/pubchem-docs/-/raw/main/pfas-tree/PFAS_Tree.pdf?inline=false">here</a>.</p> <p>Note: PubChem authors have been removed from this version to comply with a presidential decree. This version was prepared exclusively by the remaining author. </p>
FASTA file containing to the MYB encoding gene Ant1 genomic sequences corresponding to wild and cultivated tomato accessions
<p>Fasta sequence correspond to the MYB encoding gene <em>An2-like</em>. The genomic sequences correspond to <em>Solanum galagpagnese</em> accession LA1141 (this study), <em>S. lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome, and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>).</p>
FASTA file containing the MYB encoding gene An2-like genomic sequences corresponding to wild and cultivated tomato accessions
<p>FASTA sequence corresponds to the MYB encoding gene <em>An2-like</em>. The genomic sequences correspond to <em>Solanum galagpagnese</em> accession LA1141 (this study), <em>S. lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome (Hosmani et al., 2019), <em>S. lycopersicum </em>variety Indigo Rose (Yan et al., 2020), <em>S. lycopersicum</em> accession LA1996 [MN242011.1 (Colanero et al., 2020)], <em>S. chilense </em>accession LA1930 [MN242012.1 (Colanero et al., 2020)], and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), Indigo Rose [MN433087 (Yan et al., 2020)], <em>S. lycopersicum </em>accession LA1996 [MN242011.1, EF433417.1 (Sapir et al., 2008; Colanero et al., 2020)], <em>S. chilense</em> accession LA1930 [MN242012.1 (Colanero et al., 2020)] were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>) and the National Center for Biotechnology Information (NCBI)(available at NCBI: <a href="https://www.ncbi.nlm.nih.gov">https://www.ncbi.nlm.nih.gov</a>).</p>
FASTA file containing the MYB encoding genes at the Aft locus with genomic sequences corresponding to wild and cultivated tomato accessions
<p>FASTA sequences correspond to the MYB encoding genes <em>An2-like </em>and <em>Ant1</em>. The genomic sequences were combined correspond to <em>Solanum galagpagnese</em> accession LA1141 (this study), <em>S. lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome (Hosmani et al., 2019), LA1996 [MN242011.1, EF433417.1(Sapir et al., 2008; Colanero et al., 2020)], and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), <em>S. lycopersicum </em>accession LA1996 [MN242011.1, EF433417.1 (Sapir et al., 2008; Colanero et al., 2020)], <em>S. chilense</em> accession LA1930 [MN242012.1 (Colanero et al., 2020)] were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>) and the National Center for Biotechnology Information (NCBI) (available at NCBI: <a href="https://www.ncbi.nlm.nih.gov/">https://www.ncbi.nlm.nih.gov</a>).</p>
(Fastq Files) Amplicon sequencing of ama1 and mdr1 to track within-host P. falciparum diversity in Kilifi, KENYA
<p>These data were generated from amplicon sequencing of <em>Plasmodium falciparum</em> <em>ama1 </em>and<em> </em><em>mdr1</em> genes in samples collected from Kilifi, at the coast of Kenya.</p> <p>The two papers that reference these data will soon be included here:</p> <ol> <li> The Journal of Infectious Diseases - https://doi.org/10.1093/infdis/jiac144</li> <li>Wellcome Open Research - https://wellcomeopenresearch.org/articles/7-95</li> </ol> <p>Two objectives were explored:</p> <ol> <li>To determine temporal changes in the genetic diversity of malaria parasites in asymptomatic and febrile infections.</li> <li>To track within-host parasite diversity, throughout treatment in a clinical drug trial.</li> </ol>
Data files for Competitive ability depends on mating system and ploidy level across Capsella species
<p>The three data files associated with the article:</p> <p>Competitive ability depends on mating system and ploidy level across Capsella species. Annals Of Botany 2022: doi.org/10.1093/aob/mcac044</p> <p>See README for details on each files</p>
OntoChem PFAS CORE and Patent Files for MetFrag
<p>These are MetFraggable versions of the PFAS lists produced by OntoChem by performing literature mining of the CORE database (27K entries) and Google Patent collections (1.7M entries). The MetFrag versions have undergone filtering to remove entries that prevent MetFrag running (i.e. multiple-entry formulas and certain elements). Each file contains a tag whether the PFAS fits three definitions, A, B and C. The CORE database also contains the number of references in which the PFAS entry was found. Each file is available as CSV or in compressed form.</p> <ul> <li>PFAS Definition A: Each compound that contains a CF<sub>2</sub> group</li> <li>PFAS Definition B: Each compound that contains a (AH)(AH)(F)C-C(AH)F<sub>2</sub> group, where AH groups could be hydrogen or any other atom and the bond between both aliphatic carbon atoms is a single bond</li> <li>PFAS Definition C: Each compound that contains a (R<sup>1</sup>)(R<sup>2</sup>)(F)C-C(R<sup>3</sup>)F<sub>2</sub> group is considered a PFAS, where the R groups are any atom except hydrogen and the bond between both aliphatic carbon atoms is a single bond</li> </ul> <p><strong><em>Please note these files are very large (especially patents) and should not be uploaded to MetFragWeb directly - they will be available from the dropdown menu. These are provided for command line users, and any other workflows interested in these files! </em></strong>The patent file is 1.7 million entries and can cause some delay in the command line, compared with smaller database files.</p> <p>Full details are available in this preprint by Barnabas et al (2022) DOI: <a href="https://doi.org/10.26434/chemrxiv-2022-nmnnd-v2">10.26434/chemrxiv-2022-nmnnd-v2</a></p> <p>Update 20/04/2022: uploaded files with updated CID mappings post-PubChem deposition.</p>
MetFrag Local CSV: CompTox (7 March 2019 release) MetaData File
<p>These are the CSV files that can be used as a local database in MetFrag (https://msbi.ipb-halle.de/MetFrag/), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, updated versions of these files are integrated in the web interface.</p> <p>This upload includes the SelectMetaData version of the CompTox MetFrag file from the 7 March 2019 release (DOI:<a href="https://doi.org/10.23645/epacomptox.7525199.v2">10.23645/epacomptox.7525199.v2</a>).</p>
Data files belonging to the paper "Dealing with clustered samples for assessing map accuracy by cross-validation"
<p>Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. In the paper related to these data, we present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data. </p> <p> </p> <p>The study area is western Europe, constrained in the north at 52° latitude and at -10° and 24° longitude The projection is IGNF:ETRS89LAEA (Lambert azimuthal equal area projection).</p> <p> </p> <p><strong>Files:</strong></p> <p>agb.tif = above ground biomass (AGB) map from version 3 of the 2017 CCI-Biomass product (<a href="https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8">https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8</a>)<br> AGBstack.tif = covariates used for predicting AGB<br> aggArea.tif = coarse grid used for simulation in the model-based methods<br> ocs.tif = soil organic carbon stock (OCS) map (0-30 cm) from Soilgrids (<a href="https://www.isric.org/explore/soilgrids">https://www.isric.org/explore/soilgrids</a>)<br> OCSstack.tif = covariates used for predicting OCS<br> strata.xxx = 100 compact geo-strata (ESRI shape) created with the spcosa package; used for generating clustered samples<br> TOTmask.tif = mask of the area covered by the covariates</p> <p> </p> <p><strong>Details and data sources of the covariates in AGBstack.tif and OCSstack.tif:</strong></p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Note</strong></p> </td> </tr> <tr> <td> <p>ai</p> </td> <td> <p>Aridity Index</p> </td> <td> <p><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></p> </td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio1</p> </td> <td> <p>Mean annual air temperature [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio5</p> </td> <td> <p>Mean daily maximum air temperature of the warmest month [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio7</p> </td> <td> <p>Annual range of air temperature [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio12</p> </td> <td> <p>Annual precipitation [kg/m<sup>2</sup>]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio15</p> </td> <td> <p>Precipitation seasonality [kg/m<sup>2</sup>]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>gdd10</p> </td> <td> <p>Growing degree days heat sum above 10°C</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>clay</p> </td> <td> <p>Clay content [g/kg] of the 0-5cm layer</p> </td> <td> <p><a href="https://soilgrids.org/">https://soilgrids.org/</a></p> <p> </p> </td> <td> <p>Only used for AGB</p> </td> </tr> <tr> <td> <p>sand</p> </td> <td> <p>Sand content [g/kg] of the 0-5cm layer</p> </td> <td><a href="https://soilgrids.org/">https://soilgrids.org/</a></td> <td>as above</td> </tr> <tr> <td> <p>pH</p> </td> <td> <p>Acidity (Ph(water)) of the 0-5cm layer</p> </td> <td><a href="https://soilgrids.org/">https://soilgrids.org/</a></td> <td>as above</td> </tr> <tr> <td> <p>glc2017</p> </td> <td> <p>Landcover 2017</p> </td> <td> <p><a href="https://land.copernicus.eu/global/products/lc">https://land.copernicus.eu/global/products/lc</a>, reclassified to: closed forest, open forest, natural non-forest veg., bare & sparse veg. cropland, built-up, water</p> </td> <td> <p>Categorical variable</p> </td> </tr> <tr> <td> <p>dem</p> </td> <td> <p>Elevation</p> </td> <td> <p><a href="https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem">https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem</a></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>cosasp</p> </td> <td> <p>Cosine of slope aspect</p> </td> <td> <p>Computed with the terra package from elevation</p> </td> <td>Computed @25m resolution; next aggregated to 0.5km</td> </tr> <tr> <td> <p>sinasp</p> </td> <td> <p>Sine of slope aspect</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TPI</p> </td> <td> <p>Topographic position index</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TRI</p> </td> <td> <p>Terrain ruggedness index</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TWI</p> </td> <td> <p>Topographic wetness index</p> </td> <td> <p>Computed with SAGA from 500m resolution (aggregated) dem</p> </td> <td> </td> </tr> <tr> <td> <p>gedi</p> </td> <td> <p>Forest height</p> </td> <td> <p><a href="https://glad.umd.edu/dataset/gedi">https://glad.umd.edu/dataset/gedi</a></p> </td> <td> <p>Zone: NAFR</p> </td> </tr> <tr> <td> <p>xcoord</p> </td> <td> <p>X coordinate</p> </td> <td> <p>Using a mask created from the other covariates</p> </td> <td> </td> </tr> <tr> <td> <p>ycoord</p> </td> <td> <p>Y coordinate</p> </td> <td>Using a mask created from the other covariates</td> <td> </td> </tr> <tr> <td> <p>Dcoast</p> </td> <td> <p>Distance from coast</p> </td> <td> <p>Using a land mask created from the other covariates</p> </td> <td> </td> </tr> </tbody> </table> <p> </p>
New maps of global geologic provinces and tectonic plates: global tectonics data and QGIS project file
<p>The global tectonics data compilation is a set of raster and vector data that are useful for investigating tectonics past and present. The datasets are useful on their own or can be used in GIS software, which includes the QGIS project file for convenience. The datasets include our new models for tectonic plate boundaries and deformation zones, geologic provinces and orogens. Additional datasets include earthquake and volcano locations, geochronology, topography, magnetics, gravity, and seismic velocity.</p> <p>The global tectonics collection is suitable for research and educational purposes.</p>
Hunting for vampires and other unlikely forms of parity violation at the Large Hadron Collider: lhe files
<p>Parton-level simulations in the PV-mSME model with various values of lambdaPV and for the standard model (lambdaPV=0).</p> <p>This dataset includes one example lhe file generated by MadGraph for each sample used in the <a href="https://arxiv.org/abs/2205.09876">paper</a>.<br> <br> The MadGraph version and the modifications we make to its generated code are included in the code sharing dataset on <a href="https://github.com/Rupt/paper-hunting-vampires">git</a> and <a href="https://doi.org/10.5281/zenodo.6827723">Zenodo</a>.<br> <br> Main files are named `liv_3j_4j_${lambdaPV}_0.lhe.gz`, where lambdaPV is a floating point number with "." replaced with "p".<br> <br> Other files named `liv_rot_${hour}_0.lhe.gz ` are from the rotated PV-mSME from the appendix that studies the effect of a rotating planet. Each rotation in radians is <em>hour * 2 pi / 24 </em>(for discrete rotations in a 24 hour day).<br> <br> The suffix "_0" encodes that each was generated with the first in our sequence of random seeds.</p> <p>`sm_3j_4j_0.lhe.gz` is simulated from the Standard Model, which is physically equivalent to lambdaPV = 0.</p>
Protein structure files for the paper "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.
<p>This archive contains models of HRAS, KRAS, and NRAS homo- and heterodimers with various mutations discussed in the paper, "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.<br> as well as crystallographic dimers of these proteins as identified by the ProtCAD database, http://dunbrack2.fccc.edu/ProtCAD/Results/PfamArchClusterInfo.aspx?GroupId=8 (cluster 5). Several of the models are shown in Supp. Figure 11b and the crystallographic dimers of RAS that provide evidence for the possible biological relevance of these models are shown in Supp. Figure 11a.</p> <p>The crystallographic dimers were identified by clustering all possible interfaces generated by symmetry operators in crystals of HRAS, KRAS, and NRAS as described in the paper: Xu, Q., Dunbrack, R.L. ProtCID: a data resource for structural information on protein interactions. <em>Nat Commun</em> <strong>11</strong>, 711 (2020). https://doi.org/10.1038/s41467-020-14301-4.</p> <p>The models were created by superposing monomers of HRAS, KRAS, or NRAS onto the alpha4-alpha5 dimer present in the crystal of PDB entry 3k8y. Mutations were made in PyMOL. The structures were relaxed with the FastRelax protocol and the Ref2015 scoring function in the program Rosetta, which uses the backbone-dependent rotamer library of Shapovalov and Dunbrack to repack side chains.</p> <p>The crystallographic dimers are contained in a zipped PyMOL session. The mmCIF format for all the structures is present in a zip file, Tang_et_al_crystallographic_and_modeled_RAS_dimer_ciffiles.zip. The PyMOL session and zip file contains 87 HRAS dimers, 14 KRAS dimers, and 1 NRAS dimer, all having the interface consisting of the alpha4 and alpha5 helices. The PyMOL session also contains the modeled structures. Only Mg ions and GTP/GNP/GDP ligands are shown. Others are present but hidden and may be displayed by PyMOL ("show sticks, het").</p> <p> </p>
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.