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13,117 results for “Maps”
Daily maps for global daylight length at 30 arc seconds resolution (2022)
<p>Overview:<br> Daily maps for global daylight length, calculated for the year 2022.</p> <p>Processing steps:<br> For each day within the year 2022, the photoperiod (sunshine hours on flat terrain) are calculated using the SOLPOS algorithm developed by the National Renewable Energy Laboratory (NREL), USA. Resultant values have been converted from hours to minutes.</p> <p>File naming scheme (DDD = day within year) (min is abbreviation for minute):<br> <code>daylight_min_2022_DDD.tif</code></p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 90<br> south: -90<br> west: -180<br> east: 180</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> unit: minutes</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.2.0</p> <p>License: CC-BY-SA 4.0</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: National Renewable Energy Laboratory (NREL): SOLPOS 2.0 sun position algorithm (https://www.nrel.gov/grid/solar-resource/solpos.html)</p>
Data from: A semi-automated approach to classify and map ecological zones across the dune-beach interface
<p>This is the raw data behind the publication: </p> <p><strong>A semi-automated approach to classify and map ecological zones across the dune-beach interface</strong></p> <p><strong>Abstract: </strong>Habitat classification and mapping underpins most conservation and management tools, because habitats are often used as a surrogate for all biodiversity. Some habitat boundaries are easy to delineate; however, sandy shores are ecotones or ecoclines given their dynamic interface between the marine and the terrestrial realms. Although methods for mapping habitats along shorelines have been broadly applied, we aim to test a semi-automated approach to mapping across-shore “sub-environments” in this transition zone at a finer scale. Using an empirical dataset of photographs covering a small area (three across-shore transects from each of two different areas) with a high resolution, we tested seven machine learning algorithms to determine which one had the best classification accuracy, and to identify which environmental variables are the main determinants of classifications. The randomForest, stochastic gradient boosting, and C5.0 algorithms most accurately classified the photographs as the correct sub-environment. Based on the randomForest algorithm, the variables entropy, drift cover rate, local slope, segmented vegetation cover and number of points with sand or marine litter had the highest influence on the classification. There was no sensitivity to spatial variation alongshore. This approach can be used to map sub-environments at larger scales using drone technology to capture georeferenced digital photographs systematically. Consequently, coastal habitats can be mapped at a finer scale without causing disturbance to this especially sensitive ecotone.</p>
Global map of Martian fluvial systems
<p>This dataset represents an update of previous global maps of Martian fluvial systems. We included all the valleys longer than 20 km and mapped them as vector-based polylines within the QGIS software, using the more recent and, to date, the best resolution THEMIS (Thermal Emission Imaging Spectrometer) daytime IR mosaic (100 m/pixel). In addition, we used, where necessary (for small-scale systems and valleys with high erosion), CTX (Contex Camera) data, with a resolution up to 6 m/pixel. The imagery data were coupled with the MOLA (Mars Orbiter Laser Altimeter Mosaic) mosaic which has a spatial resolution of 463 m/pixel. At low latitudes, we used an equidistant cylindrical projection, while at high latitudes, we used sinusoidal and polar stereographic projections to represent and analyze the data. Topographic information and data of higher image quality (new THEMIS mosaic plus CTX data) than those of previous manual maps, allowed us to identify new structures and more tributaries for a large number of systems. An attribute table is associated to our dataset including useful information such as coordinates, total length and an approximative maximum age indication for each system. The latter has been obtained coupling our map with the global geologic map of Tanaka et al. (2014) which represents, to date, the most accurate dating of the planet surface.</p> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI:<br> 10.5281/zenodo.1051038<br> <br> Please also cite these works: </p> <p>Alemanno et al.: 2018, Global Map of Martian Fluvial Systems: Age and Total Eroded Volume Estimations, Earth and Space Science Journal, 5, 560-577, doi:<a href="https://doi.org/10.1029/2018EA000362">https://doi.org/10.1029/2018EA000362</a><br> Orofino et al.: 2018, Estimate of the water flow duration in large Martian fluvial systems. Planetary and Space Science Journal, 163, 83-96. doi: <a href="https://doi.org/10.1016/j.pss.2018.06.001">10.1016/j.pss.2018.06.001</a><br> Alemanno G.: 2018, Study of the fluvial activity on Mars through mapping, sediment transport modelling and spectroscopic analyses. PhD dissertation thesis, <a href="https://arxiv.org/abs/1805.02208">arXiv:1805.02208</a> [astro-ph.EP].</p>
Flow map data of the singel pendulum, double pendulum and 3-body problem
<p>This dataset was constructed to compare the performance of various neural network architectures learning the flow maps of Hamiltonian systems. It was created for the paper: <a title="Paper" href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4555181" target="_blank" rel="noopener">A Generalized Framework of Neural Networks for Hamiltonian Systems</a>.</p> <p>The dataset consists of trajectory data from three different Hamiltonian systems. Namely, the single pendulum, double pendulum and 3-body problem. The data was generated using numerical integrators. For the single pendulum, the symplectic Euler method with a step size of 0.01 was used. The data of the double pendulum was also computed by the symplectic Euler method, however, with an adaptive step size. The trajectories of the 3-body problem were calculated by the arbitrarily high-precision code <a title="Brutus" href="https://comp-astrophys-cosmol.springeropen.com/articles/10.1186/s40668-014-0005-3" target="_blank" rel="noopener">Brutus</a>.</p> <p>For each Hamiltonian system, there is one file containing the entire trajectory information (*_all_runs.h5.1). In these files, the states along all trajectories are recorded with a step size of 0.01. These files are composed of several <a title="Pandas" href="https://pandas.pydata.org/" target="_blank" rel="noopener">Pandas</a> DataFrames. One DataFrame per trajectory, called "run0", "run1", ... and finally one large DataFrame in which all the trajectories are combined, called "all_runs". Additionally, one Pandas Series called "constants" is contained in these files, in which several parameters of the data are listed.</p> <p>Also, there is a second file per Hamiltonian system in which the data is prepared as features and labels ready for neural networks to be trained (*_training.h5.1). Similar to the first type of files, they contain a Series called "constants". The features and labels are then separated into 6 DataFrames called "features", "labels", "val_features", "val_labels", "test_features" and "test_labels". The data is split into 80% training data, 10% validation data and 10% test data.</p> <p>The code used to train various neural network architectures on this data can be found on GitHub at: <a title="Code" href="https://github.com/AELITTEN/GHNN" target="_blank" rel="noopener">https://github.com/AELITTEN/GHNN</a>.</p> <p>Already trained neural networks can be found on GitHub at: <a title="NeuralNets_GHNN" href="https://github.com/AELITTEN/NeuralNets_GHNN" target="_blank" rel="noopener">https://github.com/AELITTEN/NeuralNets_GHNN</a>.</p> <table> <tbody> <tr> <td> </td> <td><strong>Single pendulum</strong></td> <td><strong>Double pendulum</strong></td> <td><strong>3-body problem</strong></td> </tr> <tr> <td>Number of trajectories</td> <td>500</td> <td>2000</td> <td>5000</td> </tr> <tr> <td>final time in all_runs</td> <td>T (one period of the pendulum)</td> <td>10</td> <td>10</td> </tr> <tr> <td>final time in training data</td> <td>0.25*T</td> <td>5</td> <td>5</td> </tr> <tr> <td>step size in training data</td> <td>0.1</td> <td>0.1</td> <td>0.5</td> </tr> </tbody> </table> <p> </p>
Supplementary data to `Do science maps from open access literature capture the overall topic structure of an academic field?`
<p>The dataset contains the 8,528 academic articles records related to Sustainable Food research sourced with the query `TS=("sustainab*" NEAR/2 "food*")` .</p> <p>They are the records present in the largest component of the citation network, as specified in the manuscript. </p> <p>The dataset was sourced from OpenAlex based on the original data used in the manuscript and it is composed of the following columns:</p> <table> <tbody> <tr> <td><em><strong>Column</strong></em></td> <td><em><strong>Description</strong></em></td> </tr> <tr> <td>Id</td> <td>OpenAlex ID</td> </tr> <tr> <td>DOI</td> <td>Document Object Identifier</td> </tr> <tr> <td>display_name</td> <td>The article title</td> </tr> <tr> <td>publication_year</td> <td>The publication year of the article</td> </tr> <tr> <td>open_access</td> <td>An object with details of the open access status of the article</td> </tr> </tbody> </table> <p>We choose the `.rdata` format for easy loading in R. Use the function `load()` to add the data frame to the enviroment. </p>
A complete map of specificity encoding for a partially fuzzy protein interaction
<p>All data required to run analyses for "A complete map of specificity encoding for a partially fuzzy protein interaction". Please see <a href="https://github.com/lehner-lab/fuzzy_specificity">https://github.com/lehner-lab/fuzzy_specificity</a> for instructions. </p>
Lattice diagrams of elastic maps
<p>This collection of figures is a supplement to the presentation by Gupta and Tape (2024) and builds upon the work of Tape and Tape (2021, 2022, 2024). The collection contains this file, 28 composite pdf files, and three additional composite pdf files. We examine 28 elastic maps, each of which is represented by a 6 x 6 symmetric matrix having 21 parameters (in general). For each map we calculate the closest elastic map in each of 8 symmetry classes, and we depict these 8 elastic maps within a lattice diagram.</p>
Interactive map of distribution of gene fragments indicative of cyanotoxin biosynthesis and cyanotoxins in the European Alps
<p><span>Distribution of cyanotoxins and cyanotoxin biosynthesis genes in Alpine region determined by LC-MS/MS and (q)PCR. Cyanotoxins and cyanotoxin genes are mapped on separate layers, and two basemaps are available (simple and relief). Results can be filtered by location, sample type, water body type, cyanotoxins and cyanotoxin genes. Note that cyanotoxin analyses were not performed on all sampling points.</span></p>
Genotypes for Neurospora nested association mapping population
<p>Genotype file that contains genotypes all the Neurospora association mapping population developed in Kronholm lab. See https://github.com/ikron/Neurospora_NAM_population</p> <p>The file is a tab-delimited text file in hapmap format and contains first contains the columns: rs alleles chrom pos strand assembly center protLSID assayLSID panel GCcode and then the subsequent columns are strains identifiers. The column 'rs' is the name of the SNP, column 'alleles' shows which two alternative bases occur, the column 'chrom' is the chromosome, column 'pos' is the coordinate, columns 'strand', 'center', 'protLSID', 'assayLSID', 'panel' and 'GCcode' contain all missing data. They are included for hapmap format compatibility. The column 'assembly' indicates the version of the Neurospora crassa reference genome that the coordinates are based on. And in this case it is NC12 for all SNPs.</p> <p>Missing data is indicated by 'N'</p>
Distribution Map of Festuca dolichophylla (suplemental material-TS1)
<p>The distribution map of <em>Festuca dolichophylla</em> relies on diverse data sources. Geographical coordinates (latitude and longitude) and country initials (countryCode) were extracted from Tropicos, the Gbif repository (up to May 2019), and the iDigBio database (up to July 2021). Additionally, data from other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999), were integrated. The Gbif data points are associated with gbifID numbers for reference. Please note that this compilation provides essential information for understanding the distribution of <em>F. dolichophylla</em> across various regions.</p> <h3>Software</h3> <p>Organized data by geographic coordinates was uploaded to <strong>ArcGIS Pro v. 3.2.0</strong> for map production. Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of <em>F. dolichophylla</em>.</p> <h2>Methods</h2> <div> <p>The dataset for the distribution map of <em>Festuca dolichophylla</em> was meticulously collected from various sources.</p> <ol> <li> <p><strong>Data Collection</strong>:</p> <ul> <li><strong>Tropicos</strong>: Data were extracted from Tropicos until December 2023.</li> <li><strong>Gbif Repository</strong>: Data was sourced from the Gbif repository until May 2019.</li> <li><strong>iDigBio Database</strong>: Additional data points were retrieved from the iDigBio database up to July 2021.</li> <li><strong>Other Sources</strong>: We also incorporated data from various other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999).</li> </ul> </li> <li> <p><strong>Data Organization and Processing</strong>:</p> <ul> <li>All collected data points were meticulously organized by coordinates.</li> <li>We ensured consistency by cross-referencing and validating the data.</li> <li>The dataset was then uploaded to <strong>ArcGIS Pro v. 3.2.0</strong> for map production.</li> <li>Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of <em>F. dolichophylla</em>.</li> </ul> </li> </ol> </div> <h2>Funding</h2> <div> <p>Neotropical Grassland Conservancy, Award: Memorial grant 2020</p> </div>
A map of canine sequence variation relative to a Greenland wolf outgroup
<p><span>For over 15 years, canine genetics research relied on a reference assembly from a Boxer breed dog named Tasha (i.e., canFam3.1). Recent advances in long-read sequencing and genome assembly have led to the development of numerous high-quality assemblies from diverse canines. These assemblies represent notable improvements in completeness, contiguity, and the representation of gene promoters and gene models. Although genome graph and pan-genome approaches have promise, most genetic analyses in canines rely upon the mapping of Illumina sequencing reads to a single reference. The Dog10K consortium, and others, have generated deep catalogs of genetic variation through an alignment of Illumina sequencing reads to a reference genome obtained from a German Shepherd Dog named Mischka (i.e, canFam4, UU_Cfam_GSD_1.0). However, alignment to a breed-derived genome may introduce bias in genotype calling across samples. Since the use of an outgroup reference genome may remove this effect, we have reprocessed 1,929 samples analyzed by the Dog10K consortium using a Greenland wolf (mCanLor1.2) as the reference. We efficiently performed remapping and variant calling using a GPU-implementation of common analysis tools. The resulting call set removes the variability in genetic differences seen across samples while maintaining general patterns of breed relationships. Using this sequence data, we inferred the history of population sizes and found that village dog populations experienced a 9-13 fold reduction in historic effective population size relative to wolves. </span></p>
New glacier thickness and bed topography maps for Svalbard - Dataset
<p>The dataset includes three Geotiff files that include:</p> <p>1) A bed topography map of Svalbard (heights in m a.s.l.) [Bed_map.tiff]</p> <p>2) An ice thickness map of Svalbard (in m) [Thickness_map.tiff]</p> <p>3) A mask file that with values from 0-3 indicating non-glacier areas (0), glaciers modelled with the Parallel Ice Sheet model (1), glaciers modelled with the Instructed Glacier Model (2), and surging glaciers (3). </p> <p>For a description of the methods used to generate the datasets, we refer to the manuscript "A new glacier thickness and bed map for Svalbard", to be submitted to The Cryosphere Discussions.</p>
S103 | NORMANUVCB | NORMAN Dataset of Curated UVCB Mappings
<p>This is the collection associated with list S103 | NORMANUVCB | NORMAN Dataset of Curated UVCB Mappings on the NORMAN Suspect List Exchange. <a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A curated NORMAN Network effort to provide information for substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs).</p> <p>v0.1.3: added BioHackathon UVCBs. v0.1.4: added EAWAGSURF. v0.1.5: added molecular formula to chemicals mapping for better table sorting. v0.1.6: added HESI UVCBs. v0.1.7: adjusted source display for annotation content of UVCBs. v0.1.8 added NIST polymers. v0.1.9 updated NIST polymers. </p>
A standards mapping for bio-products
<p><a href="https://www.uni.com/en/">UNI, Italian standards body</a>, has realized this first version of a mappig of around 150 standards at International (ISO/IEC/ITU for example), European (CEN/ETSI), and national (UNI) level related to the topics relevant within Biorecer project. </p> <p>The mapping includes the reference number of the document, its title, where available the scope and the technical committee developing each document. </p> <p>This mapping will be implemented and paired with a Open Standards Research Tool, which will be freely available on <a href="https://biorecer.eu/" target="_blank" rel="noopener">Biorecer website</a> (ideally by September 2024). </p> <p>Moreover, the methodology behind the mapping, an extension of the analysis will be detailed in a dedicated public project deliverable. </p> <p><strong>Disclaimer</strong>: Data as of May 2024. </p>
DEM Intercomparison eXercise (DEMIX) - Maps of completeness criteria scores for global DEMs
<h2>Introduction</h2> <p>This introduction gives a brief overview of the context in which the dataset has been produced. Readers curious about the detailed standards and procedures described in this section are encouraged to open the resources linked to this dataset.</p> <h3>The Digital Elevation Model Intercomparison eXercise (DEMIX)</h3> <p>This work is part of the Digital Elevation Model Intercomparison eXercise (DEMIX), initiated by the <a href="https://ceos.org/ourwork/workinggroups/wgcv/current-activites/#:~:text=DEMIX%3A%20Digital%20Elevation%20Model%20Intercomparison,elevation%20model%20for%20their%20application.">Committee on Earth Observation Satellites (CEOS)</a>. This initiative aims at "<a href="https://isprs-archives.copernicus.org/articles/XLIII-B4-2021/395/2021/">providing harmonised terminology and methods, as well as practical guidelines and results allowing the intercomparison of continental or global Digital Elevation Models (DEM)</a>" (Strobl et al., 2021). Several publications have defined the framework of DEMIX, from <a href="https://doi.org/10.3390/rs13183581">the terminology and definitions</a> (Guth et al., 2021) to the <a href="https://doi.org/10.1109/TGRS.2024.3368015">DEM ranking methods</a> (Bielski et al., 2024). An additional methodology paper has been publicated regarding the assessment of <a href="https://doi.org/10.3390/ijgi13030096">planimetric displacements between DEMs</a> (Riazanoff et al., 2024), which are a common source of biases in DEM comparisons.</p> <h3>The DEMIX grid</h3> <p>Studies performed within the DEMIX framework rely on the <a href="../records/7504791">DEMIX grid</a> (Guth et al., 2023), a geodetic grid (EPSG:4326) dividing the world in areas of approximately 10x10km. These standard areas are called DEMIX tiles, and can be precisely located thanks to their identifier.</p> <h3>Criteria and scores</h3> <p>Within DEMIX, several criteria have been defined to assess the quality of DEMs. These criteria take as input a DEM and a DEMIX tile, and provide as output the score of the DEM for this specific tile. Repeating this process over several DEMs and DEMIX tiles of interest allow for a comparison of scores, leading to a ranking of DEMs. <a href="https://doi.org/10.1109/TGRS.2024.3368015">DEMIX rankings are based on the Randomized Complete Block Design (RCBD)</a> (Bielski et al., 2024).</p> <h2>This dataset</h2> <p>This dataset is composed of global maps of one map per (DEM, criterion) tuple. Each GeoTIFF map can be superimposed with the <a href="../records/7504791">DEMIX grid</a> (Guth et al., 2023) in a GIS (tested in QGIS 3.16).</p> <h3>Completeness criteria</h3> <p>The completeness criteria have originally been defined by Peter Strobl. A brief description of each criterion is given in the next table. Please see the column "Original document" and files of this repository for the complete definitions.</p> <table> <tbody> <tr> <td><strong>Criterion</strong></td> <td><strong>Description</strong></td> <td><strong>Requirements</strong></td> <td><strong> Original document</strong></td> </tr> <tr> <td>A01 - Product fractional cover</td> <td>Fraction of a DEMIX tile <strong>covered</strong> by the DEM product</td> <td>None</td> <td>See document "DEMIX_CDD-A01_20211103.docx"</td> </tr> <tr> <td>A02 - Valid data fraction</td> <td>Fraction of a DEMIX tile <strong>covered</strong> by <strong>valid </strong>pixels of the DEM product</td> <td>"No data" or "void" value in metadata</td> <td>See document "DEMIX_CDD-A02_20211103.docx"</td> </tr> <tr> <td>A03 - Primary data fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels generated from the <strong>main source of data</strong> of the DEM product</td> <td>"No data" or "void" value in metadata + source data/editing mask</td> <td>See document "DEMIX_CDD-A03_20211103.docx"</td> </tr> <tr> <td>A04 - Valid land fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels of <strong>land </strong>of the DEM product</td> <td>"No data" or "void" value in metadata + water body mask</td> <td>See document "DEMIX_CDD-A04_20211103.docx"</td> </tr> <tr> <td>A05 - Primary land fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels of <strong>land </strong>generated from the <strong>main source of data </strong>of the DEM product</td> <td>"No data" or "void" value in metadata + water body mask + source data/editing mask</td> <td>See document "DEMIX_CDD-A05_20211103.docx"</td> </tr> </tbody> </table> <h3>DEMs and ancillary data</h3> <p>The following DEM products and ancillary layers have been used to generate the dataset.</p> <table> <tbody> <tr> <td><strong>Identifier</strong></td> <td><strong>Used layers</strong></td> <td><strong>Data access</strong></td> </tr> <tr> <td> <p>ASTGTM v003</p> </td> <td>ASTER GDEM elevations (dem.tif) + editing / source masks (num.tif)</td> <td><a href="https://lpdaac.usgs.gov/products/astgtmv003/">https://lpdaac.usgs.gov/products/astgtmv003/</a></td> </tr> <tr> <td> <p>ASTWBD v001</p> </td> <td>ASTER GDEM water body mask (att.tif)</td> <td><a href="https://lpdaac.usgs.gov/products/astwbdv001/">https://lpdaac.usgs.gov/products/astwbdv001/</a></td> </tr> <tr> <td> <p>AW3D30 v2003</p> </td> <td>ALOS World 3D elevations (DSM.tif) + editing / source / water body masks (MSK.tif)</td> <td><a href="https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm">https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm</a></td> </tr> <tr> <td>COP-DEM_GLO-30-DGED v2019_1</td> <td>Copernicus DEM GLO-30 elevations (DEM.tif) + editing (EDM.tif) + source (SRC.tif) + water body (WBM.tif) masks</td> <td><a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model</a></td> </tr> <tr> <td>COP-DEM_GLO-90-DGED v2019_1</td> <td>Copernicus DEM GLO-90 elevations (DEM.tif) + editing (EDM.tif) + source (SRC.tif) + water body (WBM.tif) masks</td> <td><a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model</a></td> </tr> <tr> <td> <p>NASADEM_HGT v001</p> </td> <td>NASADEM elevations (.hgt) + editing / source (.num) + water body (.swb) masks</td> <td><a href="https://lpdaac.usgs.gov/products/nasadem_hgtv001/">https://lpdaac.usgs.gov/products/nasadem_hgtv001/</a></td> </tr> <tr> <td> <p>SRTMGL1 v003</p> </td> <td>SRTMGL1 elevations (.hgt)</td> <td><a href="https://lpdaac.usgs.gov/products/srtmgl1v003/">https://lpdaac.usgs.gov/products/srtmgl1v003/</a></td> </tr> <tr> <td> <p>SRTMGL1N v003</p> </td> <td>SRTMGL1 editing / source / water body masks (.num)</td> <td><a href="https://lpdaac.usgs.gov/products/srtmgl1nv003/">https://lpdaac.usgs.gov/products/srtmgl1nv003/</a></td> </tr> </tbody> </table> <h3>Computation of scores</h3> <p>For each DEMIX tile and DEM, each "fractional cover" has been computed using the following procedure:</p> <ol> <li><strong>Crop DEMIX tile layers</strong> - The tiles of each DEM layer (elevations, editing, sources and water bodies) are cropped to the extent of the DEMIX tile.</li> <li><strong>Compute standardized layers</strong><strong> </strong>- Given the cropped DEM layers, four standardized layers are produced, which are: <ul> <li>Heights layer - Containing the heights of the DEM</li> <li>Land/water mask layer - Indicating whether DEM pixels are land or water: <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = WATER</li> <li>4 = LAND</li> </ul> </li> <li>Source mask layer - Indicating the source data of DEM heights (or "edited" value): <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = PRIMARY_DATA</li> <li>4 = EXTERNAL_DATA</li> <li>5 = EDITED</li> </ul> </li> <li>Valid mask layer - Indicating if the DEM pixels are valid or not: <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = VALID</li> </ul> </li> </ul> </li> <li><strong>Retrieve pixel number N</strong><em><strong> </strong>-<strong> </strong></em>The total pixel number N is computed for one of the layers (all layers have the same number of pixels).</li> <li><strong>Retrieve criterion pixel number C </strong>-<strong> </strong>The criterion pixel number C is computed based on the standard layers, more precisely: <ul> <li>A01 - Product fractional cover - Number of pixels of <strong>valid mask layer equal to 1, 2 or 3</strong></li> <li>A02 - Valid data fraction - Number of pixels of <strong>valid mask layer equal to 3</strong></li> <li>A03 - Primary data fraction - Number of pixels of <strong>source mask layer equal to 3</strong></li> <li>A04 - Valid land fraction - Number of pixels of <strong>land/water mask layer equal to 4</strong></li> <li>A05 - Primary land fraction - Number of pixels of <strong>source mask layer equal to 3</strong> and<strong> land/water mask layer equal to 4</strong></li> </ul> </li> <li><strong>Compute the final score S</strong><strong> </strong>- The final score S is expressed as the following percentage: <strong>S = ceil(C/N*100)</strong></li> </ol> <h2>Known issues</h2> <p>The "SRTMGL1N v003" is known to have "tile repeating issues", where part of the data is wrongly flagged as water. This issue has been reported with no particular response from the providers of the DEM (see <a href="https://forum.earthdata.nasa.gov/viewtopic.php?t=2752">https://forum.earthdata.nasa.gov/viewtopic.php?t=2752</a>).</p> <p><strong>References:</strong></p> <ul> <li>Guth, P.L.; Strobl, P.; Gross, K.; Riazanoff, S. <em>DEMIX 10k Tile Data Set (1.0)</em> [Data set]. Zenodo 2023. <a href="https://doi.org/10.5281/zenodo.7504791" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7504791</a></li> <li>Guth, P.L.; Van Niekerk, A.; Grohmann, C.H.; Muller, J.-P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; López-Vázquez, C.; Carabajal, C.C.; Albinet, C.; Strobl, P. <em>Digital Elevation Models: Terminology and Definitions</em>. Remote Sens. 2021, 13, 3581. <a href="https://doi.org/10.3390/rs13183581">https://doi.org/10.3390/rs13183581</a></li> <li>Riazanoff, S.; Corseaux, A.; Albinet, C.; Strobl, P.A.; López-Vázquez, C.; Guth, P.L.; Tadono, T. <em>Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models</em>. <em>ISPRS Int. J. Geo-Inf.</em> 2024, <em>13</em>, 96. <a href="https://doi.org/10.3390/ijgi13030096">https://doi.org/10.3390/ijgi13030096</a></li> <li>Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth, P.L.; Hawker, L.; Gesch, D.; Trevisani, S.; Herrera-Cruz, V.; Riazanoff, S.; Corseaux, A.; Reuter, H.I.; Strobl, P.A.; <em>Novel Approach for Ranking DEMs: Copernicus DEM Improves One Arc Second Open Global Topography</em> in <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 62, pp. 1-22, 2024, Art no. 4503922. <a href="https://doi.org/10.1109/TGRS.2024.3368015">https://doi.org/10.1109/TGRS.2024.3368015</a></li> <li>Strobl, P.A.; Bielski, C.; Guth, P.L.; Grohmann, C.H.; Muller, J.P.; López-Vázquez, C.; Gesch, D.B.; Amatulli, G.; Riazanoff, S.; Carabajal, C. The Digital Elevation Model Intercomparison eXperiment DEMIX, a community based approach at global DEM benchmarking. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, XLIII-B4-2021, 395–400. <a href="https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021">https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021</a></li> </ul>
Supplementary data - Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry – a perspective
<p>Analysis files and scripts for <a href="https://doi.org/10.1101/2024.06.21.600107" target="_blank" rel="noopener">associated manuscript</a>. </p> <ul> <li>CR3022.zip: script (in Rust) and necessary data to run said script for CR3022 analysis with the results from running the script.</li> <li>MA-analysis-script.zip: script (in Rust) and necessary data to run said script for automated analysis of MA benchmark results.</li> <li>MA-analysis-data.zip: data from running the MA-analysis-script, containing all MA and Stitch output files.</li> <li>MA-analysis-data-EMPEM.zip: data from running MA and Stitch on the EMPEM benchmark.</li> </ul>
Dautan et al 2024 " Gut-Initiated Alpha Synuclein Fibrils Drive Parkinson's Disease Phenotypes: Temporal Mapping of non-Motor Symptoms and REM Sleep Behavior Disorder"
<p><span>Parkinson’s disease (PD) is characterized by progressive motor as well as less recognized non-motor symptoms that arise often years before motor manifestation, including sleep and gastrointestinal disturbances. Despite the heavy burden on the patient’s quality of life, these non-motor manifestations are poorly understood. To elucidate the temporal dynamics of the disease, we employed a mice model involving injection of alpha-synuclein (αSyn) pre-formed fibrils (PFF) in the duodenum and antrum as a gut-brain model of Parkinsonism. Using anatomical mapping of αSyn PFF propagation and behavioral and physiological characterizations, we unveil a correlation between post-injection time the temporal dynamics of αSyn propagation and non-motor/motor manifestations of the disease. We highlight the concurrent presence of aggregates in key brain regions, expressing acetylcholine or dopamine and their functions in sleep duration, wakefulness, and particularly REM-associated atonia corresponging to REM behavioral disorder-like symptoms. This study presents a novel and in-depth exploration into the multifaceted nature of PD, unraveling the complex connections between α-synucleinopathies, gut-brain connectivity, and the emergence of non-motor phenotypes.</span></p>
Mapping between zbMATH Open identifiers, DOIs, ORCIDs and arXiv identifiers
<p>The second version of the mapping between zbMATH Open identifiers for <a href="https://www.wikidata.org/w/index.php?title=Property:P1556&oldid=1755821772">authors</a> and <a href="https://www.wikidata.org/w/index.php?title=Property:P894&oldid=1766254659">documents</a> and <a href="https://www.wikidata.org/wiki/Property:P356">DOIs</a> and <a href="https://www.wikidata.org/wiki/Property:P496">ORCIDs</a> in CSV format.</p> <ul> <li>The file authors.csv contains the mapping between zbMATH Open author id and ORCIDs for 38 159 authors.</li> <li>The file documents.csv contains the mapping between zbMATH Open document id and DOI for 2 813 563 documents.</li> </ul> <p>Beginning from this version, we also provide the mapping between <a href="https://www.wikidata.org/w/index.php?title=Property:P894&oldid=1766254659">documents</a> and <a href="https://www.wikidata.org/w/index.php?title=Property:P818&oldid=2151126552">arXiv</a> in CSV format</p> <ul> <li>The file arxiv.csv contains the mapping between zbMATH Open document id and arXiv identifiers for 528 640 documents.</li> </ul> <p>See https://zbmath.org/about/ (section Full Text Links) for a live version of this dataset. That version is more current but less reproducible. Moreover, the dataset here is restricted to documents with a permanent zbMATH Open identifier in the form <code>Zbl d+.d+</code>.</p> <p> </p> <p> </p>
Mapping practices of online community management
<p>Results of an online survey conducted during the period 13-31st March 2018. Responses were collected through a Google form; instructions and context were made available on a <a href="http://www.cottica.net/2018/03/13/mapping-online-community-management-practices-can-i-have-a-little-help-with-my-thesis/">web page</a>, the link of which was disseminated through Facebook, Twitter and on the e-mint mailing list (dedicated to professional online community managers on Yahoo. </p> <p>Each row of the file represents one questionnaire; each column represents one question.</p> <ul> <li>The first 9 questions are all of the format "To manage your online community, which of these courses of actions do you take, and how often?". The answers were given on a Likert-4 scale.</li> <li>The 10th question was "Do you want to add any other activity that uses up significant chunks of your community management time?". The answers were given in free form text.</li> <li>The 11th question was "How old is the community you manage? If you manage more than one, refer to the oldest." The answers were given as multiple choice, with three possible choices.</li> <li>The 12th question was "How large is the community you manage? If you manage more than one, refer to the largest.". The answers were given as multiple choice, with five possible choices.</li> </ul> <p>This work is part of my PhD Thesis.</p>
Map of Co-Seismic Landslides for the M 7.8 Kaikoura, New Zealand Earthquake
<p>Prepared by the Research Group on Earthquake Geology in Greece (http://eqgeogr.weebly.com/)</p> <p>Version 2 (updated)</p> <p>With the release of new Sentinel-2 images, and other available resources for the M7.8 Kaikoura earthquake, we present an update of the Map of Co-Seismic Landslides and Surfaces Ruptures (As of 27/11/2016). Landslides were mapped using Sentinel-2 satellite images from Copernicus, European Space Agency, dated November and December 2016. Images were visually compared with previous last available S2A images without cloud cover (13 September and 26 October) and landslides and large slope failures were manually mapped. Areas covered by cloud are omitted and shown on map. 5875 landslide sites are shown in the map. A small number of landslides could have been mis-identified due to insufficient resolution of the images, small gaps of cloud cover or for other reasons. Also, re-activated landslides on the central mountainous area were unabled to identify due to imagery restrictions (medium resolution, relief shadows etc). Some local gaps in Sentinel imagery still exist due to cloud cover, but we believe the current map is very close to the major distribution of mass movement effects. Surface ruptures were mapped using Sentinel-2 imagery and approximate position from photos of the post-earthquake aerial surveys of Environment Canterbury Regional Council (http://ecan.govt.nz)</p> <p>KML file contains7355 landslide spots.</p>
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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.
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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.