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13,116 results for “map”
Indicative distribution map for Ecosystem Functional Group MT2.2 Large seabird and pinniped colonies
<p>This archive contains indicative distribution maps and profiles for <strong>MT2.2 Large seabird and pinniped colonies</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.1). Please refer to Keith <em>et al.</em> (2020) and Keith <em>et al.</em> (2022) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group F2.10 Subglacial lakes
<p>This archive contains indicative distribution maps and profiles for <strong>F2.10 Subglacial lakes</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.1). Please refer to Keith <em>et al.</em> (2020) and Keith <em>et al.</em> (2022) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Thickness map of the Patagonian Icefields
<p>Ice thickness field for the Patagonian icefields relying on mass-conservation approach, which assimilates both glacier retreat data as well as an abundant record of direct thickness measurements. The thickness map has a time stamp of 2000. This map is provided together with error estimates and the basal topography beneath the icefields based on c-SRTM (v2.1) (Farr, T. et al. The Shuttle Radar Topography Mission. Reviews of Geophysics 45 (2007), http://dx.doi.org/10.1029/2005RG000183.)</p>
Map of the temples of Allat in the Near East
<p>This map presents the localisation spots of the temples of Allat, located only in the Near East. The map is a result of data collection and building the database in the NodeGoat software. This map is based also on the information from the epigraphical and archaeological sources.</p>
Raw data for the submitted manuscript entitled "Mapping and Disposal of Irrigation Pipes for a Sustainable Management of Agricultural Plastic Waste", authors Ileana Blanco, Giuliano Vox, Fabiana Convertino, and Evelia Schettini
<p><span>The file regards the evaluation of plastic indexes and agricultural plastic waste quantities in Apulia region due to the use of irrigation pipes. The data is used to identify the critical areas for plastic waste production due to irrigation pipes.</span></p>
GIXD data of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3), processed q-space maps
<p>This dataset contains grazing incidence x-ray diffraction (GIXD) maps projected in q-space and polar projection. The underlying raw data is published in <a href="https://doi.org/10.5281/zenodo.6683616">10.5281/zenodo.6683616</a> and processed with <a href="https://doi.org/10.5281/zenodo.6683658">10.5281/zenodo.6683658</a>. This data describes a time series of diffraction images acquired with 10 Hz.</p> <p> </p> <p>Parameters of the provided data:</p> <ul> <li> <p>Q-space-maps</p> </li> </ul> <p> </p> <ul> <li> <ul> <li> <p>Horizontal axis (Q<sub>xy</sub>) range: (0, 3.2) Å<sup>-1</sup></p> </li> <li> <p>Vertical axis (Q<sub>z</sub>) range: (0, 3.2) Å<sup>-1</sup></p> </li> <li> <p>Resolution: 1350x1350 pixels</p> </li> <li> <p>Origin (lower left coordinate in q): (0, 0)</p> </li> </ul> </li> <li> <p>Polar data</p> <ul> <li> <p>Horizontal axis (||<strong>q</strong>||) range: (0, 4.53) Å<sup>-1</sup></p> </li> <li> <p>Vertical axis (ф) range: (0, 90) deg</p> </li> <li> <p>Resolution: 512x1024 pixels</p> </li> <li> <p>Origin (lower left coordinate in q): (0, 0)</p> </li> </ul> </li> </ul>
Mapping of a Mid-depth Salinity Maximum Intrusion south of New England in June 2021
<div> <div> <p>This dataset contains data from a process-oriented research cruise aboard the R/V Neil Armstrong from June 18th to July 2nd. The goal of this cruise was to map the three-dimensional structure of a mid-depth salinity maximum intrusion of warm salinity slope water extending onto the continental shelf south of New England. This was done through the use of Autonomous Underwater Vehicles (two REMUS 100 vehicles and one Tethys class AUV (Long Range AUV or LRAUV)), a towed Rockland Scientific Vertical Microstructure Profiler (VMP 250), and ship-board CTD and ADCP measurements. More details about the processing, data coverage, and usage can be found in the accompanying manuscript. This cruise took place on the shelf waters south of Cape Cod, MA, extending to the shelf break, with all of the data collected between 40°N to 41°N and 71.5°W to 70°W. Attached is a data map showing the location of all data included within this dataset. </p> </div> <div> <p> </p> </div> <div> <p>File Descriptions: </p> </div> <div> <p><strong>Datamap.jpg </strong></p> </div> <div> <p>A map of the locations of all data included within this dataset. </p> </div> <div> <p> </p> </div> <div> <p><strong>CTD_summer2021.mat </strong></p> </div> <div> <p>This file contains profiles from the ship-board CTD (SeaBird 911+). Raw data was processed and gridded into 1 decibar bins using standard procedures in Seasave V 7.26.7.121 (Look at cnv file header for details about processing). This file is organized as a structure, with each variable in the data being a different field called by dot notation and each row with the structure being a different CTD profile. Biooptical variables are not quality-controlled. </p> </div> <div> <ul> <li> <p>CTD.time: the time of each profile in the MATLAB datetime format (from the processed SeaBird header file) in GMT </p> </li> <li> <p>CTD.lon: degrees longitude of the profile (from the processed SeaBird header file) </p> </li> <li> <p>CTD.lat: degrees latitude of the profile (from the processed SeaBird header file) </p> </li> <li> <p>CTD.pres: the pressure in decibar at each location of the profile </p> </li> <li> <p>CTD.sal: the seawater practical salinity in psu </p> </li> <li> <p>CTD.temp: the seawater in-situ temperature in °C </p> </li> <li> <p>CTD.flor: seawater fluorescence in mg/ m3 </p> </li> <li> <p>CTD.depth: depth at each location within the profile in meters </p> </li> <li> <p>CTD.density: sigmatheta (the potential seawater density with respect to a reference pressure of 0 db) in kg.m3 minus 1,000kg/m3 </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p><strong>CTD_Darter_MMMdd.mat and CTD_Edgar_MMMdd.mat </strong></p> </div> </div> <div> <div> <p>These files contain the data from the REMUS 100 missions, with Darter and Edgar being the two different REMUS 100 vehicles. </p> </div> <div> <ul> <li> <p>Conductivity: conductivity in mS/cm </p> </li> <li> <p>Depth: depth in meters </p> </li> <li> <p>Latitude: degrees latitude </p> </li> <li> <p>Longitude: degrees longitude </p> </li> <li> <p>Mission_number: the number of the REMUS mission </p> </li> <li> <p>Mission_time: time during the mission in seconds since midnight in GMT </p> </li> <li> <p>Salinity: the seawater practical salinity in psu </p> </li> <li> <p>Sound_speed: the sound speed in m/s </p> </li> <li> <p>Temperature: the seawater temperature in °C </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p><strong>LRAUV_20210623T194917.mat and LRAUV_20210624T145829.mat </strong></p> </div> <div> <p>These files contain data from the Tethys Class LRAUV (Long Range AUV) missions. Each file contains 10 structure variables. </p> </div> </div> <div> <div> <ul> <li> <p>CTD_Seabird: structure containing the bin median temperature in °C and salinity in PSU. </p> </li> <li> <p>depth: the depth at each data point in meters. </p> </li> <li> <p>fix_residual_percent_distance_traveled: underwater dead-reckoned navigation error (based on GPS fix when on surface) as a percentage of distance traveled </p> </li> <li> <p>latitude: Latitude at each data point (not corrected for vehicle drift in underwater current) </p> </li> <li> <p>latitude_fix: latitude of GPS fix (vehicle surfaced) </p> </li> <li> <p>longitude: Longitude at each data point (not corrected for vehicle drift in underwater current) </p> </li> <li> <p>longitude_fix: longitude of GPS fix (vehicle surfaced) </p> </li> <li> <p>platform_battery_charge: The battery charge in ampere-hour </p> </li> <li> <p>time_fix: time in seconds since January 1, 1970 (epoch time) </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p><strong>VMPtransact_YYYYMMdd.mat</strong></p> <p>Vertical Microstructure Profiler (Rockland Scientific VMP 250) </p> </div> <div> <p>These files contain the processed data for each Vertical Microstructure Profiler (Rockland Scientific VMP 250) transect, consisting of multiple profiles. Data has been gridded on a 1 decibar equidistant grid using standard procedures in Rockland Scientific’s processing software. Note: Bio-optical variables and dissipation rates have not been quality-controlled. </p> </div> <div> <ul> <li> <p>Time: Time in MATLAB datenum format (days since 0000-00-00 00:00:00) in GMT </p> </li> <li> <p>z: Pressure in decibar </p> </li> <li> <p>T: in-situ temperature in degC </p> </li> <li> <p>cnd: conductivity in mS/cm </p> </li> <li> <p>Chl: Chlorophyll from fluorescence in mg/ m3 </p> </li> <li> <p>turb: Turbidity in NTU </p> </li> <li> <p>eps: dissipation rate inferred from microstructure shear in m^2/s^3. (Note: Dissipation estimates come from standard fitting of microstructure data within a 1 decibar bin to a turbulence spectrum within Rockland Scientific’s standard processing. The dissipation data in the provided files has not been quality-controlled. </p> </li> </ul> </div> <div> <p> VMP-data was georeferenced by comparing the time stamps of VMP and processed ADCP files. </p> </div> <div> <p> </p> </div> <div> <p><strong>ADCP_ar50_wh300.mat </strong></p> </div> <div> <p>This file contains the data from the shipboard ADCP (Teledyne WH300 kHz). ADCP data was processed aboard using standard procedures in UHDAS/CODAS (University of Hawaii Technical Services Program, servicing UNOLS vessels (<a href="https://currents.soest.hawaii.edu/docs/adcp_doc/index.html" target="_blank" rel="noreferrer noopener">https://currents.soest.hawaii.edu/docs/adcp_doc/index.html). Vertical bin size is 2 m. </a>u: zonal (positive towards east) velocity component in m/s </p> <ul> <li> <p>v: meridional (positive towards north) component in m/s </p> </li> </ul> </div> </div> <div> <div> <ul> <li> <p>txy: time, longitude, and latitude of the velocity profiles. Time is in decimal days, with noon of Jan 1 being 0.5 decimal days and noon of January 20th being 19.5 decimal days of the reference year. For another example, 6am on June 18, 2021, is decimal day 168.25. All times are in GMT. </p> </li> <li> <p>refyear: The reference year from which the decimal days are calculated. </p> </li> <li> <p>depth: vertical coordinate of the velocity bin center </p> </li> <li> <p>pgood: percent good, a quality parameter showing the fraction of good pings within an ensemble average. </p> </li> <li> <p>spd_u: zonal ship speed in m/s </p> </li> <li> <p>spd_v: meridional ship speed in m/s </p> </li> <li> <p>tr_temp: ADCP transducer temperature in deg C </p> </li> <li> <p>amp: backscatter amplitude in relative units </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> </div>
Supplementary dataset to publication: Complete Genome Sequence of Ovine Mycobacterium avium subsp. paratuberculosis Strain JIII-386 (MAP-S/type III) and Its Comparison to MAP-S/type I, MAP-C, and M. avium Complex Genomes.
<p>This is the modified supplemented material to the publication “Complete genome sequence of ovine Mycobacterium avium subsp. paratuberculosis strain JIII-386 (MAP-S/type III) and its comparison to MAP-S/type I, MAP-C, and M. avium complex genomes”.</p> <p>The complete circular genome of Mycobacterium avium subsp. paratuberculosis (MAP) strain JIII-386 from Germany, closed by Nanopore technology in this study, was presented and compared with the draft genome of JIII-386, previously published in [doi:10.1093/gbe/ew154], the closed genome of the MAP-S/type I strain Telford, the MAP-S/type III draft genome of strain S397, twelve closed MAP-C (type II) strains and eight closed Mycobacterium avium (M. a.) strains of subsp. hominissuis (MAH) and subsp. avium (MAA). Structural comparisons clearly revealed the mosaic nature of MAP genomes, the differences between MAP subtypes I, II and III, and the higher diversity of MAP-S compared to MAP-C genomes. </p> <p>The material provides a wealth of detailed results from these analyses and comparisons. These include a list of identified ncRNA and Riboswitches, as well as additional genes in finished JIII-386, the gene content of identified prophage regions, copy number of identified transposable elements and a list of selected virulence-associated genes in the different MAP-type (I - III) strains. The genomic islands identified and included genes along with their predicted functions were presented for six MAP genomes (belonging to MAP-S/type I and III, and MAP-C), one MAH genome and one MAA genome. One table shows the corresponding genomic islands in the genomes of JIII-386, Telford and three MAP-C genomes. Furthermore, homologous genes of known MAP-S specific Large Sequence Polymorphisms regions (LSP<sup>S</sup> = LSP-S) were recorded in different MAP-S type strains, one MAH and one MAA strain, as well as genes of deletions #1 (LSP<sup>A</sup>-20), #2, and s-delta-1, previously described as MAP-S-specific deletions, their presence or absence in 3 MAP-S, 12 MAP-C, 4 MAH, and 4 MAA strains were listed. Different presence or absence of genes, but also identified frameshifts or disruptions of various virulence-associated genes could lead to the different MAP-type specific phenotypic characteristics. Comprehensive core and pan genome analyses (results listed in six tables) revealed unique genes and genes likely to have been acquired by horizontal gene transfer in different MAP types and subtypes, but also emphasized the highly conserved and close relationship, and the complex evolution of M. a. strains.</p> <p> </p>
Forest Map of New Caledonia
<h1>Description</h1> <p>This dataset contains the shapefile of the Caledonian forest produced by digitization at a scale of 1:3,000 from a mosaic of satellite images (Sentinel 2, Quickbird, Pléiades) and aerial photographs provided by the department of infrastructure, topography and land transport (DITTT) of the government of New Caledonia and available on the georep map server (<a href="https://georep.nc/" target="_blank" rel="noopener">georep.nc</a>). Satellite images and aerial photographs were taken between 2009 and 2021 with a maximum spatial resolution of 0.5 m. We classified the vegetation as a forest when the plants (which must be over 5 meters tall) formed a continuous canopy, obscuring the ground surface over an area of at least 0.5 hectares (FAO 2020). Finally, we consider a polygon as an isolated forest fragment if it is more than 10 meters away from another polygon. When the distance is less than 10 meters, the polygons are merged.</p> <p>Each of the 12,058 digitized polygons was extensively cross-checked with the Tropical Rainforest Cover Change (TMF) dataset using 41 years of Landsat time series (<a href="https://www.biorxiv.org/content/10.1101/2020.09.17.295774v1" target="_blank" rel="noopener">Vancutsem et al., 2021</a>) and the ETH model providing 10 m resolution of global canopy height (<a href="https://www.nature.com/articles/s41559-023-02206-6" target="_blank" rel="noopener">Lang et al., 2023</a>). This consistency analysis between photo-interpretation and radiometric detection was conducted on a 2 km² grid, with verification of each polygon when the divergence exceeded 15%. In total, 1564 polygons, either barely discernible in the images or exhibiting a homogeneous canopy texture, were visually verified by helicopter overflight. At least, forest polygons were verified through databases of plant occurrences collected in the forest, in particular the <a title="NOU Herbarium" href="http://publish.plantnet-project.org/project/nou" target="_blank" rel="noopener">Herbarium of New Caledonia database</a> (NOU) and the <a title="NC-PIPPN" href="https://amap.cirad.fr/reseauxparcelles/npippn.php" target="_blank" rel="noopener">New Caledonian Plant Inventories and Permanent Plots Network </a>(NC-PIPPN).</p> <h1>Content</h1> <p>This dataset was generated, analyzed, and validated using a suite of open-source software tools, including QGIS, PostgreSQL, PostGIS, Python, and the GDAL library, operating on a Linux platform. The compressed file includes six essential files formatted for an ESRI GIS system, utilizing the WGS84 international coordinate system. It is compatible for upload into spatial databases such as PostgreSQL/PostGIS..</p> <p>Each entry in the attribute table represents a forest fragment (a polygon) with associated fields (restricted to 10 characters) :</p> <table> <tbody> <tr> <td><strong>Field</strong></td> <td><strong>Type</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>id</strong></td> <td>INTEGER</td> <td>Unique identifier</td> </tr> <tr> <td><strong>pn</strong></td> <td>BOOLEAN</td> <td>True or False, indicating if the polygon overlaps with the Northern province</td> </tr> <tr> <td><strong>ps</strong></td> <td>BOOLEAN</td> <td>True or False, indicating if the polygon overlaps with the Southern province</td> </tr> <tr> <td><strong>pil</strong></td> <td>BOOLEAN</td> <td>True or False, indicating if the polygon overlaps with the Islands province (Loyalty Islands)</td> </tr> <tr> <td><strong>area_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares</td> </tr> <tr> <td><strong>dry_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares within the dry life zone according to Holdridge</td> </tr> <tr> <td><strong>moist_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares within the moist life zone according to Holdridge</td> </tr> <tr> <td><strong>rain_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares within the rain life zone according to Holdridge</td> </tr> <tr> <td><strong>created_by</strong></td> <td>TEXT</td> <td>Creator of the polygon</td> </tr> <tr> <td><strong>date_creat</strong></td> <td>DATE</td> <td>Date when the polygon was first created</td> </tr> <tr> <td><strong>date_updat</strong></td> <td>DATE</td> <td>Date when the polygon was updated</td> </tr> </tbody> </table> <h1>Limitations</h1> <p>Currently, only the North and South provinces are available, an area of 16,395 km² out of a total of 18,345 km². Digitization of the Loyaulties Islands will be available soon. This dataset is periodically updated with corrections made by field observations and the addition of new expert interpretations, especially on smaller islands that have not been digitized before. We plan to produce the whole map step by step.</p>
Indicative distribution map for Ecosystem Functional Group M1.10 Rhodolith/Maërl beds
<p>This archive contains indicative distribution maps and profiles for <strong>M1.10 Rhodolith/Maërl beds</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.1). Please refer to Keith <em>et al.</em> (2020) and Keith <em>et al.</em> (2022) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
spermatogenesis across mammals - Ensembl Release 112 mapping
<p>This repo contains remapped single-cell Anndata (h5ad) objects from the original publication "The molecular evolution of spermatogenesis across mammals (Florent Murat, Noe Mbengue et al 2023; https://doi.org/10.1038/s41586-022-05547-7)". Data was (pseudo-)remapped against Ensembl Release 112 genomes and annotations with kb-python (v0.28.2). For Macaca mulatta the NCBI GCF_003339765.1_Mmul_10_genomic.fna and GCF_003339765.1_Mmul_10_genomic.gtf was used due to low protein number in Ensembl Release 112 file Macaca_mulatta.Mmul_10.pep.all.fa.gz. Only filtered counts are provided. </p> <p>If you use this data, please cite Murat and Mbengue et al. 2023.</p>
CryoEM Maps and Associated Data Submitted to the 2015/2016 EMDataBank Map Challenge
<p>Files and metadata associated with the EMDataBank/Unified Data Resource for 3DEM 2015/2016 Map Challenge hosted at challenges.emdatabank.org are deposited.</p> <p>All members of the Scientific Community--at all levels of experience--were invited to participate as Challengers, and/or as Assessors.</p> <p>Seven benchmark raw image datasets were selected for the challenge. Six are selected from recently described single particle structure determinations with image data collected as multi-frame movies; one is based on simulated (in silico) images. All of the raw image datasets are archived at pdbe.org/empiar.</p> <p>27 Challengers created 66 single particle reconstructions from the targets, and then uploaded their results with associated details. 15 of the reconstructions were calculated using the SDSC Gordon supercomputer.</p> <p>This map challenge was one of two community-wide challenges sponsored by EMDataBank in 2015/2016 to critically evaluate 3DEM methods that are coming into use, with the ultimate goal of developing validation criteria associated with every 3DEM map and map-derived model.</p> <p> </p>
Mappings for "Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach"
<p>Studies identified false and non-actionnable alarms as a factor for alarm fatigue in intensive care units.</p> <p>To annotate patient alarms, and analyse the alarm situation in intensive care units, we conceptualized and performed data mappings related to airway management and medication interventions. The mappings were based on information retrieved from the patient data management system (PDMS) and clinical expertise. For the airway management mappings, we used additional resources such as ISO 19223:2019 or ventilator instruction manuals. The mappings do not include patient data.</p> <p>As the mappings are generic, they could be used in other contexts than alarm annotation and research.</p> <p><strong>1. Respiratory Management Mappings:</strong></p> <ul> <li>General tables summarizing the 1) categories based on ISO 19223:2019 to describe respiratory support therapies (RSTs), 2) defining the invasiveness level of a RST and 3) listing the abbreviations used in the mappings</li> <li> <p>Tables including PDMS entries for airway devices (ADs), ventilation devices (VDs), and ventilation modes (VMs)</p> </li> <li> <p>Mapping of AD entries (from the PDMS) to defined categories</p> </li> <li> <p>Mapping of VDs, VMs, and ADs to defined RSTs, including information on invasiveness</p> </li> <li> <p>Table specifying suitable ventilation parameters in the context of each RST</p> </li> </ul> <p><strong>2. Medication Mappings:</strong></p> <ul> <li> <p>General tables providing information on physiological alarm conditions (PACs), interventions, routes, and techniques of administration of interest</p> </li> <li> <p>Mapping of routes of administration to techniques of administration including PDMS entries</p> </li> <li> <p>Mapping of active ingredients (including SNOMED CT Fully Specified Names and Identifiers), related PDMS information, and routes and techniques of administration to defined PAC and interventions</p> </li> </ul>
Four lipidomics datasets (mouse liver, mouse pancreatic islets, mouse soleus muscle and mouse visceral adipose tissue), generated for the publication Mehl et al., "A multiorgan map of metabolic, signalling, and inflammatory pathways that coordinately control fasting glycemia in mice"
<p>Mehl, Thorens et al present a multiomics study aimiing to<span> identify the pathways that are coordinately regulated in pancreatic </span><span>b</span><span>-cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2 and Balb/c mice a regular chow or a high fat diet for 3, 10 and 30 days. We measured fasted glycemia, insulinemia and whole-body insulin resistance. Transcriptomic and lipidomic analysis were used in a data fusion approach to identify organ-specific pathways related to the glycemic levels across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels were associated with reduced expression of mRNAs encoding hormone and neurotransmitter receptors as well as OXPHOS, cadherins, integrins and gap junction proteins. Higher glycemia and whole-body insulin resistance were associated, in muscle, with reduced expression of mRNAs encoding insulin signaling proteins and enzymes of the glycolysis, Krebs’ cycle and OXPHOS pathways, as well as endocytosis and exocytosis proteins; in hepatocytes, with lower expression of mRNAs of the insulin signaling pathway, of branched chain amino acid catabolism and of OXPHOS; in adipose tissue, with increased expression of mRNAs of innate immunity and lipid catabolism. These data provide a map of the pathways that are coordinately recruited in the investigated tissues to control fasting glycemia and a resource for further studies of interorgan communication in glucose homeostasis. </span></p>
Reference data and documentation for Skills4EOSC Deliverable D6.1 Mapping of existing professional networks
<p>This record presents the data underlying <strong>Skills4EOSC Deliverable D6.1 Mapping of existing professional networks</strong> and relevant documentation of the search string.</p>
Detecting cosmic voids via maps of geometric optics parameters
<ul> <li>lensing-ddbb4ac.pdf - research data in pdf format</li> <li>void_matches*.dat - plain text results files corresponding to Table 3 and Figures 2, 4, 6, 8.</li> <li>lensing-ddbb4ac-journal.tar.gz - source package for producing the article pdf, together with the reproducibility package, but without the git history; appropriate for ArXiv</li> <li>lensing-ddbb4ac-git.bundle - git source package that can be unbundled with 'git clone lensing-e4f7af0-git.bundle' and used for reproducibility: to download data, do calculations, analyse them, plot them and produce the research data pdf</li> <li>software-ddbb4ac.tar.gz - this should contain all the software, apart from a minimal POSIX-compatible system and LaTeX packages, needed for compiling and installing the software used in producing this work</li> <li>lensing-ddbb4ac-snapshot.tar.gz - source files of the project; these should be enough, provided that external software packages can be downloaded, to reproduce the full project</li> </ul> <p>The authors grant a perpetual, non-exclusive licence to distribute this pdf preprint.</p> <p>All the other materials here are free-licensed, as stated in the individual files and packages.</p>
X-PSI Parameter Recovery for Temperature Map Configurations Inspired by PSR J0030+0451
<p>Posterior sample files associated with the preprint "X-PSI Parameter Recovery for Temperature Map Configurations Inspired by PSR J0030+0451 " by Vinciguerra et al. (2023; <a href="https://doi.org/10.48550/arXiv.2209.12840">arXiv</a>; almost submitted to for publication in ApJ) and Jupyter notebook scripts to reproduce the corresponding figures.</p> <p>Also included are examples of model modules in the Python language using the X-PSI framework.</p> <p>Please refer to the READme for detailed information.</p>
30 m Normalized Difference Vegetation Index Maps of Pure Pixels over China for Estimation of Fractional Vegetation Cover (2014, 2018, 2022)
<p>Using multi-angle remote sensing data, we generated 30-m maps for the normalized difference vegetation index (NDVI) of fully-covered vegetation (<em>Vv</em>) and bare soils (<em>Vs</em>) across China in 2014, 2018 and 2022. These pixel-wise <em>Vv</em> and <em>Vs</em> maps can be integrated with the vegetation index (VI)-based model to facilitate the accurate and rapid estimation of fractional vegetation cover (FVC) across various spatial resolutions and large scales. The products were produced using a multi-angle algorithm (MultiVI), which effectively addressed the spatial variability inherent in <em>Vv</em> and <em>Vs</em> and enhanced the accuracy of FVC estimations in comparison to traditional statistical methods. The estimated FVC demonstrated a root mean square deviation (RMSD) of approximately 0.1 when evaluated against field-measured FVC across different experimental sites.</p>
Map of Soil Organic Carbon: Region of Murcia (Spain)
This data package contain four soil organic carbon (SOC) maps resulted from the best data-model agreement of the analysis carried out in the frame of the Ph.D. Thesis ‘MODELING ORGANIC CARBON FOR QUANTIFICATION OF RESERVOIRS IN TERRESTRIAL ECOSYSTEMS AT THE NATIONAL LEVEL’ (Pilar Durante). Theses maps correspond to the estimates of SOC concentration (SOCc, g/kg) and SOC stocks (SOCs, tC/ha), and their associated spatially explicit uncertainties maps, for the Region of Murcia at 0-30 cm and 100 m spatial resolution. To achieve this, we evaluated four different digital soil mapping (DSM) approaches to estimate SOCc and SOCs for the Region of Murcia (11,313 km2), a topographic and climatic complex area in southern Iberian Peninsula, at three spatial resolutions (100m, 250m, 1000m). Using a local SOC database (255 soil profiles), we founded that a Quantile Regression Forest (QRF) approach had the best data-model agreement at 100 m spatial resolution, with the best balance of accuracy, external validation, and interpretability. The QRF model showed a mean SOCc of 12.18 g/kg with an overall uncertainty of 10.54 g/kg and an accuracy percentage of 79%; meanwhile the mean SOCs was 27,572 GgC with an uncertainty of 0.016 GgC. The analysis showed that using local environmental covariates and local soil information to predict SOC within this region resulted in a relative improvement between ~40% (for SOCc) and ~65% (for SOCs) when compared with SOC products derived from national and global databases. Our results provided evidence that large discrepancy exists between national and global estimates for reporting SOC at a local scale. Consequently, local-to-regional efforts are needed to better describe SOC spatial variability to reduce uncertainty and improve the assessment of soil resources.
Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City (2016-2019)
This dataset incorporates Mexico City related essential data files associated with Beth Tellman's dissertation: Mapping and Modeling Illicit and Clandestine Drivers of Land Use Change: Urban Expansion in Mexico City and Deforestation in Central America. It contains spatio-temporal datasets covering three domains; i) urban expansion from 1992-2015, ii) district and section electoral records for 6 elections from 2000-2015, iii) land titling (regularization) data for informal settlements from 1997-2012 on private and ejido land. The urban expansion data includes 30m resolution urban land cover for 1992 and 2013 (methods published in Goldblatt et al 2018), and a shapefile of digitized urban informal expansion in conservation land from 2000-2015 using the Worldview-2 satellite. The electoral records include shapefiles with the geospatial boundaries of electoral districts and sections for each election, and .csv files of the number of votes per party for mayoral, delegate, and legislature candidates. The private land titling data includes the approximate (in coordinates) location and date of titles given by the city government (DGRT) extracted from public records (Diario Oficial) from 1997-2012. The titling data on ejido land includes a shapefile of georeferenced polygons taken from photos in the CORETT office or ejido land that has been expropriated by the government, and including an accompany .csv from the National Agrarian Registry detailing the date and reason for expropriation from 1987-2007. Further details are provided in the dissertation and subsequent article publication (Tellman et al 2021). The Mexico City portion of these data were generated via a National Science Foundation sponsored project (No. 1657773, DDRI: Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City). The project P.I. is Beth Tellman with collaborators at ASU (B.L Turner II and Hallie Eakin). Other collaborators include the National Autonomous University of Mexico (UNAM),
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