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62 results for “Digital Mapping”
Historical Plat Maps of Dane County Digitized and Converted to GIS (1962-2005)
We constructed a time-series spatial dataset of parcel boundaries for the period 1962-2005, in roughly 4-year intervals, by digitizing historical plat maps for Dane County and combining them with the 2005 GIS digital parcel dataset. The resulting datasets enable the consistent tracking of subdivision and development for all parcels over a given time frame. The process involved 1) dissolving and merging the 2005 digital Dane County parcel dataset based on contiguity and name, 2) further merging 2005 parcels based on the hard copy 2005 Plat book, and then 3) the reverse chronological merging of parcels to reconstruct previous years, at 4-year intervals, based on historical plat books. Additional land use information such as 1) whether a structure was actually constructed (using the companion digitized aerial photo dataset), 2) cover crop, and 3) permeable surface area, can be added to these datasets at a later date.
Regional landform and landscape digital maps for the Eastern Guiana Shield
<p>Archive containing digital <strong>maps of 'landform types' and 'landscape units' for French Guiana and the State of Amapa (Brazil).</strong> These maps accompany the paper 'Using textural analysis for regional landform and landscape mapping, Eastern Guiana Shield', <em>Geomorphology</em> (doi:10.1016/j.geomorph.2 018.03.017) and have been produced according to the methods presented therein.</p> <p><br> </p>
Supplemental catalogs for "The Sloan Digital Sky Survey Reverberation Mapping Project: Sample Characterization"
<p>We have compiled additional properties for the SDSS-RM sample in several ancillary catalogs. Below are the notes on these supplemental catalogs. There are .readme files for each additional catalog. We also include the quality assurance plots for the global spectral fits.</p> <p><strong>QA-0000-56837.ps.gz </strong>The full set of 849 quality assessment plots for the global spectral fitting. Each plot includes a top panel showing the continuum (brown) and Fe II (blue) model components; the red line is the sum of the two. The cyan diamonds are pixels masked as absorption or bad pixels. The gray brackets near the top of the panel indicate the windows used for the continuum+Fe II fit. The bottom panels present the emission line fits for five line complexes.</p> <p><strong>allqso_sdssrm.fits</strong> A FITS table of all 1214 known quasars in the 7 square degree SDSS-RM field. Only 849 of them received a fiber in the SDSS-RM spectroscopy. This table lists the basic target information of these quasars.</p> <p><strong>QSObased_Expanded_SDSSRM_107.fits</strong> The narrow MgII/FeII absorber catalog for SDSS-RM quasars, following the methodology outlined in Zhu & Ménard (2013). Each entry corresponds to one quasar. The search for narrow absorbers includes systems that have absorber redshift close to the quasar systemic redshift (|dz|<0.04). MgII absorbers blueshifted from the quasar by dz>0.04 and also redward of CIV by dz>0.02 are of high purity. MgII absorbers with |dz|<0.04 or those at wavelength blueward of CIV, or those with FeII detection but no MgII detections (likely due to bad pixels), while included in this catalog, should be treated with caution, and may contain a small fraction of false positives (mainly CIV absorbers).</p> <p>For convenience, we also provide a version of the absorber catalog organized by absorbers (<strong>Expanded_SDSSRM_107.fits</strong>), i.e., each entry corresponds to one absorber system.</p> <p><strong>rmqso32_aegis_multi_lambda.fits</strong> Multi-wavelength data compiled from Nandra et al. (2015) or 32 SDSS-RM quasars in the AEGIS field.</p> <p><strong>spitzer_seip_rm_match_1.5arcsec.fits</strong> Spitzer IRAC and MIPS data from the Spitzer Enhanced Imaging Products (SEIP) source list for 176 SDSS-RM quasars, with a matching radius of 1.5 arcseconds. This file also compiles infrared fluxes (if available) from 2MASS (Skrutskie et al. 2006).</p> <p><strong>spec_2014_BALrobust.csv</strong> List of 95 BALQSOs (including mini-BALQSOs) identified from the first-year coadded spectroscopy. This file includes BAL flags on CIV, AlIII, MgII, and FeII/FeIII. It also includes notes on individual objects.</p> <p><strong>PS1_MD07_LC_sdssrm.fits</strong> PS1 Medium Deep light curves for the SDSS-RM quasars used to compute PS1_NMAG_OK and PS1_RMS_MAG in the main catalog. Note this is the unofficial release of the PS1 MD07 data, which was approved by the PS1 collaboration. These photometric light curves may differ slightly from the final official release of the PS1 Medium Deep field data. </p>
CHEK To-be Digital Building Permit process map
<p>To-be process map for digital building permit process as developed within the HORIZON EUROPE project 'Change toolkit for Digital Building Permit' (CHEK) https://chekdbp.eu </p> <p>It is described in the CHEK project deliverable D1.1.</p> <p>This project has received funding from the European Union’s Horizon Europe programme under Grant Agreement No.101058559</p> <p>The file is provided in<br> - SVG format, open vector editable format;<br> - Visio format, proprietary but editable format;<br> - PDF format.</p>
UKRI Digital Research Infrastructure Mapping Survey Dataset (for Net Zero Scoping Project)
<p>This dataset was generated as an output for the DRI Mapping exercise carried out during the UKRI Net Zero Digital Research Infrastructure (DRI) Scoping Project undertaken from 2021-2023. The "README.md" provides more information about the dataset and how to use it.</p> <p>The report associated with this dataset is available at:</p> <p>https://doi.org/10.5281/zenodo.7805987</p>
PEATGRIDS: Mapping global peat thickness and carbon stock via digital soil mapping approach, dataset
<p>PEATGRIDS: a dataset containing the first peat thickness and carbon stock maps estimated over peatlands area across the globe at ~1 km x ~1 km resolution. Carbon stock was calculated across all depths of the predicted peat thickness, multiplied by peat bulk density (BD) and carbon content (CC) across five depths: 0-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Mapping effort was performed using quantile random forest regression based on remotely sensed data and environmental covariates, including topography, climate, soil properties, and land cover. The maps cover areas potentially as peatlands according to the UNEP's global peatland map obtained from the <a title="Global Peat Database" href="https://greifswaldmoor.de/global-peatland-database-en.html" target="_blank" rel="noopener">Global Peat Database</a>. We may update this dataset in the future, please consider using the latest version. </p> <p>Note: This version (2.0.1) clarifies the metric units for carbon stock per area in the previous version (2.0). </p>
[[Deprecated]] DIGITAL SOIL TEXTURE MAPS OF ARGENTINA
<p>A new version has been uploaded by Guillermo Schulz.</p>
DIGITAL SOIL TEXTURE MAPS OF ARGENTINA
<p>Soil fractions of Argentina in g/100g, Clay, Silt and Sand, for 4 standard depth intervals (0–15, 15-30, 30–60, 60–100) at 1000 m resolution. Including textural classes for the four standard layers and error estimation using random forest.</p> <p>Global accuracy based on cross-validation</p> <table> <tbody> <tr> <td> <p><strong>sp</strong></p> </td> <td> <p><strong>RMSE</strong></p> </td> <td> <p><strong>Rsquared</strong></p> </td> <td> <p><strong>MAE</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 0-15 cm</strong></p> </td> <td> <p><strong>16.189</strong></p> </td> <td> <p><strong>0.640</strong></p> </td> <td> <p><strong>11.069</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 15-30 cm</strong></p> </td> <td> <p><strong>16.320</strong></p> </td> <td> <p><strong>0.629</strong></p> </td> <td> <p><strong>11.213</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 30-60 cm</strong></p> </td> <td> <p><strong>16.676</strong></p> </td> <td> <p><strong>0.618</strong></p> </td> <td> <p><strong>11.364</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 60-100 cm</strong></p> </td> <td> <p><strong>16.762</strong></p> </td> <td> <p><strong>0.587</strong></p> </td> <td> <p><strong>11.472</strong></p> </td> </tr> <tr> <td> <p><strong>silt 0-15 cm</strong></p> </td> <td> <p><strong>12.011</strong></p> </td> <td> <p><strong>0.638</strong></p> </td> <td> <p><strong>8.352</strong></p> </td> </tr> <tr> <td> <p><strong>silt 15-30 cm</strong></p> </td> <td> <p><strong>11.807</strong></p> </td> <td> <p><strong>0.608</strong></p> </td> <td> <p><strong>8.388</strong></p> </td> </tr> <tr> <td> <p><strong>silt 30-60 cm</strong></p> </td> <td> <p><strong>11.504</strong></p> </td> <td> <p><strong>0.561</strong></p> </td> <td> <p><strong>8.168</strong></p> </td> </tr> <tr> <td> <p><strong>silt 60-100 cm</strong></p> </td> <td> <p><strong>11.728</strong></p> </td> <td> <p><strong>0.583</strong></p> </td> <td> <p><strong>8.263</strong></p> </td> </tr> <tr> <td> <p><strong>clay 0-15 cm</strong></p> </td> <td> <p><strong>8.766</strong></p> </td> <td> <p><strong>0.475</strong></p> </td> <td> <p><strong>5.721</strong></p> </td> </tr> <tr> <td> <p><strong>clay 15-30 cm</strong></p> </td> <td> <p><strong>10.723</strong></p> </td> <td> <p><strong>0.452</strong></p> </td> <td> <p><strong>7.432</strong></p> </td> </tr> <tr> <td> <p><strong>clay 30-60 cm</strong></p> </td> <td> <p><strong>11.211</strong></p> </td> <td> <p><strong>0.557</strong></p> </td> <td> <p><strong>7.842</strong></p> </td> </tr> <tr> <td> <p><strong>clay 60-100 cm</strong></p> </td> <td> <p><strong>11.005</strong></p> </td> <td> <p><strong>0.536</strong></p> </td> <td> <p><strong>7.734</strong></p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Redistribution of the shapefile of the digital soil map of the Flemish Region (status 2017-06-20)
<p>This is a redistribution of the shapefile of the '<a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">Digitale bodemkaart van het Vlaams Gewest: bodemtypes, substraten, fasen en varianten van het moedermateriaal en de profielontwikkeling</a>' (digital soil map of the Flemish Region: soil types, substrates, phases, variants of the parent material, and profile development), originally published by 'Databank Ondergrond Vlaanderen’ (Subsurface Database of Flemish Region, DOV) under a CC-BY compatible license. Its shapefile has been redistributed as the <code>soilmap</code> data source, used in reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes. These workflows rely on a stable, clean (datafile-only) and uniform representation of each data source version, represented by a Zenodo DOI.</p> <p>The Belgian soil map was drawn up by intensive soil mapping from the 1950s to 1970s (Dudal et al., 2005). The map is based on the Belgian soil classification system. It is a national system that was set up exclusively for Belgian soils. The digital soil map of the Flemish Region is documented by Van Ranst & Sys (2000). Each spatial polygon, accurately digitized from the soil map sheets (published at map scale 1:20000) at scale 1:5000, includes information on soil types, substrates, phases, variants of the parent material and profile development. If available, the general characteristics and photos of a representative soil profile and environment can be obtained for each soil type. Finally, for each location it is possible to call in a scan of the analog soil map sheet, the corresponding explanation booklet and the basic maps on a 1: 5000 scale. The digital soil map was updated in 2017 with information from several military domains, a uniform soil type was generated for the polder area, and several mistakes were corrected.</p> <p>The data source is owned by ‘Vlaams Planbureau voor Omgeving’ (Flemish Planning Bureau for Environment, Department of Environment of the Flemish government).</p>
Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping
<p>This dataset include the topsoil (0-30cm) soil organic carbon (SOC) stocks in mineral soils under major land classes (forest, grassland, shrub land, savannas, cropland, cropland/natural vegetation mosaic, and sparely vegetated land) from 1981 to 2018. The long-time series of SOC stocks were estimated by using a space-for-time digital soil mapping (DSMst) model where the RothC-simulated SOC stocks were incorporated as one of the dynamic covariates of the DSMst model.</p> <p>The detail information on the products were given below:</p> <p>Name: DSMst-RothC 5-km global topsoil SOC stock products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.041666667 degree</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Extent: -180°, -90°: 180°, 90°</p> <p>Data format: GeoTIFF</p> <p>Compression: LZW</p> <p>Data type: Float32</p> <p>Unit: t C ha<sup>-1</sup></p>
Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps
<p><strong>Title:</strong></p> <p>Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps</p> <p><strong>Citation:</strong></p> <p>Seeger, K.; Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Zenodo, <a href="https://doi.org/10.5281/zenodo.7875965">https://doi.org/10.5281/zenodo.7875965</a>.</p> <p><strong>Supplement to:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, and Brill, D. (2023): Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2022-1425">https://doi.org/10.5194/egusphere-2022-1425</a>.</p> <p><strong>Abstract:</strong></p> <p>The local digital elevation model (DEM) of the Ayeyarwady Delta, referred to as AD-DEM, was generated based on elevation data of topographic maps at scale of 1:50,000 published in 2014 while source data was compiled between 2000 and 2004. Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate ~5100 elevation points (spot heights) and ~13600 elevation points extracted from contour data of the topographic maps. Elevation values higher than 10 m were excluded from interpolation and the SRTM water body mask created in 2000 was applied to the processed AD-DEM. The AD-DEM was transformed from its original vertical reference of local mean sea level at Kyaikkhami tide gauge to continuous mean sea level based on the mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) that we transposed to EGM96) in order to account for sea level variations along the Myanmar coast.</p> <p>The AD-DEM contains itself some uncertainty due to the lack of evenly distributed spot heights in areas of the upper delta, for which a separate shapefile is provided. However, we highlight to consider the AD-DEM as being the currently best available model against the background of the lacking possibility of ground truthing and being independent from satellite-based measurements.</p> <p>For further information on data processing, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material.</p> <p>File name: ADDEM_Con250m_lesseq10_MDT_AD_MMR2000_masked_maskedSRTM.tif</p> <p>File format: GEOTIFF file</p> <p>Spatial reference: MMR2000_46N</p> <p>Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) transposed to EGM96</p> <p>Cell size: 750 × 750 m</p> <p>File name: DataPoorAreas_MMR2000.shp</p> <p>File format: ESRI Shapefile</p> <p>Spatial reference: MMR2000_46N</p>
Spatial models of topsoil properties in Romania using digital soil mapping techniques
<p>The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques, accepted for publication in:</p> <p>Cristian Valeriu Patriche, Bogdan Roșca, Radu Gabriel Pîrnău, Ionuț Vasiliniuc, <em>Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning</em><strong>, PLOS ONE</strong>, 2023</p> <p>The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques. The file names indicate the soil variable and the method used for interpolation (RK – regression - kriging, EML – ensemble machine learning, GWR_OK – Geographically Weighted Regression – Ordinary kriging).</p> <p>The raster data is classified and saved in tif format with a resolution of 100 x 100 m. The spatial reference is Stereographic projection 1970 (Pulkovo_1942_Adj_58_Stereo_70).</p> <p>The soil variables are classified as follows:</p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Classes</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p><strong>2</strong></p> </td> <td> <p><strong>3</strong></p> </td> <td> <p><strong>4</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>7</strong></p> </td> </tr> <tr> <td> <p><em>pH</em></p> </td> <td> <p>≤ 5</p> <p>(strongly acid)</p> </td> <td> <p>5.1 – 5.8 (moderately acid)</p> </td> <td> <p>5.9 – 6.8</p> <p>(weakly acid)</p> </td> <td> <p>6.9 – 7.2</p> <p>(neutral)</p> </td> <td> <p>7.3 – 8.4</p> <p>(weakly alkaline)</p> </td> <td> <p>8.5 – 8.8 (moderately alkaline)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>EC (mS m<sup>-1</sup>)</em></p> </td> <td> <p>≤ 12.75</p> </td> <td> <p>12.76 – 16.49</p> </td> <td> <p>16.50 – 20.04</p> </td> <td> <p>20.05 – 24.18</p> </td> <td> <p>24.19 – 29.11</p> </td> <td> <p>29.12 – 35.23</p> </td> <td> <p>≤ 35.24</p> </td> </tr> <tr> <td> <p><em>OC (g kg<sup>-1</sup>)</em></p> </td> <td> <p>< 7.5</p> <p>(very low)</p> </td> <td> <p>7.5 – 17.4</p> <p>(low)</p> </td> <td> <p>17.4 – 37.8 (moderate)</p> </td> <td> <p>37.8 – 61.0</p> <p>(high)</p> </td> <td> <p>> 61</p> <p>(very high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>CaCO<sub>3</sub></em></p> <p><em>(g kg<sup>-1</sup>)</em></p> </td> <td> <p>0</p> <p>(no carbonates)</p> </td> <td> <p>1 – 10</p> <p>(low)</p> </td> <td> <p>11 – 40</p> <p>(medium 1)</p> </td> <td> <p>41 – 80</p> <p>(medium 2)</p> </td> <td> <p>81 – 107</p> <p>(medium 3)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>P (mg kg<sup>-1</sup>)</em></p> </td> <td> <p>< 4</p> <p>(extremely low)</p> </td> <td> <p>4 – 8</p> <p>(very low)</p> </td> <td> <p>8 – 18</p> <p>(low)</p> </td> <td> <p>18 – 36</p> <p>(medium)</p> </td> <td> <p>36 – 72</p> <p>(high)</p> </td> <td> <p>> 72</p> <p>(very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>N (g kg<sup>-1</sup>)</em></p> </td> <td> <p>≤ 1</p> <p>(very low)</p> </td> <td> <p>1.1 – 1.4</p> <p>(low)</p> </td> <td> <p>1.5 – 2.0</p> <p>(medium 1)</p> </td> <td> <p>2.1 – 2.7</p> <p>(medium 2)</p> </td> <td> <p>2.8 – 6.0</p> <p>(high)</p> </td> <td> <p>> 6</p> <p>(very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>K (mg kg<sup>-1</sup>)</em></p> </td> <td> <p>≤ 40 *</p> <p>(extremely low)</p> </td> <td> <p>41 – 65 *</p> <p>(very low)</p> </td> <td> <p>66 – 130</p> <p>(low)</p> </td> <td> <p>131 – 200 (medium)</p> </td> <td> <p>201 – 300</p> <p>(high)</p> </td> <td> <p>> 300</p> <p> (very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Clay (%)</em></p> </td> <td> <p>≤ 25</p> <p> (low 1)</p> </td> <td> <p>26 – 32</p> <p>(low 2)</p> </td> <td> <p>33 – 40</p> <p>(medium 1)</p> </td> <td> <p>41 – 45</p> <p>(medium 2)</p> </td> <td> <p>≥ 46</p> <p> (high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Silt (%)</em></p> </td> <td> <p>< 25</p> <p>(medium 1)</p> </td> <td> <p>25 – 32</p> <p>(medium 2)</p> </td> <td> <p>33 – 40</p> <p>(high 1)</p> </td> <td> <p>41 – 50</p> <p>(high 2)</p> </td> <td> <p>> 50</p> <p>(high 3)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Sand (%)</em></p> </td> <td> <p>< 15</p> <p>(low 1)</p> </td> <td> <p>15 – 25</p> <p>(low 2)</p> </td> <td> <p>26 – 35</p> <p>(low 3)</p> </td> <td> <p>36 – 56</p> <p>(medium)</p> </td> <td> <p>> 56</p> <p>(high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p>* classes not present on the Romanian territory</p> <p> </p> <p> </p> <p> </p>
Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>.</p> <p>The R code was written for R version 3.6.3.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p>
A 30-meter terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine
<p>This dataset contains the China terrace map at 30 m resolution in 2018. The map values and their corresponding classes are as follows:</p> <p><em>0: Non-terrace 1: Terrace 255: No data</em></p> <p>The 30 m China terrace map can also be viewed online at <a href="https://cbw.users.earthengine.app/view/chinaterracemap">https://cbw.users.earthengine.app/view/chinaterracemap</a></p> <p><strong>Citations:</strong></p> <p>When using this dataset, please cite both the dataset and the following data description article:</p> <p><em>Cao, B., Yu, L., Naipal, V., Ciais, P., Li, W., Zhao, Y., Wei, W., Chen, D., Liu, Z., and Gong, P.: A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine, Earth Syst. Sci. Data, 13, 2437–2456, https://doi.org/10.5194/essd-13-2437-2021, 2021.</em></p> <p> </p>
Geologic Map of Tyre Impact Crater on the Galilean Moon Europa - Digitized and Modified Versions (2024)
<p><strong>Geologic Map of Tyre Impact Crater on the Galilean Moon Europa - Digitized and Modified Versions (2024)</strong></p> <p>Files and deliverable documentation of the process of digitizing the geologic map of Tyre, Kadel et al. 2000. This may be useful if you are looking to learn how to digitize a geologic map using some form of mapping software, or to learn about the surface geology of Jupiter's icy moon Europa. </p> <p>Includes:</p> <ul> <li>read.me with supporting information</li> <li>map package</li> <li>Georeferenced PDF figures </li> <li>Shapefiles </li> </ul>
Digitalización de Mapa de Departamento de Lavalle Provincia de Mendoza año 1918. Digitalization of the Map of the Department of Lavalle, Province of Mendoza, 1918.
<h1><strong>Mapa de Departamento de Lavalle Provincia de Mendoza, año 1918. </strong></h1> <table> <tbody> <tr> <td><strong>Autor</strong></td> <td>Dirección General de Obras Públicas de Mendoza. Escala: 1:100.000. Copia del Original. Confeccionado con las mensuras administrativas judiciales existentes en el archivo de la dirección General de Obras Públicas.</td> </tr> <tr> <td><strong>Origen</strong></td> <td>Biblioteca personal Daniel Cobos, IANIGLA</td> </tr> <tr> <td><strong>Digitalización</strong></td> <td>Cámara Nikon D3200 18-55mm. + Escáner cenital (ver método).</td> </tr> <tr> <td><strong>Operador<br></strong></td> <td>Martín Federico Ortiz - Becario Doctoral del (INCIHUSA - Conicet).</td> </tr> <tr> <td><strong>Método</strong></td> <td>Se dividió el mapa en cuadrantes y se realizaron cuatro fotografías con cámara réflex montada en escáner cenital<strong> </strong>que luego se unieron con ayuda de Adobe Lightroom y Adobe Photoshop. Se realizó retoques de enfoque, contraste y balance de blancos, ya que el estado de la tinta, color y escritos del mapa físico no se encuentran en buenas condiciones.</td> </tr> </tbody> </table> <p>El mapa llegó al Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA) del Consejo Nacional de Investigaciones Científicas y Técnicas (Conicet) en sus primeros años quizás por el profesor Daniel Cobos. Cuando Daniel se jubiló, los mapas y cartas de aeronavegación fueron desechados. El Lic. Alberto Ripalta recupera este "fondo patrimonial" y lo archiva. Luego, el Doctor Facundo Rojas <strong>(1)</strong>,<strong> </strong>investigador del IANIGLA, al tomar conocimiento de lo anterior establece contacto con Alberto con el interés de digitalizar este mapa. Así, mediante el escáner cenital open source de uso para múltiples dispositivos (cámara réflex, compacta, smartphone) desarrollado en el marco del proyecto SIIP Tipo 4<em> “Las memorias del agua cuentan. Prácticas de </em><em>archivo, investigación y transición digital del Archivo Histórico del Agua de la Provincia de </em><em>Mendoza</em> dirigido por Doctor Facundo Martín del Instituto de Ciencias Humanas, Sociales y Ambientales (INCIHUSA-Conicet). </p> <p> </p> <p><strong>(1)</strong> Director del proyecto "Humanidades digitales en la Geografía e Historia ambiental cuyana. Primera parte: Antecedentes, archivos y patrimonio." (SIIP-UNCUYO 2021).</p>
McMurdo Dry Valleys Bathymetric Values From Contour Map Digitizing
As part of the Long Term Ecological Research in the McMurdo Dry Valleys of Antarctica, bathymetric data was collected for Lakes Hoare, Fryxell and Bonney. This table contains the values for depth, perimeter length, polygon area and total area per contour used for contour map digitizing.
soilmap_simple: a simplified and standardized derivative of the digital soil map of the Flemish Region
<p>The data source <code>soilmap_simple</code> is a simplified and standardized derived form of the '<a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a>' (the shapefile of which we named <code>soilmap</code>, for analytical workflows in R) published by 'Databank Ondergrond Vlaanderen’ (DOV). It is a GeoPackage that contains a <strong>spatial polygon layer</strong> ‘<code>soilmap_simple</code>’ in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>), plus a <strong>non-spatial table</strong> ‘<code>explanations</code>’ with the meaning of category codes that occur in the spatial layer. Further documentation about the digital soil map of the Flemish Region is available in Van Ranst & Sys (2000) and Dudal et al. (2005).</p> <p>This version of <code>soilmap_simple</code> was derived from version '<code>soilmap_2017-06-20</code>' (<a href="https://doi.org/10.5281/zenodo.3387008">Zenodo DOI</a>) as follows:</p> <ul> <li>all attribute variables received English names (purpose of standardization), starting with prefix <code>bsm_</code> (referring to the 'Belgian soil map');</li> <li>attribute variables were reordered;</li> <li>the values of the morphogenetic substrate, texture and drainage variables (<code>bsm_mo_substr</code>, <code>bsm_mo_tex</code> and <code>bsm_mo_drain</code> + their <code>_explan</code> counterparts) were filled for most features in the 'coastal plain' area. <ul> <li>To derive morphogenetic texture and drainage levels from the geomorphological soil types, a conversion table by Bruno De Vos & Carole Ampe was applied (for earlier work on this, see Ampe 2013).</li> <li>Substrate classes were copied over from <code>bsm_ge_substr</code> into <code>bsm_mo_substr</code> (<code>bsm_ge_substr</code> already followed the categories of <code>bsm_mo_substr</code>).</li> </ul> These steps coincide with the approach that had been taken to construct the <code>Unitype</code> variable in the <code>soilmap</code> data source;</li> <li>only a minimal number of variables were selected: those that are most useful for analytical work.</li> </ul> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/b3c6696/src/generate_soilmap_simple">'n2khab-preprocessing' at commit b3c6696</a> for the creation from the <code>soilmap</code> data source.</p> <p>A reading function to return <code>soilmap_simple</code> (this data source) or <code>soilmap</code> in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p><strong>The attributes</strong> of the spatial polygon layer <code>soilmap_simple</code> can have <code>mo_</code> in their name to refer to the <em>Belgian Morphogenetic System</em>:</p> <ul> <li><code>bsm_poly_id</code>: unique polygon ID (numeric)</li> <li><code>bsm_region</code>: name of the region</li> <li><code>bsm_converted</code>: boolean. Were morphogenetic texture and drainage variables (<code>bsm_mo_tex</code> and <code>bsm_mo_drain</code>) derived from a conversion table (see above)? Value <code>TRUE</code> is largely confined to the 'coastal plain' areas.</li> <li><code>bsm_mo_soilunitype</code>: code of the soil type (applying morphogenetic codes within the coastal plain areas when possible, just as for the following three variables)</li> <li><code>bsm_mo_substr</code>: code of the soil substrate</li> <li><code>bsm_mo_tex</code>: code of the soil texture category</li> <li><code>bsm_mo_drain</code>: code of the soil drainage category</li> <li><code>bsm_mo_prof</code>: code of the soil profile category</li> <li><code>bsm_mo_parentmat</code>: code of a variant regarding the parent material</li> <li><code>bsm_mo_profvar</code>: code of a variant regarding the soil profile</li> </ul> <p>The <strong>non-spatial table</strong> <code>explanations</code> has following variables:</p> <ul> <li><code>subject</code>: attribute name of the spatial layer: either <code>bsm_mo_substr</code>, <code>bsm_mo_tex</code>, <code>bsm_mo_drain</code>, <code>bsm_mo_prof</code>, <code>bsm_mo_parentmat</code> or <code>bsm_mo_profvar</code></li> <li><code>code</code>: category code that occurs as value for the corresponding attribute in the spatial layer</li> <li><code>name</code>: explanation of the value of <code>code</code></li> </ul>
Towards Green Cartography & Visualization: An automated, semantically-enriched method of generating energy-aware color schemes for digital maps and visualizations
<p>Towards Green Cartography & Visualization: An automated, semantically-enriched method of generating energy-aware color schemes for digital maps and visualizations</p>
Digital Map of World Countries
<p>The dataset contains the borders of the world countries. It is encoded in the Esri™ shapefile format. It is based on data originally downloaded by the site <strong><a href="https://gadm.org/download_country_v2.html">https://gadm.org/download_country_v2.html</a>. </strong></p> <p>The Laboratory of Geomatics at the University of Pavia edited the original data by modifying some polygons and adding some fields in the associated table.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.