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Datasets for "Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Classification Algorithms in Reiner Gamma and Mare Ingenii"
<p>Final surface reflectance data at 2.6 m/pixel resolution with floating point values are available as GeoTiff and ASCII text files. Definition files for the K-Means and MLC algorithms in classifying swirl units are also available as ASCII text files. See README file for further details.</p> <p>Data used in the research article:</p> <p>Chuang, F.C., M.D. Richardson, J.R. Weirich, A.A. Sickafoose, and D.L. Domingue, 2022. Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii. The Planetary Science Journal, 3:231. doi://10.3847/PSJ/ac8f43</p> <p> </p>
Processing of MODIS-Aqua data with Self-Organizing Maps NeuroVaria method for the southern canary upwelling system
<p>Abstract</p> <p>This ocean color dataset is derived from MODIS_Aqua sensor measurements covering the Southern Canary upwelling system. The raw L1A measurements were downloaded from NASA's Ocean Color web site and then processed using the Ocean Biology Processing Group's (OBPG) Multi-Sensor Level-1 to Level-2 (MSL12) code. The l2gen program, based on its standard process, generates Level-2 parameters consisting of the top of atmosphere radiance, the radiance of each ocean and atmosphere component, the measurement angles, Level-2 flags, ... The top of atmosphere radiance is pre-corrected to keep only a dependence on the diffuse transmittance, the aerosol contribution and the water leaving radiance.</p> <p><br> The pre-corrected product and measurement angles are assimilated using the Self-Organizing Map<br> NeuroVaria (SOM-NV) code (Diouf et al., 2013). SOM-NV is an algorithm based on two statistical models<br> that classify a dataset into a map, and then use the information from that map to deliver atmospheric and oceanic parameters from the satellite observation.</p> <p>The parameters of interest are the remote sensing reflectance spectra (Rrs(λ)) and the aerosol optical thickness (AOT) at 869 nm (aot_869). The Rrs at blue (443 and 488 nm) and green (547 nm) are used to calculate chlorophyll-a concentration from the OBPG OCx algorithm (chl_ocx, O'Reilly et al., 1998; Mobley et al., 2016).</p> <p>These geophysical parameters are projected onto a fixed grid at 1/96° resolution and archived in a daily netcdf format files. Each file contains five visible reflectances Rrs(λ) (with λ = 412, 443, 488, 531, and 547 nm), chl_ocx, aot_869, and latitude and longitude coordinates. These parameters are described in the files, along with the global attributes.</p> <p><br> The netcdf files are formatted as follows: SOM-NV-Ayyyydddhhmmss.nc; where yyyy = year; ddd = Julian<br> day; hh = hour; mm = minute; ss = second. The extension "Ayyyydddhhmmss.nc", corresponds to the name<br> of the MODIS_aqua file of the day. When two input files exist for the same day, within 5 minutes, the two<br> scans are concatenated and the orbit keeps the name of the second file.<br> All files are compressed internally to a size of 4, to facilitate transfers.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>Résumé</p> <p>Ce jeu de données de couleur de l’eau est issu des mesures du capteur MODIS_Aqua sur la partie sud du système d’upwelling des Canaries. Les mesures brutes L1A ont été téléchargées du site Ocean Color de la NASA, puis traitées à l’aide du code de traitement « Multi-Sensor Level-1 to Level-2 (MSL12) » du groupe Ocean Biology Processing Group (OBPG). La version standard du programme l2gen génère les paramètres de niveau 2 constitués de la luminance totale mesurée, de la luminance de chaque composante du système océan-atmosphère, des angles de mesures, des masques de niveau 2, …. La luminance totale est pré-corrigée pour ne garder qu’une dépendance à la transmittance diffuse, à la contribution des aérosols et à la luminance marine.<br> <br> Le produit pré-corrigé et les angles de mesure sont assimilés à l’aide du code Self-Organizing Map NeuroVaria (SOM-NV) de Diouf et al. (2013). SOM-NV est un algorithme basé sur deux modèles statistiques qui permettent de classer un ensemble de données sur une carte, puis d’utiliser les informations de cette carte pour restituer les paramètres atmosphériques et océaniques de l’observation satellite.<br> <br> Les paramètres restitués sont les spectres de réflectance marine (Rrs(λ)) et l’épaisseur optique des aérosols (AOT) à 869 nm (aot_869). Les Rrs au bleu (443 et 488 nm) et au vert (547 nm) servent à calculer la concentration en chlorophylle-a à partir de l’algorithme OCx de OBPG (chl_ocx).<br> <br> Ces paramètres géophysiques sont projetés sur une grille fixe à 1/96° de résolution et archivés au format de fichiers netcdf journaliers. Chaque fichier netcdf contient cinq réflectances du visible Rrs(λ) (avec λ = 412, 443, 488, 531 et 547 nm), la chl_ocx, l’aot_869, et les coordonnées latitude et longitude. Ces paramètres sont décrits dans les fichiers, ainsi que les attributs globaux.</p> <p><br> Les fichiers netcdf sont formatés comme suite : SOM-NV-Ayyyydddhhmmss.nc ; avec yyyy = année ; ddd =<br> jour julien ; hh = heure ; mm = minute ; ss = seconde. L'extension "Ayyyydddhhmmss.nc", correspond au<br> nom du fichier MODIS_aqua du jour. Dans le cas où deux fichiers existent pour un même jour, à 5 minutes<br> près, les deux scans sont concaténés et l'orbite garde le nom du deuxième fichier.<br> Tous les fichiers sont compressés en interne à un niveau 4, pour faciliter le transfert.</p>
FRUC multiple sensor forest dataset including absolute, map-referenced localization
<p><strong>FRUC Datasets (Forest environment dataset)</strong></p> <p>This dataset was collected as part of the work conducted by the Forestry Robotics @ University of Coimbra team (<a href="https://www.youtube.com/@forestryroboticsuc">https://www.youtube.com/@forestryroboticsuc</a>; part of the Institute of Systems and Robotics, <a href="https://www.isr.uc.pt/">https://www.isr.uc.pt/</a>) within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-03269; <a href="http://semfire.ingeniarius.pt/">http://semfire.ingeniarius.pt/</a>) and the Semi-Autonomous Robotic System for Forest Cleaning and Fire Prevention (SafeForest, CENTRO-01-0247-FEDER-045931) research projects. Its purpose is to allow researchers in forestry robotics to have an in-depth analysis of a florests environment; obtain an a priori map for robot operations (e.g. path plannning, landscaping, etc…) and to train segmentation algorithms;</p> <p> </p> <p>The dataset in question includes data from multiple sensors and absolute, map-referenced localization which can be used to register the sensor data to a fixed coordinate system. It was collected at the <a href="https://www.google.com/maps/place/Mata+Nacional+do+Choupal/@40.2208522,-8.4429989,842m/data=!3m1!1e3!4m6!3m5!1s0xd22f91d7cec3b95:0xb02aedc4d8380d48!8m2!3d40.2222536!4d-8.4438944!16s%2Fm%2F026jw89?hl=pt-PT">Choupal National Woods, Coimbra, Portugal</a> (40<sup>◦</sup>13′13.3′′N;8<sup>◦</sup>26′38.1′′W). The dataset was collected during a partly clouded day in a forest environment by performing <strong>two circular loop</strong> laps amounting to a total distance of approximately <strong>800m,</strong> with a total duration of <strong>14 minutes and 22 seconds</strong>. The scenario is rich in features relevant to forestry robotics applications, including trees, bushes, tree trunks, etc. To better handle the multimodal nature of the acquired data, the dataset is bundled into <a href="http://wiki.ros.org/rosbag">rosbags</a>, a file format used by the <a href="http://wiki.ros.org/">ROS (Robot Operating System)</a> to record and play back data.</p> <p><strong>More specifically, the datasets include:</strong></p> <ul> <li><strong>RGB Images</strong> from an Intel Realsense D435i</li> <li>Aligned <strong>Depth Images</strong> from an Intel Realsense D435i</li> <li>Left and Right Mono Images from a Mynt Eye s1030</li> <li><strong>Point Clouds</strong> from a Livox Mid-70 LiDAR</li> <li>Unfiltered <strong>acceleration, gyroscopic and magnetic</strong> data from a Xsens MTi IMU</li> <li>Unfiltered <strong>acceleration, gyroscopic </strong>data from an Intel Realsense D435i</li> <li><strong>GNSS Fix data</strong> from a Xiaomi Mi Mix 3 device</li> </ul> <p><strong>Description of files:</strong></p> <ol> <li>The dataset is contain in <strong>choupal.bag</strong>.</li> <li>The <strong>rosbag_info.txt </strong>contains the information of each rosbag;</li> <li>The <strong>sensor_box.urdf </strong>contains all the required transforms;</li> <li>The <strong>sensor_box.stl</strong> contains the 3D model of the apparatus;</li> <li>The <strong>choupal.launch </strong>publishes the sensor transforms and plays the dataset;</li> <li>The <strong>localization.bag</strong> contains the final graph of poses extracted with Cartographer republished as nav_msgs/odom at 4.98Hz.</li> <li>The <strong>localization_15Hz.bag</strong> contains a map-referenced localization extracted with Cartographer at a higher frequency, but the poses are interpolated. If you don't require a high frame rate, please use the <strong>localization.bag</strong> instead.</li> </ol> <p><strong>Usage:</strong></p> <ol> <li>Extract the <em>fruc_dataset_choupal_launch.zip </em>into a catkin workspace</li> <li>Install the necessary dependencies of the package: <ol> <li> <pre><code class="language-bash">cd [/path/to/catkin_ws]</code></pre> <p> </p> </li> <li> <pre><code class="language-bash">rosdep install --from-paths src --ignore-src -y -r</code></pre> </li> </ol> </li> <li>Copy the <strong>rosbags </strong>into the <em>fruc_dataset_choupal_launch/rosbag/</em></li> <li>Edit the <em>fruc_dataset_choupal_launch/launch/choupal.launch </em>file to your use case: <ol> <li>Change the <em>file_path </em>argument if the rosbags are not in the default location;</li> <li>Set <em>localization_file</em> to <em> </em>the path of the desired localization bag, leave it empty to run the dataset without localization.</li> </ol> </li> <li>Compile the package and source the environment: <ol> <li> <pre><code class="language-bash">catkin_make [/your_catkin_workspace/]</code></pre> <p> </p> </li> <li> <pre><code class="language-bash">source [/your_catkin_workspace/devel/setup.bash]</code></pre> <p> </p> </li> </ol> </li> <li>Launch the files: <pre><code class="language-bash">roslaunch fruc_dataset_choupal_launch choupal.launch</code></pre> </li> </ol>
LIPID MAPS® Structure Database (LMSD) formatted for MetFrag
<p>This repository contains the LIPID MAPS® Structure Database (<a href="https://www.lipidmaps.org/databases/lmsd/overview">LMSD</a>) formatted for use in <a href="https://msbi.ipb-halle.de/MetFrag/">MetFrag</a> (and other workflows).</p> <p><em>LIPID MAPS® Lipidomics Gateway is a free, comprehensive website for researchers interested in lipid biology. Use <a href="https://www.lipidmaps.org"> https://www.lipidmaps.org</a> to stay abreast of developments each month from across the field, and explore the rich information collections, tools and resources from the LIPID Metabolites And Pathways Strategy (LIPID MAPS®) Consortium. </em><br> </p> <p>The workflow used to create this file (by B. Talavera Andújar) can be found here: <a href="https://gitlab.lcsb.uni.lu/eci/simple-utilities/sdf2csv">https://gitlab.lcsb.uni.lu/eci/simple-utilities/sdf2csv</a></p> <p><strong>Reference:</strong> LMSD: LIPID MAPS® structure database, Sud M., Fahy E., Cotter D., Brown A., Dennis E., Glass C., Murphy R., Raetz C., Russell D., and Subramaniam S., Nucleic Acids Research, 2006, DOI: <a href="https://doi.org/10.1093/nar/gkl838"> 10.1093/nar/gkl838 </a></p>
Vari (Attica) archaeological map
<p>The AutoCAD file provides the first accurate archaeological map of the Vari area, SW Attica (Greece). The file includes all pre-Classical archaeological discoveries from Late Bronze Age to the end of the Archaic period (1400-500 BC).</p>
Augmented emission maps: several petrol and diesel (Euro 5 - 6d-Temp) vehicle-specific augmented emission maps
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">10.5281/zenodo</a> refers to a meta-data document that provides the full description of the standardized emission map</p>
Complementary data for Iqbal et al. (2023): Geological Mapping and Chronology of Lunar Landing Sites: Apollo 14
<p>Complementary data for Iqbal et al. (2023): Geological Mapping and Chronology of Lunar Landing Sites: Apollo 14</p> <p>Data contains the Geotiff of our geologic map that can be used in any geoinformation system (GIS), and map plate for interpreting the map.</p> <p><strong>If you use these data, please cite BOTH the Icarus publication and the Zenodo dataset</strong></p> <p>Iqbal, W., Hiesinger, H., Borisov, D., van der Bogert, C. H., & Head III, J. W. (2023). Geological mapping and chronology of lunar landing sites: Apollo 14. <em>Icarus</em>, <em>406</em>, 115732. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.icarus.2023.115732" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.icarus.2023.115732</a></p> <p>Iqbal, W., Hiesinger, H., Borisov, D., van der Bogert, C. H., & Head III, J. W. (2023). Complementary data for Iqbal et al. (2023): Geological Mapping and Chronology of Lunar Landing Sites: Apollo 14 [Data set]. In Icarus (Bd. 406, S. 115732). Zenodo. <a href="https://doi.org/10.5281/zenodo.8124259" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8124259</a></p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact <a href="mailto:lwueller@uni-muenster.de" rel="noopener noreferrer nofollow">iqbalw@uni-muenster.de</a></p> <p>Wajiha Iqbal, Institut für Planetologie, Universität Münster, Germany.</p>
Ecological soil map NICHE Flanders - Ecologische NICHE bodemkaart Vlaanderen
<p>NL</p> <p><strong>ECOLOGISCHE NICHE BODEMKAART VLAANDEREN</strong></p> <p>De NICHE bodemkaart voor Vlaanderen is een ecologisch getinte vereenvoudigde bodemkaart die als input dient voor het ecohydrologisch model NICHE Vlaanderen (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>). </p> <p>De bodem speelt een belangrijke rol voor standplaatscondities en het voorkomen van plantengemeenschappen. In NICHE Vlaanderen worden zogenaamde ‘ecologische bodemtypes’ gedefinieerd. De bodemkenmerken worden daarbij vereenvoudigd tot een paar ecologisch relevante kenmerken: de korrelgrootte en de aanwezigheid van organische stof, die bepalend zijn voor de vochtcondities, zuurgraad en het trofieniveau in de bodem.</p> <p>De NICHE bodemkaart onderscheidt 12 klassen:</p> <table> <tbody> <tr> <td>Cijfercode</td> <td>Lettercode</td> <td>Omschrijving</td> </tr> <tr> <td>2</td> <td>K1</td> <td>alluviale kleigronden, arm aan organisch materiaal</td> </tr> <tr> <td>3</td> <td>KV</td> <td>alluviale kleigronden, rijk aan organisch materiaal, venige klei, klei op veen</td> </tr> <tr> <td>5</td> <td>Le</td> <td>eolische leemgronden</td> </tr> <tr> <td>6</td> <td>MK</td> <td>maritieme klei</td> </tr> <tr> <td>7</td> <td>P</td> <td>trilveen</td> </tr> <tr> <td>8</td> <td>V</td> <td>veen</td> </tr> <tr> <td>11</td> <td>Z1</td> <td>humusarme zandgronden (dunne humuslaag), podzol</td> </tr> <tr> <td>12</td> <td>Z2</td> <td>humusrijke zandgronden (dikke humuslaag)</td> </tr> <tr> <td>13</td> <td>ZV</td> <td>venige zandgronden, moerige zandgronden, zandige veengronden</td> </tr> <tr> <td>14</td> <td>L1</td> <td>alluviale leemgronden, arm aan organisch materiaal</td> </tr> <tr> <td>15</td> <td>LV</td> <td>alluviale leemgronden, rijk aan organisch materiaal, venige leemgronden</td> </tr> <tr> <td>10</td> <td> </td> <td>gronden die niet in aanmerking komen voor NICHE Vlaanderen:</td> </tr> <tr> <td> </td> <td>NG</td> <td>niet gespecifieerd</td> </tr> <tr> <td> </td> <td>B</td> <td>bebouwde of sterk beïnvloede gronden</td> </tr> <tr> <td> </td> <td>D</td> <td>droge gronden</td> </tr> <tr> <td> </td> <td>W</td> <td>open water</td> </tr> </tbody> </table> <p>De NICHE bodemkaart is afgeleid van de digitale bodemkaart van Vlaanderen (Digitale versie van de Bodemkaart van Vlaanderen, uitgave 20/06/2017, Databank Ondergrond Vlaanderen). De stappen om een eenheid van de bodemkaart van Vlaanderen in een NICHE bodemtype om te zetten worden in detail beschreven in het NICHE rapport (Callebaut et al. 2007: hoofdstuk 3 pp 32-45 en bijlage 3.3).</p> <p>Als er terreingegevens beschikbaar zijn, kan er afgeweken worden van deze (NICHE) bodemkaart: hoofdstuk 3.4 van het NICHE rapport (Callebaut et al. 2007) licht toe hoe een NICHE bodemtype toegekend kan worden aan een bodemprofiel op basis van de textuur, de dikte en de opeenvolging van de verschillende horizonten.</p> <p><strong>Formaat</strong></p> <p>Vectoriële geografische informatie ter beschikking gesteld als:</p> <ul> <li>shapefile (.shp, .dbf, .shx) met projectiebeschrijving (.prj), ruimtelijke indexen (.sbn, . sbx), geospatial metadata in XML formaat (.shp.xml) en stijlen (ArcView Layer Format .lyr, Styled Layer Descriptor Format .sld)</li> <li>geopackage (.gpkg) met stijlen (Styled Layer Descriptor Format .sld)</li> </ul> <p>Geografische referentiesysteem: Belge 1972 / Belgian Lambert 72 (<a href="https://epsg.io/31370">EPSG-code 31370</a>)</p> <p>Hoogtereferentiesysteem: Tweede Algemene Waterpassing (<a href="https://www.ngi.be/website/tweede-algemene-waterpassing/">TAW</a>)</p> <p><strong>Attributen</strong></p> <ul> <li>fid- Id NICHE bodemkaart (geopackage)</li> <li>gid- Id digitale bodemkaart 2017</li> <li>Bodemtype- Bodemkaarteenheden volgens het Belgische bodemclassificatiesysteem (digitale bodemkaart 2017)</li> <li>Bodemser_c- Bodemserie bestaande uit 3 letters die staan voor textuur, drainage en profiel (digitale bodemkaart 2017)</li> <li>Bodemserie - Bodemserie: beschrijving voor textuur, drainage en profiel (digitale bodemkaart 2017)</li> <li>Unitype - Bodemkaarteenheden met zeepolders omgezet naar de bodemclassificatie van de rest van Vlaanderen (digitale bodemkaart 2017)</li> <li>Grove_leg - Gegeneraliseerde legende van de bodemkaart (digitale bodemkaart 2017)</li> <li>Substr_V_c - Substraten waarvan de lithologische aard verschilt van die van de oppervlakkige laag (lithologische discontinuiteit) (digitale bodemkaart 2017)</li> <li>Textuur_c - Grondsoort, aard van het moedermateriaal (digitale bodemkaart 2017)</li> <li>Drainage_c - Natuurlijke draineringsklasse, natuurlijke drainage (digitale bodemkaart 2017)</li> <li>Profontw_c - Profielontwikkeling (digitale bodemkaart 2017)</li> <li>Fase_c - Secundaire bodemkenmerken (digitale bodemkaart 2017)</li> <li>Varimoma_c - Variant van het moedermateriaal (digitale bodemkaart 2017)</li> <li>Variprof_c - Variant van de profielontwikkeling (digitale bodemkaart 2017)</li> <li>Streek - Landbouwstreek (digitale bodemkaart 2017)</li> <li>NICHE_let - Lettercode van het NICHE bodemtype</li> <li>NICHE_cijf - Cijfercode van het NICHE bodemtype</li> </ul> <p><strong>Wijzigingen sinds vorige versie</strong></p> <p>Sinds vorige versie (1.1):</p> <ul> <li>NICHE bodemkaart niet meer gebaseerd op versie 2001 van de bodemkaart, maar op versie 20/06/2017 incl. een update met enkele militaire domeinen (Beverlo, Kleine Brogel, Brasschaat) en met een unibodemtype voor de classificatie van de zeepolders, alsook een verbetering van verschillende fouten.</li> <li>namen van de velden veranderd</li> <li>cijfercode van de NICHE bodemtypes aangepast (2 t.e.m. 15 i.p.v. 20 000 t.e.m. 150 000)</li> <li>enkele verbeteringen van de vertaling tussen bodemtypes en NICHE bodemtypes: <ul> <li>sV, sV(o): ZV (i.p.v. V in v1.1)</li> <li>uvPep: LV (i.p.v. L1 in v1.1)</li> <li>GDa3x: LV (i.p.v. L1 in v1.1)</li> <li>v-Zdpb(z): Z1 (i.p.v. ZV in v1.1)</li> </ul> </li> </ul> <p><strong>Disclaimer</strong></p> <p>Deze kaart geeft de best beschikbare informatie maar is een vereenvoudiging van de werkelijkheid op terrein. Ten allen tijde geldt de reële situatie op terrein voor toepassing t.b.v. het beleidsmatig en wettelijk kader.</p> <p>EN</p> <p><strong>ECOLOGICAL SOIL MAP NICHE FLANDERS</strong></p> <p>The NICHE soil map for Flanders is an ecologically tinted simplified soil map that serves as input for the ecohydrological model NICHE Flanders (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>).</p> <p>Soil plays an important role for habitat conditions and the occurrence of plant communities. In NICHE Flanders, so-called 'ecological soil types' are defined. The soil characteristics are thereby simplified to a few ecologically relevant characteristics: the grain size and the presence of organic matter, which determine the moisture conditions, acidity and the trophy level in the soil.</p> <p>The NICHE soil map distinguishes 12 classes:</p> <table> <tbody> <tr> <td>Numerical code</td> <td>Letter code</td> <td>Description</td> </tr> <tr> <td>2</td> <td>K1</td> <td>alluvial clay soils, poor in organic matter</td> </tr> <tr> <td>3</td> <td>KV</td> <td>alluvial clay soils, rich in organic matter, peaty clay, clay on peat</td> </tr> <tr> <td>5</td> <td>Le</td> <td>aeolian loamy soils</td> </tr> <tr> <td>6</td> <td>MK</td> <td>maritime clay</td> </tr> <tr> <td>7</td> <td>P</td> <td>quaking bog</td> </tr> <tr> <td>8</td> <td>V</td> <td>peat</td> </tr> <tr> <td>11</td> <td>Z1</td> <td>humus-poor sandy soils (thin humus layer), podzol</td> </tr> <tr> <td>12</td> <td>Z2</td> <td>humus-rich sandy soils (thick humus layer)</td> </tr> <tr> <td>13</td> <td>ZV</td> <td>peaty sandy soils, swampy sandy soils, sandy peat soils</td> </tr> <tr> <td>14</td> <td>L1</td> <td>alluvial loamy soils, poor in organic matter</td> </tr> <tr> <td>15</td> <td>LV</td> <td>alluvial loamy soils, rich in organic matter, peaty loams</td> </tr> <tr> <td>10</td> <td> </td> <td>grounds that do not qualify for NICHE Flanders:</td> </tr> <tr> <td> </td> <td>NG</td> <td>not specified</td> </tr> <tr> <td> </td> <td>B</td> <td>built-up or heavily influenced soils</td> </tr> <tr> <td> </td> <td>D</td> <td>dry soils</td> </tr> <tr> <td> </td> <td>W</td> <td>open water</td> </tr> </tbody> </table> <p>The NICHE soil map is derived from the digital soil map of Flanders (Digital version of the Soil map of Flanders, published on 20/06/2017, Database of the Subsoil in Flanders/Databank Ondergrond Vlaanderen). The steps to convert a unit of the soil map of Flanders into a NICHE soil type are described in detail in the NICHE report (Callebaut et al. 2007: chapter 3 pp 32-45 and appendix 3.3).</p> <p>If field data is available, it is possible to deviate from this (NICHE) soil map: chapter 3.4 of the NICHE report (Callebaut et al. 2007) explains how a NICHE soil type can be assigned to an observed soil profile based on the texture, the thickness and the succession of the different horizons.</p> <p><strong>Format</strong></p> <p>Vectorial geographic information provided as:</p> <ul> <li>shapefile (.shp, .dbf, .shx) with a description of the projection (.prj), spatial indexes (.sbn, .sbx), geospatial metadata in XML format (.shp.xml) and styles (ArcView Layer Format .lyr, Styled Layer Descriptor Format .sld)</li> <li>geopackage (.gpkg) with styles (Styled Layer Descriptor Format .sld)</li> </ul> <p>Geographical reference system: Belge 1972 / Belgian Lambert 72 (<a href="https://epsg.io/31370">EPSG code 31370</a>)</p> <p>Elevation Reference System: Second General Leveling (<a href="https://www.ngi.be/website/tweede-algemene-waterpassing/">Tweede Algemene Waterpassing - TAW</a>)</p> <p><strong>Attributes</strong></p> <ul> <li>fid - Id NICHE soil map (geopackage)</li> <li>gid - Id digital soil map 2017</li> <li>Bodemtype - Soil map units according to the Belgian soil classification system (digital soil map 2017)</li> <li>Bodemser_c - Core soil series: soil series consisting of 3 letters that stand for texture, drainage and profile (digital soil map 2017)</li> <li>Bodemserie - Core soil series: description for texture, drainage and profile (digital soil map 2017)</li> <li>Unitype - Soil map units with sea polders converted to the soil classification of the rest of Flanders (digital soil map 2017)</li> <li>Grove_leg - Generalized legend of the soil map (digital soil map 2017)</li> <li>Substr_V_c - Substrates whose lithological nature differs from that of the superficial layer (lithological discontinuity) (digital soil map 2017)</li> <li>Textuur_c - Soil type, nature of the parent material (digital soil map 2017)</li> <li>Drainage_c - Natural drainage class, natural drainage (digital soil map 2017)</li> <li>Profontw_c - Profile development (digital soil map 2017)</li> <li>Fase_c - Secondary soil features (digital soil map 2017)</li> <li>Varimoma_c - Variant of the parent material (digital soil map 2017)</li> <li>Variprof_c - Variant of the profile development (digital soil map 2017)</li> <li>Streek - Agricultural region (digital soil map 2017)</li> <li>NICHE_let - Letter code of the NICHE soil type</li> <li>NICHE_cijf - Numerical code of the NICHE soil type</li> </ul> <p><strong>Changes since previous version</strong></p> <p>Since previous version (1.1):</p> <ul> <li>NICHE soil map no longer based on version 2001 of the soil map, but on version 20/06/2017, including an update with some military domains (Beverlo, Kleine Brogel, Brasschaat) and with a uni-soil type for the classification of the sea polders, as well as the correction of various errors.</li> <li>names of the fields changed</li> <li>numerical code of the NICHE soil types changed (2-15 instead of 20 000-150 000)</li> <li>a few improvements to the translation between soil types and NICHE soil types: <ul> <li>sV, sV(o): ZV (instead of V in v1.1)</li> <li>uvPep: LV (instead of L1 in v1.1)</li> <li>GDa3x: LV (instead of L1 in v1.1)</li> <li>v-Zdpb(z): Z1 (instead of ZV in v1.1)</li> </ul> </li> </ul> <p><strong>Disclaimer</strong></p> <p>This map provides the best available information but is a simplification of the reality on the field. The real situation on the field prevails at all times over the NICHE soil map for all policy and legal applications.</p>
Indicative distribution map for Ecosystem Functional Group T1.4 Tropical heath forests
<p>This archive contains indicative distribution maps and profiles for <strong>T1.4 Tropical heath forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) 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>
Mapping Croatian scientific diaspora in the UK
<p>This data contains anonymized answers from the study focusing on mapping Croatian researchers and scientists in the UK. We wanted to better understand circumstances and motivation behind the move to the UK, differences of doing research in UK and Croatia and finally extent, challenges and incentives for collaborations with Croatia. <br>This is part of the bigger project of Mapping Croatian scientific diaspora and was a pilot project for building platform mapa-znanstvenika.hr. <br>We collected 40 responses from broad range of researchers from early stages (bachelor, master, PhD students) to professors and lecturer, coming from broad range of scientific fields. </p>
Regional Heat Vulnerability Map and Cooling Solutions: A webtool of the Healthy Urban Environments Initiative
## Regional Heat Vulnerability Map and Cooling Solutions The regional heat vulnerability map and cooling solutions webtool offers two data sources for equitable heat mitigation. The dashboard layers vulnerability data onto land surface temperature regional rankings to identify areas with high and low heat exposure and vulnerability as well as the existing assets in each census block group. Additional layers can be added into the heat vulnerability map to highlight how heat affects critical infrastructures including schools, mobile home parks, parking lots, public transportation stops, pedestrian thoroughfares, and bikeways. The solutions tab showcases a variety of heat mitigation solutions and the research behind them. Heat-related solutions and resources from urban Maricopa County are included, including solutions funded through the Healthy Urban Environment Initiative. The data catalogued here are the underlying data that populate the webtool. ## Healthy Urban Environment (HUE) Initiative - Overview HUE is a solutions-focused research, policy and technology incubator to create healthier communities across Maricopa County (central Arizona, USA) through collaboration between researchers, practitioners and community members. As such, HUE funded rapid development, testing and deployment of heat-mitigation and air-quality improvement strategies and technologies. Heat emerged as the urgent focus, as urban centers across the desert Southwest continue to grow in size and density, aggravating existing challenges posed by the expansion of the built environment. In Phoenix, AZ, this expansion of the built environment creates conditions which magnify the intensity and duration of heat – making it difficult for residents to achieve thermal comfort throughout the day and night. Further, the legacies of urban sprawl and transportation planning in the Phoenix, Arizona metropolitan area have contributed to challenges with atmospheric pollutants. Importantly, urban heat and air qua
Long-term (1993-2019) dynamics of tree populations on a mapped 3-ha permanent plot in old-growth northern hardwood forest, Huron Mts., Marquette Co., MI, USA
This data-set includes multiple remeasurements, over 25 years, of all woody stems >2 cm diameter (total of 2125 stems) on a 2.72-ha stem-mapped plot in old-growth northern hardwood forest in the Huron Mountains region of northern Marquette County, MI. The plot and surrounding forest is dominated by sugar maple (Acer saccharum) and eastern hemlock (Tsuga canadensis). Among secondary species, yellow birch (Betula alleghaniensis) and basswood (Tilia americana) are most common. Soils (identified as Kalkaska series) are developed on deep sandy glacial outwash. The plot is within a much larger region of old-growth forest, protected since ca. 1880, with only minimal disturbance associated with access tracks and trails. Numerous other forest community and dendrochronological studies support the interpretation that the area around the study plot has not experienced stand-initiating disturbance for at least 400 years. Initial mapping and measurements (1993-1995 for 2.52 ha; an additional 0.2 ha added in 1999) used a 20x20 m grid established in a near-level area of uniform substrate. All stems were identified to species, mapped on polar coordinates from the center of each grid cell (including, at first measurement, identifiable dead trees, standing and down), and diameter at breast height (dbh) measured to nearest 0.1 cm. All stems were remeasured on a five-year cycle 1999-2019, and new mortality was recorded at each remeasurement. New recruits > 2 cm dbh were added at each remeasurement.
Long-term (1993-2019) tree population measurements from a mapped 2.9-ha permanent plot in old-growth northern hardwood forest, Dukes Research Natural Area, Marquette Co., MI, USA
The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 permanent monitoring plots (data to be provided in a separate package). In 1993-95, a macroplot of 2.91 ha was established in a mixed mesic upland forest area within the RNA, in which all woody stems >2 cm diameter at breast height (DBH) were identified, measured, and mapped. In 1999 and again every five years subsequently through 2019, the macroplot was recensused; all stems were remeasured, stems newly recruited (>2 cm DBH) were measured and mapped, and any mortality since previous census was noted and described. A severe storm in 2002 resulted in extensive mortality throughout the RNA, particularly in the area in and around the macroplot.
Aspen Forest Stem Map and Tree Census at the University of Michigan Biological Station, Pellston, MI 1974-2018
In 1974, a one hectare plot was established at the University of Michigan Biological Station to further understand successional trajectories of birch and aspen forests in northern Michigan. Trees with a DBH greater than 5 cm were inventoried and later, the location of the tree within the plot was documented by the UMBS resident biologist. Plots were remeasured 5 additional times by different groups at the station.
Stem maps of eight 1 ha forest plots distributed around Ann Arbor, MI and around the University of Michigan Biological Station (UMBS)
In this project we established a network of forest inventory plots to gather the data needed to forecast future forest performance under global change. Data collected from forest inventory plots, i.e., size and location of individual trees from all ages and species, have been shown to be particularly useful to link tree species demographic rates (survival, growth, age at maturity, fecundity) with community characteristics (assemblages and species turnovers), and are also widely used to estimate biomass removal (logging) and biomass production (carbon sequestration).
Textural soil maps, Colombia, 0 - 100 cm
These are the first texture maps of Colombia, obtained from national and global digital soil mapping products. The maps were developed at five standard depths (0-5, 5-15, 15-30, 30-60, and 60-100 cm) and standardized with Additive log-ratio (ALR) transformation. The maps were harmonized at 1 square km of spatial resolution. The data packages include the following set maps: texture maps obtained through the Ensemble Machine Learning (EML) algorithms called landmap and MACHISPLIN; texture maps obtained from SoilGrids platform; residual maps of the texture of the algorithms referenced above; and finally texture maps obtained through spatial ensemble technique.
May to July 2018 ground control points GPS coordinates of tidal marsh and tidal forest plant species to be used as ground control points in habitat mapping.
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground control points (GCP) and ground reference data for habitat mapping. GCPs for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were acquired. A real time kinematic (RTK) GPS survey of GPS coordinates and ground elevations for tidal marsh vegetation was carried out in May of June of 2018. A handheld GPS was used to collect GPS coordinates for tidal forest plant species in July of 2018. A total of 101 GCPs were collected in tidal habitats, with 26 in salt, 28 in brackish and 29 in tidal fresh marsh, and another 18 in tidal fresh forest. These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
Hubbard Brook Experimental Forest: 10-ha bird plot territory maps, 1969 - 2021
Maps showing the estimated territorial boundaries of all bird species occupying the 10-ha bird plot in the Hubbard Brook Experimental Forest, 1969-2021. These data were used in estimating the abundance of bird populations during this period (e.g., Holmes and Sturges 1975, Holmes et al. 1986, Holmes and Sherry 1988, 2001, Holmes 2011). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Papers associated with this dataset: Holmes, R. T., & Sturges, F. W. (1975). Bird Community Dynamics and Energetics in a Northern Hardwoods Ecosystem. Journal of Animal Ecology, 44(1), 175–200. https://doi.org/10.2307/3857 Sherry, T. W. (1979). Competitive interactions and adaptive strategies of American Redstarts and Least Flycatchers in a northern hardwoods forest. The Auk, 96(2), 265-283. Holmes, R. T., Bonney, R. E., & Pacala, S. W. (1979). Guild Structure of the Hubbard Brook Bird Community: A Multivariate Approach. Ecology, 60(3), 512–520. https://doi.org/10.2307/1936071 Holmes, R. T., Sherry, T. W., & Sturges, F. W. (1986). Bird Community Dynamics in a Temperate Deciduous Forest: Long-Term Trends at Hubbard Brook. Ecological Monographs, 56(3), 201–220. https://doi.org/10.2307/2937074 Holmes, R. T., & Robinson, S. K. (1988). Spatial patterns, foraging tactics, and diets of ground-foraging birds in a northern hardwoods forest. The Wilson Bulletin, 377-394. Holmes, R. T., & Sherry, T. W. (1988). Assessing population trends of New Hampshire forest birds: local vs. regional patterns. The Auk, 105(4), 756-768. 10.2307/4087390 Holmes, R. T., & Sherry, T. W. (2001). Thirty-year bird population trends in an unfragmented temperate deciduous forest: importance of habitat change. The Auk, 118(3), 589-609. https://doi.org/10.1093/auk/118.3.589 Holmes, R. T. (2011). Avian population and community process
High resolution shrub cover raster maps of the Jornada Basin LTER, including JER and CDRRC (2011)
This data package contains two raster shrub cover maps of the Jornada Experimental Range (JER) and Chihuahuan Desert Rangeland Research Center (CDRRC) in southern New Mexico, USA. The maps are derived from 1m resolution National Agriculture Imagery Program (NAIP) aerial photos acquired in 2011. A one-meter resolution raster file lists cover as non-shrub or non-shrub. A hectare resolution raster file contains fractional shrub cover values (0-1) calculated from the one-meter raster.
Grass-fed beef producers and retailers map
This data package includes two shapefiles and their associated attribute tables. The two files, GFB_producers_2021-02-18.zip and GFB_retailers_2021-02-18.zip, contain all internet-discoverable (at the time of data collection, July-August 2020; with minor edits/additions circa June 2022) grass-fed beef producers and retailers in the Southwest and Southern Plains of the U.S. (Arizona, California, Colorado, Kansas, Nevada, New Mexico, Oklahoma, Texas, Utah), compiled through an internet search. The data were initially collected in August of 2020 using publicly available information from Google search engine and Google map searches with the intention of informing members of the Sustainable Southwest Beef Project (USDA NIFA grant #2019-69012-29853) team about existing grass-fed beef producers and retailers in the study area.
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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.