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13,117 results for “Maps”
Fig. 8. – Distribution maps. A. Panicum cupressifolium A in Revision of the group previously known as Panicum L. (Poaceae: Panicoideae) in Madagascar
Fig. 8. – Distribution maps. A. Panicum cupressifolium A. Camus (stars), P. danguyi A. Camus (triangles), P. dregeanum Nees (circles) and P. flacourtii A. Camus (squares); B. Panicum humbertii A. Camus (stars), P. humile Nees ex Steud. (triangles), P. ibitense A. Camus (circles) and P. inconspicuum Voronts. (squares).
Fig. 16. – Distribution maps. A in Revision of the group previously known as Panicum L. (Poaceae: Panicoideae) in Madagascar
Fig. 16. – Distribution maps. A. Panicum subalbidum Kunth (stars), P. subhystrix A. Camus (triangles), P. trichocladum K. Schum. (circles) and P. voeltzkowii Mez (squares); B. Panicum vohitrense A. Camus (stars), Trichanthecium brazzavillense (Franch.) Zuloaga & Morrone (triangles), T. parvifolium (Lam.) Zuloaga & Morrone (circles) and Urochloa maxima (Jacq.) R.D. Webster (squares).
A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types
<p>Dataset accompanying manuscript <em>"A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types". </em>Datasets contain a wall-to-wall map of vegetation types covering the study area of terrestrial Norway, produced using three methods for assembling individual predictions from Distribution models (<em>probability-based method</em>, <em>performance-based method</em> and <em>prevalence-based method</em>). </p>
Fig. 1. Map showing the collecting sites. A–C in Three Species of Grandidierella (Crustacea: Amphipoda: Aoridae) from Coastal Areas of the Tohoku and Kanto-Tokai Districts, East Japan, with the Description of Two New Species
Fig. 1. Map showing the collecting sites. A–C, Grandidierella sanrikuensis sp. nov.; D, G. rubroantennata sp. nov.; E–G, G. osakaensis Ariyama, 1996. In sites C, E and G, G. japonica Stephensen, 1938 was also collected nearby.
Fig. 5. Map showing the localities where Poecilobdella nanjingensis Yang, 1996 in First Record of Poecilobdella nanjingensis (Hirudinida: Arhynchobdellida: Hirudinidae) from Taiwan and its Molecular Phylogenetic Position within the Family
Fig. 5. Map showing the localities where Poecilobdella nanjingensis Yang, 1996 has been recorded. Closed circles indicate localities reported herein, open circles those from previous studies: aYang (1996) and bYang (2014).
Fig. 1. Maps showing nototanaid distributions. A in Nototanaids (Crustacea: Tanaidacea) from Japan, with the Description of a New Species of Nototanoides
Fig. 1. Maps showing nototanaid distributions. A, global distribution of nototanaids, including the sampling sites for Nototanoides ohtsukai sp. nov.; B, nototanaid records in Japan, with the sampling sites for Nototanoides ohtsukai sp. nov. Symbols: red diamonds, Nototanoides; green triangles, Nesotanais; blue circles, Nototanais; black circles (1–4), Gamboa, Birdotanais, Paranesotanais, and Stachyops, respectively.
Spectroscopic ellipsometry mapping of PAAO (AJ-4-04-20 sample)
<p>Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide (PAAO). The sample was made by anodization of aluminum monocrystal in 0.3 mol/L oxalic acid at 40 V for 4 minutes and 30 seconds.</p> <p>The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm<sup>2</sup>). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-4-04-20 Ellipsometry Mapping Measurements.rar" file.</p> <p>Ellipsometer: rotating compensator GES5-E (Semilab).</p> <p>Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum.</p> <p>Detector: UV-Vis CCD with 0.8 nm resolution.</p> <p>Spectral range: approximately 230-960 nm.</p> <p>Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°.</p> <p>Light beam size: microspot (365 × 470 μm<sup>2</sup> at 75° angle of incidence).</p> <p>The same sample was also measured using the same spectroscopic ellipsometry method after being covered with gold nanoparticles. The data can be found here: https://doi.org/10.5281/zenodo.7115401</p>
Spatially mapping the baseline and bisphenol-A exposed Daphnia magna lipidome using desorption electrospray ionisa-tion - mass spectrometry
<p>Data from desorption electrospray ionisation - mass spectrometry of <em>Daphnia magna</em> tissue section from control and daphnids exposed to 5 ppm of bisphenol A over their 7<sup>th</sup> adult instar, specifically four sampling points; 8, 24, 48 and 72 h after the 6<sup>th</sup> brood, as well as data acquired from a blank DESI slide.</p> <p>Data is provided in the form of *imzML files with the associated *.ibd of the same name.</p>
Fig. 1. Map showing a in A New Species of the Sand-burrowing Dogielinotidae, Haustorioides furotai, from Tokyo Bay, Japan (Crustacea: Amphipoda)
Fig. 1. Map showing a collecting site, Banzu tidal flat, Kisarazu City, Chiba Prefecture, Japan (filled circle).
Fig. 1. Map showing the sampling sites. A in Two New Species of Microloxoconcha (Crustacea: Ostracoda) from the Sublittoral Zone in Western Japan
Fig. 1. Map showing the sampling sites. A, western area of Japan; B, type locality of Microloxoconcha toyoshioae n. sp. (solid circle); C, type locality of Microloxoconcha sublittoralis n. sp. (open circle).
Original data and RML mapping used to generate RDF results for K-CAP 2021
<p>These materials include the following:</p> <ul> <li>The csvs used to track the accepted papers, authors and resources in K-CAP 2021</li> <li>The RML mappings generated to transform them to RDF (paths to data will have to be adjusted)</li> <li>The resultant RDF file generated when applying the mapping (out.ttl)</li> </ul>
Global mapping of lunar refractory elements: multivariate regression vs. machine learning
<p>The quantitative estimation of elemental concentrations at the spatial resolution of hyperspectral near-infrared (NIR) images<br> of the lunar surface is an important tool for understanding the processes relevant for the origin and evolution of the Moon. The NIR reflectance of the lunar regolith is an integrated response to the presence of refractory elements and soil alteration processes. Our approach was to define a combination of spectral parameters that are robust with respect to the effects of soil maturity.<br> We calibrated the spectral parameters with respect to elemental abundances measured by the Lunar Prospector Gamma Ray Spectrometer (LP GRS) and the Kaguya GRS (KGRS). For this purpose, we compared a classical multivariate linear regression (MLR) approach and the machine learning based support vector regression (SVR) technique applied to M3 global observations. The M 3 -based global elemental maps are consistent in distribution and range with the LP GRS and KGRS elemental maps<br> and do not show artifacts in immature areas such as small fresh craters. The results derived using MLR and SVR are compared to<br> sample-based ground truth data of the Apollo and Luna sample-return sites, where the root-mean-square deviations obtained by the<br> two regression models are similar. The main advantage of the proposed new algorithm is its ability to minimize artifacts due to space-weathering effects. The elemental maps of Mg and Ca provide additional information and reveal structures not always visible in the Fe map. The global elemental abundance maps derived for the fully calibrated M 3 observations might thus serve as important tools to investigate the lunar geology and evolution.</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>
Distribution map of occurence of bats roosting in caves in Cameroon
<p>Distribution map of occurence of bats roosts in the caves of Cameroon, as observed by the author during the 2009-2010-2011 speleological prospections. The order (Megachiroptera, Microchiroptera), based on visual assessment, is noted when available.</p>
Surveys for mapping migration paths
<p>In May-June 2021, <a href="http://cost.eu/actions/CA16111/">ETHMIGSURVEYDATA</a>—a COST Action and research network dedicated to quantitative surveys on ethnic and migrant minorities' (EMMs') integration and inclusion—organized a <em>Policy Dialogue Webinar Series </em>to share ETHMIGSURVEYDATA’s main results, as well as to showcase EMM projects that are ongoing in policy-relevant institutions. These webinars have included presentations by the European Asylum Support Office (EASO), the European Commission Joint Research Centre (JRC), the International Organization for Migration (IOM), and the Organisation for Economic Co-operation and Development (OECD).</p> <p>This is the video recording for the 2nd webinar of the <em>Policy Dialogue Webinar Series, </em>which was held on 25 May 2021: "Surveys for mapping migration paths." This video recording has also been made available on the <a href="https://www.youtube.com/watch?v=tec5qKcE81E">ETHMIGSURVEYDATA Youtube channel</a>.</p> <p>The full program for this webinar can be found below:</p> <p>Speakers</p> <ul> <li>Laura Bartolini, IOM-Italy - IOM’s Displacement Tracking Matrix and mixed migration flows in the Mediterranean</li> <li>Philippe Wanner, University of Geneva - Measuring Migrants’ Integration in Switzerland. The Migration-Mobility Survey</li> <li>Cris Beauchemin, Institut national d'études démographiques (Ined) - Trajectories and Origins Surveys (France): Beyond the “immigration bias”</li> </ul> <p>Chair</p> <ul> <li>Marcello Carammia, University of Catania</li> </ul>
Map. Ancient Vidarbha showing find-spots of Vākāṭaka inscriptions
<p>Map. Ancient Vidarbha showing find-spots of Vākāṭaka inscriptions (red = copper plate charter, brown = stone inscription).</p>
Ca2+ activity maps of astrocytes tagged by axo-astrocytic AAV transfer
<p>Astrocytes exhibit localized Ca<sup>2+</sup> microdomain (MD) activity thought to be actively involved in information processing in the brain. However, functional organization of Ca<sup>2+</sup> MDs in space and time in relationship to behavior and neuronal activity is poorly understood. Here, we first show that Adeno-Associated Virus (AAV) particles transfer anterogradely from axons to astrocytes. Then we use this axo-astrocytic AAV transfer to express genetically encoded Ca<sup>2+</sup> indicators at high contrast circuit-specifically. In combination with two-photon microscopy and unbiased, event-based analysis we investigated cortical astrocytes embedded in the vibrissal thalamocortical circuit. We found a wide range of Ca<sup>2+</sup> MD signals, some of which were ultrafast (≤300 ms). Frequency and size of signals were extensively increased by locomotion but only subtly with sensory stimulation. The overlay of these signals resulted in behavior dependent maps with characteristic Ca<sup>2+</sup> activity hotspots, maybe representing memory engrams. These functional subdomains are stable over days, suggesting subcellular specialization.</p>
Interpolated depth to water table (groundwater) maps for the continental United States
<p><strong>DATA:</strong></p> <p>This is a collection of depth to water table maps with uncertainty estimates for the continental United States for years 1989 and 2019. Data used to create these maps were obtained from the National Ground-Water Monitoring Network (NGWMN). Data included 14,351 sites and 17,632,047 observations for the years 1989-2019. To improve our inference a set of auxiliary variables proven to have a relation with depth to water table were included. We paired point estimates of depth to water table data with environmental data, as well as terrain variables derived out of a base digital elevation model (DEM) created by NASA at a 1x1km resolution. Climatic layers (temperature, precipitation, and snow melt equivalent) for 1989-2019 were obtained from Daymet (Version 4), which provides a continuous grid of historical monthly and annual weather data, with a 1x1km spatial resolution (Thornton et al., 2020). Out of the DEM, primary (slope, aspect) and secondary terrain attributes (curvatures, upslope contributing areas) were used to calculate a compound topographic index (CTI). </p> <p> </p> <p><strong>MODELING FRAMEWORK:</strong></p> <p>Water table depth analyses were conducted using a three-step interpolation approach: 1) we utilized gradient boosted regression trees (GBRT) to make predictions, 2) we used kriging interpolation on GBRT residuals to reduce bias from spatial autocorrelation, to incorporate a spatial correlation structure and to create uncertainty maps, and 3) we then combined the GBRT and kriging predictions for the final map. This method is equivalent to a Universal Kriging, where in our case, we evaluated the trend using GBRT. Model metrics were calculated for the training (80% of the data) and validation (20% of the data) datasets to evaluate overall performance.</p> <p>*** Uncertainty is greater surrounding the 1989 interpolations due to a lower number of observations.</p>
2017–2018 Land Cover Map of Pyrénées-Atlantiques
<p>This archive contains:</p> <p><br> ├── classification_dpt64_16_classes.tif : the 16 classes land cover map</p> <p>├── classification_dpt64_16_classes_confusion_matrix.png : the confusion matrix. Have a look at it, it is performed on a different dataset than the one used for training the classifier.</p> <p>├── classification_dpt64_21_classes.tif : the 21 classes land cover map including post-treatments (<a href="https://outlook.office.com/owa/redir.aspx?REF=IplRG6_B9OG8Rjvmh5Epv5dpru9Rpr5pwX-IxbR_kY-VTC7cUdfZCAFodHRwczovL2ZyYW1hZ2l0Lm9yZy9TY2h3YWFiL3Byb2pldF9wcmVkYXRldXJzNjQvLS9ibG9iL21haW4vc2NyaXB0cy9DbGFzc2lmaWNhdGlvblBvc3RQcm9jZXNzLnB5">https://framagit.org/Schwaab/projet_predateurs64/-/blob/main/scripts/ClassificationPostProcess.py</a>)</p> <p>├── colorFile.txt : color file for symbology</p> <p>├── configfile_iota2.cfg : iota2 configuration file (in case you are already using iota2. If not, what are you waiting for ?)</p> <p>├── document_methodologique.pdf : technical report (french) for the classification </p> <p>├── nomenclature.txt : nomenclature file</p> <p>├── reference_data_2018.shp : the training and validation data set in its 2018 version (for crops)</p> <p>├── reference_data_2019.shp : the training and validation data set in its 2019 version (for crops)</p> <p>├── reference_photo_interpretation.shp : the part of the training and validation data set that has been photo interpreted with a field giving the potential species or combinations of associated vegetation</p> <p>├── reference_tree_nomenclature.png : a visual about the reference data</p> <p>├── stratification_3_zones.shp : the stratification layer that has helped improve classification results. It is based on landscape entities (<a href="https://outlook.office.com/owa/redir.aspx?REF=-inpMPbdxkotoNFtrpo0ptiZmF3YJRSM91bnORcpCHKVTC7cUdfZCAFodHRwczovL2RhdGEubGU2NC5mci9leHBsb3JlL2RhdGFzZXQvZW50aXRlLXBheXNhZ2VyZS8.">https://data.le64.fr/explore/dataset/entite-paysagere/</a>) </p> <p>├── style_16_classes.qml : the Qgis style layer 16 classes</p> <p>└── style_21_classes.qml : the Qgis style layer 21 classes<br> </p> <p>Description:</p> <p><br> The land cover map of the French department Pyrénées-Atlantiques (64) is based on Sentinel-2 (L2A level) satellite images performed with Iota² chain (<a href="https://outlook.office.com/owa/redir.aspx?REF=xF93blZK9Vxs-N7pnBh9q489HLjMtSEZRU6VkhywO-qVTC7cUdfZCAFodHRwczovL2ZyYW1hZ2l0Lm9yZy9pb3RhMi1wcm9qZWN0L2lvdGEyLw..">https://framagit.org/iota2-project/iota2/</a>). The algorithm used is Random Forest. The time series used ranges from 2017 to 2018.</p> <p>During the development phase of this classification, the collection of additional training data on the photo-interpreted classes 'landes basses' (low heath shrublands), 'landes hautes' (high heath shrublands) and 'landes hautes avec arbres' (high heath shrublands with young-growth forest) has led to a remarkable increase of the number of pixels of these classes and with it the visual quality of the map. However, this increase has been linked with only minor to almost no significant improvement of the F-scores on these classes. Some are still massively confused with other land covers like grasslands and broadleaf mature forests. Especially the mixed class 'landes hautes avec arbres' (high heath shrublands with young-growth forest).<br> <br> We take it as a limit of the reference data that is built from divers data sources and would always beneficiate from more training samples of shrubby classes and a better precision of the class 'forêt de feuillus' (broadleaf mature forests). But this could also show the limit of pixel-oriented classifications for mixed/textured classes (classes with high intra-class heterogeneity). Experimentations using a contextual method – the Auto-context method now being included in Iota2 thanks to Dawa Derksen and Iota2 developers (<a href="https://outlook.office.com/owa/redir.aspx?REF=-QXKxpHR73RRUGsNGVs-FtQHPORNqP2y2wLSVVK_L-uVTC7cUdfZCAFodHRwOi8vbGFubmlzdGVyLnVwcy10bHNlLmZyL29zby9kb25uZWVzd3d3X1RoZWlhT1NPL2lvdGEyX2RvY3VtZW50YXRpb24vZGV2ZWxvcC9hdXRvQ29udGV4dC5odG1s">http://lannister.ups-tlse.fr/oso/donneeswww_TheiaOSO/iota2_documentation/develop/autoContext.html</a>) – has unfortunately not been conclusive on that matter yet.</p>
Figs. 372–377. Distribution maps. 372 in Noideattella and Tolegnaro, Two New Genera of Goblin Spiders from Madagascar, with Comments on the Gamasomorphoid and Silhouettelloid Oonopids (Araneae, Oonopidae)
Figs. 372–377. Distribution maps. 372. Noideattella amboa (circles), Noideattella assumptia (triangles). 373. Noideattella gamela (circles), Noideattella famafa (triangles). 374. Noideattella saka (circles), Noideattella mamba (triangles). 375. Noideattella lakana (circles), Noideattella farihy (triangles), Noideattella tany (squares). 376. Noideattella tsiba (circles), Noideattella fantara (triangles). 377. Tolegnaro sagani (circles), Tolegnaro kepleri (triangles).
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.