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13,117 results for “Maps”
46 Block--63k or 50k scale Maps of South Asia--additional 1--20240105
<p>56 new map files to add to the set of 63k/50k Maps of South Asia, Block 46. An updated Index Map for Block 46 is included in this set of maps.</p>
84 Block--63k or 50k scale South Asia Maps
<p>19 maps of the Survey of India covering block 84.</p>
Evaluating robot workspaces with performance maps
<p><strong>Evaluating robot workspaces with performance maps</strong></p> <p><em>Aline Kluge-Wilkes, Presley Demuner Reverdito</em></p> <p>The following data set was created while validating a proposed method to evaluate robot workspaces with different performance metrics. </p> <p>The underlying paper will be presented at the CIRP CATS 2024, the 10th Conference on Assembly Technology and Systems, hosted in Karlsruhe from 24-26.April.2024 and published thereafter.</p> <p>Deploying mobile robots in line-less and mobile assembly systems without predefined formations necessitates understanding the robot's capabilities and determining the tasks it can perform. The data set concerns performance maps to assess the feasibility of assembly tasks within the robot's workspace. These maps offer quantifiable metrics to compare the suitability of base placements for mobile robots concerning the feasibility of specific assembly tasks.</p> <p>Performance maps are a discretised representation of a particular robot's distribution of selected performance metrics. The current implementation focuses on calculating manipulability, dexterity, and condition number. Using a URDF robot model and task poses as input, the metrics are computed across distributed poses within the robot's workspace at a specified resolution, forming the performance map.</p> <p>The data set contains the application of the performance map to exemplary tasks and robots.</p> <p>A Design of Experiments (DoE) was conducted to investigate the influence of three predictor variables on robot manipulation performance. The predictor variables considered in the study were as follows:</p> <p>1. Resolution (R): Two levels of resolution, 0.08m and 0.12m, were examined to assess their impact on robot performance representation.</p> <p>2. Number of poses (N): Two different quantities of poses, 25 and 50, were studied to understand how they affect the representation of the robot's capabilities.</p> <p>3. Robot Model: Two distinct robot models, the ABB IRB1600-1.2 and the ABB IRB120-0.58, were chosen for the experiments.</p> <p> </p> <p>Eight experiments were designed and executed, each with a unique combination of the predictor variables. These experiments are summarised in the Excel sheet "DOE-tests-and-results". The first table overviews the eight experiments. The following eight tables indicate the positions and the results: the respective calculated robot's performance metrics. </p> <p>The .json files specify the poses for which the respective performance metrics (dexterity index, reciprocal condition number, and manipulability index) per robot were calculated.</p> <p>The included .png files visualise the distribution of each index for each conducted experiment in a performance map. The maps were created with Matplotlib. </p> <p> </p> <p>As a comparison, the "capability maps" of both robot models with both resolutions --- generated with the ROS (Robot Operating System) library Reuleaux (http://wiki.ros.org/reuleaux) --- are provided. The generated files are given in .h5 format. The respective files are named as "CM_...". </p> <p>-------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Acknowledgement:</p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy --- EXC-2023 Internet of Production --- 390621612.</p> <p><br><br> </p>
Data from: mapping endemic freshwater fish richness to identify high priority areas for conservation: an ecoregion approach
<p>Freshwater ecosystems are experiencing accelerating global biodiversity loss. Thus, knowing where these unique ecosystems' species richness reaches a peak can facilitate their conservation planning. By hosting more than 290 freshwater fishes, Iran is a major freshwater fish hotspot in the Middle East. Considering the accelerating rate of biodiversity loss, there is an urgent need to identify species rich areas and understanding of the mechanisms driving biodiversity distribution. In this study, we gathered distribution records of all endemic freshwater fishes of Iran (85 species) to develop their richness map and determine the most critical drivers of their richness patterns from an ecoregion approach. We performed a generalized linear model (GLM) with quasi-Poisson distribution to identify contemporary and historical determinants of endemic freshwater fish richness. We also quantified endemic fish similarity among the 15 freshwater ecoregions of Iran. Results showed that endemic freshwater fish richness is highest in the Zagros Mountains while moderate level of richness was observed between Zagros and Alborz Mountains. High, moderate and low richness of endemic freshwater fish match with Upper Tigris & Euphrates, Namak, and Kavir & Lut Deserts ecoregions respectively. Kura - South Caspian Drainages and Caspian Highlands were the most similar ecoregions and Orumiyeh was the most unique ecoregion according to endemic fish presence. Precipitation and precipitation change velocity since the Last Glacial Maximum were the most important predictors of endemic freshwater fish richness. Areas identified to have the highest species richness have high priority for the conservation of freshwater fish in Iran, therefore, should be considered in future protected areas development.</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>
High-resolution mapping of soil carbon stocks in the western Amazon
<h2>Dear Researchers and Interested Parties,</h2> <p> It is with great enthusiasm that we share our page on Zenodo, where we provide <strong>detailed maps</strong> (30 m resolution) of <strong>soil carbon stocks in Rondônia, Brazil</strong>. These maps were generated using machine learning techniques, using the Random Forest model implemented in the caret package. This initiative aims to provide a deeper and more accurate understanding of the spatial distribution of carbon in the soil, contributing significantly to environmental studies and climate change mitigation strategies in the region.</p> <h2>Available resources:</h2> <h3>High Resolution Maps:</h3> <p>We provide detailed maps of the estimates and uncertainties of soil carbon stocks at different depths (0-5; 5-15; 15-30; 30-60 and 60-100 cm). The maps include mean values (Mg ha<sup>-1</sup>), quantiles (Mg ha<sup>-1</sup>) and coefficients of variation (%), all in "tif" format, with a spatial resolution of 30 m and SAD 1969 Lambert South America projection system (<a href="https://epsg.io/102015">EPSG :102015</a>).</p> <p>The entire process was conducted in open source (R Language). The codes and database used <strong>can be found in the <a href="https://github.com/moquedace/ro_soil_carbon_stock" target="_blank" rel="noopener">GitHub repository</a></strong>, and more information about the methodology is available in the following publication:</p> <p>Moquedace, C. M., Baldi, C. G. O., Siqueira, R. G., Cardoso I. M., Souza, E. F. M., Fontes, R. L. F., Francelino, M. R., Gomes, L. C., Fernandes-Filho, E. I. High-resolution mapping of soil carbon stocks in the Western Amazon. <em>Geoderma Regional</em>, v. 36, p. e00773, 2024. DOI: <a href="https://doi.org/10.1016/j.geodrs.2024.e00773">10.1016/j.geodrs.2024.e00773</a></p> <h2>Availability objectives:</h2> <h3>Promote scientific collaborations:</h3> <p>We encourage researchers, scientists, and organizations to explore and use this data to enrich their own research and projects related to soil carbon and climate change.</p> <h3>Enhance environmental understanding:</h3> <p>By providing open access to these maps, we aim to contribute to a deeper understanding of environmental processes in Rondônia, Brazil and, by extension, enable the implementation of sustainable strategies.</p> <h3>Stimulating innovation:</h3> <p>We believe that sharing this data will stimulate innovation in modeling and spatial analysis methods, driving advances in the prediction of soil carbon stocks, especially in the Amazon.</p> <h2>Thank you in advance for your interest and collaboration. Together, we can advance knowledge and the search for sustainable solutions to important environmental challenges.</h2> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Predictive mapping of tree species assemblages in an African montane rainforest
<p>Conservation of mountain ecosystems can benefit from knowledge of habitats and their distribution patterns. This benefit is particularly true for diverse ecosystems with high conservation values such as the "Afromontane" rainforests. We mapped the vegetation of one such forest: the rugged Bwindi Impenetrable Forest, Uganda—a World Heritage Site known for its many restricted-range plants and animal taxa including several iconic species. Given variation in elevation, terrain and human impacts across Bwindi, we hypothesised that these factors influence the composition and distribution of tree species. To test this, detailed surveys were carried out using stratified random sampling. We established 289 georeferenced sample sites (each with 15 trees ≥20 cm dbh) ranging from 1,320 to 2,467 m a.s.l. and measured 4,335 trees comprising 89 species that occurred in four or more sample sites. These data were analysed against twenty-one digitally mapped biophysical variables using various analytical techniques including non-metric multidimensional scaling (NMDS) and random forests. We identified six tree species assemblages with distinct compositions. Among the biophysical variables, elevation had the strongest correlation with the ordination (r<sup>2</sup>=0.5; <em>p</em><0.001). The "out-of-bag" (OOB) estimate of the error rate for the best final model was 50.7% meaning that nearly half of the variation was accounted for using a limited set of variables. We demonstrate that it is possible to predict the spatial pattern of such a forest based on sampling across a highly complex landscape. Such methods offer accurate mapping of composition that can guide conservation.</p>
Data from: Male mouse recombination maps for each autosome identified by chromosome painting
<p>Linkage maps constructed from genetic analysis of gene order and crossover frequency provide few clues to the basis of the genomewide distribution of meiotic recombination, such as chromosome structure, that influences meiotic recombination. To bridge this gap, we have generated the first cytological recombination map that identifies individual autosomes in the male mouse. We prepared meiotic chromosome (synaptonemal complex [SC]) spreads from 110 mouse spermatocytes, identified each autosome by multicolor fluorescence in situ hybridization of chromosome- specific DNA libraries, and mapped 12,000 sites of recombination along individual autosomes, using immunolocalization of MLH1, a mismatch repair protein that marks crossover sites. We show that SC length is strongly correlated with crossover frequency and distribution. Although the length of most SCs corresponds to that predicted from their mitotic chromosome length rank, several SCs are longer or shorter than expected, with corresponding increases and decreases in MLH1 frequency. Although all bivalents share certain general recombination features, such as few crossovers near the centromeres and a high rate of distal recombination, individual bivalents have unique patterns of crossover distribution along their length. In addition to SC length, other, as-yet-unidentified, factors influence crossover distribution leading to hot regions on individual chromosomes, with recombination frequencies as much as six times higher than average, as well as cold spots with no recombination. By reprobing the SC spreads with genetically mapped BACs, we demonstrate a robust strategy for integrating genetic linkage and physical contig maps with mitotic and meiotic chromosome structure.</p>
QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat
<p>The mapping population comprises of 180 RILs developed from a cross between GW322 and KAUZ.</p> <p><strong>Phenotypic data</strong><br>Phenotypic evaluation was conducted across two consecutive crop seasons (2021-22 and 2022-23) under late sown irrigation (LSIR) and late sown restricted irrigation (LSRI) conditions at ICAR-IARI, New Delhi. Various physiological and agronomic traits of importance were measured. The component traits of yield including days to heading (DH), normalized difference vegetation index (NDVI), SPAD chlorophyll content (SPAD), plant height (PH), spike length (SL), thousand-grain weight (TGW), grain weight per spike (GWPS), biomass (BM) and grain yield per plot (PY) were measured under heat and combined stress conditions.</p> <p><strong>Genotypic data</strong><br>DNA was isolated from 21-day-old seedlings using the CTAB method (Murray and Thompson, 1980). DNA quality check was done using 0.8% agarose gel electrophoresis. The Axiom Breeders' array containing 35K Single Nucleotide Polymorphism (SNP) was employed for the genotyping of RILs and parents.</p>
Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
<p>The mapping population consists of 166 recombinant inbred lines (RILs) derived from a cross between HD3086 and HI1500.</p> <p><strong>Phenotypic data</strong><br>The RILs population along with parents were evaluated under four conditions namely timely sown irrigation (TSIR) taken as control, timely sown restricted irrigation (TSRI), late sown irrigation (LSIR), and late sown restricted irrigation (LSRI) conditions at Delhi, and under restricted irrigation condition at Indore. From each plot, 20 random spikes were harvested and spikes from each plot were threshed separately. While cleaning, care was taken to prevent metal and dust contamination. The grain iron concentration (GFeC) and grain zinc concentration (GZnC) were measured using Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000 M/s Oxford Inc, USA). The thousand kernel weight (TKW) was recorded by counting 1000 grains manually and weighted with an electronic balance.</p> <p><strong>Genotypic data</strong><br>DNA was extracted from 21 days old seedlings using CTAB method (Murray and Thompson, 1980). Genomic DNA quality was determined using 0.8% agarose gel electrophoresis with λ DNA as the standard and quantified using nanodrop. The 35K SNP Axiom breeders' array was used for genotyping of parents and the RILs population.</p>
Paper is not enough: Crowdsourcing the T<sub>1</sub> mapping common ground via the ISMRM reproducibility challenge
Dataset provided for NeuroLibre preprint. Author repo: https://www.github.com/rrsg2020/note NeuroLibre fork:https://github.com/roboneurolibre/note <p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/23">NeuroLibre technical screening.</a></p> <p><strong><a href="https://neurolibre.org" target="NeuroLibre">https://neurolibre.org</a></strong></p>
GIS data of urchin barren mapping in Northeastern New Zealand
<p>On shallow rocky reefs in northeastern Aotearoa, New Zealand, urchin barrens are recognised as indicators of the ecosystem effects of overfishing reef predators. Yet, information on their extent and variability is lacking. We use aerial imagery to map the urchin barrens and kelp forests on reefs (<30 m depth) across seven locations, including within two long-established marine reserves and a marine protected area that allows recreational fishing. Urchin barrens were present in all locations and were restricted to reefs <10-16 m deep. This archive contains ArcGIS shapefiles and layer files for all of the maps used in this study. The study area extends from Cape Reinga in the far north of the North Island to Tawharanui in the Hauraki Gulf near Auckland. Regional scale base maps of the prominent marine habitats were included along with the seven fine-scale maps where the kelp forests and urchin barrens were mapped.</p>
Spatial mapping of the hepatocellular carcinoma landscape identifies unique intratumoural perivascular-immune neighbourhoods
<p>The uploaded data includes results from imaging mass cytometry (IMC) data collected from hepatocellular carcinoma patients. The associated publication can be found <a href="https://journals.lww.com/hepcomm/fulltext/2024/11010/spatial_mapping_of_the_hcc_landscape_identifies.11.aspx">here</a> (Marsh-Wakefield <em>et al.</em>, 2024, <em>Hepatology Communications</em>).</p> <p>The CSV file contains segmented cells from IMC data. This includes the Patient, ROI, and Group each cell is assigned. Marker signal intensities underwent arcsine transformation and were rescaled. The “simprof_cluster” column contains the final iteration of clustering following initial X-shift clustering.</p> <p>Notes on additional columns:</p> <ul> <li>“Sample” is barcoded such that the first three digits are the ablation number, followed by the region on the TMA, the group, and the patient. I.e., “[ablation.number]_[TMA.location]_[group]_[patient]”.</li> <li>“x” and “y” refer to the coordinates of samples.</li> <li>“Group” refers to the tissue type. Included non-tumour (NT), invasive margin (IM), and tumour (T) regions.</li> <li>The area for each ROI has been calculated (µm^2 and mm^2).</li> <li>“Batch” refers to TMA.</li> <li>In most cases each area from each patient has three ablation sites. Three samples have an extra ablation site due to technical difficulties during the ablation, and hence have a “split” sample.</li> </ul> <p>The PDF file contains patient information associated with the IMC data.</p> <p>DOI of dataset:</p> <p>10.5281/zenodo.10622397</p> <p>Any further questions can be addressed to Felix Marsh-Wakefield felix.marsh-wakefield@sydney.edu.au</p>
Mapping SARS-CoV-2 antigenic relationships and serological responses
<p>During the SARS-CoV-2 pandemic, multiple variants escaping pre-existing immunity emerged, causing concerns about continued protection. Here, we use antigenic cartography to analyze patterns of cross-reactivity among a panel of 21 variants and 15 groups of human sera obtained following primary infection with 10 different variants or after mRNA-1273 or mRNA-1273.351 vaccination. We find antigenic differences among pre-Omicron variants caused by substitutions at spike protein positions 417, 452, 484, and 501. Quantifying changes in response breadth over time and with additional vaccine doses, our results show the largest increase between 4 weeks and >3 months post-2nd dose. We find changes in immunodominance of different spike regions depending on the variant an individual was first exposed to, with implications for variant risk assessment and vaccine strain selection.</p>
EyeCatchingMaps, a Dataset to Assess Saliency Models on Maps
<div>EyeCatchingMaps, a Dataset to Assess Saliency Models on Maps</div> <div> </div> <div> <p>This dataset is made up of a total of 322 maps. It is made up of maps from google maps, ign and osm.</p> <p>It also includes maps from school textbooks.</p> </div> <div> </div> <div>In the folder map we have all the map of the datatset</div> <div> </div> <div>in the folder coord_on_map, for each map, a file containing all the fixation points of each participant, named ’coord_fixation_name_map.png.csv’ in CSV format with the following elements and during a fixation, a person looks at an area and can move slightly. The fixation will then be represented by a few points:</div> <div>- worl_index : the id of fixation</div> <div>- id_fixation : the id of fixation point</div> <div>- time : the time of the fixation point in relation to the time of the survey</div> <div>- x : x coordinate of the point of fixation on the image</div> <div>- y : y coordinate of the point of fixation on the image</div> <div>- dispersion : distance between all gaze locations during a fixation, in degree</div> <div>- accuracy : in degree</div> <div>- precision :in degree</div> <div>- participant : Participant ID</div> <div>- distance : distance between eye-tracker and screen incm</div> <div>- time_to_map : the time of the fixation point in relation to the map display</div> <div>- height : height of the map</div> <div>- width : width of the map</div> <div> </div> <div>In the folder heatmap we have heatmaps for each map named ’heatmap_name_map.png’.</div> <div> </div> <div> </div> <div> <div> <div>in the folder 'script' there are the different scripts used to produce the data</div> </div> </div>
Current and Future Flood maps for Flood risk assessment under the Shared Socioeconomic Pathways in the Greater Accra region, Ghana
<p>The study used 15 flood conditioning factors in simulating current and future flood conditions under the SSP scenarios using the Frequency Ratio (FR) model </p>
Causal functional maps of brain rhythms in working memory
<p>Results of the meta-modeling of transcranial alternating current stimulation (tACS) studies in working memory. Two files are for theta and gamma maps in MNI brain space. The files accompany the paper "Causal functional maps of brain rhythms in working memory" by Miles Wischnewski*, Taylor A Berger, Alexander Opitz, and Ivan Alekseichuk**. Correspondance: *m.wischnewski@rug.nl or **ialeksei@umn.edu</p> <p> </p>
Replication Package: Product-Line Engineering for Smart Manufacturing: A Systematic Mapping Study on Security Concepts
<p><strong>Welcome to the public repository for the additional content of the paper "Product-Line Engineering for Smart Manufacturing: A Systematic Mapping Study on Security Concepts", accepted at the ICSOFT 2024.</strong></p> <p>This repository provides additional information to the conducted mapping study, including the following file:</p> <ul> <li>analysis_sheet_ICSOFT2024.csv: sheet containing information regarding the analysis results of 43 included papers based on the extraction criteria.</li> </ul>
Satellite Image Maps and GIS File of the Masara, Maco, Davao de Oro Landslide
<p>These maps and GIS file provide visualizations of the landslide that occurred in Brgy. Masara, Maco, Davao de Oro, Philippines on 06 February 2024.<br><br>The PlanetScope Super Dove satellite image was acquired four (4) days after the landslide event (10 February 2024). Using the satellite image, the extents of the landslide were manually delineated using GIS software. We estimated that the larger landslide has a surface area of approximately 85,960 sq. m., while the smaller one measures approximately 759 sq. m. Unfortunately, some portions of the image near the landslide area are contaminated by cloud shadows, making it challenging to visualize and accurately map the extent of the landslide.<br><br>Also included is a high-resolution satellite image depicting Brgy. Masara before the occurrence of the landslide. The image was acquired sometime in November 2019. The manually delineated extent of the landslide area (from the PlanetScope image) is overlaid to easily visualize the impact of the landslide on the nearby community.<br><br>Credits:<br><br>* GIS analysis and map preparation: Jojene R. Santillan<br>* Imagery © 2024 Planet Labs Inc., Maxar<br><br>Access to the PlanetScope image was made possible by the Planet Education and Research Program.<br><br>****<br>Disclaimers:</p> <p>The maps and GIS files ("Products") shall be used for non-commercial, reference purposes only. They shall not be used as replacements to authoritative maps and information provided/issued by mandated government agencies.</p> <p>Accuracy and Limitations: The accuracies of the Products are dependent on the source datasets, and limitations of the software and algorithms used and implemented procedures. The Products are provided “as is” without any warranty of any kind, expressed or implied. CCGeo does not warrant that the Products will be complete, and meet the needs or expectations of the user, or that the operations or use of the maps and GIS files will be error-free.</p> <p><br>Limitation of Liability: CCGeo and Caraga State University will not be held liable for any incidental, consequential, special, exemplary, or indirect damages (including lost profits and lost data) arising from or relating to the use of the maps and GIS files.</p>
SNP markers used for QTL mapping in the inbred lines
<p><span>Young leaves of the 175 inbred lines and their seven parents were collected from seedlings grown in a greenhouse. </span><span>About 200 mg bulk leaf sample from three plants of a line was placed in 2 ml safe-lock </span><span>Eppendorf tube and stored at ‒80 </span><span>˚C for one night prior to crushing using a Mixer Mill (TissueLyser II, Qiagen, Germany). Genomic DNA was extracted using SIGMA DNA extraction kit (Sigma-Aldrich, St. Louis, MO, USA) following the manufacturer's instruction. DNA concentration and purity of the samples were assessed using a NanoDrop 2000c spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The samples were processed and sequenced using tunable genotyping-by-sequencing (tGBS®) method by Data2Bio (Ames, IW, USA). Genomic DNA was digested using two restriction enzymes NSpI (5′-RCATG^Y-3′) and BfuCI/Sau3AI (5′-^GATC-3′) which created 3´and 5´overhangs, respectively. Two single-stranded oligos, one containing a sample-specific internal barcode and the other a universal oligo, were ligated to the complementary 3´ and 5´ overhangs, respectively. </span>All 175 inbred lines' and seven parents' treated DNA was pooled for construction of the tGBS library and sequencing. The raw sequence data were demultiplexed by barcode, which was subsequently removed bioinformatically from each sequence. The barcode-trimmed sequence reads of genotype were further trimmed using the trimming software, Lucy (Chou & Holmes, 2001; Li & Chou 2004) to remove low-quality reads based on Phred quality scores of Q15.</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.