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13,117 results for “Maps”
Map 3 in The Pseudolathra species of the East Palaearctic and the Oriental regions (Coleoptera: Staphylinidae: Paederinae)
Map 3: Distributions of Pseudolathra pulchella (triangles; open triangles: female-based records), P. vellicans (filled circles), P. transversicollis (open squares), based on revised records.
Map 1 in The Pseudolathra species of the East Palaearctic and the Oriental regions (Coleoptera: Staphylinidae: Paederinae)
Map 1: Distributions of Pseudolathra unicolor (triangles; open triangles: female-based records), P. tichomirovae (open squares), P. lineata (filled squares), P. cordiformis (filled diamond), and P. brevincisa (open circle), based on revised records.
API Deprecation: A Systematic Mapping Study
<p>Dataset for our systematic mapping study on API Deprecation:</p> <p>L. Bonorden and M. Riebisch, "API Deprecation: A Systematic Mapping Study," in <em>48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2022)</em>, Maspalomas, Spain, 2022.</p> <p> </p> <p>We provide the decisive criteria for excluded studies and the complete classification for included studies.</p> <p>Furthermore, we provide a list of related works—i.e., references and citations identified through snowballing on the included studies. The IDs in the data set relate to entries in the Semantic Scholar Academic Graph, which may be accessed via <em>semanticscholar.org/paper/{ID}</em>.</p> <p><em>(The list of related works has been added in version 2 of this data set.)</em></p>
Genome-wide mapping of individual replication fork velocities using nanopore sequencing
<p>These data are related to the <strong>"Genome-wide mapping of individual replication fork velocities using nanopore sequencing" </strong>manuscript by Theulot, Lacroix et al. (https://doi.org/10.1038/s41467-022-31012-0) and to the GitHub repository NanoForkSpeed (https://github.com/LacroixLaurent/NanoForkSpeed).</p> <p>BT1_run4.tar.gz contains the nanopore reads from a typical experiment as fast5 files.</p> <p>BT1_run4_mega.zip contains the result of the BrdU basecalling as described on the GitHub page in a modified bam file format as described on the GitHub page NanoForkSpeed/BrdU_Basecalling..</p> <p>BT1_run4_Megalodon_00_smdata.rds contains the result of the parsing function for the modified bam file as described on the GitHub page NanoForkSpeed/BrdU_Basecalling.</p> <p>BT1_run4_Megalodon_00_NFS_data.rds contains the result of the fork detection procedure on the BT1_run4_Megalodon_00_smdata.rds file as described on the GitHub page NanoForkSpeed/Forks_Detection.</p> <p>BT1_run4_merged_NFS_data.rds contains the result of the NFS_merge function as described on the GitHub page NanoForkSpeed/Forks_Detection.</p> <p> </p>
TimeSpec4LULC: A Smart-Global Dataset of Multi-Spectral Time Series of MODIS Terra-Aqua from 2000 to 2021 for Training Machine Learning models to perform LULC Mapping
<p>TimeSpec4LULC is a smart open-source global dataset of multi-spectral time series for 29 Land Use and Land Cover (LULC) classes ready to train machine learning models. It was built based on the seven spectral bands of the MODIS sensors at 500 m resolution from 2000 to 2021 (262 observations in each time series). Then, was annotated using spatial-temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE).</p> <p>TimeSpec4LULC contains two datasets: the original dataset distributed over 6,076,531 pixels, and the balanced subset of the original dataset distributed over 29000 pixels.</p> <p>The original dataset contains 30 folders, namely "Metadata", and 29 folders corresponding to the 29 LULC classes. The folder "Metadata" holds 29 different CSV files describing the metadata of the 29 LULC classes. The remaining 29 folders contain the time series data for the 29 LULC classes. Each folder holds 262 CSV files corresponding to the 262 months. Inside each CSV file, we provide the seven values of the spectral bands as well as the coordinates for all the LULC class-related pixels.</p> <p>The balanced subset of the original dataset contains the metadata and the time series data for 1000 pixels per class representative of the globe. It holds 29 different JSON files following the names of the 29 LULC classes.</p> <p>The features of the dataset are:</p> <p>- ".geo": the geometry and coordinates (longitude and latitude) of the pixel center.</p> <p>- "ADM0_Code": the GAUL country code.</p> <p>- "ADM1_Code": the GAUL first-level administrative unit code.</p> <p>- GHM_Index": the average of the global human modification index.</p> <p>- "Products_Agreement_Percentage": the agreement percentage over the 15 global LULC products available in GEE.</p> <p>- "Temporal_Availability_Percentage": the percentage of non-missing values in each band.</p> <p>- "Pixel_TS": the time series values of the seven spectral bands.</p>
Galactic maps of HI, reconstructed from the HI4PI 21-cm line survey
<p>Maps of galactic HI, reconstructed from the HI4PI 21-cm line survey data (HI4PI Collaboration 2016, A&A 594, A116), <a href="http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/585/A41">Strasbourg astronomical Data Center</a>, using a Bayesian variational inference approach.</p> <p>Here, we provide the mean and standard deviation of the gas density (in cm<sup>-3</sup>) on a three-dimensional 768<span class="math-tex">\(\times\)</span>768<span class="math-tex">\(\times\)</span>64 grid, spanning -24 kpc < x < 24 kpc, -24 kpc < y < 24 kpc and -2 kpc < z < 2 kpc, both for the BEG03 and the SBM15 gas flow models:</p> <ul> <li> <p>HI_dens_mean_BEG03.fits</p> </li> <li> <p>HI_dens_stddev_BEG03.fits</p> </li> <li> <p>HI_dens_mean_SBM15.fits</p> </li> <li> <p>HI_dens_stddev_SBM15.fits</p> </li> </ul> <p>In addition, we also publish the mean and standard deviation of the mass surface density (in M<sub>sun</sub> pc<sup>-2</sup>) on a two-dimensional 768<span class="math-tex">\(\times\)</span>768 grid, spanning -24 kpc < x < 24 kpc and -24 kpc < y < 24 kpc, again for both gas flow models:</p> <ul> <li> <p>HI_surf_mass_dens_mean_BEG03.fits</p> </li> <li> <p>HI_surf_mass_dens_stddev_BEG03.fits</p> </li> <li> <p>HI_surf_mass_dens_mean_SBM15.fits</p> </li> <li> <p>HI_surf_mass_dens_stddev_SBM15.fits</p> </li> </ul> <p>Throughout, we have assumed the optically thin limit for the gas and adopted the conversion factor <span class="math-tex">\(X_{{\rm HI}}\simeq 1.823\times 10^{18}\)</span> molecules cm<sup>-2</sup> (K km s<sup>-1</sup>)<sup>-1</sup>. Refer to the FITS headers for more detailed information and see <a href="https://arxiv.org/abs/2202.02341">arXiv:2202.02341</a> for a detailed description of the reconstruction as well as a comparison with earlier studies.</p> <p>When using the deprojected gas maps in your research work, please refer to this Zenodo dataset and the following publication<br> P. Mertsch and V.H.M. Phan, "Bayesian inference of three-dimensional gas maps: II. Galactic HI", <a href="https://arxiv.org/abs/2202.02341">arXiv:2202.02341</a>.</p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl 103)
<p>Ensembl 103 derived ID mapping database for use with BridgeDb.</p> <p><br> This work was funded by the <a href="https://fairplus-project.eu/">FAIRplus project</a> (grant agreement no 802750) and <a href="https://www.nwo.nl/en/researchprogrammes/open-science/open-science-fund/open-science-fund-2021-awarded-grants">NWO Open Science Fund</a> (grant no <a href="https://www.nwo.nl/en/projects/203001121">203.001.121</a>).</p>
Fig. 1. Maps showing the localities where adeonid bryozoans were collected. A. Collecting localities around Japan. Gray arrows indicate warm currents, the unfilled arrow indicates the cold current. B in Diversity and distribution of adeonid bryozoans (Cheilostomata: Adeonidae) in Japanese waters
Fig. 1. Maps showing the localities where adeonid bryozoans were collected. A. Collecting localities around Japan. Gray arrows indicate warm currents, the unfilled arrow indicates the cold current. B. Enlargement from previous, showing the localities around Sagami Bay, Sagami Sea, and Hachijo-jima Island. C. Enlargement from map A, showing the localities along the Nansei Islands, from Tanegashima to Okinawa.
Map of Russian forest for the year 2009
<p>The map of Russian forest for the year 2009 is a part of Integrated Land Information System (ILIS) for Russia (Gordov et al., 2013). It contains a land cover map (Schepaschenko 2011, 2015) and associated forest data that are based on the State Forest Register (SFR). Downscaling of the SFR regional data was accomplished using a multi-sensor remote sensing approach, geographically weighted regression and reference data obtained using visual interpretation of very high resolution imagery (Schepaschenko et al., 2019). The map contains the spatial distribution of the forest parameters including the major tree species, the average age, the relative stocking, site index, and biomass conversion factors (Schepaschenko et al., 2018).</p> <p>The data base has spatial resolution of 5 arc second (or ca 150 m) and contains following fields:</p> <p>ID_FOR - unique ID of forest<br> SPEC_MOD - tree species <br> AGE - average forest age<br> SI - site index<br> RS - relative stocking (tree density)<br> GS_ha - growing stock, m3/ha<br> bef_mn - biomass expansion factor (BEF)<br> bef_sd - BEF standard deviation<br> bcef_mn - biomass conversion and expansion factor (BCEF)<br> bcef_sd - BCEF standard deviation<br> RtS_mn - root-to-shoot ratio (R:S)<br> RtS_sd - R:S standard deviation</p> <p> </p> <p>How to cite:</p> <p>Schepaschenko et al. (2011) <a href="https://doi.org/10.1080/1747423X.2010.511681">https://doi.org/10.1080/1747423X.2010.511681</a></p> <p>Schepaschenko et al (2015) <a href="https://doi.org/10.1134/S1995425515070136">https://doi.org/10.1134/S1995425515070136</a> </p> <p>Schepaschenko et al (2018). <a href="https://doi.org/10.3390/f9060312">https://doi.org/10.3390/f9060312</a>.</p> <p> </p>
Map. Ancient Karṇāṭaka showing find-spots of Kadamba inscriptions
<p>Map. Ancient Karṇāṭaka showing find-spots of Kadamba inscriptions. © Institute of Oriental Studies of the Russian Academy of Science</p>
Map. Ancient Vidarbha showing find-spots of Vākāṭaka inscriptions
<p>Map. Ancient Vidarbha and neighbouring regions showing find-spots of Vākāṭaka inscriptions. © Institute of Oriental Studies of the Russian Academy of Science</p>
Normative brain mapping of interictal intracranial EEG to localise epileptogenic tissue
<p>Code & data to reproduce figures in "Normative brain mapping of interictal intracranial EEG to localise epileptogenic tissue", Taylor et al, 2021 (Brain)</p>
Saudi Arabian Capitals Urban Land Cover Maps: 1985-2019
<p>A CCDC algorithm was used to produce 13 sets of geographical maps. Each set represents one capital city of Saudi Arabia: Riyadh (which also serves as the country’s capital), Buridah, Ha’il, Dammam, Makkah, Madinah, Arar, Skakah Tabuk, Albaha, Abha, Jazan, and Najran. Each of the 13 datasets contains 35 annual maps from 1985 to 2019. The file format of these datasets is the .hdr file. The dataset is free to download. </p> <p>In the case of using the urban land cover maps, please cites the website and the paper:</p> <ol> <li>Aljaddani AH, Song X-P, Zhu Z. Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a Landsat Time Series. <em>Remote Sensing</em>. 2022; 14(10):2382. https://doi.org/10.3390/rs14102382</li> <li>Aljaddani, Amal H, Song, Xiao-Peng, & Zhu, Zhe. (2022). Saudi Arabian Capitals Urban Land Cover Maps: 1985-2019 (Version: 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6210073</li> </ol> <p> </p> <p> </p>
Data: Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals
<p><strong>Data used in:</strong></p> <p>Schönauer, M., Prinz, R., Väätäinen, K., Astrup, R., Pszenny, D., Lindeman, H., et al. (2022). Spatiotemporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 102730. doi: 10.1016/j.jag.2022.102730</p>
Concept maps as a novel assessment tool in medical education
<p>This dataset accompanies the manuscript entitled "Concept maps as a novel assessment tool in medical education". The dataset contains data from eight participants in a pilot study investigating the use of Concept Maps (CMs) in medical education. Each participant constructed one CM for three different Problem-Based Learning (PBL) cases. Participant CMs, demographic data, results from pre-intervention questionnaire on their learning style (VARK Learning Styles Self-Assessment Questionnaire) and post-intervention questionnaire results measuring the level of clinical and critical thinking are included. In addition expert CMs are also provided for each of the three PBL cases.</p> <p> </p> <p><br> <br> <br> </p>
Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits
<p>Supporting data associated with the manuscript "Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits". These include:</p> <ul> <li><strong>StringTie2.438.GEUVADIS.gtf.gz</strong> - Assembled transcriptome using RNA-seq from 438 samples from the GEUVADIS project by StringTie2</li> <li><strong>puQTL_nominal_output.txt.gz</strong> - puQTL results from QTLtools nominal pass</li> <li><strong>puQTL_conditional_output.txt.gz</strong> - puQTL results from QTLtools conditional pass</li> <li><strong>eQTL_nominal_output.txt.gz</strong> - eQTL results from QTLtools nominal pass</li> <li><strong>eQTL_conditional_output.txt.gz</strong> - eQTL results from QTLtools conditional pass</li> </ul> <p>See <a href="https://qtltools.github.io/qtltools/">https://qtltools.github.io/qtltools/</a> for column descriptions of QTL mapping results.</p>
Project files provided as supporting information to the manuscript "Making sense of complex systems through resolution, relevance, and mapping entropy"
<p>README file to the project files provided as supporting information to the manuscript “Making sense of complex systems through resolution, relevance, and mapping entropy”</p> <p>Feb. 25, 2022</p> <p>Authors: Roi Holtzman, Marco Giulini and Raffaello Potestio</p> <p>==================================</p> <p>The dataset contains the following files:</p> <p>- A README file with the description of the pymap program for describing how different selections of *N* out of *n* degrees of freedom (mappings) affect the amount of information retained about a full data set.<br> - The pymap.py program<br> - The pymap.yml support file<br> - The data.tar tarball with the setup data<br> - The results.tar tarball with the output data<br> ===</p>
Underlying data for IsoAligner: dynamic mapping of amino acidpositions across protein isoforms
<p>The human isoform library (list_of_gene_objects_25th_july_final.txt) for the IsoAligner webtool is generated from these resources.</p>
Maps of thermal inertia, dielectric constant and brightness temperature of asteroid (16) Psyche derived from ALMA data
<p>These data and results are in support of the findings by Cambioni, S., de Kleer, K. and Shepard, M. in their paper "The Heterogeneous Surface of Asteroid (16) Psyche", Journal of Geophysical Research: Planets, link: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021JE007091. </p> <p>If using any of this material, please cite the above article as doi: 10.1029/2021JE007091</p> <p> </p>
Future water level, discharge, and flood maps under climate change and infrastructure impacts along the Cambodian Mekong.
<p>Baseline and future (2036-2065) river water levels and discharges at 4 gauging stations along the Cambodian Mekong (Kratie, Kampong Cham, Chrouy Changva, and Neak Loeung) under different scenarios of climate change (RCP 4.5 and 8.5) and infrastructural developments. Average depth and duration flood maps are also included for each scenario.</p> <p> </p> <p>A full description of the methods and results can be found in the article: </p> <p>Alexander J. Horton, Nguyen V. K. Triet, Long P. Hoang, Sokchhay Heng, Panha Hok, Sarit Chung, Jorma Koponen, and Matti Kummu. (2022). The Cambodian Mekong floodplain under future development plans and climate change. <em>Nat. Hazards Earth Syst. Sci.</em></p>
ScienceDex guides
Understand access before you commit
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.