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13,117 results for “Maps”
Map of islands and shallow water areas in the Spermonde Archipelago (Indonesia)
<p>This repository contains data and code used to make a map of islands and shallow water areas for the Spermonde Archipelago, Indonesia. The map was obtained using a two-stepped classification approach, described below, and simple statistics and graphs were then calculated in python.</p> <p>This work was inspired by the "Geoscientific Project" of Mr. Dennis Flenner, University of Bremen, who classified the same area with SENTINEL2 and QGIS tools. This work was supported through grant SEASCHANGE (RO-5245/1-1) from the Deutsche Forschungsgemeinschaft (DFG) as part of the Special Priority Program (SPP)-1889 “Regional Sea Level Change and Society”.</p>
Benchmark maps and assignments for multi-agent path finding
<p><strong>The dataset</strong></p> <p>The dataset is produced within the SafeLog project and it is used for benchmarking of multi-agent path planning algorithms. Specifically, the dataset consists of a set of 21 maps with increasing density and a set of 500 random assignments, each for a group of 100 agents for planning on each of the maps.</p> <p>All of the maps, in the form of a graph G = {V, E}, are built on the same set of 400 vertices V. The sets of edges Ej, where j ∈ (0; 20), in the maps then form a set ranging from a spanning tree to a mostly 4-connected graph. These maps were created by generating a complete square graph with the size of 20*20 vertices. The graph was then simplified to a spanning tree, and, finally, approximately 50 random edges from the complete graph were added 20 times, to create the set of 21 maps of density ranging from 800 to 1500 edges in the graph. <br> </p> <p><strong>Content and format</strong></p> <p>The following files are included in the dataset</p> <p><em>test_nodes.txt </em>- 400 nodes of a 20*20 square map in the form "id x y"<br> <em>testAssignment.txt</em> - 50499 random pairs of nodes ids from test_nodes.txt<br> <em>test_edgesX.txt</em> - pairs of adjacent nodes ids from test_nodes.txt forming edges<br> - X = 0 - tree<br> - X = 20 - full graph<br> - created starting at a full graph and repeatedly erasing edges until a tree remains</p> <p>To illustrate the maps in the dataset, we provide three images (1008.png, 1190.png, and 1350.png) showing maps with 1008 (1190, 1350) edges. </p> <p><strong>Citation</strong></p> <p>If you use the dataset, please cite:</p> <p>[1] Hvězda, J., Rybecký, T., Kulich, M., and Přeučil, L. (2018). <a href="https://doi.org/10.1109/ITSC.2018.8569712"><em>Context-Aware Route Planning for Automated Warehouses</em></a>. Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC).</p> <p><em>@inproceedings{Hvezda18itsc,<br> author = {Hvězda, Jakub and Rybecký, Tomáš and Kulich, Miroslav and Přeučil, Libor},<br> title = {Context-Aware Route Planning for Automated Warehouses},<br> booktitle = {Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC)},<br> publisher = {IEEE Intelligent Transportation Systems Society},<br> address = {Maui},<br> year = {2018},<br> doi = {10.1109/ITSC.2018.8569712},<br> }</em></p> <p> </p> <p>[2] Hvězda, J., Kulich, M., and Přeučil, L. (2019). <a href="https://link.springer.com/chapter/10.1007/978-3-030-31993-9_18">On Randomized Searching for Multi-robot Coordination</a>. In: Gusikhin O., Madani K. (eds) Informatics in Control, Automation and Robotics. ICINCO 2018. Lecture Notes in Electrical Engineering, vol 613. Springer, Cham.</p> <p><em>@inbook{Hvezda19springer,<br> author = {Hvězda, Jakub and Kulich, Miroslav and Přeučil, Libor},<br> title = {On Randomized Searching for Multi-robot Coordination},<br> booktitle = {Informatics in Control, Automation and Robotics},<br> publisher = {Springer},<br> address = {Cham, CH},<br> year = {2019},<br> series = {Lecture Notes in Electrical Engineering},<br> language = {English},<br> url = {https://link.springer.com/chapter/10.1007/978-3-030-31993-9_18},<br> doi = {10.1007/978-3-030-31993-9},<br> }</em></p> <p><br> [3] Hvězda, J., Kulich, M., and Přeučil, L. (2018). <a href="http://www.scitepress.org/PublicationsDetail.aspx?ID=ppwUqsGaX18=\&t=1">Improved Discrete RRT for Coordinated Multi-robot Planning</a>. Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - (Volume 2).</p> <p><em>@inproceedings{Hvezda18icinco,<br> author = {Hvězda, Jakub and Kulich, Miroslav and Přeučil, Libor},<br> title = {Improved Discrete RRT for Coordinated Multi-robot Planning},<br> booktitle = {Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - (Volume 2)},<br> publisher = {SciTePress},<br> address = {Madeira, PT},<br> year = {2018},<br> language = {English},<br> url = {http://www.scitepress.org/PublicationsDetail.aspx?ID=ppwUqsGaX18=\&t=1},<br> doi = {10.5220/0006865901710179},<br> access = {full}<br> }</em></p> <p> </p> <p> </p> <p> </p>
Protein elution profiles accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p>Key to files</p> <p><strong>Experiment_Order.csv</strong></p> <ul> <li>Description: Meta details of each experiment.</li> <li>Format: experiment_name,ExperimentID_order,tissue,experiment_type,spec,ExperimentID</li> </ul> <p><strong>Fraction_Details.csv</strong></p> <ul> <li>Description: Meta details of each fraction</li> <li>Format:FractionID,frac_order,ExperimentID</li> </ul> <p><strong>plant_virNOG_orthology.csv.gz</strong></p> <ul> <li>Description: Conversion between orthogroup and protein IDs.</li> <li>Format:ID,ProteinID,spec</li> </ul> <p><strong>orthogroup_annotation.csv.gz</strong></p> <ul> <li>Description: Orthogroup annotations</li> <li>Format:ID,Annotation,arath_genenames,arath_Entries,arath_Entry_names,arath_Protein_names,disruptions,tair_disruptions,lloyd2012_LOFs,arath_functions,arath_misc,pathway,unipathway,BioCyc,Reactome,BRENDA,kegg_pws,ec,arath_masses,arath_protein_names,arath_GO,devstages,tissues,tair,araport,orysj_genenames,orysj_Entries,orysj_Entry_names,orysj_Protein_names,orysj_disruptions,orysj_functions,orysj_misc</li> </ul> <p><strong>panplant_tidy_elution_virNOG.csv.gz</strong></p> <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong> for all experiments. Includes parts per million in each fraction. </li> <li>Format: ExperimentID,FractionID,ID,Total_PeptideCount,spec,ExperimentID_order,FractionID_order,abundance_ppm</li> </ul> <p><strong>panplant_tidy_elution_protcount.csv.gz</strong></p> <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed <strong>proteins</strong> for all experiments. </li> <li>Format: ExperimentID,FractionID,ProteinID,ProteinCount,spec,ExperimentID_order,FractionID_order</li> </ul> <p><strong>panplant_wide_elution_virNOG.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong></li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>panplant_wide_elution_virNOG_annot.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong>, includes annotation columns.</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> <p><strong>panplant_wide_elution_expnorm.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong></li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>panplant_wide_elution_expnorm_annot.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong>, including columns with annotations</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> <p><strong>[experiment_name].virNOG.wide.gz</strong></p> <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong> in one experiment</li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <ul> </ul> <p><strong>[experiment_name].protcount.wide.gz</strong></p> <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) counts for all observed <strong>proteins </strong>in one experiment</li> <li>Format: ProteinID,[Fractions]</li> </ul> <p><strong>[species]_specconcat.virNOG.wide.gz</strong></p> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups </strong>from a particular species. Only present for species with more than one experiment. </li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>[species]_specconcat.protcount.wide.gz</strong></p> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>proteins</strong> from a particular species. Only present for species with more than one experiment. </li> <li>Format: ProteinID,[Fractions]</li> </ul> <p> </p> <ul> </ul> <p>Species codes</p> <p>|Code | Species | Common name | Use |<br> |---|---|---|<br> | arath | Arabidopsis Thaliana | Arabidopsis | <br> | braol | Brassica oleracea | Broccoli |<br> | cansa | Cannabis sativa | hemp | <br> | cerri | Ceratopteris richardii | C-fern | <br> | chlre | Chlamydomonas reinhardtii | Chlamydomonas |<br> | chqui | Chenopodium quinoa | Quinoa | <br> | orysj | Oryza sativa var. japonica | Rice |<br> | selml | Selaginella moellendorffii | Selaginella | <br> | sollc | Solanum lycopersicum | Tomato | <br> | wheat | Triticum Aestivum | Wheat | <br> | soybn | Glycine max | Soybean | <br> | cocnu | Cocos nucifera | Coconut | </p> <p>| maize | MAIZE | maize | </p> <p> </p>
Adaptation and energy demand systematic mapping of the literature dataset
<p>Those files form a database of all the informations extracted during the associated systematic review : <a href="https://doi.org/10.1088/1748-9326/abc044">When adaptation increases energy demand: a systematic map of the literature</a></p> <p>ReadMe.pdf provides a detailled notice of the dataset.</p> <p>This dataset contains 1 SQLite file and for convenience 9 CSV files which are the tables contained in the SQLite file. CSV files are given both in Windows and Linux format in separate folders.</p>
Figs 29–31. Maps and habitat. 29 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Figs 29–31. Maps and habitat. 29. Map of Chile with position of type locality of Mumetopia interfeles Roháček sp. nov. (blue open square). 30. Satellite view of part of Valparaíso city, with collecting site indicated by red arrow. 31. Habitat of M. interfeles sp. nov., northern end of grassy area between houses visited by cats. Photo by J. von Tschirnhaus (31), other sources: Vemaps.com (29), Google Earth Pro (30).
Process map for casting a long-term experimental campaign on RC shrinkage cracking.
<p>This dataset presents the process map that was developed for the casting of a long-term experimental campaign on reinforced concrete (RC) slabs subjected to the combined effect of restrained shrinkage and vertical loads.<br> This experimental campaign was performed in the scope of the FCT project "IntegraCrete: A comprehensive multi-physics and multi-scale approach to the combined effects of applied loads and thermal/shrinkage deformations in reinforced concrete structures''.<br> The results from this experimental campaign are presented in Gomes et al (2020), while the conceptualization, planning and experimental procedures are described in detail in Gomes et al (2021). The latter is supported by this process map to describe the micromanagement plan that was devised for the casting day.<br> 14 people were involved in the casting of 3 slabs and 2 complementary specimens inside a highly instrumented climatic chamber, as well as 38 specimens for concrete characterization at different ages and 12 load blocks to use as vertical loads. This process map was developed with the standard Business Process Model and Notation (BPMN).</p>
High resolution global industrial and smallholder oil palm map for 2019
<p>The dataset contains 634 100x100 km tiles, covering areas where oil palm plantations were detected. The file '<em>grid.shp</em>' contains the grid that covers the potential distribution of oil palm. The file '<em>grid_withOP.shp</em>' shows the 100x100 grid squares with presence of oil palm plantations. The classified images (‘<em>oil_palm_map</em>’ folder, in geotiff format) are the output of the convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites. The images have a spatial resolution of 10 meters and contain three classes: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm. The file ‘<em>Validation_points_GlobalOilPalmLayer_2019.shp</em>’ includes the 13,495 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the labelled class assigned by visual interpretation, and the attribute ‘predClass, which reflects the predicted class by the convolutional neural network. The ‘Class’ and ‘predClass’ values are the same as the raster files: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm.</p> <p>See article for additional information:</p> <p>Descals, Adrià, et al. "High-resolution global map of smallholder and industrial closed-canopy oil palm plantations." <em>Earth System Science Data</em> 13.3 (2021): 1211-1231.</p> <p> </p> <p>Changelog v1:</p> <p>- The analysis was extended to Sri Lanka, South India, and countries in Eastern Africa where oil palm can potentially grow.</p> <p>- The validation dataset only includes the points drawn by simple random sampling and stratified random sampling in the grid cells where the IUCN industrial layer detected oil palm.</p> <p>- The 'Class' and 'predClass' values in the validation dataset were reclassified with the same values as the raster images: [1] Industrial plantations, [2] Smallholder plantations, and [3] Other land covers/uses.</p>
World map presentation of water vapour perturbation emitted by hypersonic aircraft at 32-38 km altitude
<p>World map presentation of water vapour perturbation by hypersonic aircraft in a layer at 32-38 km altitude. In this movie emitted water vapour accumulates for one month in one-hour steps (interpolated from data with five-hour time intervals for a more fluid film). Transport of perturbation dominated by horizontal movement through atmospheric air mass is visible.</p>
Dataset for Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation
<p>Dataset of paper "Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation".</p> <p>The measurement data contains indoor mapping results using millimeter-wave 5G NR signals at 28 GHz. The measurement campaign was conducted in an indoor office environment in Hervanta Campus of Tampere University. Six different sets of measurements contain the range profiles after the proposed radar processing. The shared data contains the IQ data of both transmit and receive signals used during the measurement campaign.</p> <p>The file "main.m" shows how to process and plot the shared data.</p>
Reproduction package for the paper "Mapping the spectral index of Cas A: evidence for flattening from radio to infrared"
<p>This is a basic reproduction package for the paper "Mapping the spectral index of Cas A: evidence for flattening from radio to infrared" by V. Domček et al. (2021). It provides raw, intermediate and final data sets, including figures and scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data. </p> <p>An open access version of the paper can be found at https://arxiv.org/abs/2005.12677</p>
XRF maps of Paracas textiles and threads
<p>XRF dataset used in two publications:</p> <p>Javér, A., M. Hacke, C. Thays Delgado, and K. Thuresson. 2017. Paracas textiles – Colour and condition. Investigation of the mordants and state of degradation of the Paracas textile collections in Peru and Sweden. In ICOM-CC 18th Triennial Conference Preprints, Copenhagen, 4–8 September 2017, ed. J. Bridgland, art. 1804. Paris: International Council of Museums.</p> <p>and</p> <p>Sabatini, F., Bacigalupo, M., Degano, I. <em>et al.</em> Revealing the organic dye and mordant composition of Paracas textiles by a combined analytical approach. <em>Herit Sci</em> <strong>8, </strong>122 (2020). https://doi.org/10.1186/s40494-020-00461-5</p>
Mapping present and future predicted distribution patterns for a meso-grazer guild in the Baltic Sea
<p>Baltic Sea communities consisting of key and endemic species are threatened by climate change. Using Ecological niche modelling, we map predicted distribution patterns under recent and future climate change scenarios (2050) for a food-web consisting of a guild of meso-grazers (Idotea spp.), their host algae (Fucus vesiculosus and F. radicans) and their fish predator (Gasterosteus aculeatus). Brackish water species depend on two important abiotic factors: temperature and salinity. We assess which of these environmental factors determines the distribution limits of the grazers in the Baltic Sea today. For species in a semi-enclosed sea area such as the Baltic Sea, climate-induced changes may lead to dramatic food-web effects. We assess the consequences of the predicted climate-induced habitat range changes for this unique Baltic community.<br /> </p>
Autonomic Provisioning and Application Mapping on Spot Cloud Resources
<p>1. Attached files: </p> <p>500_100_fmincon_0.95_1.mat <br /> Experiment with 500 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>500_100_heuristic_0.95_1.mat<br /> Experiment with 500 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_70_fmincon_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, exact algorithm.</p> <p>2000_70_heuristic_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.9_1.mat<br /> Experiment with 2000 users, 100ms max response time, 90% availability, exact algorithm.</p> <p>2000_100_heuristic_0.9_1.mat <br /> Experiment with 2000 users, 100ms max response time, 90% availability, our algorithm.</p> <p>2000_100_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>2000_100_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, exact algorithm.</p> <p>2000_100_heuristic_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, our algorithm.</p> <p>2000_300_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, exact algorithm.</p> <p>2000_300_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, our algorithm.</p> <p>10000_100_fmincon_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>10000_100_heuristic_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2. Data format:</p> <p>MATLAB data format, can be load from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Communication Characterization and Optimization of Applications Using Topology-Aware Task Mapping on Large Supercomputers (Data)
<p>Auxiliary materials for ICPE 2016 paper titled:<br /> "Communication Characterization and Optimization of Applications Using Topology-Aware Task Mapping on Large Supercomputers".</p> <p> </p>
Bandung (Indonesia) area InSAR mean velocity maps
<p>Bandung is the capital of the West Java province of Indonesia. The larger metropolitan area of Bandung has a population of more than 8 million people, and a strong exposure to a variety of geohazards. Satellite SAR data provide information on ground deformation, needed to monitor and model the various sources of these hazards and to perform multi-hazard risk analysis.</p> <p>We show the results of an ALOS-1 and COSMO-SkyMed SAR data investigation over the Bandung metropolitan area retrieved by means of InSAR multi temporal technique aimed mainly at mapping urban subsidence.</p>
Example data map compressed with ISO29500-2 with 3 entry point for ArcGIS, MiraMon and OWS context file.
<p>A simple map consisting of a 1:1 000 000 country boundaries vector file, produced by the FAO (United Nations – FAOStat; geodata.grid.unep.ch/options.php?selectedID=2135) on top of a 5’ digital elevation model raster file (produced by the NOAA and NGDC; geodata.grid.unep.ch/options.php?selectedID=1414). Data has been obtained from the UNEP EDE Data Portal (UNEP 2013). Vector file is a Shapefile (a de facto standard) (ESRI 1998), while the raster file consists of either a raw signed 16-bit data or a long known TIFF file (Adobe 1992, Perkins 1995). Metadata and symbolization files are included. OPC specifies how to explicitly relate different parts using .rels files. These files are XML files with the same name as that of its respective source part, adding “.rels” and placed in a “rels” folder. Each of these files lists the target parts related to its source and the semantics of this relation.</p> <p>OPC can define entry points to the data by listing them in a “.rels” part in the root “rels” folder. In this file, three map files: for the ESRI software a world.mxd map, for the MiraMon software a world.mmm map, and a world.xml map in the form of an atom file following the new Web Service common standard (OGC OWS) context document. A geospatial application reading the package will determine which entry part it better supports to start recovering the data.</p>
A Synoptic Map of Halo Substructures from the Pan-STARRS1 3π Survey
<p>Panoramic maps of the entire Milky Way halo north of Dec. ~ -30 degrees (~30,000 deg^2), constructed by applying the matched-filter technique to the Pan-STARRS1 3π Survey dataset. The details can be found in Bernard et al. 2016 (http://adsabs.harvard.edu/abs/2016arXiv160706088B).</p> <p>mf_26dist_equ_FeHm1.5_12gyr_gr.fits.gz contains the 26 distance slices from 3.5 to 35 kpc obtained using the [Fe/H]=-1.5 and 12 Gyr old model in the g,r bands.</p> <p>In mfstack_equ_FeHm1.5_12gyr_gri.fits.gz, the g,r and g,i models have been combined, and slices have been co-added to obtain 3 broad distance ranges.</p> <p>Best seen in SAOImage DS9, opened as multi-extension cubes, in log scale and inverted colour map, and smoothed with a 3pix Gaussian. </p>
FIGURES 26 – 28. Pneuminion distribution maps. — 26. P. balfourbrownei. — 27. P. impressum. — 28. P in A revision of the South African endemic water beetle genus Pneuminion Perkins (Coleoptera: Hydraenidae)
FIGURES 26 – 28. Pneuminion distribution maps. — 26. P. balfourbrownei. — 27. P. impressum. — 28. P. nanum.
FIGURES 23 – 25. Pneuminion distribution maps. — 23. P. velamen. — 24. P. semisulcatum. — 25. P. t u b u m in A revision of the South African endemic water beetle genus Pneuminion Perkins (Coleoptera: Hydraenidae)
FIGURES 23 – 25. Pneuminion distribution maps. — 23. P. velamen. — 24. P. semisulcatum. — 25. P. t u b u m.
FIGURES 21 – 22. Pneuminion distribution maps. — 21. All Pneuminion collecting sites. — 22. P in A revision of the South African endemic water beetle genus Pneuminion Perkins (Coleoptera: Hydraenidae)
FIGURES 21 – 22. Pneuminion distribution maps. — 21. All Pneuminion collecting sites. — 22. P. endroedyi.
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.