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13,117 results for “Maps”
Aggregated frequencies of transcription initiations observed in FANTOM5 CAGE data on GRCm38, including alignments with low mapping qualities
<p><strong>Overview</strong></p> <p>Aligned reads of the FANTOM5 CAGE data have been used after filtering (ones with mapping quality less than 20 or percent identity less than 85% were discarded) for general purpose, resulting in the data set consisting of only the reads aligned with confidence. The filtering process made possible to interpret the data without ambiguity, however it also limited interpretation of paralogous or duplicated regions within the genome. Here all of the 5'-ends of the CAGE read alignments, including the ones with low mapping quality, were counted. The counts in the individual profiles were aggregated and summed up. </p> <p>This data set is produced for mouse, in the same way to the one for human data set http://doi.org/10.5281/zenodo.1410835</p> <p> </p> <p><strong>Data files</strong></p> <p>The resulting data files are formatted as bigWig (https://genome.ucsc.edu/FAQ/FAQformat.html#format6.1). '*.fwd.bw' and '*.rev.bw' represent forward and reverse strand on the genome, respectively. </p> <p> </p> <p><strong>Methods</strong></p> <p>The BAM files under https://fantom.gsc.riken.jp/5/datafiles/reprocessed/mm10_v7/basic/ were subjected to 5'-end counting by bedtools v2.27.1 (https://github.com/arq5x/bedtools2), followed by conversion into bigWig with jksrc v366 (http://hgdownload.cse.ucsc.edu/admin/).</p>
Augmented emission maps: 1598 cc 85 kW Euro 6 diesel engine
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1598 cc 85 kW Euro 6 diesel engine that has been applied in several Volkswagen group models (Volkswagen Golf, Golf Sportsvan, Touran and T-roc, Audi A1, A1 Sportback, A3, A3 Limousine, A3 Sportback and Q2, Seat Ibiza, Arona, Leon, Ateca and Toledo, Skoda Rapid, Octavia, Karoq).</p> <p>Note that the Hyundai/Kia engine with the same specifications (but a different alliance code) is not covered.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: 1199 cc 55 kW Euro 4 petrol engine
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1199 cc 55 kW Euro 4 petrol engine that has been applied in the Opel Corsa, Agila, and Astra.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: the 1461 cc 81 kW Euro 6 diesel engine
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1461 cc 81 kW Euro 6 diesel engine that has been applied in several Renault, Nissan and Mercedes-Benz models (Kadjar, Megane, Scenic, Talisman, Captur, Clio, Kangoo; Qashqai, Juke, Pulsar, NV200; Citan).</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: 1199 cc 55 kW Euro 5a diesel engine
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1199 cc 55 kW Euro 5a diesel engine that has been applied in the Volkswagen Polo, Seat Ibiza, Skoda Fabia and Skoda Roomster.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
Mapping the Local Spatial Charge in Defective Diamond by Means of N-V Sensors—A Self-Diagnostic Concept
<p>Mapping the Local Spatial Charge in Defective Diamond by Means of N-V Sensors—A Self-Diagnostic Concept</p> <p>Electrically active defects have a significant impact on the performance of electronic devices based on wide-band-gap materials. This issue is ubiquitous in diamond science and technology, since the presence of charge traps in the active regions of different classes of diamond-based devices (detectors, power diodes, transistors) can significantly affect their performance, due to the formation of space charge, memory effects, and the degradation of the electronic response associated with radiation-induced damage. Among the most common defects in diamond, the nitrogen-vacancy (N-V) center possesses unique spin properties that enable high-sensitivity field sensing at the nanoscale. Here, we demonstrate that N-V ensembles can be successfully exploited to perform direct local mapping of the internal electric-field distribution of a graphite-diamond-graphite junction exhibiting electrical properties dominated by trap- and space-charge-related conduction mechanisms. By means of optically detected magnetic resonance measurements, we performed both point-by-point readout and spatial mapping of the electric field in the active region at different bias voltages. In this novel “self-diagnostic” approach, defect complexes represent not only the source of detrimental space-charge effects but also a unique tool for their direct investigation, by providing an insight on the conduction mechanisms that could not be inferred in previous studies on the basis of conventional electrical and optical characterization techniques.</p>
Distribution. SE Africa from SE Zambia, S Malawi, W Mozambique, Zimbabwe, and N Botswana to Swaziland and C & E South Africa, and isolated records from Uganda and Tanzania; there are also apparently records from Togo, Benin, C Nigeria, NE Egypt, and Lesotho, but they need validation and are not mapped here. in Rhinolophidae
Distribution. SE Africa from SE Zambia, S Malawi, W Mozambique, Zimbabwe, and N Botswana to Swaziland and C & E South Africa, and isolated records from Uganda and Tanzania; there are also apparently records from Togo, Benin, C Nigeria, NE Egypt, and Lesotho, but they need validation and are not mapped here.
Analyzed papers in an open IoT platform systematic mapping study
<p>As part of our systematic mapping study related to Open IoT Platforms, here we present the overall included and analyzed papers for our study. As a process, the papers are classified into five categories, such as used, indicated, proposed, defined and no explanation studies.</p> <p>The result of the classification process is: -use: 134 papers, -indicate: 56 papers -propose: 28 papers, -define: 3 papers, and -no explanation: 59 papers</p>
FIGURES 18–26. Distribution maps for Metallactus hamifer species-group. M in Revision of the Metallactus hamifer species-group (Coleoptera: Chrysomelidae: Cryptocephalinae)
FIGURES 18–26. Distribution maps for Metallactus hamifer species-group. M. abditus (18); M. agonista (19); M. albivittis (20); M. albopictus (21); M. bivitticollis (22); M. chamorroi (23); M. crassicollis (24); M. dicaprioi (25); M. hamifer (26). Grey squares: localities reported in a generic way.
FIGURE4.Map of selected records of Siphamia species recorded in Queensland and Papua New Guinea waters in 2003–2005, including the type locality of Siphamia guttulata. Some symbols represent more than one specimen. in Redescription and distributional range extension of the Speckled Siphonfish, Siphamia guttulata (Pisces: Apogonidae)
FIGURE4.Map of selected records of Siphamia species recorded in Queensland and Papua New Guinea waters in 2003–2005, including the type locality of Siphamia guttulata. Some symbols represent more than one specimen.
A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing
<p>The Krogan Laboratory used affinity-purification mass spectrometry to identify 332 high confidence SARS-CoV-2-human protein-protein interactions, including 67 druggable human proteins or host factors targeted by 69 known drugs. Results may be relevant to antiviral drug production.</p>
Pogo models of axisymmetric and realistic corrosion maps
<p>Pogo model files used in PRSA paper by AAE Zimmermann, P Huthwaite, B Pavlakovic, 2020, 'High-resolution thickness maps of corrosion using SH1 guided wave tomography'.</p> <p>These models utilise frames to excite various transducer types:</p> <p><strong>Directional rectangular SH</strong> transducer array on surface</p> <pre><code class="language-bash">pogoSolve3d modelname --startFrame 1 --stopFrame 120</code></pre> <p><strong>SH0 point source</strong> transducer array</p> <pre><code class="language-bash">pogoSolve3d modelname --startFrame 121 --stopFrame 240</code></pre> <p><strong>SH1 point source </strong>transducer array</p> <pre><code class="language-bash">pogoSolve3d modelname --startFrame 241 --stopFrame 360</code></pre> <p><strong>SH2 point source</strong> transducer array</p> <pre><code class="language-bash">pogoSolve3d modelname --startFrame 361 --stopFrame 480</code></pre> <p><strong>SH point source</strong> transducer array on surface</p> <pre><code class="language-bash">pogoSolve3d modelname --startFrame 481 --stopFrame 600</code></pre> <p> </p>
FIGURE 11. Map depicting localities where B in Description of Bradinopyga konkanensis sp. nov. (Odonata: Anisoptera Libellulidae) from the coastal region of Maharashtra, India
FIGURE 11. Map depicting localities where B. konkanensis has been observed so far, which includes: type locality, two localities where paratype males were collected, & additional seven localities where this species has been photographed.
Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals (SDGs)"
<p><strong>This package contains data on five text analysis types (term extraction, contract analysis, topic modeling, network mapping), based on the survey data where researchers selected research output that are related to the 17 Sustainable Development Goals (SDGs). This is used as input to improve the current SDG classification model v4.0 to v5.0</strong></p> <p><a href="https://sustainabledevelopment.un.org/sdgs">Sustainable Development Goals</a> are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">Aurora SDG dashboard</a> is the initial outcome as <em>proof of practice</em>.)</p> <p>The initiative started from the Aurora Universities Network in 2017, in the working group "<a href="https://aurora-network.global/activity/societal-impact-and-relevance-of-research-sirr/">Societal Impact and Relevance of Research</a>", to investigate and to make visible 1. what research is done that are relevant to topics or challenges that live in society (for the proof of practice this has been scoped down to the SDGs), and 2. what the effect or impact is of implementing those research outcomes to those societal challenges (this also have been scoped down to research output being cited in policy documents from national and local governments an NGO's).</p> <p><strong>Context of this dataset | classification model improvement workflow</strong></p> <p>The classification model we have used are 17 different search queries on the Scopus database.</p> <ul> <li>SDG search queries version 4.0 (SQv4) have been created, Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3817443"><em>Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v4.0</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817443</a></li> </ul> </li> <li>A survey has been distributed to senior researchers to test the robustness of SQv4. Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3798385"><em>Survey data of "Mapping Research output to the Sustainable Development Goals SDGs"</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3798385</a></li> </ul> </li> <li>This text analysis has been made as one of the inputs to improve the classification model. Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3832090"><em>Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals SDGs"</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3832090</a></li> </ul> </li> <li>Improved SDG search queries version 5.0 (SQv5) have been created, Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3817445"><em>Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v5.0</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817445</a></li> </ul> </li> </ul> <p><strong>Methods used to do the text analysis</strong></p> <ol> <li><strong>Term Extraction</strong>: after text normalisation (stemming, etc) we extracted 2 terms in bigrams and trigrams that co-occurred the most per document, in the title, abstract and keyword</li> <li><strong>Contrast analysis</strong>: the co-occurring terms in publications (title, abstract, keywords), of the papers that respondents have indicated relate to this SDG (y-axis: True), and that have been rejected (x-axis: False). In the top left you'll see term co-occurrences that a clearly relate to this SDG. The bottom-right are terms that are appear in papers that have been rejected for this SDG. The top-right terms appear frequently in both and cannot be used to discriminate between the two groups.</li> <li><strong>Network map</strong>: This diagram shows the cluster-network of terms co-occurring in the publications related to this SDG, selected by the respondents (accepted publications only).</li> <li><strong>Topic model</strong>: This diagram shows the topics, and the related terms that make up that topic. The number of topics is related to the number of of targets of this SDG.</li> <li><strong>Contingency matrix</strong>: This diagram shows the top 10 of co-occurring terms that correlate the most.</li> </ol> <p><strong>Software used to do the text analyses</strong></p> <p>CorTexT: The <a href="https://www.cortext.net/">CorTexT Platform</a> is the digital platform of LISIS Unit and a project launched and sustained by IFRIS and INRAE. This platform aims at empowering open research and studies in humanities about the dynamic of science, technology, innovation and knowledge production.</p> <p><strong>Resource with interactive visualisations</strong></p> <p>Based on the text analysis data we have created a website that puts all the SDG interactive diagrams together. For you to scrall through. <a href="https://sites.google.com/vu.nl/sdg-survey-analysis-results/">https://sites.google.com/vu.nl/sdg-survey-analysis-results/</a></p> <p><strong>Data set content</strong></p> <p>In the dataset root you'll find the following folders and files:</p> <ul> <li><strong>/sdg01-17/</strong> <ul> <li>This contains the text analysis for all the individual SDG surveys.</li> </ul> </li> <li><strong>/methods/</strong> <ul> <li>This contains the step-by-step explanations of the text analysis methods using Cortext.</li> </ul> </li> <li><strong>/images/</strong> <ul> <li>images of the results used in this README.md.</li> </ul> </li> <li><strong>LICENSE.md</strong> <ul> <li>terms and conditions for reusing this data.</li> </ul> </li> <li><strong>README.md</strong> <ul> <li>description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.</li> </ul> </li> </ul> <p>Inside an <strong>/sdg01-17/</strong>-folder you'll find the following:</p> <ul> <li>This contains the step-by-step explanations of the text analysis methods using Cortext.</li> <li><strong>/sdg01-17/sdg04-sdg-survey-selected-publications-combined.db</strong> <ul> <li>his contains the title, abstract, keywords, fo the publications in the survey, including the and accept or rejection status and the number of respondents</li> </ul> </li> <li><strong>/sdg01-17/sdg04-sdg-survey-selected-publications-combined-accepted-accepted-custom-filtered.db</strong> <ul> <li>same as above, but only the accepted papers</li> </ul> </li> <li><strong>/sdg01-17/extracted-terms-list-top1000.csv</strong> <ul> <li>the aggregated list of co-occuring terms (bigrams and trigrams) extracted per paper.</li> </ul> </li> <li><strong>/sdg01-17/contrast-analysis/</strong> <ul> <li>This contains the data and visualisation of the terms appearing in papers that have been accepted (true) and rejected (false) to be relating to this SDG.</li> </ul> </li> <li><strong>/sdg01-17/topic-modelling/</strong> <ul> <li>This contains the data and visualisation of the terms clustered in the same number of topics as there are 'targets' within that SDG.</li> </ul> </li> <li><strong>/sdg01-17/network-mapping/</strong> <ul> <li>This contains the data and visualisation of the terms clustered in co-occuring proximation of appearance in papers</li> </ul> </li> <li><strong>/sdg01-17/contingency-matrix/</strong> <ul> <li>This contains the data and visualisation of the top 10 terms co-occuring</li> </ul> </li> </ul> <p>note: the .csv files are actually tab-separated.</p> <p><strong>Contribute and improve the SDG Search Queries</strong></p> <p>We welcome you to join the Github community and to fork, branch, improve and make a pull request to add your improvements to the new version of the SDG queries. <strong><a href="https://github.com/Aurora-Network-Global/sdg-queries">https://github.com/Aurora-Network-Global/sdg-queries</a></strong></p>
MVs and FVs for Planck 2015 and 2018 temperature maps obtained by polyMV
<p>The files located in <em>data_mvs.zip</em> are the tables of Multipole Vectors (MVs) for Planck 2015 and 2018 temperature maps (full sky and masked maps using the Common Temperature mask of PR3). The files located in <em>data_fvs.zip</em> are the tables of Fréchet vectors (FVs) for Planck 2015 and 2018 temperature maps. MVs and FVs were obtained using <a href="https://oliveirara.github.io/polyMV/">polyMV</a>.</p> <p>Any publications making use of this data should cite this paper: R. A. Oliveira, T. S. Pereira, and M. Quartin, <strong>CMB statistical isotropy confirmation at all scales using multipole vectors</strong>, <a href="https://doi.org/10.1016/j.dark.2020.100608">Phys. Dark Univ. 30 (2020) 100608</a> (<a href="https://arxiv.org/abs/1812.02654">arXiv:1812.02654 [astro-ph.CO]</a>).</p> <p>Please refer to the above paper for further details.</p>
Mapping the world's free-flowing rivers: CSV Dataset
<p>These data were originally generated by: Grill, G., Lehner, B., Thieme, M. <em>et al.</em> Mapping the world’s free-flowing rivers. <em>Nature</em> 569, 215–221 (2019). <a href="https://doi.org/10.1038/s41586-019-1111-9">https://doi.org/10.1038/s41586-019-1111-9</a>.</p> <p>The dataset here is a CSV generated from the original data curated and generated by Grill <em>et al.</em> to facilitate processing of the data using a variety of computational tools. The attribute table of the data generated in the study were extracted from an ArcGIS geodatabase format using QGIS and converted to a CSV.</p> <p>The description of the variables and attributes can be found in the documentation of the original dataset: <a href="https://figshare.com/articles/Mapping_the_world_s_free-flowing_rivers_data_set_and_technical_documentation/7688801">https://figshare.com/articles/Mapping_the_world_s_free-flowing_rivers_data_set_and_technical_documentation/7688801</a>. </p>
Land surface phenology based crop maps for Continental United States 2000-2018
<p>This data collection contains annual maps of crop types for Continental United States for the period 2000-2018 at spatial resolution of ~231m derived using <a href="https://doi.org/10.3334/ORNLDAAC/1299">MODIS Land Surface Phenology</a>. This data collection is a companion to the paper <strong><em>Konduri, V., Kumar, J., Hargrove, W. W., Hoffman, F. M., Ganguly, A. R. (2020) <a href="https://doi.org/10.1016/j.rse.2020.112048">Mapping Crops Within the Growing Season Across the United States. Remote Sensing of Environment</a>, Vol 251, 2020 </em></strong><a href="https://doi.org/10.1016/j.rse.2020.112048">https://doi.org/10.1016/j.rse.2020.112048</a>, which describes the methodology for development of these datasets, validation metrics and analysis.</p> <p> </p> <p><strong><strong>Files in collection (28): </strong></strong></p> <ul> <li><strong><em>crop_map_predicted_[YEAR].nc</em>: </strong>Annual crop type maps for YEAR = 2000-2018</li> <li><strong><em>Crop_map_legend.csv</em>: </strong>Legend for crop type categories in the maps (Category numbers and legends for crop types are consistent with those used by <a href="https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php">USDA Crop Data Layer</a>)</li> <li>Maps of earliest date (Day of Year) of classification for eight dominant crop types for year 2015: <ul> <li><strong>earliest_classification_date_corn_CONUS.nc</strong> : Earliest date of classification for corn</li> <li><strong>earliest_classification_date_soybeans_CONUS.nc</strong> : Earliest date of classification for soybeans</li> <li><strong>earliest_classification_date_winter_wheat_CONUS.nc</strong> : Earliest date of classification for winter wheat</li> <li><strong>earliest_classification_date_fallow_CONUS.nc</strong> : Earliest date of classification for fallow</li> <li><strong>earliest_classification_date_other_hay_non_alfalfa_CONUS.nc</strong> : Earliest date of classification for other hay/non-alfalfa</li> <li><strong>earliest_classification_date_alfalfa_CONUS.nc</strong> : Earliest date of classification for alfalfa</li> <li><strong>earliest_classification_date_sorghum_CONUS.nc</strong> : Earliest date of classification for sorghum</li> <li><strong>earliest_classification_date_rice_CONUS.nc</strong> : Earliest date of classification for rice</li> </ul> </li> </ul> <p> </p> <p><strong><strong>Data formats:</strong></strong></p> <ul> <li>All map products are in gridded <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> format.</li> <li>Annual crop type maps use category types described in Crop_map_legend.csv. </li> <li>Earliest date of classification maps are encoded as Day of the Year.</li> </ul> <p> </p> <p><strong><strong>Projection for geospatial data</strong> (in <a href="https://live.osgeo.org/en/overview/proj4_overview.html">PROJ4 format</a>):</strong></p> <pre><code class="language-bash">PROJCS["US_National_Atlas_Equal_Area", GEOGCS["sphere", DATUM["unknown", SPHEROID["Spherical_Earth",6370997,"inf"]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]], PROJECTION["Lambert_Azimuthal_Equal_Area"], PARAMETER["latitude_of_center",45], PARAMETER["longitude_of_center",-100], PARAMETER["false_easting",0], PARAMETER["false_northing",0], UNIT["Meter",1]]</code></pre> <p><strong>Paper Citation:</strong></p> <blockquote> <p><strong><em>Konduri, V., Kumar, J., Hargrove, W. W., Hoffman, F. M., Ganguly, A. R. (2020) Mapping Crops Within the Growing Season Across the United States. Remote Sensing of Environment (in revision)</em></strong></p> </blockquote>
FIGURE 16. Distribution map for 11 in A revision of the Brazilian species of Lysmata Risso, 1816 (Decapoda: Caridea Lysmatidae), with discussion of the morphological characters used in their identification
FIGURE 16. Distribution map for 11 species of Lysmata Risso, 1816 currently known from the Brazilian coast and offshore islands. Abbreviations: Alagoas (AL), Amapá (AP), Atol das Rocas (AR), Bahia (BA), Ceará (CE), Espírito Santo (ES), Maranhão (MA), Pará (PA), Paraíba (PB), Paraná (PR), Pernambuco (PE), Piauí (PI), Rio Grande do Norte (RN), Rio Grande do Sul (RS), Rio de Janeiro (RJ), Santa Catarina (SC), São Paulo (SP), São Pedro e São Paulo (SPSP), Sergipe (SE), Trindade e Martim Vaz (TMV).
Say Fong (Laos). Map, 1903.
<p>Map of Say Fong published in G. Maspero, Say Fong: Une ville morte, <em>Bulletin de l'École française d'Extrême-Orient</em> 3, no. 1 (Janvier-Mars 1903): 1-17.</p>
Cutting through the Jungle: Disambiguating Model-based Traceability Terminology — Terminology Mapping Table
<p>This dataset contains the mapping of model-based traceability terminology developed in "Cutting through the Jungle: Disambiguating Model-based Traceability Terminology" to the secondary and primary sources that have been used as part of the validation of the terminology.</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.