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13,117 results for “Maps”
Geographical Distribution Maps of Western Palearctic Weevil Taxa
<p>The electronic supplement belongs to the research article Schütte A, Stüben PE, Astrin JJ (2023) Molecular Weevil Identification Project: A Thoroughly Curated Barcode Release of 1300 Western Palearctic Weevil Species (Coleoptera: Curculionoidea) - Biodiversity Data Journal 11.</p> <p>The ZIP file contains 613 distribution maps from Western Palearctic weevil taxa. The distribution maps showing Europe originate from the Curculio Institute's website (www.curci.de). Additional information on distribution range and known synonyms were based on the information from the Löbl catalogs (Löbl & Smetana 2011, Löbl & Smetana 2013). The maximum distribution range of each species was measured in km with Google Earth's ruler function.</p> <p>An unzip software is needed to access the *.JPG files within the *.ZIP file. Microsoft operating systems support *.zip files natively since Windows XP. MAC operating systems offer the "archive utility" to access *.zip files. Android users must install an app like Winzip, WinRAR, or 7ZIP. The iOS 13 operating system and onwards allow unzipping *.zip archives natively (iPhone and iPad). The *.zip filetype support can be installed on Linux operating systems via the terminal command: "sudo apt-get install unzip". Command to unzip: "unzip \*.zip". The *.JPG files can be opened with any picture viewer or internet browser.</p> <p>References<br> Löbl L, Smetana A (2011) Catalogue of the Coleoptera. Vol. 7, Curculionoiodea I, Stenstrup, Apollo Books, 373 pp.<br> Löbl L, Smetana A (2013) Catalogue of the Coleoptera. Vol. 8, Curculionoiodea II, Leiden & Boston, Brill, 700 pp.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Noncanonical electromechanical coupling paths in cardiac hERG potassium channel (semi-binary contact maps)
<p>Matrices of the semi-binary contact maps of the following open and closed systems: WT, A527L, A614G, L524R, L529H, L532H, T425L, T618L, W563L.</p> <p>The residue numbering is not the official one because the first residues (397) of hERG (PAS domain) were not included in our simulations so that each subunit comprizes 466 residues. Moreover, the four subunits were numbered consecutively. The official numbering of a residue can be easily recovered. The general rule is:</p> <p>official residue - 397 = our residue</p> <p>For example, the official T425 corresponds to T28 in the first subunit (425-397), T494 in the second subunit (425-397+466), T960 in the third subunit (425-397+466+466), and T1426 in the fourth subunit (425-397+466+466+466).</p>
Additional results for article "A new approach for the generation of real-time GNSS low-latitude ionospheric scintillation maps"
<p>Complete set of interpolation error and correlation metrics for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options using the SSS cross-validation scheme for the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Complete table of interpolation errors and correlation.csv”.</p> <p>Complete set of scintillation maps for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options covering the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Scintillation maps for the 10-hour dataset.zip”.</p> <p>Comparison plots of the scintillation maps generated by the approaches GDA, IDW, RBF and GPR with the pre-processing options SAR, SMR and VQI for each of the 40 intervals of time of 16 minutes covering the 10-hour dataset - file “Set of maps for all 4 approaches with the SAR, SMR and VQI sets of options.zip”.</p> <p>Sequence of scintillation maps for the 8-hour dataset generated by the GPR(VQI) approach for the three time resolutions (1, 2, and 16-minute) - file “Scintillation maps for the 8-hour comparison dataset.zip”.</p> <p>Animations corresponding to the sequence of maps generated by the GPR(VQI) approach for the 8-hour dataset, and for the three time resolutions (1, 2, and 16-minute) - file “Animations of GPR(VQI) maps for the 8-hour comparison dataset.zip”.</p>
Deposited data for 'Structural and functional map for forelimb movement phases between cortex and medulla'; Yang, Kanodia and Arber; 2023
<p>Primary source data for figures in '<strong>Structural and functional map for forelimb movement phases between cortex and medulla</strong>'; <a href="https://doi.org/10.1016/j.cell.2022.12.009">Yang et al. 2023</a> </p> <p> </p> <p> </p>
Mapping of districts to control zones of German Transmission System Operators (TSOs)
<p><strong>Mapping of districts to control zones of German Transmission System Operators (TSOs)</strong></p> <p>This dataset provides a mapping of districts in Germany at NUTS3 level to control zones that are maintained by four transmission system operators (50Hertz, Amprion, TenneT, TransnetBW). A visualisation of the mapping is provided it GER_NUTS3_TSOs.png.</p> <p>I can provide no guarantee of the accuracy or completeness of the provided information. In case the control zones of the TSOs change, or an error is detected, I do not assume liability for the error. The output file(s) can also be re-generated with the provided python script (GER_NUTS3_TSOs.py), and manipulated if necessary. In case you detect an error, please reach out to me so we can correct it in order to provide as accurate information as possible.</p> <p><strong>Licensing</strong></p> <p>This dataset is created on top of a dataset of German political administrative boundaries at NUTS3 level covering Germany provided by geoBoundaries (DEU_ADM2.shp). geoBoundaries is an online, open license database of global political administrative boundaries (i.e., state, county). For further information, see https://www.geoboundaries.org/.</p> <p>For the processing, I am also using the dataset "Verwaltungsgebiete 1:2500000" (vg_12500.shp) provided by the "Bundesamt für Kartographie und Geodäsie". The dataset is licensed as "Data licence Germany - attribution - version 2.0", and can be retrieved at https://gdz.bkg.bund.de/index.php/default/verwaltungsgebiete-1-2-500-000-stand-31-12-vg2500-12-31.html. A reference to the license is provided under the following link: https://www.govdata.de/dl-de/by-2-0.</p> <p>The files GER_NUTS3_TSOs.png, GER_NUTS3_TSOs.shp, GER_NUTS3_TSOs.shx, GER_NUTS3_TSOs.prj, GER_NUTS3_TSOs.dbf and GER_NUTS3_TSOs.cpg are released under the Creative Commons 4.0 Attribution International (CC BY 4.0).</p> <p>The file GER_NUTS3_TSOs.py is released under a MIT license.</p>
Land Cover Fraction Mapping with FORCE - Supplemental Data
<p> </p> <p>This upload contains data required to replicate a <a href="https://github.com/franzschug/force/blob/develop/docs/source/howto/lcf.rst">tutorial </a>that applies regression-based unmixing of spectral-temporal metrics for sub-pixel land cover mapping with synthetically created training data. The tutorial uses the <a href="https://github.com/davidfrantz/force">Framework for Operational Radiometric Correction for Environmental monitoring</a>.</p> <p>This dataset contains intermediate and final results of the workflow described in that tutorial as well as auxiliary data such as parameter files.</p> <p>Please refer to the above mentioned tutorial for more information.</p> <p> </p>
GIS Shapefiles for In Situ Geologic Map based on PMars 2020 Perseverance Rover Observations
<p>These are shapefiles for in situ geologic maps illustrated in Crumpler et al., 2023. n Situ Geologic Context Mapping Transect on the Floor of Jezero Crater from Mars 2020 Perseverance Rover Observations. Journal of Geophysical Research-Planets, Mars 2020 Results Special Collection</p>
Processed cadastre map for Aarhus municipality 2018
<p>Cadastre map for Aarhus municipality</p> <p>The whole of Denmark is divided into cadastres, which represent legally binding property boundaries. The 2018 cadastre map comprises a total of 81,437 cadastre units within Aarhus municipality. The cadastre map contains some general land use information, such as whether a cadastre unit is part of the road or rail network and if it is primarily used for agricultural production.</p> <p>Furthermore, each cadastre unit contains a unique identification string, which can be linked to building specific information from the Danish Building and Dwelling (BBR) register and to ownership information from the Central Business (CVR) register.</p> <p>Based on this information, for the 2018 cadastre, we compiled information on ownership categories, building specific information (e.g. use, such as residential, business etc.) and whether a cadastre unit is publicly accessible. For some of the information, the cadastre map was overlaid with other spatially explicit datasets.</p> <p>The different columns in the compiled cadastre map are described in the table below</p> <p><em>Information contained in the 2018 cadastre map for Aarhus municipality</em></p> <table> <tbody> <tr> <td> <p>Column Name</p> </td> <td> <p>Description</p> </td> <td> <p>Unit</p> </td> <td> <p>Comment</p> </td> </tr> <tr> <td> <p>parcel_id</p> </td> <td> <p>Unique ID for cadastre parcel</p> </td> <td> <p>Number</p> </td> <td> </td> </tr> <tr> <td> <p>cvr_name</p> </td> <td> <p>Owner name from CVR (central business) register</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> <tr> <td> <p>road_rail_code</p> </td> <td> <p>Code depicting if a cadastre is designated as road or railway line: 0 = Other; 1 = road; 2 = rail</p> </td> <td> <p>Number</p> </td> <td> <p>Based on information contained in the cadastre dataset</p> </td> </tr> <tr> <td> <p>road_rail_name</p> </td> <td> <p>Name for cadastres designated as roads or railways</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> <tr> <td> <p>code_BBR</p> </td> <td> <p>Code depicting use of buildings from BBR (building and dwelling register)</p> </td> <td> <p>Number</p> </td> <td> <p>In case of more than one building use, the cadastre is assigned the building use with the largest area within the cadastre</p> </td> </tr> <tr> <td> <p>name_BBR</p> </td> <td> <p>Name for use of buildings from BBR (building and dwelling register)</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> <tr> <td> <p>built_year</p> </td> <td> <p>Building year of oldest existing building on the cadastre</p> </td> <td> <p>Year</p> </td> <td> </td> </tr> <tr> <td> <p>ownership_code</p> </td> <td> <p>Code for type of ownership</p> </td> <td> <p>Number</p> </td> <td> <p>Based on re-classification of owner name from CVR-register</p> </td> </tr> <tr> <td> <p>ownership_name</p> </td> <td> <p>Name for type of ownership</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> <tr> <td> <p>ownership_agg_code</p> </td> <td> <p>Code for aggregated type of ownership</p> </td> <td> <p>Number</p> </td> <td> <p>Based on re-classification of owner name from CVR-register</p> </td> </tr> <tr> <td> <p>ownership_agg_name</p> </td> <td> <p>Name for aggregated type of ownership</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <table> <tbody> <tr> <td> <p>public_access_code</p> </td> <td> <p>Code depicting if a cadastre is publicly accessible: 0 = No public access; 1 = public access; 2 = road</p> </td> <td> <p>Number</p> </td> <td> <p>cadastres depicted as publicly accessible include: all publicly owned areas, unless they are farmed; common areas owned by private owner/housing associations; common areas owned by cooperative housing associations; private forests; privately owned nature areas, if these are not fenced</p> </td> </tr> <tr> <td> <p>public_access_name</p> </td> <td> <p>Name for public accessibility</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> <tr> <td> <p>build_code_01</p> </td> <td> <p>Code for building use for 1st aggregation level</p> </td> <td> <p>Number</p> </td> <td> <p>Based on aggregation of use categories from BBR (building and dwelling register)</p> </td> </tr> <tr> <td> <p>build_name_01</p> </td> <td> <p>Name for building use for 1st aggregation level</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> <tr> <td> <p>build_code_02</p> </td> <td> <p>Code for building use for 2nd aggregation level</p> </td> <td> <p>Number</p> </td> <td> </td> </tr> <tr> <td> <p>build_name_02</p> </td> <td> <p>Name for building use for 2nd aggregation level</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> <tr> <td> <p>build_code_03</p> </td> <td> <p>Code for building use for 3rd aggregation level</p> </td> <td> <p>Number</p> </td> <td> </td> </tr> <tr> <td> <p>build_name_03</p> </td> <td> <p>Name for building use for 3rd aggregation level</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> <tr> <td> <p>public_access_greenspace_type_code</p> </td> <td> <p>Code for type of publicly accessible green space: 0 = No publicly accessible green-blue space; 1 = Institution; 2 = Common area; 3 = Cemetery; 4 = Sport/leisure; 5 = Other green-blue space</p> </td> <td> <p>Number</p> </td> <td> <p>Based on combination of ownership, building type and public accessibility</p> </td> </tr> <tr> <td> <p>public_access_greenspace_type_name</p> </td> <td> <p>Name for type of publicly accessible green space</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> <tr> <td> <p>golf_course_code</p> </td> <td> <p>Code depicting if a cadastre is used as golf course: 0 = ND; 1 = golf course</p> </td> <td> <p>Number</p> </td> <td> <p>Based on owner name form CVR register</p> </td> </tr> <tr> <td> <p>golf_course_name</p> </td> <td> <p>Name for golf course</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> <tr> <td> <p>riding_school_horse_racing_track_code</p> </td> <td> <p>Code depicting if a cadastre is used as riding school or horse racing track: 0 = ND; 1 = riding school/horse racing trach</p> </td> <td> <p>Number</p> </td> <td> <p>Based on owner name form CVR register</p> </td> </tr> <tr> <td> <p>riding_school_horse_racing_track_name</p> </td> <td> <p>Name for riding school/horse racing track</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <table> <tbody> <tr> <td> <p>primary_school_code</p> </td> <td> <p>Code depicting if the cadastre belongs to a primary school: 0 = no primary school; 1 = primary school</p> </td> <td> <p>Number</p> </td> <td> <p>Based on overlay with feature point dataset from Aarhus municipality</p> </td> </tr> <tr> <td> <p>primary_school_type_code</p> </td> <td> <p>Code depicting school type: 0 = No primary school; 1 = public school; 2 = public special school; 3 = private school; 4 = private continuation school</p> </td> <td> <p>Number</p> </td> <td> <p>Based on overlay with feature point dataset from Aarhus municipality</p> </td> </tr> <tr> <td> <p>primary_school_type_name</p> </td> <td> <p>Name of school type</p> </td> <td> <p>Text</p> </td> <td> </td> </tr> <tr> <td> <p>primary_school_name</p> </td> <td> <p>Name of school</p> </td> <td> <p>Text</p> </td> <td> <p>Based on overlay with feature point dataset from Aarhus municipality</p> </td> </tr> <tr> <td> <p>primary_school_count_pupils</p> </td> <td> <p>Number of pupils attending school (2022)</p> </td> <td> <p>Number</p> </td> <td> <p>Based on school webpages</p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Quantitative monitoring and mapping of the damage in Syria archeological site Afamia (Apamea).
<p>This data is associated with the paper</p> <p><strong>Remote sensing and map quantitative data in an armed conflict zone.</strong></p> <p><strong>Case study: Quantitative monitoring and mapping of the damage in Syria archeological site Afamia (Apamea).</strong></p> <p>This data is arranged in CSV file.</p> <p>TheCSV file (Data-Afamia-Syria-MOBAIED 2023) contains 143,426 rows and 9 columns that represent the 143,426 entities (points) which can be projected under an adapted GIS.</p> <p>The ID for each entity is specified at the column (A), the spatial coordinates are determined under WGS 84 / UTM zone 37N at the columns (B, C, D, E). </p> <p>The category of each entity is described for each of the years of observation at the columns (F, G, H, I) using the following code:</p> <p>(0) Outside of the archeological site Apamea</p> <p>(1) inside of the archeological site Apamea </p> <p>(2) Fields</p> <p>(3) Other</p> <p>(4) Intact Site</p> <p>(5) Intact Fields</p> <p>(7) Previous Clandestine Excavations</p> <p>(8) Recent Clandestine Excavations</p> <p>(9) Traces of former excavations</p>
Mapping Jewish Communities of the Byzantine Empire
<p>The aim of the project was to map the Jewish presence in the Byzantine empire using GIS (Geographical Information Systems). All references (published and unpublished) to Jewish communities in the Byzantine Empire were gathered and collated. The data were incorporated in a GIS which will be made freely available to the general public using web maps at www.byzantinejewry.net.</p> <p> </p>
Fine-mapped summary statistics for protein coding regions (i.e. cis regions) based on the Olink Explore 1536 and Explore Expansion technologies
<p>This data set contains fine-mapping results performed by SuSie for cis regions ±500kb around the protein coding gene) for protein targets as measured by the Olink Explore 1536 and Explore Expansion technologies in 1,180 individuals from EPIC Norfolk study (https://www.epic-norfolk.org.uk/). Only protein targets where fine-mapping predicted at least one credible set were included in the results. </p>
Global distribution map of Rhenish stoneware during the 16th to 18th century
<p>The dataset provides a distribution map of Rhenish stonewares between the 16th and 18th century. The data was collected from published archaeological data (print and online) available to the author. According the published information the pottery was classified to different wares (Cologne, Frechen, Siegburg, Raeren, Westerwald). Values are given for individual sherd numbers. If no information was given in the publication, the value is set to "1". Bibligraphic reference is given by author - date. Full bibliographic reference can be found in the pdf-file.</p> <p>The csv-file contains next to location name, bibliographic reference and pottery counts values for longitude and latitude. The coordinate reference is WGS 84 - EPSG:4326.</p>
Dutch grid map layers for biodiversity surveys
<p>This dataset contains map layers in GeoPackage format with the following grids used in biodiversity surveys in the Netherlands:</p> <table> <tbody> <tr> <td><strong>Layer name</strong></td> <td><strong>Size (m)</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>IvonGrid Kwartierhok</td> <td>1250x1042 </td> <td>Used between 1902 and 1950 by IVON botanical surveys.</td> </tr> <tr> <td>IvonGrid Uurhok</td> <td>5000x4168 </td> <td>Used between 1902 and 1950 by IVON botanical surveys. Each square contains 16 'kwartierhok' squares with corresponding first four characters in column name.</td> </tr> <tr> <td>RdGrid Kilometerhok</td> <td>1000</td> <td>Used since 1950 in biodiversity surveys. </td> </tr> <tr> <td>RdGrid Uurhok</td> <td>5000</td> <td>Used since 1950 in biodiversity surveys. Each square contains 25 kilometer squares.</td> </tr> </tbody> </table> <p> </p> <p>The RdGrid maps are available in the WGS84 and RD New (EPSG:28992) spatial reference systems, as they are aligned to rounded RD coordinates. IvonGrid is only available in WGS84.</p> <p><strong>Grid square codes</strong></p> <p>Kilometerhok (1x1 km) and Uurhok (5x5) grids have corresponding grid square codes. A code consists of three concatenated numbers for map sheat (1-62), 5 km square (11-58) and 1 km square (11-55). E.g. a 5 km square code looks like 4017. In literature and on collection labels this often written as 40.17 or 40-17.</p> <p><strong>Maintanance</strong></p> <p>This dataset is being curated by FLORON Plant Conservation Netherlands since 1988 for use in botanical surveys (previously by Theo Peterbroers, Bart Vreeken and Ruud Beringen).</p> <p><strong>History</strong></p> <p>IVON stands for "Instituut voor Vegetatie-onderzoek Nederland", an NGO founded in 1930 was most active until the 1950's and then succeeded by FLORON in 1988. More information about the repartition of the IVON grid can be found in Atlas van de Nederlandse Flora part 1 (Mennema et al. 1989). The original map was hand drawn on topographical maps by J.W.C. Goethart en W. J. Jongmans (Van Ooststroom 1956) originally following the Bessel 1841 projection (EPSG:7004). The grid cells are equal in size, although not rounded to kilometers. The size was probably chosen to fit maps that were commercially available at that time.</p> <p> </p> <p><strong>References</strong></p> <p>Mennema, J., Quene-Boterenbrood, Plate, C. L., Van der Meijden, R., & Weeda, E. J. (1989). Atlas van de Nederlandse flora. Kosmos, Amsterdam.</p> <p>van Ooststroom, S.J. (1956). Het I.V.O.N. – Werk. Correspondentieblad ten dienste van de floristiek en het vegetatie-onderzoek van Nederland 1: 2–3.</p>
Mapping ecosystem types and land cover types in the Seychelles granitic islands, using Earth Engine and Sentinel-2
<p>We share here maps produced using Earth Engine: https://code.earthengine.google.com/?accept_repo=users/bsenterre/gis</p> <p>The maps include a land cover classification based on Sentinel-2, at 10m resolution, using an Object-Based Image Analysis approach, for the Seychelles granitic islands. Based on the land cover, landform (modeled using TauDEM), altitude and expert knowledge, we then derived a model of ecosystem types, with 3 maps: current distribution, potential distribution and prehuman distribution.</p> <p>A report exists (18th May 2022) that describes in detail the methodology, and it is being used for the preparation of a publication. The maps uploaded here are in raster format (geotif), crs=4326, and are accompanied by QGIS legend files (.qml), so they should load in QGIS with their legend automatically.</p>
Results of the crowd-mapping action within the project TeRRIFICA [Dataset No. 1 dated 2022-09-19]
<p>The dataset includes the results of the crowd-mapping action within the project "Territorial RRI fostering innovative climate action" - TeRRIFICA (Horizon 2020 under GA 824489) dated 2022-09-19. The data are points added to the map by the users (volunteers) and represent locations where climate change-related issues occur regarding air temperature, air quality, water, soil, and wind (SPOTS). The second part of the dataset is related to the crowd-mapping users and their anonymized characteristics (USERS). More details are available at https://terrifica.eu/.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Predictive high-resolution mapping of sea floor rock cover for the UK and Ireland
<p>Predicted seabed rock cover using the machine learning algorithm Catboost and marine environmental predictors.</p> <p>Supporting data for T4.1 of Horizon 2020 project FutureMARES.</p>
Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in an Alzheimer disease model
<p>Amyloid-β plaques and neurofibrillary tau tangles are the neuropathologic hallmarks of Alzheimer’s disease (AD), but the spatiotemporal cellular responses and molecular mechanisms underlying AD pathophysiology remain poorly understood. Here we introduce STARmap PLUS to simultaneously map single-cell transcriptional states and disease marker proteins in brain tissues of AD mouse models at a voxel size of 95 95 350 nm. This high-resolution spatial transcriptomics map revealed a core-shell structure where disease-associated microglia (DAM) closely contact amyloid-β plaques, whereas disease-associated astrocyte-like cells (DAA-like) and oligodendrocyte precursor cells (OPC) are enriched in the outer shells surrounding the plaque-DAM complex. Hyperphosphorylated tau emerged mainly in excitatory neurons in the CA1 region accompanied by infiltration of oligodendrocyte subtypes into the axon bundles of hippocampal alveus. The integrative STARmap PLUS method bridges single-cell gene expression profiles with tissue histopathology at subcellular resolution, providing an unprecedented roadmap to pinpoint the molecular and cellular mechanisms of AD pathology and neurodegeneration.</p>
GEOLAB - Transnational Access project QC-CEM - Mapping quick clay with geophysical methods
<p>Quick clay is characterised by complete collapse and liquid-like mobility when overloaded. Quick clay is found primarily in Norway and Sweden, but also exists in Finland, Russia, Canada and Alaska. Quick clay landslides, with their retrogression characteristics and extreme mobility, pose significant risk to human lives, infrastructure, property and surrounding ecosystems. Hence, the proper characterization of quick clay sites is essential for ensuring the safety and resilience of infrastructure in Norway and elsewhere in Europe.<br> The current practice for mapping quick clay in Norway relies heavily on borehole data with either rotary sounding or total sounding and core samples tested in the laboratory. The only method for identifying quick clay with certainty is physical testing in the laboratory, but it is time-consuming, expensive and gives limited information, i.e., only at the depths and locations where the samples are taken. In Norway, rotary sounding and total soundings are frequently used in mapping of quick clay. There is increasing interest in using geophysical methods such as Electrical Resistivity Tomography (ERT) to supplement the results from soundings, particularly in early stage of ground investigation for mapping of quick clay. ERT is a near surface geophysical method that uses direct current to measure the earth's electrical resistivity. The current is injected into the subsurface through steel electrodes installed 10-20 cm into the ground, and the apparent resistivity distribution along a profile or area is measured. Using data processing and inverse modelling a 2D or 3D resistivity model of the subsurface can be derived.<br> Geophysical methods such as ERT show capability to identify not quick clay such as sand, silt, dry crust, moraine and bed rock reasonably accurate, but the identification of quick clay is still generally limited. The detection of leached clay (thus potentially quick clay) is however possible.<br> Transnational Access project QC-CEM is funded through the 1st call for proposal for the GEOLAB project. This project aims at testing various geophysical methods for their capability for soil characterisation, particularly for detecting quick clay.</p> <p>The objectives of the QC-CEM project are:<br> (i) to test different configurations of Electrical Resistivity Tomography survey for detection of quick clay<br> (ii) to test innovative and efficient electromagnetic based methods for mapping of quick clay. Results from this investigation is not available to share at this stage.<br> (iii) to investigation the effectiveness of cross-interpretation using different geophysical methods for soil characterisation. The results from this activity will be published in open publication after they are processed.</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.