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13,117 results for “Maps”
Landslide Influencing Factors for Landslide Susceptibility Mapping in Lombardy, Italy
<p>A selection of Landslide Influencing Factors used for Landslide susceptibility mapping through machine learning models covering the Lombardy region in Italy.</p> <p>A list of the factors:</p> <ul> <li>Averaged Hourly Precipitation for the year of 2020. Source: Precipitation data from ARPA Lombardia.</li> <li>Digital Elevation Model [meters], 5m/pix. Source: GeoPortale Lombardia.</li> <li>Eastness [unitless], 5m/pix. Source: DTM derived.</li> <li>Faults buffer (50, 100, 200, 500, >500), [meters], 1:10 000. Source: GeoPortale Lombardia, derived.</li> <li>Land Use Land Cover Map. Source: GeoPortale Lombardia.</li> <li>Lithology, 5m/pix. Source: GeoPortale Lombardia.</li> <li>Normalized Difference Vegetation Index, [unitless], 10m/pix. Source: Sentinel-2 derived for the year 2020.</li> <li>Northness [unitless], 5m/pix. Source: DTM derived.</li> <li>Orientation of the slope faces [degrees], 5m/pix. Source: DTM derived.</li> <li>Plan Curvature [unitless], 5m/pix. Source: DTM derived.</li> <li>Profile Curvature [unitless], 5m/pix. Source: DTM derived.</li> <li>River buffer (50, 100, 200, 500, >500), [meters], 1:10 000. Source: GeoPortale Lombardia, derived.</li> <li>Road buffer (50, 100, 200, 500, >500), [meters], 1:10 000. Source: GeoPortale Lombardia, derived.</li> <li>The 90<sup>th</sup> percentile of hourly precipitation for the year of 2020. Source: Precipitation data from ARPA Lombardia.</li> <li>Topographic Wetness Index, 5m/pix . Source: DTM derived</li> </ul> <p>The maps are in WGS 84/ UTM zone 32N (EPSG:32632).</p> <p>The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:</p> <p><em>Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a case</em><br><em>study in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263</em></p> <p>The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.</p> <p>The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.</p> <p> </p>
Ocean Heat Content Anomalies in the North Atlantic based on mapping Argo data using local Gaussian processes defined over space
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2005-2022, in the North Atlantic, north of 20N) subtracting the time mean over the period 2005-2021 from the monthly time series of OHC. OHC fields are mapped using a locally stationary Gaussian process (defined over space) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). In this product, mapping is done in latitude and longitude with monthly subsets of data. Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA for 0-2000 dbar. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. </p>
ADAPT Global Solar Magnetic Maps - 2010 Sep 18-20 (w/ & w/o farside active region input)
<p>ADAPT (Air Force Data Assimilative Photospheric flux Transport) model global solar magnetic maps using HMI magnetograms with ("wfar") and without ("orig") estimated farside active region flux, for the 3 day period: September 18-20, 2010. The farside emergence of NOAA AR11109 (approximately on 18sep2010, based on STEREO observations of farside) is estimated by modeling the HMI vector observation on the east-limb (i.e., at a CMD of approximately -61.5 degrees) back ~6 days. </p>
On the estimation of the shear modulus of a honeycomb sandwich panel from X-ray mapping of its core: supplementary files
<p>This dataset is a Supplementary Material accompanying a paper [1] entitled </p> <p><em>On the estimation of the shear modulus of a honeycomb sandwich panel from X-ray mapping of its core</em></p> <p>by Q. Leclère <em>et al.</em>, published in the <a href="https://doi.org/10.25518/2684-6500.161">Journal of Structural Dynamics</a>. (Diamond Open Access)</p> <p>This dataset contains two data types from two experimental setups implemented on the same sandwich panel sample.</p> <p>.TIF images are X-ray radiographies of the sample, showing the honeycomb structure of the core. (1pixel=0.19mm)</p> <p>.UNV file is the result of a measurement of the vibration field of the sample excited by a piezoactuator realized using a Laser Doppler Vibrometer (regular grid, 2mm resolution). </p> <p>More information in the paper, that has to be cited in any publication exploiting this dataset.</p> <p>[1] Q. Leclère, K. Ege, F. Marchetti, N.B. Roozen, Ph. Duvauchelle, M. Tahraoui & V. Kaftandjian, «On the estimation of the shear modulus of a honeycomb sandwich panel from X-ray mapping of its core.», <em>Journal of Structural Dynamics</em> [En ligne], Issue 2, URL : https://popups.uliege.be/2684-6500/index.php?id=161.</p>
Stability (similarity) values of output maps generated using different background point sampling strategies
<p>These tables represent the model settings and similarity values of Species Distribution Models generated using different background pointssampling strategies. The similarity values are our own newly designed way to investigate model stability and were determined by comparing an output map of an SDM fitted using real data (original map) with an output map of an SDM fitted using virtual occurrences generated from the original map.</p>
MAP 4 in HIVA NASSERZADEH, GIUSEPPE PLATIA & SAYEH SERRI (2023) Click beetles (Coleoptera: Elateridae) of Khuzestan province (Iran): with three new species, Craspedostethus izehensis sp.n., Dicronychus khuzestanicus sp. n. and Gurjevelater catei sp. n.
MAP 4. Distribution map of Craspedostethus, Ectamenogonus and Lacon species in Khuzestan.
SPACEtomo training dataset for Yeast lamella map segmentation using nnU-Netv2
<p><strong>Training data used to train a nnU-Net 2D segmentation model for TEM montages of cryoFIB-milled lamellae from Yeast.</strong></p> <p>Medium magnification map pieces (2.283 nm/pixel) from starved <em>S. cerevisiae</em> were manually segmented into 17 categories (see publication) and used to train a 2D nnU-Net. </p> <p>This training dataset contains 50 images and segmentations of starved Yeast cells (kindly provided by Anna Bieber and Cristina Capitanio) and 10 additional images of Baker's Yeast under exponential growth conditions.</p>
Fungal Divergent Actin: Trait mapping and function hypothesis
<p>This projects investigates the potential new function of a divergent actin form mainly found in fungal species (Fungal Divergent Actin (FDA)) using trait mapping and association analysis as a novel strategy to generate hypotheses about protein function. We use the ProteinCartography pipeline (https://doi.org/10.57844/arcadia-a5a6-1068), combined with trait mapping and trait association analysis to generate hypotheses about the function of FDA. </p> <p>The overall idea is to identify a working set of fungal species for which we are able to confidently determine the presence or absence of fungal divergent actin, gather fungal trait information for these species (ecological traits, structural traits, genetic traits etc...) and determine whether there is any correlation between the presence/absence of FDA and one of the fungal trait. Any correlation between FDA presence/absence and fungal trait can then be used to infer hypothesis about the function of FDA.</p> <p>Our approach is divided into four main steps:</p> <p>- Step 1: Expanding the initial set of fungal species that possess FDA</p> <p>- Step 2: Defining the 'working set of species' (set of fungal species for which we can determine their FDA status (presence or absence))</p> <p>- Step 3: Curating fungal trait information</p> <p>- Step 4: Statistical modeling of the association of FDA and chosen fungal traits</p> <p> </p> <p>In this upload, we provide files that are parts of Steps 1 & 2:</p> <p> - ProteinCartography folder of the ProteinCartography run for the FDA proteins: fungal_divergent_actins_PC.zip</p> <p> - .csv table of all fungal proteins and associated species in Uniprot that have available structure in AlphaFold: Fungi_prot_uniprot.csv</p> <p> </p> <p><strong>ProteinCartography run: fungal_divergent_actins_PC.zip</strong></p> <p><em>(Step 1 - Expanding the initial set of fungal species that possess FDA)</em></p> <p>The aim of the first step, is to detect as many fungal species as possible that possess FDA. For this, we used ProteinCartography (https://doi.org/10.57844/arcadia-a5a6-1068), to screen for protein that have similar structures than the previously identified Fungal Divergent Actin (REF actin pub) and expand the original cluster of FDA.</p> <p>After identifying 6 representative sequences of Fungal Divergent Actins, we used each of the six proteins as input proteins for "Search Mode'' of the pipeline ProteinCartography. Full details on the ProteinCartography pipeline can be found in the GitHub repository and accompanying pub (https://doi.org/10.57844/arcadia-a5a6-1068).</p> <p>The fungal_divergent_actins_PC.zip contains all the inputs and outputs of the ProteinCartography run:</p> <ul> <li>fasta and pdb files for all 6 input proteins</li> <li>configuration file for the ProteinCatography run</li> <li>all output files (including main files presented in the Pub: similarity matrix, semantic analysis and aggregated_features_pca_umap in the subfolder output/clusteringresults</li> </ul> <p> </p> <p><strong>Uniprot list of fungal protein with available structure: </strong>Fungi_prot_uniprot.csv</p> <p><em>(Step 2 - Defining the 'working set of species')</em></p> <p>The aim of Step 2 is to identify the list of fungal species for which we can confidently determine the FDA presence/absence status. While ProteinCartography allows us to identify species that possess FDA, we also need to be able to confidently tell when a species doesn't possess FDA. Thus, the working set is defined as the set of species for which the presence or absence of FDA was putatively established.</p> <p>Because ProteinCartography relies on protein structures available in UniProt and AlphaFold, we decided to define our working set as any fungal species that has a minimum of 6000 protein structures in AlphaFold. Then we consider that any species of this set that is not part of the extended cluster does not possess FDA.</p> <p>Fungi_prot_uniprot.csv is the list of fungal proteins and associated species that have structure avaialable in AlphaFold from UniProt. This serves as the initial file to eventually count the number of proteins with available structures per species and identify the working set of fungal species - To obtain this list we conducted an 'Advanced search' in UniProt using the following query: </p> <ul> <li>'Fungi' in Taxonomy field </li> <li>' * ' for the field AlphaFoldDB cross-reference (found within the Cross reference /3D structure field)</li> </ul>
The Arabidopsis Leaf Quantitative Atlas: a cellular and subcellular mapping through unified data integration
<p>Additional data accompanying Tolleter et. al. The Arabidopsis Leaf Quantitative Atlas: a cellular and subcellular mapping through unified data integration <em>Quantitative Plant Biology</em>. 2024.</p> <p>Quantitative analyses and models are required to connect a plant's cellular organization with its metabolism. However, quantitative data are often scattered over multiple studies, and finding such data and converting them into useful information is time consuming. Consequently, there is a need to centralize the available data and to highlight the remaining knowledge gaps. Here, we present a step-by-step approach to manually extract quantitative data from various information sources, and to unify the data format. First, data from Arabidopsis leaf were collated, checked for consistency and correctness, and curated by cross-checking sources. Second, quantitative data were combined by applying calculation rules. They were then integrated into a unique comprehensive, referenced, modifiable and reusable data compendium representing an Arabidopsis reference leaf. This atlas contains the metrics of the 15 cell types found in leaves at the cellular and subcellular levels.</p> <p>Table_S1_2023_12_12.xlsx: All supplementary data relating to the Arabidopsis Leaf Quantitative Atlas</p> <p>ChloroKB data export.ipynb: A jupyter notebook containing code to convert Quantitative_data_2023_12_13.xlsx (from http://umr5558-chlorokb.univ-lyon1.fr:8080/ChloroKB_Grails/static/images/csv/Quantitative_data.csv) into Table_S5_2023_12_13.xlsx.</p> <p>Variable_conversion_table.xlsx ,Localisation_conversion_table.xlsx, Localisation_conversion_table_isolated_organelles.xlsx: Conversion factors required by ChloroKB data export.ipynb for unit conversion. </p>
Voxel-wise maps for the paper: Longitudinal associations of magnetic susceptibility with clinical severity in Parkinson's disease
<p>This upload contains voxel-wise group level QSM data, and statistical maps for group level results associated with the paper: Longitudinal associations of magnetic susceptibility with clinical severity in Parkinson's disease.</p> <p>The assocaited code for reproducing these statistical maps can be found here: <a href="https://github.com/gecthomas/QSM_PD_longitudinal">gecthomas/QSM_PD_longitudinal (github.com)</a></p>
GTA: San Andreas Los Santos full map 3D
GTA: San Andreas Los Santos full map 3D is the representation made by rockstars. buy this and be happy Source: Objaverse 1.0 / Sketchfab
Australia Map - Low Poly
Low poly Australia Map with each state being a separate entity. Can be imported to Unity/Unreal for map display and individual data display for each state. Please feel free to comment the country of your choice and get it modelled!! Source: Objaverse 1.0 / Sketchfab
Map Of The World
The oldest known map of the world. Located in the British Museum. Date: 6thC BC (approx) Photos taken November 2018 with Sony a6000. Processed in Agisoft Photoscan. https://www.britishmuseum.org/research/collection_online/collection_object_details.aspx?assetId=404485001&objectId=362000&partId=1 Official British Museum scan: https://skfb.ly/6WWzY Source: Objaverse 1.0 / Sketchfab
Africa 3D map
At EROS Data Center is a large globe that NASA commissioned to familiarize the astronauts with Earth's geography. This was in part so the astronauts could figure out where they were while in orbit. I snapped about 63 photos with my LG G4 in manual mode to create this raised relief map. Enjoy! Source: Objaverse 1.0 / Sketchfab
Catalina Island 3D map
Ranch Life in the Island's Interior Nestled in a small valley nearly 12 miles outside of Avalon is El Rancho Escondido, "the hidden ranch." Still a working ranch, it was originally started as the Wrigley family's Arabian Horse Ranch. In the early 1930s, Philip and Helen Wrigley set out to develop a breeding program for Santa Catalina Island's wild horses. By the 1950s those efforts evolved into the breeding of champion Arabian horses that were exhibited at shows and competitions across the United States. El Rancho Escondido had become world-class horse ranch as well as a major attraction in the island's interior. In 1977 Philip Wrigley passed away. Helen Wrigley continued to visit the island but the children took over running the ranch. Although activities at El Rancho Escondido have modified over the years, the intent has remained constant: to share Catalina's interior with visitors. The Ranch is now home to Rusack Vineyards, producing chardonnay, pinot noir and zinfandel grapes. Source: Objaverse 1.0 / Sketchfab
1996 Topographic Map of Chan Chich
Just testing ways to visualize old maps. This map was drawn in 1996 as a flat 2D map. I turned it into a 3D map to help find flaws in the map and to understand it better. Source: Objaverse 1.0 / Sketchfab
bendy map chapter 2
map from Windows Android Nintendo Switch Xbox One PlayStation 4 IOS Linux MacOS game: Bendy and the ink mashine 2017 Source: Objaverse 1.0 / Sketchfab
Map of New York City
New York City encompasses five county-level administrative divisions called boroughs: Manhattan, Brooklyn, Queens, The Bronx, and Staten Island. All boroughs are part of New York City, and each of the boroughs is coextensive with a respective county, the primary administrative subdivision within New York State. Queens and The Bronx are concurrent with the counties of the same name, while Manhattan, Brooklyn, and Staten Island correspond to New York, Kings, and Richmond Counties respectively. Source: Objaverse 1.0 / Sketchfab
Map of Mt. Kosciusko & surrounding districts
The Map of Mt. Kosciusko is an experiment in combining accurate height data with a historical map, while also exploring the history and significance of the region. It is inspired by a model developed by <a href="https://sketchfab.com/3d-models/new-york-phoenicia-quadrangle-ff012503e2a7412f88268f0a1afef08e" target="_blank">Thomas Flynn</a>. The original historical map is available through the <a href="https://nla.gov.au/nla.obj-234012997/view" target="_blank">National Library of Australia</a>. Source: Objaverse 1.0 / Sketchfab
92 Block--63k or 50k scale South Asia Maps
<p>6 maps of the Survey of India covering block 92.</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.