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423 results for “spatial use”
Data used for Investigating the role of Amazonian mesoscale wind patterns and strength on the spatial distribution of Martian bedrock exposures
<p>This upload contains four distinct zipped files with several different datasets contained within used for the analysis in publication "Investigating the role of Amazonian mesoscale wind patterns and strength on the spatial distribution of Martian bedrock exposures" by Gary-Bicas et al., 2022 Description for each dataset is below.</p> <p>- External data (Contains data used for thermophysical and morphological analysis).</p> <ul> <li>binary files NBmap2007.bin (Putzig and Mellon, 2007) and nmap2003.bin( Putzig et al., 2005) . These contain global Mars thermal inertia maps using the Thermal Emission Spectrometer (TES) onboard Mars Global Surveyor (MGS) </li> <li>Comma separated files with terminations "...USGS.csv" these are files extracting data for the studies' regions from the Mars global USGS geologic map #3292 (Tanaka et al., 2014) </li> <li>Shape file for bedrock designations (bedrock.[shp,shx,prj,dbf]) created by Cowart et al., 2019 where they mapped locations with bedrock exposures on Mars. We also include comma separated value files of the same maps for locations studied in this analysis (files with termination "...bedrock.csv")</li> <li>Shape file included has the locations of craters identified in all study regions for analysis (craters.point.[shp,shx,prj,dbf] and craters.polygon.[shp,shx,prj,dbf]) paired with the comma separated value intracrat.csv</li> <li>Shape file with study locations for analysis (windo_modeling_locations_revised3.[shp,shx,prj,dbf])</li> </ul> <p>- MRAMS data files.zip</p> <ul> <li>Contains 11 simulated climate states for each of the ten study location in analysis as well as Jezero crater using the Mars Regional Atmospheric Modeling System (MRAMS, Rafkin and Michaels, 2019). For each simulated case there are 4 seasonal time steps equating to 44 simulated cases for each study region in total (484 files) see associated python software publication indicating ingestion and processing of MRAMS datasets</li> </ul> <p>- MRAMS output files.zip</p> <ul> <li>After ingesting the datasets in MARS data files.zip into a python algorithm (see associated software publication) values for Wind Erosion Potential were extracted from the datasets and weighted sums were conducted to get annual values (see manuscript publication and associated python software publication,"MRAMS Data Output.ipynb") data was output into comma separated values for ease of use</li> </ul> <p>-MRAMS elevation and slope files.zip</p> <ul> <li>MRAMS data from output files.zip was further ingested into other algorithms to extract elevation and terrain slope values (see associated python software publication, "MRAMS Data Output.ipynb") that were output into comma separated values for ease of use</li> </ul> <p> </p> <p> </p>
Figure 3 from: Underwood E, Taylor K, Tucker G (2018) The use of biodiversity data in spatial planning and impact assessment in Europe. Research Ideas and Outcomes 4: e28045. https://doi.org/10.3897/rio.4.e28045
Figure 3 Steps in the Environmental Impact Assessment Process.
Figure 2 from: Underwood E, Taylor K, Tucker G (2018) The use of biodiversity data in spatial planning and impact assessment in Europe. Research Ideas and Outcomes 4: e28045. https://doi.org/10.3897/rio.4.e28045
Figure 2 Steps in the Strategic Environmental Assessment Process.
Figure 2b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 2b "Network Edition" features. - Example of a road network uploaded into the application
Supplementary files for: A geographic information system-based global variable renewable potential assessment using spatially resolved simulation
<p>File 1: Supplementary materials on methods</p> <p>File 2: Definition of the zones in this study</p> <p>File 3: Statistics on land cover pixels updated from OSM</p> <p>File 4: Validation of the eligibility masks with existing installation sites</p> <p>File 5: Validation of the estimated CF profiles for the European countries</p> <p>File 6: Summary of the available area, technical potential and capacity potential of wind and solar power in this study</p> <p>File 7: Aggregation of the estimated 10-year hourly CF into 288 month-hour profile</p> <p>File 8: The estimated 10-year hourly CF of all available technologies and CF tranches in each zone</p>
Eq 1 from: Pellegrini TG, Sales LP, Aguiar P, Ferreira RL (2016) Linking spatial scale dependence of land-use descriptors and invertebrate cave community composition. Subterranean Biology 18: 17-38. https://doi.org/10.3897/subtbiol.18.8335
Eq 1 -
Dataset of spatial transcriptomics of lung adenocarcinoma for analyzing the tumor microenvironment using topological analysis
<p>The human lung adenocarcinoma dataset for 'STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data'. </p>
Datasets used in Detecting Plumes in Mobile Air Quality Monitoring Time Series with Density-based Spatial Clustering of Applications with Noise v01
<p>This repository contains the following data sets related to Detecting Plumes in Mobile Air Quality Monitoring Time Series with DBSCAN published in . Please cite the following: .</p> <p>Validated_Data.csv: A .csv file containing the validation set used in the study. Column headings are the following:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been manually flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p> <p>Labeled_DBSCAN_Anomalies.csv: A .csv file containing points labeled as anomalies by the DBSCAN algorithm described in the manuscript. Columns are the following.</p> <p>"BC": Black carbon measurement (ng/m^3)<br> "CO2": Carbon dioxide measurement (ppm)<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2 (ppb)<br> "UFP": Ultrafine particle count (p/cc)<br> "Anomaly": Whether the DBSCAN algorithm has labeled this point as "Anomaly" (2) or "Normal" (1)<br> "Uniq_Fac": Factor spanning from 1-277 grouping measurements taken on separate days by car. E.g. all measurements with Uniq_Fac=1 were grouped and analyzed together.<br> "LST": Timestamp (US/Central)<br> "Road_Class": TigerLINE census road class designation for the given point. Possible road classes are S1100 - Primary Road, S1200 - Secondary Road, S1400 - Local Road, S1630 - Ramps, S1640 - Service Drives, S1730 - Private Roads<br> "X": Universal Transverse Mercator Easting for Zone 15N (m).<br> "Y": Universal Transverse Mercator Northing for Zone 15N (m).</p> <p>*_To_Be_Validated.csv: A series of files where * denotes the following.</p> <p>"DB": DBSCAN Algorithm<br> "QOR": QOR Algorithm<br> "QAND": QAND Algorithm<br> "Drew": Drewnick Algorithm</p> <p>Each file contains the following columns:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p>
Mechanistic Studies of Chronic COPD Using Single-Cell Sequencing and Single-Cell Spatial Transcriptomics
ClinicalTrials.gov study NCT07208123. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prediction of Therapeutic Response to Neoadjuvant Chemotherapy in Muscle Invasive Bladder Cancer Patients Using Spatial Transcriptomics
ClinicalTrials.gov study NCT06373055. IPD Sharing: NO. Countries: 1. Publications: 0.
Using Tissue-based Spatial Data to Understand How Obesity-related Tumor Metabolites Fuel Prostate Cancer Progression
ClinicalTrials.gov study NCT06292897. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Feasibility of Using Prism Adaptation to Treat Spatial Neglect and Motor Function in Stroke
ClinicalTrials.gov study NCT02419222. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Palliative Spatially Fractionated (GRID) Radiotherapy Using Intensity Modulated Proton Therapy
ClinicalTrials.gov study NCT05831579. IPD Sharing: YES. Countries: 1. Publications: 0.
Using Repetitive Transcranial Magnetic Stimulation (rTMS) in the Rehabilitation of Chronic Spatial Neglect After Stroke
ClinicalTrials.gov study NCT01000103. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Rehabilitating Unilateral Neglect Using Spatial Working Memory Training
ClinicalTrials.gov study NCT02608190. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Use of a Vibrotactile Sensory Prosthesis in Patients With Postural Imbalance and Spatial Disorientation
ClinicalTrials.gov study NCT00146952. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Study on Predicting the Risk of Distant Metastasis in Breast Cancer Using AI-Generated Spatial Pathological Maps
ClinicalTrials.gov study NCT07244094. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Spatial and Temporal Characterization of Gliomas Using Radiomic Analysis
ClinicalTrials.gov study NCT06036381. IPD Sharing: NO. Countries: 1. Publications: 0.
Identification of Specific Molecular Signatures in Pediatric, Adolescent, and Young Adult Rhabdomyosarcoma Through Spatial Proteome Analysis Using Mass Spectrometry
ClinicalTrials.gov study NCT06924463. IPD Sharing: NO. Countries: 1. Publications: 0.
Characterization of immune cell populations in the tumor microenvironment of colorectal cancer using high definition spatial profiling [VDJ]
GEO Series GSE280312. Homo sapiens. 16 samples. Type: Other; Expression profiling by high throughput sequencing.
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.