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6,381 results for “spatial”
Dataset: Spatial Data Starter Kit for OnSSET Energy Planning in Kitui County, Kenya
<p>This is a set of openly-available data pre-processed to facilitate county-level energy planning using the Open Source Spatial Electrification Tool (OnSSET) in Kitui County, Kenya. It provides a ready-to-use starter kit of data inputs for county-level OnSSET analysis. The work to identify these data is submitted for publication - publication details will be added here as soon as possible upon release. These data are contained in spatial data files used to create the input for OnSSET in Kitui, and a prepared CSV data input for OnSSET in Kitui (<em>kitui_OnSSET_data</em>). The following spatial data files are included in the dataset:</p> <ul> <li>kitui_admin: A vector (.geojson) file containing the administrative boundaries of Kitui county. </li> <li>kitui_clusters: A vector (.geojson) file locating population clusters generated in data processing for OnSSET.</li> <li>kitui_demand: A raster (.tif) file containing merged health, agriculture, commercial, and residential demands for Kitui county in kWh.</li> <li>kitui_elevation: A raster (.tif) containing elevation information.</li> <li>kitui_GHI: A raster (.tif) file containing global horizontal irradiance data for Kitui county.</li> <li>kitui_hydro: A vector (.geojson) file containing the locations of hydropower stations in Kitui county. Note that there are none, and that this is expected.</li> <li>kitui_night_lights: A raster (.tif) file capturing the light emitted from Kitui county at night.</li> <li>kitui_power_stations: A vector (.geojson) file showing the locations of power stations in Kitui county.</li> <li>kitui_roads: A vector (.geojson) file showing the main roadways in Kitui county.</li> <li>kitui_transformers: A vector (.geojson) file showing transformer locations in Kitui county.</li> <li>kitui_transmission_lines: A vector (.geojson) file locating transmission lines in Kitui county. </li> <li>kitui_travel_hours: A raster (.tif) file showing travel time to the nearest market center in Kitui county.</li> <li>kitui_wind_100: A raster (.tif) file of wind speeds at 100 m in Kitui county.</li> </ul> <p>This dataset has been produced through work undertaken in the Climate Compatible Growth Programme.</p>
Data set for the publication entitled "Azithromycin alters spatial and temporal dynamics of airway microbiota in idiopathic pulmonary fibrosis"
<p>Set of files containing data used for microbiota analysis by 16S rRNA amplicon sequencing.</p> <p>The study cohort included patients with idiopathic pulmonary fibrosis from four centres in Switzerland, treated with azithromycin or placebo, sampled sequentially by oropharyngeal swab.</p> <p>This work is available in medRxiv and has been submitted</p>
Spatially Aggregated Rain Radar Forecast for the Koeln Weiden, Germany
<p>radar_forecast.csv contains time series data generated by spatial aggregation of rain radar forecasts constructed using robust local optical flow extrapolation.</p>
High temporal and spatial resolution emission inventory for maritime shipping emissions on the North Sea and Baltic Sea (2015)
<p>A temporally and spatially highly resolved emission inventory for the North Sea and Baltic Sea for the year 2015, created with current emission factors and ship activity data. The emissions inventory is available as 396 csv files, one for each day in 2015 and December 2014, grouped as monthly archives. </p> <p><strong>Note that due to the underlying ship activity data and the geographic boundaries, the time index in the <em>Datetime </em>column in the <em>ship_emissions_YYYYMMDD.csv</em> files is not equidistant.</strong> For example, since vessels leave the geographic area and reenter later, no data is available for the time the vessel is not within the area.</p> <p>The underlying model source code is available on Github, with a release of the associated version on Zenodo: [](https://doi.org/10.5281/zenodo.6951672)</p> <p> </p>
Effects of audio-motor training on spatial representations in long-term late blindness
<p>Datasets for behavioural data:</p> <p>-<em>Auditory horizontal localization task</em></p> <p>- <em>Auditory vertical localization task</em></p> <p>- <em>Position matching task</em></p> <p>- <em>Proprioceptive midline task</em></p> <p>Dataset for EEG data:</p> <p>- Spatial bisection task: mean ERP amplitude for 50-90ms timew window for each trial, separately for condition, session and roi</p> <p> </p>
Climate based seed zones for Mexico: spatial grids to guide reforestation under observed and projected climate change
<p>This database entry provides climate-based seed zone system for Mexico to address climate change observed over the last 30 years and projected climate change for the 2050s. The database corresponds to a journal publication by Castellanos-Acuña et al. (2018), available at https://doi.org/10.1007/s11056-017-9620-6. This seed zone classification is based on bands of two climate variables that have often been shown to drive genetic adaptation of tree species: mean coldest month temperature (MCMT), and an aridity index (AHM). MCMT was divided into ten bands of 3°C intervals, with the limits of these bands being, temperatures below <2°C, 2-5°, 5-8°, 8-11°, 11-14°, 14-17°, 17-20°, 20-23°, 23-26°, >26°C. AHM was divided into seven bands with intervals that are approximately equal width under a log-transformation: <20, 20-30, 30-45, 45-65, 65-95, 95-140, and >140 °C/mm. The gridded files provided in this database entry, the classes are coded as integer numbers, with the last digit representing the AHM class (1-7) and the first or first and second digit representing the MCMT class (1-10).</p>
Dataset for: Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses
<p>This repository contains supplementary material for the paper titled `Application of Machine Learning for the Spatial<br> Analysis of Binaural Room Impulse Responses' Available at: <a href="http://dx.doi.org/10.3390/app8010105">dx.doi.org/10.3390/app8010105</a> . These programs and audio files are distributed in the hopes that they will prove useful under the Creative Commons Attribution 4.0, with no warranty; or the implied warranty of merchantability or fitness for a particular problem. Please give appropriate credit for use of the material provided in this repository back to the author. </p> <p>In order to use the MatLab code the Auditory Toolbox by Malcolm Slaney [1] and the Cochleagram function distributed by Bin Gao [2] are required.</p> <p>The python scrips require the following Python libraries to be installed: Numpy[3], SciPy[4] and Tensorflow [5].</p> <p>The MatLab code was tested using MatLab R2017a on a Computer running windows 7.</p> <p>The python code was tested using Python 3.2.5, using an anaconda Python environment - in windows command line.</p> <p>--</p> <p>The repository contains:</p> <p>Folders:</p> <p><br> 1.) neg90 - This folder contains the gaussian normalisation parameters stored as text files and the weights and biases for the trained neural network - these are all for the -90° rotation neural network.</p> <p>2.) pos90 - This folder contains the gaussian normalisation parameters stored as text files and the weights and biases for the trained neural network - these are all for the +90° rotation neural network.</p> <p>3.) testData - this folder contains pre-generated test data for the different binaural dummy head microphones, speaker, and signal type combinations.</p> <p>Python Scripts:</p> <p><br> 1.) AnalyseDoA.py - A python script that can be run to test the neural network using the pre-generated test data - running the script will allow the user to input the binaural dummy head, speaker, and signal type. The important variables generated by this script are DoA - the direction of arrival for each signal in the feature vector, and yDiff - the difference between the predicted DoA and the expected direction of arrival</p> <p>2.) DirectionAnalysis.py - This python file contains a set of function that are used to define the neural network, and run it. The function called DoAPrediction takes the feature vector generated by the MatLab code as its input argument, these features will then be passed to the neural network, and the output of this function is the direction of arrival predicted by the neural network for each signal. The functions: DoAAnalysis_neg90 and DoAAnalysis_pos90 are called by the DoAPrediction function, these functions create the neural network using the NN function, import the weights and biases, and passes the feature matrix (provided as input) through the neural network - the output of these functions are the predicted direction of arrival.</p> <p>MatLab files:</p> <p><br> 1.) runAnalysis.m - This MatLab script analyses the dataset provided as part of this repository. Users can change the variables head ('KEMAR' or 'KU100'), signalType ('directSound' or 'reflection'), and speaker ('EquatorD5' or 'Genelec8030'). This script will produce the gaussian normalised feature vector and expected direction of arrival for all signals with the defined head, signal type, and speaker combination. These variables are then saved in .mat files so they can be imported by the python scripts.</p> <p>2.) BinauralModelCochlea.m - This MatLab function analyses a given binaural signal and outputs the interaural cross-correlation, interaural level difference, interaural time difference, the cochlea output for the left and right channel and the centre frequencies of the gammatone filter band. The input variables are: IR - the signal to be analysed, N - the number of gammatone filters, freqLow - the lowest centre frequency of the gammatone filter bank (centre frequency of the first gammatone filter), and freqHigh - the highest centre frequency of the gammatone filter bank (the centre frequency of the Nth gammatone filter). This function requires Malcolm Slaney's Auditory Toolbox [1] and Bin Gao's Cochleagram function [2] in order to work.</p> <p>3.) generateFeatureVector.m - This MatLab function generates a feature vector from an input binaural signal x, and a version of the signal captured after the binaural dummy head has been rotated by either +90° or -90° degree (variables xPos90 and xNeg90 respectively). If the sampling frequency (Fs) isn't 44100, the signals are resampled to be at 44100. This file also contains a function 'gaussianNormalisationTestData' which gaussian normalises the data using the mean and standard deviation calculated from the data used to train the neural networks - the mean and standard deviation values are stored in the folder GMParams in the pos90 and neg90 folders.</p> <p>4.) generateTestData.m - This MatLab function analyses the included binaural dataset, it takes the input variables: head - the binaural dummy head used for the measurements either 'KEMAR' or 'KU100', speaker - the speaker used for the measurements either 'EquatorD5' or 'Genelec8030', and signalType - the type of signal being analysed either 'directSound' or 'reflection'.</p> <p>Text files:</p> <p><br> 1.) noLayers.txt - a text file containing the number of layers used when training the neural network - with the current version of the code the neural network contains only 1 layer.</p> <p>2.) README.txt - Read me file containing information about the repository.</p> <p>Audio files:</p> <p><br> This repository contains 1152 binaural signals half of which are direct sounds segmented from a binaural room impulse responses and the other half are reflections segmented from binaural room impulse responses (detailed in the paper this material supports) the direct sounds are recorded at angles from 0° to 357.5° in steps of 2.5° and the reflections are recorded at angles of 1° to 358.5° in steps of 2.5°. In the paper only recordings relating to signals recorded with the Equator D5 are analysed.</p> <p>The combination of audio files include:</p> <p>1.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker<br> 2.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker<br> 3.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker<br> 4.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker<br> 5.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker<br> 6.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker<br> 7.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker<br> 8.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker</p> <p>The files are stored using the following file naming convention:<br> head_Test3_speaker_signalType_000_0_Degrees.wav - where _000_0 defines the azimuth direction of arrival so for example for a direct sound measured with the KEMAR unit and the Genelec8030 at 5 degrees would be 'KEMAR_Test3_Genelec8030_directSound_005_0Degrees.wav' and for a reflection measured with the KU100 and the Equator D5 at 298.5 degrees would be 'KU100_Test3_EquatorD5_reflection_298_5Degrees.wav'</p> <p>--</p> <p>Bibliography:<br> [1] Slaney, M. (1998). Auditory Toolbox. Palo Alto, CA. [Online]. Available: https://engineering.purdue.edu/~malcolm/interval/1998-010/ [Accessed: Oct. 27, 2017]</p> <p>[2] Gao, B. (2014). Cochleagram and IS-NMF2D for Blind Source Separation. [Online] Available: http://uk.mathworks.com/matlabcentral/fileexchange/48622-cochleagram-and-is-nmf2d-for-blind-source-separation?focused=3855900&tab=function [Accessed: Oct. 27, 2017]</p> <p>[3] NumFocus. (n.d.). NumPy. [Online]. Available: http://www.numpy.org/ [Accessed: Oct. 27, 2017]</p> <p>[4] SciPy. (n.d.). SciPy. [Online]. Available: https://www.scipy.org/ [Accessed: Oct. 27, 2017]</p> <p>[5] Google. (n.d.). TensorFlow. [Online] Available: https://www.tensorflow.org/ [Accessed: Oct. 27, 2017]</p> <p>--</p> <p>All code and audio produced by: Michael Lovedee-Turner, PhD candidate in Music Technology at the Audio Lab, Department of Electronic Engineering, University of York</p> <p>Contact: mjlt500@york.ac.uk</p>
Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile
<p>The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore-network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.</p>
3D models (NXS): Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley
<p><span>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</span></p>
3D models (true color, TIF): Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley
<p>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</p>
Community science approach reveals temporal and eutrophication-related spatial patterns in bladderwrack-associated invertebrate fauna
<p>Data related to the "Community science approach reveals temporal and eutrophication-related spatial patterns in bladderwrack-associated invertebrate fauna" paper by Salo, Nieminen, Salovius-Laurén and Rinne published in Estuarine, Coastal and Shelf Science in 2024. <a href="https://doi.org/10.1016/j.ecss.2024.108822">https://doi.org/10.1016/j.ecss.2024.108822</a></p> <p>The data describes the community data collected with the community science method described in the paper. </p>
A grid-based spatial database of current and potential mires in Estonia (EstMire)
<p>The EstMire dataset <span>includes </span><span>11,394,461 points (</span>center points of a <span>25</span><span>×</span><span>25 m regular grid</span><span>)</span> <span>covering</span> the<span> <span>known (as of 2022) and potential mires in Estonia. It was compiled and modified from multiple data sources for running a spatial simulation model, SooSim. The database includes the areas mapped by the Estonian Fund for Nature (1997–2021), wetland polygons from Estonian Topographic Database (2023), and the completed mire restoration projects carried out by the State Forest Management Centre </span></span>(2013<span>–2022</span>). Added to these sources are the<span> remaining Histosols areas from Estonian soil map, which were screened for being either so far unmapped mires or potential areas (mostly drained forests) that could develop into mires once restored. </span>Natural open- or semi-open (wooded) mires, peatland forests and areas with the recovery potential was separated by assessing tree canopy height and density based on the Lidar data provided by Estonian Land Board, and by combining this with land use data to remove regenerating clear-cuts or otherwise human modified areas. Each current or potential mire point includes its coordinates and 10 variables describing its woody cover and restoration potential, mire site type, the surrounding ditch length, and (if recently subjected to ditch renovation or restoration) the year of those interventions. The dataset consists of two tables, the current mire points (“Estmire_current.csv”) and other peatland points (“Estmire_potential.csv”).</p>
Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br>The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 82:00:30N<br>south: 18N<br>west: 32:00:30W<br>east: 70E</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://doi.org/10.5281/zenodo.7427021">https://doi.org/10.5281/zenodo.7427021</a></p>
Supplemental data for "Inequitable spatial and temporal patterns in the distribution of multiple environmental risks and benefits in Metro Vancouver"
<p><strong>DemoEnPoC2016.csv/DemoEnPoC2006.csv:</strong></p> <p>This is a table including environmental and demographic (Census variables) data at postal code level for Metro Vancouver in the year 2006 and 2016. The environmental data (SO2 metrics, PM2.5 metrics, Calculated ozone metrics, NO2 data, NDVI metrics, and Canadian Active Living Environments Index (Can-ALE) indexed to DMTI Spatial Inc. postal codes) were extracted from CANUE (Canadian Urban Environmental Health Research Consortium). The demographic data is extracted from Canadian Census analyzer (https://datacentre.chass.utoronto.ca/), the deprivation index is downloaded from from the Institut national de santé publique du Québec (INSPQ). </p> <p><strong>DGRwithLable:</strong></p> <p>This is the Dissemination Geographies Relationship File for the 2021 census year (Statistics Canada, 2021) with the lable of urban or rural, indicating which dissemination area (DA) is identified as urban and included in this study. The urban area is named as population certer. </p> <p><strong>Aggregation and SS Determination:</strong></p> <p>This script contains code for:</p> <ul> <li>Aggregating postal code level data to the Dissemination Area (DA) level.</li> <li>Eliminating rural DAs.</li> <li>Converting environmental data into ordinal categories using quartile and even break methods.</li> <li>Identifying sweet and sour spots for each DA based on these methods.</li> </ul> <p><strong>SSEJ Analysis:</strong></p> <p>This script includes code for:</p> <ul> <li>Creating violin and box plots to illustrate descriptive statistics of demographic groups across different environmental categories (sweet, sour, risky, and medium).</li> <li>Performing linear regression analyses between environmental categories and demographic variables.</li> </ul> <p><strong>SS Heatmap:</strong></p> <p>This script comprises code for:</p> <ul> <li>Summarizing the results of the linear regression analyses.</li> <li>Assessing changes in inequities among demographic groups between 2006 and 2016.</li> <li>Visualizing regression coefficients through heatmaps.</li> </ul> <p> </p>
Geo-referencing of journal articles and platform design for spatial query capabilities
<p>We analyzed the corpus of three geoscientific journals to investigate if there are enough locational references in research articles to apply a geographical search method, on the example of New Zealand. We counted place name occurrences that match records from the official Land Information New Zealand (LINZ) gazetteer in the titles, abstracts and full texts of freely available papers of the New Zealand Journal of Geology and Geophysics, the New Zealand Journal of Marine and Freshwater Research, and the Journal of Hydrology, New Zealand, for the years 1958 to 2015. We generated ISO standard compliant metadata records for each article including the spatial references and make them available in a public catalogue service.</p> <ol> <li><em>articles_georef_count_data.xlsx</em>: The counts and evaluation tracking of the place name occurrences in the journal articles.</li> <li><em>summary_final.xlsx</em>: Summary statistics for evaluation based on the counts data.</li> <li><em>article_template.xml</em>: XML template for ISO 19139 compliant metadata record filled for each article.</li> <li><em>full_article.xm</em>l: Exemplary fully filled ISO 19139 compliant metadata record.<br> </li> </ol>
Data for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect
<p>Dataset for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect, in final revision for Nature Communications.</p> <p>Includes two files - one behavioral data from uniquely identified worker bumblebees, and the second containing metadata for the colonies from which these data were generated (including experimental treatments, locations, sizes, etc.).</p>
Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task"
<p>Raw data for the simulation study " Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task" [1].</p> <p>[1] Josupeit, A., Schoenmaker, E., van de Par, S., & Hohmann, V. (2018). Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task. <em>European Journal of Neuroscience</em>, https://doi.org/10.1111/ejn.13981.</p>
Global monthly discharge dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution discharge dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre>
A Twitter Dataset for Spatial Infectious Disease Surveillance
<p>Dengue is a mosquito-borne viral disease which infects millions of people every year, specially in developing countries. Some of the main challenges facing the disease are reporting risk indicators and rapidly detecting outbreaks. Traditional surveillance systems rely on passive reporting from health-care facilities, often ignoring human mobility and locating each individual by their home address. Yet, geolocated data are becoming commonplace in social media, which is widely used as means to discuss a large variety of health topics, including the users' health status. In this dataset paper, we make available two large collections of dengue related labeled Twitter data. One is a set of tweets available through the Streaming API using the keywords dengue and aedes from 2010 to 2016. The other is the set of all geolocated tweets in Brazil during the year of 2015 (available also through the Streaming API). We detail the process of collecting and labeling each tweet containing keywords related to dengue in one of 5 categories: personal experience, information, opinion, campaign, and joke. This dataset can be useful for the development of models for spatial disease surveillance, but also scenarios such as understanding health-related content in a language other than English, and studying human mobility.</p>
Global monthly water temperature dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution water temperature dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre> <p>V1.1 update includes a improved version of the model removing some initial spikes related to rapid ice melt and streams that fall dry. The record has been reduced from 1981 tot 2014 to remove potential spinup impacts.</p> <p>The 1960-2010 data from v1.0 can be used for the earlier years.</p> <p>Consistent forcing is used for both time periods to remove potential biases that might occur otherwise.</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.