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242 results for “Spatial Dataset”
STARSS22: Sony-TAu Realistic Spatial Soundscapes 2022 dataset
<p><strong>DESCRIPTION:</strong></p> <p>The **<strong>Sony-TAu Realistic Spatial Soundscapes 2022 (STARSS22)</strong>** dataset contains multichannel recordings of sound scenes in various rooms and environments, together with temporal and spatial annotations of prominent events belonging to a set of target classes. The dataset is collected in two different countries, in Tampere, Finland by the Audio Researh Group (ARG) of **<strong>Tampere University (TAU)</strong>**, and in Tokyo, Japan by **<strong>SONY</strong>**, using a similar setup and annotation procedure. The dataset is delivered in two 4-channel spatial recording formats, a microphone array one (**<strong>MIC</strong>**), and first-order Ambisonics one (**<strong>FOA</strong>**). These recordings serve as the development dataset for the <a href="https://dcase.community/challenge2022/task-sound-event-localization-and-detection">DCASE 2022 Sound Event Localization and Detection Task</a> of the <a href="https://dcase.community/challenge2022/">DCASE 2022 Challenge</a>.</p> <p>Contrary to the three previous datasets of synthetic spatial sound scenes of TAU Spatial Sound Events 2019 (<a href="https://zenodo.org/record/2599196">development</a>/<a href="https://zenodo.org/record/3377088">evaluation</a>), <a href="https://doi.org/10.5281/zenodo.4064792">TAU-NIGENS Spatial Sound Events 2020</a>, and <a href="https://zenodo.org/record/5476980">TAU-NIGENS Spatial Sound Events 2021</a> associated with the previous iterations of the DCASE Challenge, the STARS22 dataset contains recordings of real sound scenes and hence it avoids some of the pitfalls of synthetic generation of scenes. Some such key properties are:</p> <ul> <li>annotations are based on a combination of human annotators for sound event activity and optical tracking for spatial positions,</li> <li>the annotated target event classes are determined by the composition of the real scenes,</li> <li>the density, polyphony, occurences and co-occurences of events and sound classes is not random, and it follows actions and interactions of participants in the real scenes.</li> </ul> <p>The recordings were collected between September 2021 and January 2022. Collection of data from the TAU side has received funding from Google.</p> <p><strong>REPORT & REFERENCE:</strong></p> <p>If you use this dataset please cite the report on its creation, and the related DCASE2022 task setup:</p> <p>Archontis Politis, Kazuki Shimada, Parthasaarathy Sudarsanam, Sharath Adavanne, Daniel Krause, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Yuki Mitsufuji, Tuomas Virtanen (2022). <strong>STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events</strong>. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022)</em>, Nancy, France.</p> <p>found <a href="https://dcase.community/documents/workshop2022/proceedings/DCASE2022Workshop_Politis_51.pdf">here</a>.</p> <p><strong>AIM:</strong></p> <p>The dataset is suitable for training and evaluation of machine-listening models for sound event detection (SED), general sound source localization with diverse sounds or signal-of-interest localization, and joint sound-event-localization-and-detection (SELD). Additionally, the dataset can be used for evaluation of signal processing methods that do not necessarily rely on training, such as acoustic source localization methods and multiple-source acoustic tracking. The dataset allows evaluation of the performance and robustness of the aforementioned applications for diverse types of sounds, and under diverse acoustic conditions.</p> <p><strong>SPECIFICATIONS:</strong></p> <ul> <li>70 recording clips of 30 sec ~ 5 min durations, with a total time of ~2hrs, contributed by SONY (development dataset).</li> <li>51 recording clips of 1 min ~ 5 min durations, with a total time of ~3hrs, contributed by TAU (development dataset).</li> <li>52 recording clips with a total time of ~2hrs, contributed by SONY&TAU (evaluation dataset).</li> <li>A training-test split is provided for reporting results using the development dataset.</li> <li>40 recordings contributed by SONY for the training split, captured in 2 rooms (dev-train-sony).</li> <li>30 recordings contributed by SONY for the testing split, captured in 2 rooms (dev-test-sony).</li> <li>27 recordings contributed by TAU for the training split, captured in 4 rooms (dev-train-tau).</li> <li>24 recordings contributed by TAU for the testing split, captured in 3 rooms (dev-test-tau).</li> <li>A total of 11 unique rooms captured in the recordings, 4 from SONY and 7 from TAU (development set).</li> <li>Sampling rate 24kHz.</li> <li>Two 4-channel 3-dimensional recording formats: first-order Ambisonics (FOA) and tetrahedral microphone array (MIC).</li> <li>Recordings are taken in two different countries and two different sites.</li> <li>Each recording clip is part of a recording session happening in a unique room.</li> <li>Groups of participants, sound making props, and scene scenarios are unique for each session (with a few exceptions).</li> <li>To achieve good variability and efficiency in the data, in terms of presence, density, movement, and/or spatial distribution of the sounds events, the scenes are loosely scripted.</li> <li>13 target classes are identified in the recordings and strongly annotated by humans.</li> <li>Spatial annotations for those active events are captured by an optical tracking system.</li> <li>Sound events out of the target classes are considered as interference.</li> <li>Occurences of up to 3 simultaneous events are fairly common, while higher numbers of overlapping events (up to 5) can occur but are rare.</li> </ul> <p>More detailed information on the dataset can be found in the included README file.</p> <p><strong>SOUND CLASSES:</strong></p> <p>13 target sound event classes are annotated. The classes follow loosely the <a href="https://research.google.com/audioset/ontology/index.html">Audioset ontology</a>.</p> <p> 0. <strong>Female speech, woman speaking</strong><br> 1. <strong>Male speech, man speaking</strong><br> 2. <strong>Clapping</strong><br> 3. <strong>Telephone</strong><br> 4. <strong>Laughter</strong><br> 5. <strong>Domestic sounds</strong><br> 6. <strong>Walk, footsteps</strong><br> 7. <strong>Door, open or close</strong><br> 8. <strong>Music</strong><br> 9. <strong>Musical instrument</strong><br> 10. <strong>Water tap, faucet</strong><br> 11. <strong>Bell</strong><br> 12. <strong>Knock</strong></p> <p>The content of some of these classes corresponds to events of a limited range of Audioset-related subclasses. For more information see the README file.</p> <p><strong>EXAMPLE APPLICATION:</strong></p> <p>An implementation of a trainable model of a convolutional recurrent neural network, performing joint SELD, trained and evaluated with this dataset is provided <a href="https://github.com/sharathadavanne/seld-dcase2022">here</a>. This implementation will serve as the baseline method in the DCASE 2022 Sound Event Localization and Detection Task.</p> <p><strong>DEVELOPMENT AND EVALUATION:</strong></p> <p>The current version (Version 1.1) of the dataset includes the 121 development audio recordings and labels, used by the participants of Task 3 of DCASE2022 Challenge to train and validate their submitted systems, and the 52 evaluation audio recordings without labels, for the evaluation phase of DCASE2022.</p> <p>If researchers wish to compare their system against the submissions of DCASE2022 Challenge, they will have directly comparable results if they use the evaluation data as their testing set.</p> <p><strong>DOWNLOAD INSTRUCTIONS:</strong></p> <p>The file <strong><em>foa_dev.zip</em></strong>, correspond to audio data of the <strong>FOA </strong>recording format.<br> The file <strong><em>mic_dev.zip</em></strong>, correspond to audio data of the <strong>MIC</strong> recording format.<br> The <strong><em>metadata_dev.zip</em></strong> is the common metadata for both formats.</p> <p>The file <strong><em>foa_eval.zip</em></strong>, corresponds to audio data of the <strong>FOA</strong> recording format for the evaluation dataset.<br> The file <strong><em>mic_eval.zip</em></strong>, corresponds to audio data of the <strong>MIC</strong> recording format for the evaluation dataset.</p> <p>Download the zip files corresponding to the format of interest and use your favourite compression tool to unzip these zip files.</p>
Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions
<p>Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios.</p> <p>These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):</p> <p> </p> <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre> <p> </p> <p>This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p> </p>
Description and quality metadata files for four spatial landmark datasets
<p>The files define the description and quality metadata of the spatial landmark datasets from camptocamp.org defined <a href="https://doi.org/10.5281/zenodo.6480985">here</a>.</p> <p>Two ISO standards ISO 19115-1:2014 and ISO 19157:2013, recommanded by the INSPIRE Directive for the dissemination of spatial data and the reporting of data quality are used.</p>
Dataset for the article "Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion" by Roman Barth and Haitham Shaban
<p>The data set comprises all raw microscopy images and DFCC analyses as presented in </p> <p><strong>Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion</strong></p> <p>by Roman Barth and Haitham Shaban, published in Nucleus (https://doi.org/10.1080/19491034.2022.2088988)</p> <p>There are two folders for RNAPII and DNA each, one for the raw images and one for the processed DFCC data, supplied as .mat files.</p> <p>Every folder contains three sub-folders containing the data for the conditions: +Serum, -Serum, and +DRB.</p>
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>
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>
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>
Spatial dataset for ecological niche and spatial distribution modeling of Herichthys bartoni (Cichliformes: Cichlidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic cichlid <em>Herichthys bartoni</em> in the Media Luna spring, Mexico. This data includes occurrences records by species life stage (adult, juvenile and fry), in three field sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>For more information about the codes where the previous datasets could be used, visit the following repository with URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Likewise, the UC and WDp variables used to run the ecological niche and spatial distribution model, by summer period, can be found in the following repository wirh URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
Dataset for "Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator"
<p>The dataset represents the experimental data for publication "<span>Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator</span>"</p>
Dataset for: "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)"
<p>The version 1.0 contains the supporting data for the work (still under submission) "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)".</p> <p>The following files are here available (all file are georeferenced in EPSG: 3003):</p> <p>- AVG_Rainfall_1990-2019.tif -> Raster map of the mean annual precipitation for the northern Tuscany, Italy. It encompasses the portion of the Tuscany region northern of the cities of Livorno - Florence. The interpolation was validated via a leave one out cross-validation procedure.</p> <p>- D3-1_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1921 to 1950 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy).</p> <p>- D3-2_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1951 to 1980 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy).</p> <p>- DeltaSHP_Points_AVG_Annual_Rainfall.zip -> Shape file of the raingauges locations with the mean annual precipitation values of the period 1990 to 2019.</p> <p>- RaingaugesSHP_Points_AVG_Annual_Rainfall_1990-2019.zip -> Shape file of the raingauges locations with the following information: differences in the mean annual precipitation values between the two 3-decades periods 1951 to 1980 and 1990 to 2019 (named D3-2); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1951 to 1980 and 1990 to 2019; difference in the mean annual precipitation values between the two 3-decades periods 1921 to 1950 and 1990 to 2019 (named D3-1); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1921 to 1950 and 1990 to 2019.</p>
A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors (dataset)
<p>This repository contains the software and datasets needed to reproduce the results presented in the article "<a href="https://doi.org/10.1016/j.anucene.2022.109674">A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors</a>", published in Annals of Nuclear Energy.</p>
Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways
<p>This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).</p>
Spatially corrected dataset for natural variation across Arabidopsis diversity panel in early responses to salt stress
<p>The spatially corrected data of Arabidopsis thaliana accessions, which were grown according to the established protocol for studying salt stress in soil experiment (described in detail here dx.doi.org/10.17504/protocols.io.4xzgxp6), in the PSI facility, Czech Republic.</p> <p>The spatial correction was done using the asreml package. The data was subsequently used for the Genome-Wide Association Study. </p>
[Dataset] Spatial and temporal variability of the 365-nm albedo of Venus observed by the camera on board Venus Express
<p>This is the derived data, presented in a publication entitled "Spatial and temporal variability of the 365-nm albedo of Venus observed by the camera on board Venus Express" (JGR:Planet, doi: 10.1029/2019JE006271). See the paper for details. See 'Readme.txt' for the file descriptions.</p>
Preference and familiarity mediate spatial responses of a large herbivore to experimental manipulation of resource availability: datasets
<p>Publicly available dataset for:</p> <p>N. Ranc, P.R. Moorcroft, K.W. Hansen, F. Ossi, T. Sforna, E. Ferraro, A. Brugnoli & F. Cagnacci. 2020. Preference and familiarity mediate spatial responses of a large herbivore to experimental manipulation of resource availability. Scientific Reports. https://doi.org/10.1038/s41598-020-68046-7</p>
Final spatial dataset for native Norwegian vascular plants
<p>Occurrence data for the native Norwegian vascular plant species obtained from the Global Biodiversity Information Facility (GBIF). The dataset contains 3,597,865 occurrences.</p>
Dataset for "Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux"
<p>This dataset provides measured and upscaled forest floor methane (CH4) fluxes and soil moisture.</p> <p>This dataset is related to the following manuscript:</p> <p>Vainio et al., Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux, Biogeosciences, in review. (The discussion preprint is available at https://doi.org/10.5194/bg-2020-263.)</p>
[Database] Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets
<p>This file contains the complete catalog of datasets and publications reviewed in: Di Mauro A., Cominola A., Castelletti A., Di Nardo A.. <em>Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets.</em> Water 2021.The <strong>complete catalog</strong> contains:</p> <ul> <li>92 state-of-the-art water demand datasets identified at the district, household, and end use scales;</li> <li>120 related peer-reviewed publications;</li> <li>57 additional datasets with electricity demand data at the end use and household scales.</li> </ul> <p>The following <strong>metadata</strong> are reported, for each <strong>dataset</strong>:</p> <ul> <li>Authors</li> <li>Year</li> <li>Location</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Time Sampling Resolution</li> <li>Access Policy.</li> </ul> <p>The following <strong>metadata </strong>are reported, for each <strong>publication</strong>:</p> <ul> <li>Authors</li> <li>Year</li> <li>Journal</li> <li>Title</li> <li>Spatial Scale</li> <li>Type of Study: Survey (S) / Dataset (D)</li> <li>Domain: Water (W)/Electricity (E)</li> <li>Time Sampling Resolution</li> <li>Access Policy</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Location</li> </ul> <p><strong>Authors:</strong><br> Anna Di Mauro - Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) | <a href="mailto:anna.dimauro@unicampania.it">anna.dimauro@unicampania.it</a>;<br> Andrea Cominola - Chair of Smart Water Networks | Technische Universität Berlin - Einstein Center Digital Future (Germany) | <a href="mailto:andrea.cominola@tu-berlin.de">andrea.cominola@tu-berlin.de</a>; <br> Andrea Castelletti - Department of Electronics, Information and Bioengineering | Politecnico di Milano (Italy) | <a href="mailto:andrea.castelletti@polimi.it">andrea.castelletti@polimi.it</a><br> Armando Di Nardo -Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) | <a href="mailto:armando.dinardo@unicampania.it">armando.dinardo@unicampania.it</a></p> <p><strong>Citation and reference:</strong></p> <p>If you use this database, please consider citing <a href="https://www.mdpi.com/2073-4441/13/1/36">our paper</a> </p> <p>Di Mauro, A., Cominola, A., Castelletti, A., & Di Nardo, A. (2021). Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets. Water, 13(1), 36, https://doi.org/10.3390/w13010036</p> <p><strong>Updates and Contributions:</strong></p> <p>The catalogue stored in this public repository can be collaboratively updated as more datasets become available. The authors will periodically update it to a new version. </p> <p>New requests can be submitted to the authors, so that the dataset collection can be improved by different contributors. Contributors will be cited, step by step, in the updated versions of the dataset catalogue.</p> <p><strong>Updates history:</strong></p> <ol> <li>March 1st, 2021 - Pacheco, C.J.B., Horsburgh, J.S., Tracy, J.R. (Utah State University, Logan, UT - USA) --- The dataset associated with paper <a href="https://doi.org/10.3390/s20133655">Bastidas Pacheco, C.J.; Horsburgh, J.S.; Tracy, R.J.. A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters. Sensors 2020, 20, 3655.</a> is published in the HydroShare repository, where it is available as an OPEN dataset. Data can be found here: <a href="https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51">https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51</a></li> </ol>
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.