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25 results for “Spatial database”
Open Database of Spatial Room Impulse Responses at Detmold University of Music
<p>This repository contains an open source database of Spatial Room Impulse Responses (SRIR) captured at three different performance spaces of the Detmold University of Music. It includes the following rooms: </p> <ul> <li>Detmold Konzerthaus (medium sized concert hall, ~600 seats).</li> <li>Brahmssaal (small music chamber room, ~100 seats).</li> <li>Detmold Sommertheater (theater, ~300 seats).</li> </ul> <p>The collection contains approximately 600 multichannel RIRs corresponding to several source and receiver configurations. For each room we include measurement positions on stage and at the audience area captured with both an artificial head and an open microphone array compatible with the Spatial Decomposition Method (SDM).</p> <p>The Detmold Konzerthaus holds a large scale Wave Field Synthesis system and a Room Acoustic Enhancement System. SRIRs of an ensemble of focused sources on stage and with conditions of increased artificial reverberation are also included.</p> <p>If you use this dataset for your research, please cite our work:</p> <p>Amengual Gari, S. V.; Sahin, B.; Eddy, D; Kob, M.: <strong>"Open Database of Spatial Room Impulse Responses at Detmold University of Music"</strong>, <em>149th Convention of the Audio Engineering Society, </em>2020.</p> <p> </p> <p>The database is organized in 3 sets:</p> <p><strong>- Set A: </strong></p> <p>Source: Single Source measurements.</p> <p>Receiver: Open Array and Dummy Head.</p> <p>Rooms: BS, DST, KH</p> <p>Special configurations: Artificial reverberation, music stand on stage</p> <p><strong>- Set B: </strong></p> <p>Source: Loudspeaker and WFS orchestra</p> <p>Receiver: Open Array.</p> <p>Rooms: KH</p> <p><strong>- Set C:</strong></p> <p>Source: Loudspeaker orchestra</p> <p>Receiver: Dummy Head and Omni8 array</p> <p>Rooms: KH</p> <p> </p> <p>Further details on the measurement procedure and acoustical analysis of the RIRs can be found in the following publications:</p> <p><strong>Set A</strong></p> <p>Amengual Gari, S. V., Investigations on the Influence of Acoustics on Live Music Performance using Virtual Acoustic Methods, Ph.D. thesis, 2017.</p> <p>Amengual Garí, S. V.; Kob, M: "Investigating the impact of a music stand on stage using spatial impulse responses". 142nd Convention of the Audio Engineering Society, Berlin, May 2017.</p> <p><strong>Set B</strong></p> <p>Amengual Garí, S. V.; Pätynen, J.; Lokki, T.: "Physical and perceptual comparison of real and focused sound sources in a concert hall". Journal of the Audio Engineering Society, vol. 64 (12), pp. 1014-1025, December 2016.</p> <p><strong>Set C</strong></p> <p>Sahin, B., ““Investigation of the Detmold Concert Hall auditorium acoustics by comparing preference ratings and objective measurements.”, M.Sc. Thesis, 2017.</p> <p>Sahin, B., Amengual, S. V., and Kob, M., “Investigating listeners’ preferences in Detmold Concert Hall by comparing sensory evaluation and objective measurements,” Proc. 43th DAGA, Kiel, 2017.<br> </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>
[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>
A High-Resolution Spatial Room Impulse Response Database
<p><strong>A High-Resolution Spatial Room Impulse Response Database:</strong></p> <p>A Database of various SRIRs measured in three rooms, for two receiver and 3 source positions each. The positions are shown in the floor plans. </p> <p><strong>Naming Convention:</strong></p> <p><strong>Receivers</strong>: SMA (DRIRs): spherical microphone array impulse responses on a 2702 sampling point Lebedev grid.</p> <p> KU100 (BRIRs): Neumann KU100 dummy head impulse responses on a 360 sampling point horizontal grid</p> <p> Omni_ir: Omnidirectional impulse responses measured with an Earthworks M30 microphone </p> <p><strong>Receiver positions:</strong> P1, P2 as indicated in the floor plans</p> <p><strong>Source positions:</strong> LSL: left speaker, LSR: right speaker, LSC: center speaker as indicated in the floor plans </p> <p><strong>Rooms: </strong>Audiolab, Classroom, Audimax</p> <p>_______________________________________________________________________________________</p> <p><strong>Contact:</strong><br> Tim Lübeck, Johannes M. Arend, and Christoph Pörschmann<br> TH Köln - University of Applied Sciences<br> Institute of Communications Engineering<br> Department of Acoustics and Audio Signal Processing<br> Betzdorfer Str. 2, D-50679 Cologne, Germany</p> <p><a href="https://www.th-koeln.de/personen/tim.luebeck/">https://www.th-koeln.de/personen/tim.luebeck/</a></p> <p><a href="https://www.th-koeln.de/personen/johannes.arend/">https://www.th-koeln.de/personen/johannes.arend/</a></p> <p><a href="http://www.th-koeln.de/personen/christoph.poerschmann/">https://www.th-koeln.de/personen/christoph.poerschmann/</a><br> <br> </p>
PaleoRiada: A New Integrated Spatial Database of Palaeofloods in Spain
<p>PaleoRiada is the first national geographic database that compiles data on palaeoflood records published in scientific journals, book chapters, conference presentations, and publicly accessible scientific-technical reports. This database has been implemented through a Database Management System (Microsoft Access). </p> <p>Funding:</p> <p>Grants 2022-2023 and 2023-2026, signed between the Spanish General Directorate for Water (DGA-MITERD) and the Spanish Research Council (CSIC-MCIU), which include actions 20223TE003 and 20233TE012 (Tarquín project in IGME-CSIC).</p> <p>Community of Madrid (Predoctoral research grant PIPF-2022/ECO-24879)</p> <p> </p>
Point sample database with spatial holdbacks for global forest edge model training
<p>Global point dataset sampling 50 biophysical parameters to train the global forest carbon edge model at https://github.com/springinnovate/carbon_edge_model/releases/tag/1.2.0</p> <p>Field schema:</p> <p>accessibility_to_cities_2015_30sec_compressed (Integer64)<br> altitude_10sec_compressed (Integer64)<br> baccini_carbon_data_2014_compressed (Integer64)<br> bio_01_30sec_compressed (Real)<br> bio_02_30sec_compressed (Real)<br> bio_03_30sec_compressed (Real)<br> bio_04_30sec_compressed (Real)<br> bio_05_30sec_compressed (Real)<br> bio_06_30sec_compressed (Real)<br> bio_07_30sec_compressed (Real)<br> bio_08_30sec_compressed (Real)<br> bio_09_30sec_compressed (Real)<br> bio_10_30sec_compressed (Real)<br> bio_11_30sec_compressed (Real)<br> bio_12_30sec_compressed (Real)<br> bio_13_30sec_compressed (Real)<br> bio_14_30sec_compressed (Real)<br> bio_15_30sec_compressed (Real)<br> bio_16_30sec_compressed (Real)<br> bio_17_30sec_compressed (Real)<br> bio_18_30sec_compressed (Real)<br> bio_19_30sec_compressed (Real)<br> cec_0-5cm_mean_compressed (Integer64)<br> cec_5-15cm_mean_compressed (Integer64)<br> cfvo_0-5cm_mean_compressed (Integer64)<br> cfvo_5-15cm_mean_compressed (Integer64)<br> clay_0-5cm_mean_compressed (Integer64)<br> clay_5-15cm_mean_compressed (Integer64)<br> fc_stack_hansen_forest_cover2014_compressed (Integer64)<br> gf_0.4_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> gf_1.45_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> gf_5.0_fc_stack_hansen_forest_cover2014_compressed (Real)<br> gf_5.0_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> hillshade_10sec_compressed (Integer64)<br> masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Integer64)<br> night_lights_10sec_compressed (Real)<br> night_lights_5min_compressed (Real)<br> nitrogen_0-5cm_mean_compressed (Integer64)<br> nitrogen_10sec_compressed (Integer64)<br> nitrogen_5-15cm_mean_compressed (Integer64)<br> phh2o_0-5cm_mean_compressed (Integer64)<br> phh2o_5-15cm_mean_compressed (Integer64)<br> sand_0-5cm_mean_compressed (Integer64)<br> silt_0-5cm_mean_compressed (Integer64)<br> silt_5-15cm_mean_compressed (Integer64)<br> slope_10sec_compressed (Real)<br> soc_0-5cm_mean_compressed (Integer64)<br> soc_5-15cm_mean_compressed (Integer64)<br> tri_10sec_compressed (Real)<br> wind_speed_10sec_compressed (Real)</p>
Spatial database of the repertory of all the roads in Spain in the year of grace of 1543 by Juan Villuga [shapefile]
<p>This file provides a digital version of the roads documented by Juan Villuga in 1543. The file consists of 139 geographic entities (linear features), each entity represents a road documented by Juan Villuga with its respective name. The ETRS89 30N projection has been used. The vectorization has been made from two sources: 1) Villuga, Juan. 1543. Repertorio de todos los caminos de España en el año de gracia de 1543. Barcelona: Institut Cartografic i Geològic de Catalunya, R: RL 3419. (Accessed 14/05/2014). http://cartotecadigital.icgc.cat/cdm/singleitem/collection/espanya/id/2618/rec/1; and 2) Villuga, Juan. 1950 [1543]. Repertorio de todos los caminos de España. Madrid: Reimpresiones Bibliográficas.</p> <p>Este archivo proporciona una versión digital de los caminos por Juan Villuga en 1543. El archivo consta de 139 entidades geográficas de tipo línea, cada entidad representa un camino documentado por Juan Villuga con su respectivo nombre. Se ha utilizado la proyección ETRS89 30N. La vectorización ha sido realizada a partir de dos fuentes: 1) Villuga, Juan. 1543. Repertorio de todos los caminos de España en el año de gracia de 1543. Barcelona: Institut Cartografic i Geològic de Catalunya, R: RL 3419. (Consulta 14/05/2014). http://cartotecadigital.icgc.cat/cdm/singleitem/collection/espanya/id/2618/rec/1; y 2) Villuga, Juan. 1950 [1543]. Repertorio de todos los caminos de España. Madrid: Reimpresiones Bibliográficas. </p>
mapspamc_db: a database with global spatial datasets to support the implementation of the mapspamc R package.
<p>This repository contains the mapspamc database (mapspamc_db), a collection of global spatial datasets to support the implementation of the <a href="https://github.com/michielvandijk/mapspamc">mapspamc</a> R package. The database also includes subnational crop statistics and matching country shapefiles for several country examples. For more information on how to use the mapspamc package in combination with mapspamc_db, see the <a href="https://michielvandijk.github.io/mapspamc/">mapspamc documentation</a>. Detailed information on the contents of mapspam_db, such as the sources of information and pre-processing is described in the mapspamc_db documentation (pdf file) that is part of the repository.</p>
TAU Spatial Room Impulse Response Database (TAU-SRIR DB)
<p><strong>DESCRIPTION</strong></p> <p>The <strong>TAU Spatial Room Impulse Response Database (TAU-SRIR DB)</strong> database contains spatial room impulse responses (SRIRs) captured in various spaces of Tampere University (TAU), Finland, for a fixed receiver position and multiple source positions per room, along with separate recordings of spatial ambient noise captured at the same recording point. The dataset is intended for emulation of spatial multichannel recordings for evaluation and/or training of multichannel processing algorithms in realistic reverberant conditions and over multiple rooms. The major distinct properties of the database compared to other databases of room impulse responses are:</p> <ul> <li>Capturing in a high resolution multichannel format (32 channels) from which multiple more limited application-specific formats can be derived (e.g. tetrahedral array, circular array, first-order Ambisonics, higher-order Ambisonics, binaural).</li> <li>Extraction of densely spaced SRIRs along measurement trajectories, allowing emulation of moving source scenarios.</li> <li>Multiple source distances, azimuths, and elevations from the receiver per room, allowing emulation of complex configurations for multi-source methods.</li> <li>Multiple rooms, allowing evaluation of methods at various acoustic conditions, and training of methods with the aim of generalization on different rooms.</li> </ul> <p>The RIRs were collected by staff of TAU between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement 637422 <a href="https://cordis.europa.eu/project/id/637422">EVERYSOUND</a>.</p> <p><strong><em>NOTE</em></strong><em>: This database is a work-in-progress. We intend to publish additional rooms, additional formats, and potentially higher-fidelity versions of the captured responses in the near future, as new versions of the database in this repository.</em></p> <p> </p> <p><strong>REPORT AND REFERENCE</strong></p> <p>A compact description of the dataset, recording setup, recording procedure, and extraction can be found in:</p> <p>Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). <strong>A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection</strong>. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)</em>, Tokyo, Japan.</p> <p>available <a href="https://dcase.community/documents/workshop2020/proceedings/DCASE2020Workshop_Politis_88.pdf">here</a>. A more detailed report specifically focusing on the dataset collection and properties will follow.</p> <p> </p> <p><strong>AIM</strong></p> <p>The dataset can be used for generating multichannel or monophonic mixtures for testing or training of methods under realistic reverberation conditions, related to e.g. multichannel speech enhancement, acoustic scene analysis, and machine listening, among others. It is especially suitable for the follow application scenarios:</p> <ul> <li>monophonic and multichannal reverberant single- or multi-source speech in multi-room reverberant conditions</li> <li>monophonic and multichannel polyphonic sound events in multi-room reverberant conditions </li> <li>single-source and multi-source localization in multi-room reverberant conditions, in static or dynamic scenarios</li> <li>single-source and multi-source tracking in multi-room reverberant conditions, in static or dynamic scenarios</li> <li>sound event localization and detection in multi-room reverberant conditions, in static or dynamic scenarios</li> </ul> <p> </p> <p><strong>SPECIFICATIONS</strong></p> <p>The SRIRs were captured using an [Eigenmike](https://mhacoustics.com/products) spherical microphone array. A [Genelec G Three loudspeaker](https://www.genelec.com/g-three) was used to playback a maximum length sequence (MLS) around the Eigenmike. The SRIRs were obtained in the STFT domain using a least-squares regression between the known measurement signal (MLS) and far-field recording independently at each frequency. In this version of the dataset the SRIRs and ambient noise are downsampled to 24kHz for compactness.</p> <p>The currently published SRIR set was recorded at nine different indoor locations inside the Tampere University campus at Hervanta, Finland. Additionally, 30 minutes of ambient noise recordings were collected at the same locations with the IR recording setup unchanged. SRIR directions and distances differ with the room. Possible azimuths span the whole range of $\phi\in[-180,180)$, while the elevations span approximately a range between $\theta\in[-45,45]$ degrees. The currently shared measured spaces are as follows:</p> <ol> <li>Large open space in underground bomb shelter, with plastic-coated floor and rock walls. Ventilation noise. Circular source trajectory.</li> <li>Large open gym space. Ambience of people using weights and gym equipment in adjacent rooms. Circular source trajectory.</li> <li>Small classroom (PB132) with group work tables and carpet flooring. Ventilation noise. Circular source trajectory.</li> <li>Meeting room (PC226) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.</li> <li>Lecture hall (SA203) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Small classroom (SC203) with group work tables and carpet flooring. Ventilation noise. Linear source trajectory.</li> <li>Large classroom (SE203) with hard floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Lecture hall (TB103) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Meeting room (TC352) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.</li> </ol> <p>The measurement trajectories were organised in groups, with each group being specified by a circular or linear trace at the floor at a certain distance from the z-axis of the microphone. For circular trajectories two ranges were measured, a <em>close</em> and a <em>far</em> one, except room TC352, where the same range was measured twice, but with different furniture configuration and open or closed doors. For linear trajectories also two ranges were measured, <em>close</em> and <em>far</em>, but with linear paths at either side of the array, resulting in 4 unique trajectory groups, with the exception of room SA203 where 3 ranges were measured resulting on 6 trajectory groups. Linear trajectory groups are always parallel to each other, in the same room.</p> <p>Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights. </p> <p>The SRIRs are extracted from the noise recordings of the slowly moving source across those trajectories, at an angular spacing of approximately every 1 degree from the microphone. Instead of extracting SRIRs at equally spaced points along the path (e.g. every 20cm), this extraction scheme was found more practical for synthesis purposes, making emulation of moving sources at an approximately constant angular speed easier.</p> <p>More details on the trajectory geometries can be found in the <strong>README</strong> file and the <strong>measinfo.mat</strong> file.</p> <p> </p> <p><strong>RECORDING FORMATS</strong></p> <p>As with the DCASE2019-2021 datasets, currently the database is provided in two formats, first-order Ambisonics, and a tetrahedral microphone array - both derived from the Eigenmike 32-channel recordings. For more details on the format specifications, check the README. </p> <p>We intend to add additional formats of the database, of both higher resolution (e.g. higher-order Ambisonics), or lower resolution (e.g. binaural).</p> <p> </p> <p><strong>REFERENCE DOAs</strong></p> <p>For each extracted RIR across a measurement trajectory there is a direction-of-arrival (DOA) associated with it, which can be used as the reference direction for sound source spatialized using this RIR, for training or evaluation purposes. The DOAs were determined acoustically from the extracted RIRs, by windowing the direct sound part and applying a broadband version of the MUSIC localization algorithm on the windowed multichannel signal.</p> <p>The DOAs are provided as Cartesian components [x, y, z] of unit length vectors.</p> <p> </p> <p><strong>SCENE GENERATOR</strong></p> <p>A set of routines is shared, here termed <em>scene generator</em>, that can spatialize a bank of sound samples using the SRIRs and noise recordings of this library, to emulate scenes for the two target formats. The code is similar to the one used to generate the <a href="https://doi.org/10.5281/zenodo.5476980"><strong>TAU-NIGENS Spatial Sound Events 2021</strong></a> dataset, and has been ported to Python from the original version written in Matlab.</p> <p>The generator can be found [**<strong>here</strong>**](https://github.com/danielkrause/DCASE2022-data-generator), along with more details on its use. </p> <p>The generator at the moment is set to work with the <a href="https://zenodo.org/record/2535878">NIGENS</a> sound event sample database, and the <a href="https://zenodo.org/record/4060432">FSD50K</a> sound event database, but additional sample banks can be added with small modifications.</p> <p>The dataset together with the generator has been used by the authors in the following public challenges:</p> <p>- <a href="https://dcase.community/challenge2019/task-sound-event-localization-and-detection">DCASE 2019 Challenge Task 3</a>, to generate the <strong>TAU Spatial Sound Events 2019</strong> dataset (<a href="https://doi.org/10.5281/zenodo.2599196">development</a>/<a href="https://doi.org/10.5281/zenodo.3377088">evaluation</a>)</p> <p>- <a href="https://dcase.community/challenge2020/task-sound-event-localization-and-detection">DCASE 2020 Challenge Task 3</a>, to generate the <a href="https://doi.org/10.5281/zenodo.4064792"><strong>TAU-NIGENS Spatial Sound Events 2020</strong></a> dataset</p> <p>- <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection">DCASE2021 Challenge Task 3</a>, to generate the <a href="https://doi.org/10.5281/zenodo.5476980"><strong>TAU-NIGENS Spatial Sound Events 2021</strong></a> dataset</p> <p>- <a href="https://dcase.community/challenge2022/task-sound-event-localization-and-detection">DCASE2022 Challenge Task 3</a>, to generate additional <a href="https://doi.org/10.5281/zenodo.6406873"><strong>SELD synthetic mixtures for training the task baseline</strong></a></p> <p><em><strong>NOTE</strong>: The current version of the generator is work-in-progress, with some code being quite "rough". If something does not work as intended or it is not clear what certain parts do, please contact us.</em></p> <p> </p> <p><strong>DATASET STRUCTURE</strong></p> <p>The dataset contains a folder of the SRIRs (<strong>TAU-SRIR_DB</strong>), with all the SRIRs per room in a single MAT file. The file <strong>rirdata.mat</strong> contains some general information such as sample rate, format specifications, and most importantly the DOAs of every extracted SRIR. The file <strong>measinfo.mat</strong> contains measurement and recording information in each room. Finally, the dataset contains a folder of spatial ambient noise recordings (<strong>TAU-SNoise_DB</strong>), with one subfolder per room having two audio recordings fo the spatial ambience, one for each format, FOA or MIC. For more information on how to SRIRs and DOAs are organized, check the README.</p> <p> </p> <p><strong>DOWNLOAD</strong></p> <p>The files <em>TAU-SRIR_DB.z01</em>, ..., <em>TAU-SRIR_DB.zip</em> contain the SRIRs and measurement info files.</p> <p>The files <em>TAU-SNoise_DB.z01</em>, ..., <em>TAU-SNoise_DB.zip</em> contain the ambient noise recordings.</p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <p>Combine the split archive to a single archive:</p> <pre><code>zip -s 0 split.zip --out single.zip</code></pre> <p>Extract the single archive using unzip:</p> <pre><code>unzip single.zip</code></pre> <p> </p> <p><strong>LICENSE</strong></p> <p>The database is published under a custom **<strong>open non-commercial with attribution</strong>** license. It can be found in the `LICENSE.txt` file that accompanies the data.</p>
Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>Synthesis of fuel load and fuel consumption field measurements accompanying the publication:</p><p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p><p>Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5</p><p>1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br>2Department of Earth System Science, University of California, Irvine, CA 92697, USA<br>3Woodwell Climate Research Center, Falmouth, MA 02540, USA<br>4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br>5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p><p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p><p> </p><p>Units are g C / m2</p>
Spatial Database of Pondscapes
<p>The spatial distribution of the pondscapes in Europe were obtained from global and local databases and presented in deliverable D3.2 (report on spatial distribution of pondscapes in Europe). However, the information was too coarse and necessary features of the ponds could not be retrieved from the available local databases. Therefore, available datasets from each participant country were collected and a common database was created. This database consists of spatial information such as latitude and longitude of the ponds in geographic coordinates, name, code, land cover, intensity, hydro period, surface area, perimeter, maximum depth, mean depth, altitude, measurement year, created year, information about work packages and installed sensor information. </p> <p>Geocoded and detailed GIS layers showing all ponds were created and it will be used as ground truth for mapping and validating hydro period information. A web application has been developed to display spatial distribution and available information of ponds from the individual countries. The application allows users to dynamically visualize the ponds and download pond’s available data, hydro period information and pond database spatially: https://ponderful.hidrosaf.com/.</p> <p> </p>
Spatial and climatic worldwide database of the Asian Palmate Group of Araliaceae
<p>Asian Palmante Group database (AsPGdb) is a cleaned database for the 23 genera of the Asian Palmate Group (AsPG) of the ginseng family Araliaceae. Data has been collected from March 2018 to April 2020, from multiple data sorces, mostly open access online databases, especially GBIF. </p> <p>Each record in the database contains descriptive information about the taxa and different variables related to the geographic location of each record. In addition, each record has climatic information according to the following classifications: latitudinal zonation, Köppen’s classification (Köppen and Geiger, 1936), Holdridge’s classification (Holdridge, 1996), Metzger’s classification (Metzger et al., 2012) and Ecoregions system (Olson et al., 2001). We developed climate layers of these bioclimatic classifications for use in Geographic Information Systems, either by modifying existing layers (Dinerstein et al., 2017; Beck et al., 2018) or by creating new ones (all layers are available in GitHub repository: <a href="https://github.com/vvalnun/Bioclimatic-classifications-AsPG.git">https://github.com/vvalnun/Bioclimatic-classifications-AsPG.git</a>. The complete description of the AsPG database is found in the file named "Supplementary material 1_Database description_V2". The file describes in detail the information found in each of the columns.</p> <p>To compile the AsPGdb, we clean invalid records, correct spatial uncertainty and extract climatic data for all the records of the AsPG database, using R (R Core Team, 2018) and QGIS (QGIS Development Team, 2021).</p> <p>DOI references from GBIF downloads are provided in the "References_GBIF_AsPG" file.</p>
Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions and burned area as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br><sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br><sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br><sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br><sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
Model code for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions model code as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p>Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.</p>
Spatial database of the towns of the repertoire of all the roads of Spain in the year of grace of 1543 by Juan Villuga [shapefile]
<p>Este archivo proporciona una versión digital de los pueblos categorizados por Juan Villuga en 1543. El archivo consta de 1070 entidades geográficas de tipo punto, cada entidad representa un pueblo documentado por Juan Villuga con su respectivo nombre. Se ha utilizado la proyección ETRS89 30N. La vectorización ha sido realizada a partir de dos fuentes: 1) Villuga, Juan. 1543. Repertorio de todos los caminos de España en el año de gracia de 1543. Barcelona: Institut Cartografic i Geològic de Catalunya, R: RL 3419. (Consulta 14/05/2014). <a href="http://cartotecadigital.icgc.cat/cdm/singleitem/collection/espanya/id/2618/rec/1">http://cartotecadigital.icgc.cat/cdm/singleitem/collection/espanya/id/2618/rec/1</a>; y 2) Villuga, Juan. 1950 [1543]. Repertorio de todos los caminos de España. Madrid: Reimpresiones Bibliográficas.</p> <p>La tabla de atributos contiene información de la denominación de cada pueblo dada por Juan Villuga (1543), de la categorización dada por Villuga, de la denominación dada por Gonzalo Menéndez Pidal (1951), de la denominación actual y de la precisión de la digitalización.</p> <p>Cuanto a la metodología, en primer lugar, se transcribieron los núcleos y se relacionaron con la información de sus respectivas rutas y clasificación definida por Villuga (capital, ciudad importante, ciudad pequeña, venta) a través de una tabla en formato .xml. Cada núcleo, a su vez, se asoció también con la denominación actual y las establecidas en las cartografías de Villuga y Menéndez Pidal. De esta forma, se registraron los cambios en la toponimia entre las cartografías de 1546 y 1941. Otra característica adicional que decidimos considerar fue el atributo de "exactitud" que se refiere a la calidad de la información espacial (las coordenadas). Cuando los datos de un núcleo no eran exactos al georreferenciarlos, añadimos el atributo "inexacto", de modo que la calidad de los datos también quedara registrada en la propia tabla de atributos.</p>
360° Binaural Room Impulse Response (BRIR) Database for 6DOF spatial perception research
<p>by Applied Psychoacoustics Lab, University of Huddersfield</p> <p> </p> <p>Authors: Bogdan Bacila and Hyunkook Lee</p> <p>bogdan.bacila@hud.ac.uk, h.lee@hud.ac.uk</p> <p> </p> <p><strong>Description</strong></p> <p>An open-access database for 360° binaural room impulse responses (BRIR) captured in a reverberant concert hall. Head-rotated BRIRs were acquired with 3.6° angular resolution for each of 13 different receiver positions, using a custom-made head-rotation system that was automated and integrated with the Huddersfield Acoustical Analysis Research Toolbox. The BRIRs are provided in the SOFA format. The library also contains impulse responses captured using a first-order Ambisonic microphone and an omnidirectional microphone. It is expected that the database would be useful for studying the perception of spatial attributes in a six degrees-of-freedom context.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>The impulse responses are organised into two main folders:</p> <p>* Binaural: Contains the SOFA files and MATLAB files for each position, with a 3.6° angular resolution, recorded with the Neumann KU100 binaural head.</p> <p>* FOA: Contains the First Order Ambisonics audio files in A format and B format for each individual position, recorded with an Sennheiser Ambeo microphone in an end-fire configuration. </p> <p> </p> <p><strong>Naming Convention</strong></p> <p>The files are named after their relative position on the stage and the distance from the stage:</p> <p>* C = Centre</p> <p>* L = Left</p> <p>* LW = Left Wide</p> <p> </p> <p><strong>License</strong></p> <p>This project is licensed under the CC-BY-4.0 License - see the License.txt file for details</p> <p> </p> <p><strong>Publication</strong></p> <p>This database was presented at the Audio Engineering Society 146th International Convention.</p> <p>Download link: http://www.aes.org/e-lib/browse.cfm?elib=20371</p> <p> </p> <p><strong>Referencing</strong></p> <p>If you use the database for your research, please reference it as follows.</p> <p>Bacila, B. I., & Lee, H. (2019). 360° Binaural Room Impulse Response (BRIR) Database for 6DOF Spatial Perception Research. Presented at the Audio Engineering Society Convention 146, Dublin, e-Brief 513</p>
Supplementary material 1 from: Ball-Damerow JE, Oboyski PT, Resh VH (2015) California dragonfly and damselfly (Odonata) database: temporal and spatial distribution of species records collected over the past century. ZooKeys 482: 67-89. https://doi.org/10.3897/zookeys.482.8453
California Odonata database records after data processing, as described in methods.:
Database for Automatic Spatial Audio Scene Classification in Binaural Recordings of Music
<p>This repository contains supplementary material for the paper titled ‘<em>Automatic Spatial Audio Scene Classification in Binaural Recordings of Music</em>.’</p> <p>The database consists of the five following folders:</p> <ol> <li>Binaural recordings used for training</li> <li>Binaural recordings used for testing</li> <li>Extracted features</li> <li>Classification algorithm</li> <li>Music credits</li> </ol>
Figure 3 from: Ball-Damerow JE, Oboyski PT, Resh VH (2015) California dragonfly and damselfly (Odonata) database: temporal and spatial distribution of species records collected over the past century. ZooKeys 482: 67-89. https://doi.org/10.3897/zookeys.482.8453
Figure 3 - Relationship between species richness and total number of records by county, where each point represents a California county.
Figure 5 from: Ball-Damerow JE, Oboyski PT, Resh VH (2015) California dragonfly and damselfly (Odonata) database: temporal and spatial distribution of species records collected over the past century. ZooKeys 482: 67-89. https://doi.org/10.3897/zookeys.482.8453
Figure 5 - Number of unique county records for each collection type (Calbug collaborating institutions, non-Calbug institutions, observations - Cal Odes and Odonata Central, and private collections), and number of unique county records with two, three, and four shared data types.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.