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Result dataset for our experimental analysis on multi-cepstral projection representation strategies for dysphonia detection
<p>Database containing the results of the analyzed versions of the framework proposed in our paper submitted to the journal Sensors (Basel) under the title "An experimental analysis on multi-cepstral projection representation strategies for dysphonia detection".</p> <p>In this database, we have the following information:</p> <p>"gender": Gender of individuals referring to the selected voice database. For this field, we have the following possible values: “male” for a selection of male individuals, “female” for a selection of female individuals, and “both” for a selection considering both genders.</p> <p>“Techniques”: Concerns about the techniques for extracting cepstral coefficients that we are analyzing. The identifier “nonceps” refers to the use of non-cepstral features.</p> <p>“vowel”: Vowel considered in the database selection. The following values are possible: “a”, “i” and “u”.</p> <p>“intonation”: Tone used by individuals when pronouncing the analyzed vowel. Possible values are: “h” for high; “l” for low; “n” is normal; and “lhl” for low-high-low.</p> <p>“coordinates”: Number of coordinates that make up the feature vector that represents the voice signal after the dimensionality reduction routines.</p> <p>“scale”: Normalization function used on the feature vector. The possible values of this field are the following: “MinMax” for the min-max scale; “Robust” for the robust scale; “Standard” for the standard scale; and “Unscaled” for the unscaled vector.</p> <p>“ACC”: Accuracy obtained by the analyzed version on the considered voice database clipping.</p> <p>“AUC”: Area under the ROC curve obtained by the analyzed version on the considered voice database clipping.</p> <p>“EER”: Equal Error Rate obtained by the analyzed version on the considered voice database clipping.</p> <p>“F1”: F1-score obtained by the analyzed version on the considered voice database clipping.</p> <p>“EH”: Rate of healthy voice signals classified as pathological on the considered voice database clipping.</p> <p>“EP”: Rate of pathological voice signals classified as healthy on the considered voice database clipping.</p> <p>“KFCV”: Average accuracy score of a 5-fold Cross Validation over the training dataset on the considered voice database clipping.</p> <p>“Balancing”: Indication of the use of balancing technique (SMOTE) by the considered framework version.</p> <p>“Classifier”: Classifier used, being possible the use of Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM).</p> <p>“Multi-Projection”: Multi-projection strategies employed by the evaluated technique.</p> <p>“Features”: Type of feature that defines the feature vector. In this case, the following values are possible in this field: “NonCeps” for non-cepstral features; “Ceps” for cepstral features only; and “Ceps and NonCeps” for features of cepstral and non-cepstral types.<br> .</p> <p>It is worth noting that the symbol “-”, present in some fields, represents the “non-use” of any technique of the type indicated by the field. For example, in the case of the “Balancing” field, the value “-” means that no data balancing technique was used in the evaluated version of the framework.</p>
PHUSICOS project platform dataset
<p>The PHUSICOS dataset gathers :</p> <p>- a dataset of Nature Based Solutions (NBS) actions implemented in mountainous or rural areas to cope with hydrometeorological events</p> <p>- datasets of documents of interest on NBS (publications, medias, reports, ect.)</p>
Data of yield in a mandarin crop derived from Diverfarming project
<p>Crop yield data, auxiliary data and methods metadata from a mandarin crop studied in Diverfarming project</p>
3D meshes of wood samples (KUR project)
<p>The repository contains 3D meshes of 83 wood samples from the LEIZA reference collection documented as part of the project „Mass Finds in Archaeological Collections“ funded by the Federal Cultural Foundation, from 15.04.2008 to 31.12.2011 within the framework of the "Programme for the Conservation and Restoration of Mobile Cultural Property" (KUR, see www.rgzm.de/kur). Each wood sample has two 3D meshes, one before conservation (KUR_3D_Vorzustand.zip) and one after conservation (KUR_3D_Endzustand.zip). A 3D fringe light projector (GOM ATOS III Rev.01) with a resolution of 0.25 mm was used to digitize the samples. Additional information can be found in the stored metafiles (*.json, *.ttl). The acquisition of 3D data was done during July 2009 - August 2014.</p>
Heatwaves characterization derived from observations and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_eobs_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_eobs_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_eobs_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of regions in Europe (1981-2100)
<p>This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul> <p> </p>
Heatwaves characterization derived from observations and climate projections to assess thermal behavior of regions in Europe (1981-2100)
<p>This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_eobs_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_eobs_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_eobs_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul> <p> </p>
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Project "Public services management system to improve the quality and accessibility of services" (01.2.2-LMT-K-718-03-0019) literature review screening results
<p>The results of the keyword query in Scopus search with the abstracts were screened using <i>abstractr </i>platform at <a href="http://abstrackr.cebm.brown.edu">http://abstrackr.cebm.brown.edu</a>. Four reviewers reviewed intersecting subsets of the overall list of publications in separate reviews, therefore duplicate records in the file are possible. The results from four reviews were combined into one file using functionality of <i>abstractr </i>platform. The reviews were finalized in February, 2022. Majority of the publications from the Scopus query results were automatically assigned low relevance scores thanks to the active learning algorithm used by <i>abstractr</i> and therefore were not reviewed manually.</p><p>Notes on the columns of the dataset:</p><ul><li>(internal) id - internal id added by <i>abstractr.</i></li><li>(source) id - Scopus document id followed by underscore and '1' (if publication has DOI) or 'n' (if publication has no DOI).</li><li>keywords - authors' keywords and Scopus keywords concatenated from the Scopus query results.</li><li>abstract - an abstract of a publication from the Scopus query results.</li><li>title - title of a publication from the Scopus query results.</li><li>journal - journal of a publication from the Scopus query results.</li><li>authors - authors of a publlication from the Scopus query results.</li><li>consensus - for publications reviewed by multiple reviewers the consensus decision is signified by '1', no consensus - by 'x' and unable to asses consensus by 'o'. These codes are generated by <i>abstrackr.</i></li><li>eg - the first reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>dj - the second reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>rp - the third reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>mp - the fourth reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>count (+1) - integer, the number of reviewers selecting the publication for fulltext reading. Calculated from 'eg ','dj ','rp' and 'mp' columns.</li><li>count (0) - integer, the number of reviewers not sure of selecting the publication for fulltext reading. Calculated from 'eg ','dj ','rp' and 'mp' columns.</li><li>count (-1) - integer, the number of reviewers not selecting the publication for fulltext reading. Calculated from 'eg ','dj ','rp' and 'mp' columns.</li><li>at leat once selected - binary integer, representing the final decision rule to select publications for fulltext reading and further analysis.</li></ul><p>The data were collected as a part of the project "Public services management system to improve the quality and accessibility of services" ("Viešųjų paslaugų vadybos sistema paslaugų kokybei ir prieinamumui gerinti"), grant no. 01.2.2-LMT-K-718-03-0019, funded by the Lithuanian research council.</p>
Project's repository for: Co-immersion in Audio Augmented Virtuality: the Case Study of a Static and Approximated Late Reverberation Algorithm
<p>Repository of the VR scene and the audio data used for the experiment reported in the publication <a href="https://ieeexplore.ieee.org/document/10269056" target="_blank" rel="noopener">available in Open Access</a>:</p> <blockquote> <p>Davide Fantini, Giorgio Presti, Michele Geronazzo, Riccardo Bona, Alessandro Giuseppe Privitera and Federico Avanzini (2023) "Co-immersion in Audio Augmented Virtuality: the Case Study of a Static and Approximated Late Reverberation Algorithm" in <em>IEEE Transactions on Visualization and Computer Graphics (ISMAR special issue)</em></p> </blockquote> <p>The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/README.md">README.md</a> includes some instructions to use the data in this repository.</p> <p> </p> <p><strong>AUDIO</strong></p> <p>The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/audio.zip">audio.zip</a> includes the Reaper's projects and audio files used in the experiment to provide the auditory stimuli (simultaneous reverberated speeches) to the participants. Each subfolder corresponds to a different Virtual Acoustics Environment (VAE):</p> <ul> <li><<em>LivingRoom</em>|<em>MARCo</em>|<em>METU</em>> <ul> <li><<em>Living Room</em>|<em>MARCo</em>|<em>METU</em>><em>.rpp</em>: Reaper's project for the VAE</li> <li><em>Bin</em>: folder including the speech data convolved with the late reverberation part of the reverb condition \(B\) for each source position in the VAE</li> <li><em>Freeverb</em>: folder including the speech data convolved with the late reverberation part of the reverb condition \(F_\text{d}\) for each source position in the VAE</li> <li><em>HOA</em>: <ul> <li><em>ER</em>: folder including the speech data convolved with the early reflections part (HOA in A-format) of the reference reverb condition \(H\) for each source position in the VAE</li> <li><em>Ref</em>: folder including the speech data convolved with the entire reference reverb condition \(H\) (HOA in A-format) for each source position in the VAE</li> </ul> </li> </ul> </li> </ul> <p>The reverberated speech data in the <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/audio.zip">audio.zip</a> file are obtained using third-party datasets:</p> <ul> <li>The anechoic speech data are retrieved from four speakers (F2, F5, M3, M6) of the <a href="https://doi.org/10.5281/zenodo.6257551">ACE challenge corpus</a></li> <li>The Room Impulse Responses (RIR) in High-Order Ambisonics (HOA) format used to reverberate the speeches are retrieved from: <ul> <li><a href="https://doi.org/10.5281/zenodo.5747753">Living Room</a></li> <li><a href="https://doi.org/10.5281/zenodo.3477602">Concert hall (MARCo)</a></li> <li><a href="https://doi.org/10.5281/zenodo.2635758">Classroom (METU)</a></li> </ul> </li> </ul> <p> </p> <p><strong>VR SCENE</strong></p> <p>The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/VRscene.zip">VRscene.zip</a> includes the Virtual Reality (VR) scene provided to the participants during the experiment via an Oculus Quest 2. This file includes two subfolders:</p> <ul> <li><em>UDPServer</em>: C# code for the UDP server used for sending the OSC messages for head tracking <ul> <li><em>external/SharpOSC.dll</em>: external library (<a href="https://github.com/ValdemarOrn/SharpOSC">SharpOSC</a>) used to interact with the OSC protocol</li> </ul> </li> <li><em>VR_Headtracking</em>: folder including the Unity project with the VR scene</li> </ul> <p> </p>
Supplementary material for the article "High-resolution projections of ambient heat for major European cities using different heat metrics"
<p>This dataset contains the data displayed in the figures or the article "High-resolution projections of ambient heat for major European cities using different heat metrics".</p> <p>The different files contain:</p> <ul> <li>Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc:<br> Change of yearly maximum temperature in Europe between 1981-2010 and 3 °C European warming relative to 1981-2010.</li> <li>Data_Fig2_timeseries-GSAT-ESAT_EURO-CORDEX_CMIP5_CMIP6_1971-2100_RCP85_SSP585.xlsx:<br> Time series of global mean surface air temperature (GSAT) for CMIP5 and CMIP6 models, and for European mean surface air temperature (ESAT) for EURO-CORDEX, CMIP5, and CMIP6 models for the period 1971-2100.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_E-OBS_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for E-OBS for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_ERA5-Land_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for ERA5-Land for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_EURO-CORDEX_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for the EURO-CORDEX models for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_weather-stations_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for GSOD and ECA&D stations for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig4_TX-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.</li> <li>Data_Fig5_Contribution-of-explanatory-variables-to-total-explained-variance.xlsx:<br> Contribution of different explanatory variables (climate and location factors) to the total explained variance of spatial patterns of heat metrics.</li> <li>Data_Fig6_TN-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> Nighttime heat metrics for the investigated cities: HWMId-TN at 3 °C European warming relative to 1981-2010, TN exceedances above 20 °C at 3 °C European warming relative to 1981-2010, and TNx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.</li> <li>Data_Fig7_TX-ambient-heat_CMIP5_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP5 models.</li> <li>Data_Fig7_TX-ambient-heat_CMIP6_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP6 models.</li> <li>Data_Fig8_GCM-RCM-matrix_ambient-heat_3K-European-warming.xlsx:<br> GCM-RCM matrices for the three heat metrics.</li> </ul>
Hesperomys Project v23.6.0
<p>An export of data from the <a href="https://hesperomys.com">Hesperomys Project</a> version 23.6.0. The Hesperomys Project is a database of taxonomy and nomenclature, focused on mammals but also covering some other groups, principally other fossil tetrapods. The database contains information such as:</p> <ul> <li>Taxonomic classification for all mammals, living and extinct</li> <li>References to original citations for the vast majority of names</li> <li>Type specimens and type localities for numerous names</li> </ul> <p>The full database is available online at hesperomys.com. This export contains:</p> <ul> <li>name.csv: Data on names, including taxonomic context, authority, citation, type locality, type specimen, and classification of the etymology.</li> <li>taxon.csv: Data on taxa, including classification and authority</li> <li>collection.csv: Data on collections that contain type specimens, including name, location, and number of type specimens in the database</li> </ul> <p>The code used to generate the exports is on <a href="https://github.com/JelleZijlstra/taxonomy/blob/9a68bec908264ddfcb1d623b4c427ebfb689396f/taxonomy/db/export.py">GitHub</a>.</p> <p>Release notes for version 23.6.0:</p> <ul> <li>Database <ul> <li>Add numerous new names and recently published articles. Since the previous release,<br> 1226 additional names and 1450 additional articles have been added.</li> <li>Add given names or initials for a number of name authors that were given only as<br> family names.</li> <li>Remove about 200 duplicate articles</li> <li>Remove some duplicate names, mostly in the turtles</li> <li>Remove uses of the legacy "dubious" status</li> <li>Fix some incorrect parent taxa</li> <li>Add numerous new fossil taxa based on the recent literature</li> <li>Further taxonomic changes for extant mammals for alignment with the MDD</li> <li>Simplify the treatment of justified emendations, using two names instead of three</li> <li>Change many _page_described_fields to follow a more consistent format</li> </ul> </li> <li>Backend <ul> <li>Further checks for publication dates. Distinguish between "parts" (separately<br> published portions of a larger work) and "chapters" (simultaneously published<br> portions of a larger work, usually with different authorship). Enforce that chapters<br> have the same publication date as their enclosing work. Allow ranges of years (e.g.,<br> "1848-1852") only for works composed of parts, not for names or for other kinds of<br> works.</li> <li>More sophisticated processing and validation of LSIDs in order to display correct<br> publication dates.</li> <li>Enforce that the <em>page_described</em> and <em>original_rank</em> fields are set for all names<br> with original citations.</li> <li>Add new nomenclature statuses to distinguish between unpublished names, separating<br> out "unpublished_thesis" (unpublished because named in a thesis),<br> "unpublished_electronic" (named in an electronic work that does not fulfill the<br> ICZN's criteria for publication), "unpublished_supplement" (named in electronic<br> supplementary material only), and "unpublished_pending" (not yet available, but<br> expected to be made available by print publication).</li> </ul> </li> <li>Frontend <ul> <li>Better ordering for various lists of names and articles (e.g., ordering by page)</li> </ul> </li> </ul>
Dataset for "Future projections for the Antarctic ice sheet until the year 2300 with a climate-index method"
<p>Dataset for the paper "Future projections for the Antarctic ice sheet until the year 2300 with a climate-index method" (Journal of Glaciology, <a href="https://doi.org/10.1017/jog.2023.41">doi: 10.1017/jog.2023.41</a>).</p> <p>Please see the README for details.</p> <p>V1.1: Run-specs header files for SICOPOLIS added. README updated.<br>V1: Initial upload.</p> <p>* * * * * * *</p> <p>Users should cite the original publication when using all or parts of these data.</p>
GitHub Top 25 Software Project Analysis
<p>Companion dataset for the paper "For a More Transparent Governance of Open Source" published in the Communications of the ACM, 66, 8, 28-30, 2023.</p> <p>Data collected on May, 13th, 2022.</p> <p><strong>Note:</strong> cell annotations are only visible in the Excel version of the dataset.</p>
AdriSC Climate Model Data - For the article: Projecting expected growth period of bivalves in a coastal temperate sea
<p>The recent implementation, development and successful runs of the kilometer-scale atmosphere-ocean Adriatic Sea and Coast (AdriSC) climate model for the historical period of 1987-2017 and for an extreme climate projection (RCP 8.5) for the 2070-2100 period, have provided the necessary dataset to better understand the potential impact of climate change within the Adriatic basin. Here, temperature, salinity and ocean currents were extracted and formatted from the AdriSC ocean model at 1 km resolution. This dataset was then used to reproduce in the past (1987-2017 period) and project in the future (2070-2100 period) the expected growth of five bivalve species in the northern Adriatic Sea at two different locations: Barbariga and along the western coast of Istria. </p> <p> </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>
FUME Local population projections in destination cities
<p>FUME data on projected distributions of migrants at local level between 2030 and 2050.</p> <p>The dataset contains a folder of data for each destination city as a gridded dataset at 100m resolution in GeoTIFF format. The examined destination cities are: Amsterdam, Copenhagen, Krakow and Rome. The dataset is provided as 100m grid cells based on the Eurostat GISCO grid of the 2021 NUTS version, using ETRS89 Lambert Azimuthal Equal-Area (EPSG: 3035) as coordinate system. The file names consist of the projected year, the corresponding scenario, and the reference migrant group. The projections have been performed for the years 2030, 2040 and 2050. The investigated scenarios are the following:<br> • benchmark (bs),<br> • baseline (bs),<br> • Rising East (re),<br> • EU Recovery (eur),<br> • Intensifying Global Competition (igc), and<br> • War (war).</p> <p>The migration background is derived from data about the Region of Origin (RoO) for migrants in Copenhagen and Amsterdam, and from Region of Citizenship (CoC) for migrants in Krakow and Rome.</p> <p>The case study of <strong>Copenhagen</strong> covers the two central NUTS3 areas (DK011, DK012) and the groups presented are the following:<br> • total population (totalpop),<br> • native population (DNK),<br> • Eastern EU European migrants (EU_East),<br> • Western EU Europeans migrants (EU_West),<br> • Non-EU European migrants (EurNonEU),<br> • migrants from Turkey (Turkey),<br> • the MENAP countries (MENAP; excluding Turkey),<br> • other non-Western (OthNonWest), and<br> • other Western countries (OthWestern).</p> <p>The case study of <strong>Amsterdam</strong> covers one NUTS3 area (NL329) and the presented groups are the following:<br> • total population (totalpop),<br> • native population (NLD),<br> • Eastern EU European migrants (EU East),<br> • Western EU European migrants (EU West),<br> • migrants from Turkey and Morocco (Turkey + Morocco),<br> • migrants from the Middle East and Africa (Middle East + Africa),<br> • migrants from the former colonies (Former Colonies), and<br> • migrants from the rest of the world (Other Europe etc).</p> <p>The case study of <strong>Krakow</strong> covers the Municipality of Krakow, and the presented groups are the following:<br> • total population (totalpop),<br> • native population (POL),<br> • EU/EFTA European migrants (EU),<br> • non-EU European migrants (Europe_nonEU), and<br> • migrants from the rest of the world (Other).</p> <p>The case of <strong>Rome</strong> covers the Municipality of Rome, and the presented groups are the following:<br> • total population (totalpop),<br> • native population (ITA),<br> • migrants from Romania (ROU),<br> • Philippines (PHL),<br> • Bangladesh (BGD),<br> • the EU (EU; excluding Romania),<br> • Africa (Africa),<br> • Asia (Asia; excluding Philippines and Bangladesh) and<br> • America (America).</p>
Indoor/outdoor air temperature dataset - IN-HALE Project
<p>IN-HALE project aims to identify the thermal summer conditions inside the residences of residents over 65 years of age and to identify the individual determinants of exposure to heat, as well as the barriers (material and/or immaterial) for thermal adaptation. To this end, we will monitor the indoor thermal environment of 20 dwellings, through visits of objective assessment of the thermal behavior of the dwellings and using the installation of thermal data loggers during the summer period.</p> <p>Further info: <a href="http://www.in-hale.org">www.in-hale.org</a></p>
Covid-19 Vaccine Monitoring project (CVM)-Electronic Health Record data sources Codelist
<p>This is the code list that was used to identify outcomes and covariates (those tagged as in narrow) in electronic health records of participating data sources in the the CVM study which was addressing the following questions</p> <p> </p> <p>1)<strong> To create and assess readiness of electronic health record data sources for rapid evaluation of safety signals by </strong></p> <ul> <li> <p>Providing an overview of the methods for identification of COVID-19 vaccine exposure in the data sources </p> </li> <li> <p>Monitoring the number of individuals exposed to any COVID-19 vaccine and to compare this to COVID-19 vaccine exposure (benchmark: ECDC vaccine tracker)1 </p> </li> <li> <p>Generation of updated background rates for AESIs </p> </li> </ul> <p><strong>2) To conduct rapid safety assessment studies using electronic healthcare records and support EMA safety assessments. </strong></p> <p>The protocol for this study is publicly available www.encepp.eu/encepp/viewResource.htm?id=42637. The report with results using the code list is publicly available on Zenodo as well. </p> <p> </p> <p> </p> <p> </p>
OpenAIRE Covid-19 publications, datasets, software and projects metadata.
<p>This dataset provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dataset contains the OpenAIRE COVID-19 Gateway records, identified via full-text mining and inference techniques applied to the <a href="https://explore.openaire.eu">OpenAIRE Graph</a>. The OpenAIRE Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA.</p> <p>The dataset consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at <a href="https://doi.org/10.5281/zenodo.3974226">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.8238913">10.5281/zenodo.8238913</a>.</p> <p> </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.