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12,237 results for “2019”
ENEM 2019: Respostas e Notas dos Participantes de São Paulo Divididos por Área
<p>Dados dos participantes do ENEM de 2019 do município de São Paulo divididos por área. As seguintes etapas foram aplicadas para obter os dados:</p> <ul> <li>Baixar os microdados fornecidos pelo INEP;</li> <li>Filtrar para manter apenas aqueles referentes ao ano de 2019 para os participantes do município de São Paulo;</li> <li>Arquivo convertido de CSV para Parquet;</li> <li>Filtrar e manter apenas os atributos desejados como nota, alternativas escolhidas, tipo de prova e gabarito;</li> <li>Separar o vetor de respostas dos participantes em colunas, uma coluna para cada questão, e indicar na coluna se o participante acertou a questão (1) ou errou (0). </li> </ul>
Liebesleid 1911 / 2019. Reenactment of Fritz Kreisler's 1911 "Love's Sorrow" Recording
<p>This video clip presents some intermediary results from re-enacting and digitally simulating a Fritz Kreisler (vl) and Haddon Squire (p) 1911 “Love’s Sorrow“ recording, which aimed to investigate the validity of so-called “acoustic“ recordings as sources for musical performance practice research. To do so, authentic impulse responses of some early acoustical sound recording and reproduction devices (built 1901–1933) were created and applied to modern recordings, such as the “Love’s Sorrow“ reenactment.<a>[1]</a> In the video, both the 1911 Kreisler / Squire original and the 2019 reenactment are shown consecutively, allowing for trace the sound modifications due to each step of the historical recording and reproduction processes. We found that most musical parameters deployed by the musicians – especially (changes in) articulation and timbre, but also dynamics, vibrato, and portamento – are affected significantly by the devices when played back. Therefore, it not only seems objectionable to use acoustical recordings as sources for researching historical performance practices in general, but also suggests that early recording musicians may have adjusted their studio performances remarkably in order to cope with the early technology. </p> <p>First published Nov. 20<sup>th</sup>, 2020 on YouTube (channel of the Stuttgart University of Music and Performing Arts).<a>[2]</a> For the sheet music edition of the 1911 Kreisler / Squire “Love’s Sorrow“ performance, on which the 2019 reenactment was based, see Vollmer (2021).<a>[3]</a></p> <p> </p> <p><a>[1]</a> For an extensive documentation see Vollmer, F. & Bolles, B.-A., “In Search for the ‘Phonograph Effect‘: Performed violin gestures and their sound modification by early acoustic recording and reproduction devices (1901–1933)“, forthcoming in 2022. </p> <p><a>[2]</a> See Vollmer, F. (2020). Liebesleid 1911 / 2019 – Reenactment of Fritz Kreisler’s 1911 “Love’s Sorrow” Recording, Stuttgart: University of Music and Performing Arts. <a href="https://www.youtube.com/watch?v=CRJVDpzSfLU">https://www.youtube.com/watch?v=CRJVDpzSfLU</a> (retrieved 21/12/23)</p> <p><a>[3]</a> Vollmer, F. (2021). "Love's Sorrow". Sheet music edition based on the 1911 London recording of Fritz Kreisler (1875–1962) and Haddon Squire, Stuttgart: Vollmer. <a>10.5281/zenodo.5801912</a></p>
Tower-based remote sensing data for understory vegetation at Delta Junction, Alaska 2019-2020
<p> Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from August 2019-December 2020. We provide daily averaged vegetation indices for a mix of understory lichen and moss species in a black spruce dominated forest. We compute near-infrared vegetation index (NIRv), normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and chlorophyll-carotenoid index (CCI) averaged for three understory targets at NEON Delta Junction. We also provide daily averaged photosynthetically active radiation (PAR) and solar zenith angle (SZA). Finally, we provide the average diurnal profiles of all the aforementioned metrics for 4 20-day windows in winter, spring, summer, and fall. </p>
CO2 NEE and ER + air and soil meteorological and climate parameters in Arctic tundra, Ny Ålesund (Svalbard, NO) - summer 2019
<p>The dataset “fluxes_meteoclimate_NyAlesund” is a .csv file reporting CO2 fluxes and basic meteoclimatic variables measured in the Bayelva Basin near Ny Ålesund, in the Brøgger peninsula, Spitsbergen, Norway (78°55’24’’ N, 11°55’15’’E) during the 2019 growing season peak (July-August). Average coordinates of the measuring site are: 78°55’25.7” N,11°53’29.4” E. Fluxes were measured using the flux chamber method: the Net Ecosystem Exchange (NEE) was measured with a transparent flux chamber, while the Ecosystem Respiration (ER) with a shaded chamber. Three types of sampling were performed: at a fixed point during 24h ('point' in column sampling); in points randomly distributed over a site ('site' in column sampling); and in points covered with specific species ('species' in column sampling). Flux data are complemented by measurements of soil temperature (Ts, in Celsius degrees), soil volumetric water content (VWC, in %), atmospheric pressure (Pr, in hPa), air temperature (Ta, in Celsius degrees), air moisture (RH, in %), and solar radiance (rs , in W/m2). The Green Fractional Cover (GFC, between 0 and 1) of the vegetation inscribed within the sampling surface was estimated from digital RGB pictures taken at nadir. Measurements were divided into 4 classes, depending on the prevailing cover type: bare soil (BS), vascular vegetation (V), non-vascular vegetation (NV, including lichens, mosses and bacterial soil crust) and mix of vascular and non-vascular vegetation (MIX). Class V was further split into 5 subclasses: Carex spp. (CX), Dryas octopetala (DR), Salix Polaris (SL), Saxifraga oppostifolia (SX) and Silene acaulis (SI). </p>
TROPOMI-derived ground level NO2 concentrations (2019 & 2020 Monthly Means)
<p>Monthly mean ground level NO2 concentrations derived from TROPOMI satellite NO<sub>2</sub> observations for January-June 2019 and 2020. Ground level concentrations are derived from observed column densities using the GEOS-Chem chemical transport model constrained with ground monitor observations following the method outlined in Cooper et al 2021 (DOI: 10.1038/s41586-021-04229-0)</p> <p><br> Annual mean data are provided at ~1x1 km<sup>2</sup> resolution at satellite overpass time (~1:30 PM local). Datasets are in netcdf (.nc) format.<br> </p>
TROPOMI-derived ground level NO2 concentrations (2019 Annual mean)
<p>Annual mean ground level NO2 concentrations derived from TROPOMI satellite NO<sub>2</sub> observations. Ground level concentrations are derived from observed column densities using the GEOS-Chem chemical transport model constrained with ground monitor observations following the method outlined in Cooper et al 2021 (DOI: 10.1038/s41586-021-04229-0)</p> <p><br> Annual mean data are provided at ~1x1 km<sup>2</sup> resolution at satellite overpass time (~1:30 PM local). Datasets are in netcdf (.nc) format.</p>
Annual mean TROPOMI-derived ground-level NO2 mixing ratio (2019 - North America v1)
<p>Annual mean ground-level NO2 mixing ratio for 2019 inferred from the TROPOMI satellite instrument over North America at 0.025x0.03125 degree resolution. Included is 2019 annual mean and 1.5 year mean spanning July 2018 – December 2019.</p> <p><strong>Reference:</strong></p> <p>Cooper, M.J., R.V. Martin, C.A. McLinden, and J.R. Brook (2020), Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument, Env. Res. Lett., DOI:10.1088/1748-9326/aba3a5</p>
Labeled points for four land cover classes referring to the year 2019
<p>This dataset provides hand-labeled points for four different land cover classes in coastal areas (sand, seawater, grass, trees).</p> <p>It was created based on photointerpretation of high-resolution imagery in Google Earth Pro and QGIS, referring to the year 2019.</p> <p>This dataset was used for the random forest classification of satellite imagery in the following manuscript: </p> <p>"<em>Satellite image processing for the coarse-scale investigation of sandy coastal areas</em>".</p> <p>If you use any part of this dataset, please cite as follows:</p>
Svalbox-DOM_2019-0016_Kolosseum
<b>Model description: </b><br>Digital outcrop model of Kolosseum, in Mudderbukta, Ekmanfjorden. Exhibiting outcrops of the Gipshuken Formation.<br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.2.12040).<br>Ground control point data use CRS epsg:4326.Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3862">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Wallops SuperDARN data in netCDF format (2019-Dec)
<p>2019-Dec Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
Wallops SuperDARN data in netCDF format (2019-Nov)
<p>2019-Nov Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
Svalbox-DOM_2019-0018_Kvalhovden
<b>Model description: </b><br>Kvalhovden is a mountain just north of Kvalvågen on the western shores of Storfjorden. The coastal cliffs expose palaeolandslide blocks and related slope failure features that disturb a succession of paralic sediments (Onderdonk and Midtkandal, 2010). The Carolinefjellet, Helvetiafjellet, and Rurikfjellet Formations are exposed. <br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.2.12040).<br>Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3987">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Svalbox-DOM_2019-0017_Kampesteindalen
<b>Model description: </b><br>The Kampesteindalen Fault is a normal fault striking roughly NW-SE and dipping approximately 70 degrees to the SW (Smyrak-Sikora et al., 2019). With an offset of approximately 50 metres, the fault juxtaposes the uppermost Hultberget Formation against the Ebbaelva Member of the Ebbadalen Formation.<br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.2.12040).<br>Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3983">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Web Data Commons (November 2019) Property and Datatype Usage Dataset
<p>This is a dataset about the usage of properties and datatypes in the <a href="http://webdatacommons.org/structureddata/2019-12/stats/stats.html">Web Data Commons RDFa, Microdata, Embedded JSON-LD, and Microformats Data Sets (November 2019)</a> based on the <a href="https://commoncrawl.org/2019/11/november-2019-crawl-archive-now-available/">Common Crawl November 2019 archive</a>. The dataset has been produced using the <a href="https://doi.org/10.5281/zenodo.6338129">RDF Property and Datatype Usage Scanner v2.1.1</a>, which is based on the <a href="https://jena.apache.org/">Apache Jena</a> framework. Only RDFa and embedded JSON-LD data were considered, as Microdata and Microformats do not incorporate explicit datatypes.</p> <p><strong>Dataset Properties</strong></p> <ul> <li><strong>Size</strong>: 37.0 MiB compressed, 785.7 MiB uncompressed, 3 542 700 rows plus 1 head line determined using <code class="language-bash">gunzip -c measurements.csv.gz | wc -l</code></li> <li><strong>Parsing Failures</strong>: The scanner failed to parse 5 080 473 triples (~0.053 %) of the source dataset (containing 9 547 365 107 triples).</li> <li><strong>Content</strong>: <ul> <li>CATEGORY: The category (<em>html-embedded-jsonld</em> or <em>html-rdfa</em>) of the Web Data Commons file that has been measured.</li> <li>FILE_URL: The URL of the Web Data Commons file that has been measured.</li> <li>MEASUREMENT: The applied measurement with specific conditions, one of: <ul> <li><strong>UnpreciseRepresentableInDouble</strong>: The number of lexicals that are in the lexical space but not in the value space of <code>xsd:double</code>.</li> <li><strong>UnpreciseRepresentableInFloat</strong>: The number of lexicals that are in the lexical space but not in the value space of <code>xsd:float</code>.</li> <li><strong>UsedAsDatatype</strong>: The total number of literals with the datatype.</li> <li><strong>UsedAsPropertyRange</strong>: The number of statements that specify the datatype as range of the property.</li> <li><strong>ValidDateNotation</strong>: The number of lexicals that are in the lexical space of <code>xsd:date</code>.</li> <li><strong>ValidDateTimeNotation</strong>: The number of lexicals that are in the lexical space of <code>xsd:dateTime</code>.</li> <li><strong>ValidDecimalNotation</strong>: The number of lexicals that represent a number with decimal notation and whose lexical representation is thereby in the lexical space of <code>xsd:decimal</code>, <code>xsd:float</code>, and <code>xsd:double</code>.</li> <li><strong>ValidExponentialNotation</strong>: The number of lexicals that represent a number with exponential notation and whose lexical representation is thereby in the lexical space of <code>xsd:float</code>, and <code>xsd:double</code>.</li> <li><strong>ValidInfOrNaNNotation</strong>: The number of lexicals that equals either <code>INF</code>, <code>+INF</code>, <code>-INF</code> or <code>NaN</code> and whose lexical representation is thereby in the lexical space of <code>xsd:float</code>, and <code>xsd:double</code>.</li> <li><strong>ValidIntegerNotation</strong>: The number of lexicals that represent an integer number and whose lexical representation is thereby in the lexical space of <code>xsd:integer</code>, <code>xsd:decimal</code>, <code>xsd:float</code>, and <code>xsd:double</code>.</li> <li><strong>ValidTimeNotation</strong>: The number of lexicals that are in the lexical space of <code>xsd:time</code>.</li> <li><strong>ValidTrueOrFalseNotation</strong>: The number of lexicals that equal either <code>true</code> or <code>false</code> and whose lexical representation is thereby in the lexical space of <code>xsd:boolean</code>.</li> <li><strong>ValidZeroOrOneNotation</strong>: The number of lexicals that equal either <code>0</code> or <code>1</code> and whose lexical representation is thereby in the lexical space of <code>xsd:boolean</code>, and <code>xsd:integer</code>, <code>xsd:decimal</code>, <code>xsd:float</code>, and <code>xsd:double</code>.</li> </ul> <strong>Note</strong>: Lexical representation of <code>xsd:double</code> values in embedded JSON-LD got normalized to always use exponential notation with up to 16 fractional digits (see <a href="https://github.com/jsonld-java/jsonld-java/blob/v0.13.1/core/src/main/java/com/github/jsonldjava/core/RDFDataset.java#L648">related code</a>). Be careful by drawing conclusions from according <strong>Valid…</strong> and <strong>Unprecise…</strong> measures.</li> <li>PROPERTY: The property that has been measured.</li> <li>DATATYPE: The datatype that has been measured.</li> <li>QUANTITY: The count of statements that fulfill the condition specified by the measurement per file, property and datatype.</li> </ul> </li> </ul> <p><strong>Preview</strong></p> <pre><code>"CATEGORY","FILE_URL","MEASUREMENT","PROPERTY","DATATYPE","QUANTITY" "html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2019-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","https://www.w3.org/2006/vcard/ns#longitude","https://www.w3.org/2001/XMLSchema#float","1" "html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2019-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","https://www.w3.org/2006/vcard/ns#latitude","https://www.w3.org/2001/XMLSchema#float","1" "html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2019-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","http://purl.org/goodrelations/v1#hasCurrencyValue","http://www.w3.org/2001/XMLSchema#floatfloat","10" … "html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2019-12/quads/dpef.html-embedded-jsonld.nq-01426.gz","ValidZeroOrOneNotation","http://schema.org/ratingValue","http://www.w3.org/2001/XMLSchema#integer","109" "html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2019-12/quads/dpef.html-embedded-jsonld.nq-01426.gz","ValidZeroOrOneNotation","http://schema.org/width","http://www.w3.org/2001/XMLSchema#integer","60" "html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2019-12/quads/dpef.html-embedded-jsonld.nq-01426.gz","ValidZeroOrOneNotation","http://schema.org/minValue","http://www.w3.org/2001/XMLSchema#integer","68"</code></pre> <p><strong>Note</strong>: The data contain malformed IRIs, like "<code>xsd:dateTime</code>" (instead of probably "<code>http://www.w3.org/2001/XMLSchema#dateTime</code>"), which are caused by missing namespace definitions in the original source website.</p> <p><strong>Reproduce</strong></p> <p>To reproduce this dataset checkout the <a href="https://doi.org/10.5281/zenodo.6337650">RDF Property and Datatype Usage Scanner v2.1.0</a> and execute:</p> <pre><code class="language-bash">mvn clean package java -jar target/Scanner.jar --category html-rdfa --list http://webdatacommons.org/structureddata/2019-12/files/html-rdfa.list November2019 java -jar target/Scanner.jar --category html-embedded-jsonld --list http://webdatacommons.org/structureddata/2019-12/files/html-embedded-jsonld.list November2019 ./measure.sh November2019 # Wait until the scan has completed. This will take a few days java -jar target/Scanner.jar --results ./November2019/measurements.csv.gz November2019 </code></pre>
Sub-hourly water temperature data collected by UNBC's northern hydrometeorology group (NHG) across the Nechako Watershed, 2019-2021
<p>This is a dataset of water temperature time series collected at 15 minute intervals at 24 sites across the Nechako Watershed. Data are provided in comma-delimited format with one file for each site. Each file name identifies the site location. The first column contains the date/time (UTC) in the format MM/DD/YEAR HH:MM. The second column contains the corresponding water temperature in degrees Celsius to two decimals. The third column contains a recommended flag for data quality, with "P" representing a "pass", "F" denoting a "fail", and "B" denoting "backwater conditions" with the likely presence of ice and below freezing temperatures. Additional issues encountered at specific sites are identified in the attached "read me" file along with basic metadata (site coordinates, elevation and period of record). A map of the sites is also attached to depict their locations across the Nechako Watershed. </p>
Reports about the tools for converting IFC to CityGML and CityGML to IFC delivered within the GeoBIM benchmark 2019 - Task 4
<p>Answers delivered through the online forms, about the tests performed by participants within the Task 4 - 'conversions from IFC to CityGML and from CityGML to IFC' - of the GeoBIM benchmark 2019, funded as a Scientific Initiative 2019 by the International Society of Photogrammetry and Remote Sensing (ISPRS) and co-funded by the European association for Spatial Data Research (EuroSDR).</p> <p>Full details and additional resources about the project are available in the project website: https://3d.bk.tudelft.nl/projects/geobim-benchmark/</p> <p>The dataset results from the collaboration of the authors with all the participants to the benchmark, listed at https://3d.bk.tudelft.nl/projects/geobim-benchmark/participants.html</p> <p>It is composed by 2 files:</p> <p>- the answers to the delivered online forms, organised in excel sheet;</p> <p>- the same answers organised in a more human-readable PDF, with reliable images and links.</p>
Reports about the software behaviour delivered within the GeoBIM benchmark 2019 - Task 3
<p>Answers delivered through the online forms, about the tests performed by participants within the Task 3 - support for CityGML of the GeoBIM benchmark 2019, funded as a Scientific Initiative 2019 by the International Society of Photogrammetry and Remote Sensing (ISPRS) and co-funded by the European association for Spatial Data Research (EuroSDR).</p> <p>Full details and additional resources about the project are available in the project website: https://3d.bk.tudelft.nl/projects/geobim-benchmark/</p> <p>The dataset results from the collaboration of the authors with all the participants to the benchmark, listed at https://3d.bk.tudelft.nl/projects/geobim-benchmark/participants.html</p> <p>It is composed by 2 files:</p> <p>- the answers to the delivered online forms, organised in excel sheet;</p> <p>- the same answers organised in a more human-readable PDF, with reliable images and links.</p>
Dataset del análisis de los juegos de Fight COVID Jam 2019
<p>Dataset realizado por medio del modelo "Modelo de análisis de serious games en el entorno de las game jam" (https://zenodo.org/record/6462394) en la muestra de juegos presentados en la Fight COVID Jam 2019 (https://fightcovid.gamebcn.co/).</p>
Non-crop vegetation characteristics and vocalizing bird richness across 44 sites in Iowa, USA in June 2019
<p>This data was derived from field work conducted in June 2019 where sixty AudioMoth passive acoustic monitors were placed along agricultural field margins in Iowa, USA. Twenty-five of the monitoring location were established by farmer and landowner collaborators, and the remaining (35) sites were established by the author (A.P.D.). Unique vocalizing bird species were counted in ninety-five recordings from 6 to 8 days during dawn hours per site. High resolution mapping identified non-crop vegetation and texture at spatial extents ranging from 100 to 1000 meters. Pesticide and fertilizer application were collected via a survey with collaborators. Site location names are included when the research site was an Iowa State Research and Demonstration Farm (ISRF). When the site was a collaborator, the site name was anonymized to "Collaborator" to respect the privacy of participants.</p>
Reports about the software behaviour delivered within the GeoBIM benchmark 2019 - Task 1
<p><strong>Answers delivered through the online forms</strong>, about the tests performed by participants within the <strong>Task 1 - support for IFC</strong> of the <strong>GeoBIM benchmark 2019</strong>, funded as a Scientific Initiative 2019 by the International Society of Photogrammetry and Remote Sensing (ISPRS) and co-funded by the European association for Spatial Data Research (EuroSDR).</p> <p>Full details and additional resources about the project are available in the project website: https://3d.bk.tudelft.nl/projects/geobim-benchmark/</p> <p>The dataset results from the collaboration of the authors with all the participants to the benchmark, listed at https://3d.bk.tudelft.nl/projects/geobim-benchmark/participants.html</p> <p>It is composed by 4 files:</p> <p>- the answers to the delivered online forms, organised in excel sheet;</p> <p>- the same answers organised in a more human-readable PDF, with reliable images;</p> <p>- the same answers organised in a more human-readable PDF, with low-resolution images and working links;</p> <p>- the answers regarding the IFCgeometries dataset in IFC 2x3 format, in excel;</p> <p>- the answers regarding the IFCgeometries dataset in IFC 4 format, in excel.</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.