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363 results for “harmonics”
Joseph Haydn - String Quartets Op.20 - Harmonic Analysis Annotations Dataset
<p>This dataset accompanies the Master Thesis from the same author. It is a manually-annotated corpus of harmonic analysis in **harm syntax.</p> <p>The dataset contains the following scores:<br> Haydn, Joseph<br> 1. E-flat major, op. 20 no. 1, Hob. III-31<br> I. Allegro moderato<br> II. Menuetto. Allegretto<br> III. Affettuoso e sostenuto<br> IV. Finale. Presto<br> 2. C major, op. 20 no. 2, Hob. III-32 <br> I. Moderato<br> II. Capriccio. Adagio<br> III. Menuetto. Allegretto<br> IV. Fuga a 4 soggetti<br> 3. G minor, op. 20 no. 3, Hob. III-33<br> I. Allegro con spirito<br> II. Menuetto. Allegretto<br> III. Poco adagio<br> IV. Finale. Allegro molto<br> 4. D major, op. 20 no. 4, Hob. III-34<br> I. Allegro di molto<br> II. Un poco adagio e affettuoso<br> III. Menuet alla Zingarese & Trio<br> IV. Presto e scherzando<br> 5. F minor, op. 20 no. 5, Hob. III-35<br> I. Allegro moderato<br> II. Menuetto<br> III. Adagio<br> IV. Finale. Fuga a due soggetti<br> 6. A major, op. 20 no. 6, Hob. III-36<br> I. Allegro di molto e scherzando<br> II. Adagio. Cantabile<br> III. Menuetto. Allegretto<br> IV. Fuga a 3 soggetti. Allegro</p>
INEGI Uso del Suelo y Vegetacion Land Cover Classifications for Mexico (1985, 1993, 2002, 2007, 2011), Harmonized with NLCD 2011 Legend
<p>We have taken the Uso del Suelo y Vegetacion land cover classification products for Mexico (courtesy of Mexico's Instituto Nacional de Estadistica y Geografia, or INEGI) for years 1985, 1993, 2002, 2007, and 2011 (INEGI, 2015); and harmonized their classes with the classes of the Multi-Resolution Land Characteristics Consortium (MRLC) National Land Cover Database (NLCD) (Homer et al., 2015). Details of processing, along with the processing scripts, are archived in GitHub in the <a href="https://github.com/tbohn/NLCD_INEGI/tree/v1.5">NLCD_INEGI</a> project (Bohn, 2019).</p> <p>This project contains the following g-zipped tar files:</p> <ul> <li>SERIE_I.tgz - land cover from 1985</li> <li>SERIE_II.tgz - land cover from 1993</li> <li>SERIE_III.tgz - land cover from 2002</li> <li>SERIE_IV.tgz - land cover from 2007</li> <li>SERIE_V.tgz - land cover from 2011</li> </ul> <p>On LINUX, the contents of these files can be extracted via "tar":</p> <p>tar -xvzf SERIE_I.tgz >& log.tar.txt</p> <p>On Windows, applications such as "7-zip" can extract the contents.</p> <p>Each of these .tgz files contain a folder with the same name but without the ".tgz". Within each of these folders are the following sub-folders:</p> <ul> <li>For SERIE_I to SERIE_IV: <ul> <li>metatiles/ - original land cover shapefiles, with Mexico divided into "metatiles" along UTM zones, as documented in <a href="https://github.com/tbohn/NLCD_INEGI/tree/v1.0/docs/Processing_of_INEGI_USOSV_dataset.docx">Processing_of_INEGI_USOSV_dataset.docx</a></li> <li>geo/ - shapefiles from "metatiles", reprojected into geographic</li> <li>entire/ - shapefiles from "geo" merged into a single file for the entire country</li> </ul> </li> <li>For SERIE_V: <ul> <li>entire/ - original land cover shapefile in Lambert Conical projection, covering all of Mexico</li> <li>geo/ - shapefile from "entire" reprojected into geographic</li> </ul> </li> <li>SERIE_I to SERIE_V: <ul> <li>cve_union/ - shapefiles covering all of Mexico, in geographic projection, with land cover reclassified to NLCD 2011 legend</li> <li>rasters/ - files from "cve_union", rasterized at 0.000350884 degree resolution</li> <li>ascii/ - raster files from "rasters", exported to ascii ESRI grid file format</li> </ul> </li> </ul> <p>Output files (in the "ascii" folders) are ESRI ascii raster grid files, in geographic projection, with cellsize = 0.000350884 degrees.</p>
Geo-referenced Harmonized Financial Data on Soil Defense Public Works in Italy
<p>The dataset collects financial data about public works in Italy, specifically, it focuses on soil defense investments. The data is sourced from three distinct platforms: the OpenCoesione website, the OpenBDAP database, the Ministry of Economy and Finance's open data platform, and the ReNDiS database, provided by ISPRA, that exclusively gathers information about interventions in soil defense. The data obtained is interconnected using unique project codes (CUP) to prevent duplication.</p> <p>Georeferencing involves integrating geographic references into the three datasets. It enhances the accuracy of spatial analyses of spatial defense investments and provides valuable context for understanding the geographical distribution of available financial data. By incorporating geographic references such as regions, provinces, and municipalities analysts can gain insights into the spatial patterns and relationships within the datasets. This step is crucial for effective decision-making and policy formulation in the field of soil defense investments.</p> <p>Geographical references for each project were integrated using codes and names of regions, provinces, and municipalities from the ISPRA database. This database retrieves information directly from ISTAT websites, ensuring constant updates to names and codes, thus enhancing the accuracy of spatial analyses.</p> <p>Furthermore, geographical codes facilitated the association of centroids coordinates and polygon shapes for each financial observation, enhancing spatial visualization and analysis of soil defense investments, empowering decision-makers with a deeper understanding of the geographic distribution and impact of these initiatives. This comprehensive approach allows for a deeper exploration of the geographical factors influencing soil defense investments, including identifying hotspots of activity, assessing spatial trends, and understanding the localized impact of interventions on environmental sustainability and community resilience.</p> <p>The zip folder comprises four subfolders and two files. Among the files, one is a text file containing metadata, while the other is a CSV file consolidating merged data at the national level from three repositories. The subfolders contain data categorized by region and data categorized by region sourced from the three distinct repositories.</p> <p> Datasets present 28 variables: </p> <ul> <li>Columns 1-2: descriptive variables;</li> <li>Column 3: total amount financed for each intervention;</li> <li>Columns 4-9: geo-reference variables;</li> <li>Columnn 10:25: key dates of the public works process;</li> <li>Column 26: source of the data;</li> <li>Columns 27-28: geo-referencing (centroids and areal shape).</li> </ul> <p>An additional dataset has been added comprising all Italian municipalities, including thos that lack information on soil defense investments. In such a way, there are geographical information regarding all the peninsula. </p>
The International Transport Energy Modeling (iTEM) Open Data & Harmonized Transport Database
<p>This dataset and documentation contains detailed information of the iTEM Open Database, a harmonized transport data set of historical values, 1970 - present. It aims to create transparency through two key features:</p> <ul> <li>Open-Data: Assembling a comprehensive collection of publicly-available transportation data</li> <li>Open-Code: All code and documentation will be publicly accessible and open for modification and extension. <a href="https://github.com/transportenergy">https://github.com/transportenergy</a></li> </ul> <p>The iTEM Open Database is comprised of individual datasets collected from public sources. Each dataset is downloaded, cleaned, and harmonised to the common region and technology definitions defined by the iTEM consortium https://transportenergy.org. For each dataset, we describe the name of the dataset, the web link to the original source, the web link to the cleaning script (in python), variables, and explain the data cleaning steps (which explains the data cleaning script in plain English).</p> <p>Shall you find any problems with the dataset, please report the issues here <a href="https://github.com/transportenergy/database/issues">https://github.com/transportenergy/database/issues</a>. </p> <p> </p>
Dataset used in the publication entitled "Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids" presented at 2022 20th International Conference on Harmonics and Quality of Power (ICHQP)
<p>Dataset obtained from experimental research carried out in a real power grid. Based on the dataset, the problem of decomposition in identification of sources of voltage fluctuations has been presented in the publication: Kuwałek P., Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids, <em>Proceedings of the 20th International Conference on Harmonics and Quality of Power</em>, IEEE , art. no. 43, 2022, Italy, Naples. The description of the power grid model is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
Current Harmonics Minimization of PMSM Based on Iterative Learning Control and Neural Networks: Motor Data
<p>The provided motor data corresponds to an electrical machine with 24 stator slots and 16 poles. As is common in electrical machines, this motor generates unwanted flux and current harmonics. However, the accompanying paper presents an effective solution to suppress these harmonics through the combined use of Iterative Learning Control (ILC) and Neural Networks (NNs).</p> <p>The ILC method demonstrates proficient compensation for harmonics during operations with constant speed and current reference values. Additionally, Neural Networks are trained with data derived from ILC, proving to be highly effective in suppressing harmonics even during transient operation. The simulation model used in the study is based on flux and torque maps, dependent on dq-currents and the electrical angle. These maps are obtained from Finite Element Method (FEM) simulations of an interior permanent magnet synchronous machine (IPM) and are openly published here, intended to facilitate other researchers in making direct comparisons with their own methodologies.</p> <p>Simulation results presented in the paper confirm that the integration of ILC and NNs leads to superior elimination of current harmonics during transient operations compared to using ILC alone.<br> If you use the provided maps and motor data, kindly cite the associated paper for reference: https://doi.org/10.3390/machines11080784, https://www.mdpi.com/2075-1702/11/8/784</p>
Harmonized Vegetation Continuous Fields (VCF)
<p><strong>Motivation</strong></p> <p><a href="https://www.nature.com/articles/s41586-018-0411-9">Song’s Vegetation Continuous fields (VCF) product</a>, based on AVHRR satellite data, is the longest time-series of its type, but lacks updates past 2016 due to the extensive degradation of the sensor. We used machine learning to extend this time-series using data from the <a href="https://land.copernicus.eu/global/products/lc">Copernicus Land Cover dataset</a>, which provides per-pixel proportions of different land cover classes between 2015 and 2019. In addition, we included <a href="https://modis.gsfc.nasa.gov/data/dataprod/mod44.php">MODIS VCF data</a>.</p> <p><strong>Content</strong></p> <p>This repository contains the infrastructure used to model Song-like VCF data past 2016. This infrastructure contains a yaml file that configures the modelling framework (e.g. variables, directories, hyper-parameter tuning), and that interacts with a standardized folder structure.</p> <p><strong>Modelling approach</strong></p> <p>Song's VCF dataset includes data on generic categories, namely “tree cover”, “non-tree vegetation”, and “non vegetated”. Given the Copernicus dataset has a higher thematic detail, we first aggregated these data into comparable classes. We created a “Non-tree vegetation” layer (i.e. total per-pixel proportion of crops, grasses, shrubs, and mosses), and a “Non Vegetated” layer (i.e. total per-pixel proportion of bare land, permanent water, urban, and snow). Independent data on “Tree cover” was already present.</p> <p>We then constructed a Random Forest Regression (RFReg) model to predict Song-like VCF layers between 2016 and 2019. The predictions were informed by variables on topography, climate, and fires (which limit the density of vegetation), and by variables on differences between the Copernicus VCF and MODIS-based VCF data. Because MODIS data is available past 2016, its inclusion informs our models on how MODIS data, and their differences compared to Copernicus data, relate to the values reported in Song's data.</p> <p><strong>Sampling scheme</strong></p> <p>For each VCF category, we collected samples on a country-by-country basis. Within each country, we estimated the difference in percent cover between the Song's and Copernicus VCF data, and sampled across a gradient of differences, from -100% (no cover in AVHRR and full cover in Copernicus) to +100% (full cover in AVHRR and no cover in Copernicus). We iterated through this range in intervals of 10% and sampled across a gradient of “tree cover”, “non-tree vegetation”, and “non vegetated”, in intervals of 10% from 0% to 100%. We collected at least one sample per 50 km<sup>2</sup> in 2016, the last year where all VCF-related variables (Song's, Copernicus, MODIS) are available simultaneously. The amount of samples attributed to each range of differences is proportional to the area covered by this range within the country of reference. The sampling approach was repeated for each VCF class, and the outputs were later combined into a single set of samples that exclude duplicates, resulting in 238,052 samples.</p> <p><strong>Validation</strong></p> <p>The model outputs were validated using leave-one-out cross-validation. For each VCF class, the validation framework iterates through each country where samples were collected, excluding it for validation and using the remaining samples to train a RFReg models.This resulted in R<sup>2</sup> values of 0.91, 0.87 and 0.91 for “tree cover”, “non-tree vegetation”, and “non vegetated”. respectively. The RMSE values were of 2.31%, 3.05%, and 2.25%.</p> <p>The model was applied to data from 2015, which was not used to neither predict nor validate our models. A comparison between the 2015 Song data against our predictions, which consist of 8,764,232 pixels, yielded R<sup>2</sup> values of 0.94, 0.91, and 0.97. The RMSE were 6.65%, 8.92%, and 5.96%. Additionally, we compared changes between 2015 and 2016, resulting in RMSE values of 2.83%, 3.69%, and 2.57%.</p> <p><strong>Post-processing</strong></p> <p>When observing annual VCF time-series based on Song's data, we noted that our predictions were the most plausible for “tree cover” and “non-tree vegetation”. In turn, our “non vegetated” are seemingly underestimated (see "temporal_trend_check.png"), reporting large year-to-year decreases om cover (-3.05% between 2016 and 2017, compared to -0.14% for "tree cover" and -0.26% for “non-tree vegetation”). To address this issue, we recommend deriving data on “non-vegetated” cover by computing the difference between 100% and the sum of "tree cover” and “non-tree vegetation”.</p> <div class="notranslate"> </div>
Spherical harmonic model of the planet Venus: VenusTopo719
<p><strong>VenusTopo719.shape</strong> is a spherical harmonic model of the shape of the planet Venus. This model makes use of 4-pi normalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The description of how this spherical harmonic model was constructed can be found in Wieczorek (2015).</p>
Spherical harmonic model of the shape of Earth's Moon: MoonTopo2600p
<p><em><strong>THIS MODEL IS SUPERSEDED BY <a href="../doi/10.5281/zenodo.10796953">Spherical harmonic models of the shape of the Moon (principal axis coordinate system)</a>.</strong></em></p> <p> </p> <p><strong>MoonTopo2600p.shape</strong> is a spherical harmonic model of the shape of Earth's Moon in a principal axis coordinate system. This model makes use of 4-pi normalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The description of how this spherical harmonic model was constructed can be found in Wieczorek (2015).</p>
Spherical harmonic model of the shape of Mars: MarsTopo2600
<p><strong><em>THIS MODEL IS SUPERSEDED BY </em><a href="../records/10794059"><em>Spherical harmonic models of the shape of Mars</em></a></strong></p> <p> </p> <p><strong>MarsTopo2600.shape</strong> is a spherical harmonic model of the shape of the planet Mars. This model makes use of 4-pi normalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The description of how this spherical harmonic model was constructed can be found in Wieczorek (2015).</p>
Spherical harmonic model of the magnetic field of Mars from Morschhauser et al. (2014)
<p><strong>Morschhauser2014.txt.gz</strong> is a gzipped file of the magnetic potential coefficients of Mars as published by Morschhauser et al. (2014). This is the same as the file ts01.txt in the supplemental materials of this manuscript.</p>
Satellite Laser Ranging in 5x5 Spherical Harmonics
<p>This is a variant of the weekly, 5x5 SLR product created at the University of Texas’s Center for Space Research (CSR) which is released alongside the GRACE series:</p> <p>ftp://podaac.jpl.nasa.gov/allData/tellus/preview/L2/deg_5/CSR.Weekly.5x5.Gravity_Harmonics.txt. </p> <p>The version here is averaged monthly, rather than weekly, to make it more directly comparable to the monthly GRACE data. It contains an estimate of C<sub>61</sub>/S<sub>61</sub> (but no other degree-6 harmonics) to avoid skewing the C<sub>21</sub> harmonic due to a lack of sufficient degrees of freedom during the creation of the SLR gravity product (Cheng and Ries, 2017). From January - November 1993, only four satellites were used in its creation (Starlette, Ajisai, and Lageos 1 and 2). After that point, Stella was added as well. Data exists through mid-2017.<br> <br> The layout of the files contains a header section followed by one line containing the date for the following lines, and then 22 lines of spherical harmonic data. The time lines are formatted as: IARC, MAXDEG, NP, IYEAR, IM, XMJD:<br> IARC: the arc number (ie: month number) <br> MAXDEG: spherical harmonic maximum degree/order of the field <br> NP: number of coefficient pairs (Cnm & Snm)<br> IYEAR: year<br> IM: month<br> XMJD: epoch of the arc at the first day in modified Julian date</p> <p>The data lines are formatted as: N, M, Cnm, Snm, Cnm-sigma, Snm-sigma:<br> N: spherical harmonic degree <br> M: spherical harmonic order<br> Cnm: C coefficient <br> Snm: S coefficient<br> Cnm-sig: The formal errors of the C coefficient<br> Snm-sig: The formal errors of the S coefficient<br> </p>
Harmonized Chlorophyll-a dataset from Landsat-8/9 OLI and Sentinel 2 MSI in lakes of the Yunnan-guizhou Plateau, China
<p>We generated a harmonized Chl-a dataset for the lakes in the Yunnan–Guizhou Plateau in China from 2013 to 2022 by the Landsat 8/9 and Sentinel-2A/B virtual constellation. Here, we shared the mean chlorophyll-a in nine major lakes in studied area. </p><p>These dataset were aggregated from MSI- and OLI-derived Chl-a images and were subseted using the boundary of each lake. Data format is Geo-Tiff (*.tif) and zero values are INVALID values. More details on Chl-a retrievals can be found in: </p><p>Z. Cao et al., "Harmonized Chlorophyll-a Retrievals in Inland Lakes From Landsat-8/9 and Sentinel 2A/B Virtual Constellation Through Machine Learning," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022, Art no. 4209916, doi: 10.1109/TGRS.2022.3207345.</p>
Research data on IMVS / IMPS Higher harmonics of the Si Photodioed / TiO2 nanotubes
<p>The following dataset contains research data that is the basis of research article:</p><p>"Higher harmonics of the Intensity Modulated Photocurrent/Photovoltage Spectroscopy Response - a Tool for studying Photoelectrochemical Nonlinearities"</p><p>Contensts of the package are the following:</p><p>a) Experimental results of the IMVS for the photodiode (2-electrode system)</p><ol><li>OCP vs. power density relations [2 files]</li><li>Higher harmonic spectra for varying AC components 10,20,30,40 and 50 % (DC component fixed at 50% = 2 mW) [25 files]</li></ol><p>b) Experimental results of the IMPS for the TiO2 nanotubes (3-electrode system)</p><ol><li>Photocurrent vs. power density relation for polarization potentials 200mV, 400mV, 600mV, 800mV, 1000mV [2 files]</li><li>Higher harmonic spectra for varying AC components 10,20,30,40 and 50% at +1000 mV polarization (DC component fixed at 50% = 2 mW) [20 files]</li><li>Higher harmonic spectra for varying polarization potential 200mV, 400mV, 600mV, 800mV (AC and DC components fixed at 50% and 50%) [31 files]</li></ol>
Spherical harmonic models of the gravity field of Titan
<p>This archive contains previously published models of the gravitational field of Saturn's moon Titan.</p> <ul> <li>Durante2019.sh</li> </ul> <p>All models make use of unnormalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)^m.</p>
Spherical harmonic models of the gravity field of Enceladus
<p>This archive contains previously published models of the gravitational field of Saturn's moon Enceladus.</p> <ul> <li>Iess2014.sh (SOL1)</li> <li>Park2024.sh (Case 2)</li> </ul> <p>All models make use of unnormalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)^m.</p>
Spherical harmonic models of the shape of (433) Eros
<p>This archive contains two spherical harmonic models of the shape of asteroid 433 Eros. One model is based on laser altimetry from the instrument NLR and the other is based on a stereo photoclinometry shape.</p> <p>The data used to generate the model based on laser altimetery were taken from the file <code>nlr125ar.img</code> on <a href="https://sbnarchive.psi.edu/pds3/near/NEAR_A_NLR_6_EROS_MAPS_MODELS_V1_0/data/img/">NASA's PDS website</a>. This image file was first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, and the resulting pixel registed map was then converted to a gridline registration using the function <code>grdsample</code>. Following this, the resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The data used to generate the model based on the stereo photoclinometric shape model were taken from the file <code>quad512q.tab</code> on <a href="https://sbnarchive.psi.edu/pds4/non_mission/gaskell.ast-eros.shape-model_V1_1/data/quad/">NASA's PDS website</a>. The vertices from the ICQ shape model with Q=512 were first converted from Cartesian to spherical coordinates, from which a regular gridline registered netcdf file was created using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>surface</code> with a tension of 0.6 and with a grid spacing of 0.17578125 degrees. This file was then read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics in the same manner as the NLA based model.</p> <p>The two files in this archive are</p> <ul> <li>Eros_NLR_shape_719.bshc.gz</li> <li>Eros_SPC_shape_511.bshc.gz</li> </ul> <p>The numbers 719 and 511 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 8 and ~5.7 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip.</p>
Spherical harmonic models of the gravity field of Neptune
<p>This archive contains published spherical harmonic models of the gravity field of Neptune. The coefficients are to be used with unnormalized spherical harmonic functions that exclude the Condon-Shortely phase factor of (-1)^m, and the file is formatted in a manner to be read by the <a href="https://shtools.github.io/SHTOOLS/">pyshtools</a> software (using format='shtools'). The header of the file contains the reference radius, GM, GM uncertainty, and maximum degree of the spherical harmonic expansion (all in SI units).</p> <p>* Jacobson2009.sh</p>
Ellipsoidal Harmonic Forward Model derived from Earth2014 topographies up to d/o 7200: EHFM_Earth_7200
<p>The model named EHFM_Earth_7200 was derived by layer-based forward modeling technique in ellipsoidal harmonics, the maximum degree of this model reaches 7200. The relief information was provided by Earth2014 relief model. EHFM_Earth_7200 provides very detailed (~3 km) information for the Earth’s short-scale gravity field, and it is expected to be able to augment or refine existing global gravity models. To meet the existing standard, here we provide spherical harmonic coefficients, which are transformed from original ellipsoidal harmonic coefficients.</p>
Archaeological Sites in Slovak Republic - Harmonized Dataset
<p>A registry of archaeological sites in Slovak Republic, based on the dataset "Centrálna evidencia archeologických nálezísk na Slovensku (skúšobná prevádzka)" ("Central Evidence of Archaeological Sites in Slovakia (test version)") by The Institute of Archaeology of the Slovak Academy of Sciences available as WFS from the National Geoportal (<a href="https://geoportal.gov.sk/">https://geoportal.gov.sk/</a>)</p> <p>Source data: <a href="https://maps.geop.sazp.sk:443/geoserver/wfs">https://maps.geop.sazp.sk:443/geoserver/wfs</a></p> <p>The dataset was created by correcting the information about archaeological dating (culture) and creating individual records for each dating, to enable filtering by era and culture. The language of the dataset is Slovak.</p> <p><strong>Format:</strong> ESRI Shapefile</p> <p><strong>CRS: </strong>S-JTSK / Krovak East North, EPSG: 5514</p> <p><strong>Fields:</strong></p> <p><strong>fid</strong>: Record identifier</p> <p><strong>kataster</strong>: Cadastre name</p> <p><strong>obec</strong>: Town or village name</p> <p><strong>poloha</strong>: Location name</p> <p><strong>stlok</strong>: degree of location accuracy (1 - accurate, 2 - accurate, localized according to text description, 3 - inaccurate, center of the built-up area of the cadastre, -1 - unknown)</p> <p><strong>druhnal</strong>: type of site</p> <p><strong>c_aktivity</strong>: number of archaeological activity</p> <p><strong>rokevid</strong>: year of evidence of the archaeological activity</p> <p><strong>datovanie</strong>: dating (culture)</p> <p><strong>dat_era</strong>: dating (era)</p> <p><strong>dat_popis</strong>: dating (description)</p> <p><strong>dat_orig</strong>: original dating entry before harmonization</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.