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4,283 results for “Database”
TDB2 databases for LargeRDFBench
<p>This contains TDB2 (the triple store from <a href="https://jena.apache.org/">Apache Jena</a>, version 3.17.0) directories (archived as .tar.zst files) for each of the <a href="https://github.com/dice-group/LargeRDFBench">LargeRDFBench</a> datasets. In order for <a href="https://jena.apache.org/">Jena</a> to accept the data, it had to be cleaned, amending IRIs to make them valid (as per <a href="https://datatracker.ietf.org/doc/html/rfc3987">RFC 3987</a>) and replacing _ in lang tags with - (e.g., en_US was changed to en-US).</p> <p>The input for creating these archives was the .nt.zst files from <a href="https://doi.org/10.5281/zenodo.5008280">https://doi.org/10.5281/zenodo.5008280</a>, version 1.0.0.</p> <p>To bring up a <a href="https://www.w3.org/TR/sparql11-query/">SPARQL</a> endpoint for dataset <em><strong>X</strong></em> at <a href="http://localhost:3030/X/sparql">http://localhost:3030/<em><strong>X</strong></em>/sparql</a>, follow the following steps:</p> <ol> <li>Download <strong><em>X.tdb.tar.zst</em></strong></li> <li>Run <strong>tar --zstd -xf X.tdb.tar.zst</strong> <ul> <li>The <strong>zstd</strong> package may need to be installed</li> <li>On older versions of tar, <strong>--zstd</strong> is not avaialble and <strong>-I zstd</strong> should be used instead.</li> </ul> </li> <li>Run <strong>fuseki-server --tdb2 --loc <em>X</em>.tdb --port 3030 /<em>X</em></strong> <ul> <li>Download Apache Fuseki binaries from <a href="https://jena.apache.org/download/">here</a></li> <li>Replace <strong>fuseki-server</strong> with the full path, unless it was added to your <strong>$PATH</strong></li> <li>Or use a docker container: <strong>docker run -it --rm -v $(pwd)/<em>X</em>.tdb:/data -p 3030:3030 alexishuf/fuseki:3.17.0 --tdb2 --loc=/data --port 3030 /<em>X</em></strong></li> </ul> </li> </ol>
metapsyData: R Package to Access the Metapsy Databases
<p>The <code>metapsyData</code> package allows to access the Metapsy meta-analytic psychotherapy databases direct in your <code>R</code> environment. Once installed, simply run the <code>data</code> function (e.g. <code>data(DepPsychDB)</code>) to save the data locally. The documentation of the package is also hosted by <a href="https://rdrr.io/github/metapsy-project/metapsyData/">rdrr.io</a>.</p> <p>The interactive Metapsy web application (<a href="https://www.metapsy.org/">metapsy.org</a>) uses <code>metapsyData</code> in the background. You can open the Metapsy website in <code>R</code> by running <code>open_app()</code>.</p> <p>The raw data files can be accessed in the associated GitHub repository under <code>data</code>. To search for available databases in <code>metapsyData</code>, type in <code>metapsyData::</code> in your RStudio console.</p>
Database of Medieval Nubian Identity Markers (version 1.0)
<p>The Database of Medieval Nubian Identity Markers (DBMNIM) has been created in the framework of the project <em>IaM NUBIAN. Identity and Memory in Christian Nubia: A study on strategies of (self-)presentation and preservation of the past in medieval African society.</em></p> <p>The DBMNIM is a relational database which fits into a bigger structure of a database of written sources originating from the Middle Nile Valley in Middle Ages, under continuous development by myself. The basic component of this structure is the Database of Medieval Nubian Texts (DBMNT), available online at <a href="http://www.dbmnt.uw.edu.pl/">www.dbmnt.uw.edu.pl</a>, from which the DBMNIM draws the basic metadata for any given text included in the database.</p> <p>The DBMNIM has a three-level structure, represented by three interconnected tables: “Identity markers”, “IM Variants”, and “IM References”, and contains a variety of data that allow the recognition of identity(-ies) of persons occurring in the sources. For the time being, only textual identity markers are covered in the DBMNIM, but the database is designed to include also non-textual markers (graphic signs and representations, contextual information).</p> <p>The database has been designed in FileMaker Pro 16 and the upload includes the original database file in the .fmp12 format. However, in order to ensure a wide accessibility, the contents of the database has been exported into .xml, .csv, and .xlsx files. For detailed information on the structure and contents of the DBMNIM, see the attached DBMNIM_upload_documentation_1_0.pdf file.</p>
Database of infection control and surveillance program, 2011-2020
<p>A full anonymized data set was collected as a part of the ICU infection control and surveillance program; 01/01/2011-12/31/2020</p> <p>File "Zenodo_DB_v4<a href="https://zenodo.org/api/files/6d089d03-7a43-4513-b476-92f438837941/VAE_Data_Main_0821_1338.csv">.csv</a>" contains daily data (one row is one day) on infection surveillance ordered by date.</p> <p>File "<a href="https://zenodo.org/api/files/6d089d03-7a43-4513-b476-92f438837941/Data_Dictionary_MainDB.csv">Data_Dictionary_MainDB_2021.csv</a>" contains the description of all variables from the data set.</p> <p> </p>
HANZE database of historical flood impacts in Europe, 1870-2025
<p>The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries between 1870 and 31 March 2025. The data was collected by extensive data-collection from more than 1000 sources ranging from news reports through government databases to scientific papers. The dataset includes 2687 events characterized by at least one impact statistic: area inundated, fatalities, persons affected or economic loss. Economic losses are presented both in the original currencies and price levels as well as inflation and exchange-rate adjusted to 2024 value of the euro. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Daily start and end dates, information on causes of the event, notes on data quality issues or associated non-flood impacts, and full bibliography of each record supplement the dataset. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: <a href="https://naturalhazards.eu">https://naturalhazards.eu</a>. The dataset is designed to be complimentary to HANZE-Exposure, a high-resolution model of historical exposure changes (such as population and asset value), and be easily usable in statistical and spatial analyses.</p> <p><strong>This is a preliminary update of HANZE v2.1, adding 169 floods for years 2021-2025 (until 31 March 2025). It makes only minor revisions to previous data (adds 11 pre-2021 events, revises 17 records and removes 3 events that were newly reassessed as non-flood events). A more extensive revision of the data is planned for 2026.</strong></p> <p>The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders)</p> <p><strong>HANZE flood events database </strong></p> <p>HANZE_events.csv - Flood event data</p> <p>HANZE_references.csv - List of all references</p> <p>HANZE3_events_regions_2010.zip - Flood event data as GIS file (regions v2010)</p> <p>HANZE3_events_regions_2021.zip - Flood event data as GIS file (regions v2021)</p> <p>HANZE3_events_regions_2021.zip - Flood event data as GIS file (regions v2021)</p> <p><strong>Supplementary data </strong></p> <p>S1_countries_codes_and_names.csv - Country codes/names</p> <p>S2_regions_codes_and_names_v2010.csv - Region codes/names, v2010</p> <p>S3_regions_codes_and_names_v2021.csv - Region codes/names, v2021</p> <p>S3a_regions_codes_and_names_v2024.csv - Region codes/names, v2024</p> <p>S4_list_of_all_currencies_by_country.csv - Data on all currencies used in the study area since 1870</p> <p>S5_currency_conversion_rates.csv - Conversion rates applied to compute losses in 2024 euros</p> <p>S6_GDP_deflators_by_country.csv - Gross domestic product deflator by country, 1870-2025</p> <p>S7_floods_removed_from_HANZE.csv - Flood events in HANZE v1 and v2, which were excluded from v3</p> <p>Regions_v2010_simplified.zip - Map of subnational regions used in the database, v2010</p> <p>Regions_v2021_simplified.zip - Map of subnational regions used in the database, v2021</p> <p>Regions_v2024_simplified.zip - Map of subnational regions used in the database, v2024</p>
Lunar Missions Database (MoonDB)
<p>The MoonDB is a database of past and present spacecraft in cislunar space, compiled through publicly available sources. It was designed to better understand the rationale behind missions, focusing on their final results, current status, funding agencies and nations. Particular attention has also been given to the surface of the Moon, where landing sites have been identified. The database is mainly based on data collected by NASA in the Master Catalogue of the NASA Space Science Data Coordinated Archive (NSSDCA) [1]. Other sources are also considered, such as the Satellite Catalog (SATCAT) from the Space-Track project [2], created by the US Combined Force Space Component Command (CFSCC). The work by McDowell (2020) [3] has also been considered an inspiration for this work, although the primary source of information has remained the NASA catalogue.</p> <p>Some structural and logical changes have been introduced to follow the needs of this research project. Following a list provided by the NSSDCA, a certain number of tentative USSR missions were added to the statistics [4]. Most spacecraft were destroyed due to a launch failure and were not disclosed to the public: the available information results from an investigation.</p> <p>Additional information and a version changelog are provided in the readme file.<br> </p>
SEEtheSkills resources database from the Interregional research on the status of energy skills
<p>This dataset includes a list of resources identified during the interregional research on the status of energy skills, done in the frame of SEEtheSkills project. The comprehensive overview of the information created in the area of Energy Efficiency (EE) and Renewable Energy Systems (RES), goes both wide, by trying to identify as many different examples as possible, and deep, by digging into the examples themselves. The key areas the research focused on: skills defined in national roadmaps; skills developed as part of previous BUS projects; developed training schemes; the number of trained workers and professionals; companies that design and produce EE materials; status of Recognition of Previous Learning (RPL); status of demand for energy skills; level of awareness of energy skills; available certification schemes; legal obligations promoting the use of energy skills and their timelines, predictions for future development of energy skills. The survey covers mainly the five countries participating in the project Slovenia, Spain, Netherlands, Slovakia and North Macedonia, but also beyond their geographical coverage.</p>
Database for RailRad calculation method for simulating sound radiated by railway track vibrations
<p>This dataset contains precalculated acoustic transfer functions for efficiently calculating the sound radiated by railway track vibrations.</p> <p>The transfer functions contained in each file describe the complex sound pressure produced at a number of receiver locations given a unit velocity at a source element on the railway track surface, per frequency and at a fixed wavenumber along the track.</p> <p>Four different acoustic geometries are included: (1) a standard UIC60 rail in free space, (2) the rail in an acoustic half space, (3) the rail located above a slab track surface, and (4) identical geometry to (3) but including an acoustically hard hull of a passenger train geometry above the track.</p> <p>More information about the exact location of source and receiver coordinates can be found in the .hdf5 files, in the subgroup 'info'. The transfer functions themselves are located in the dataset 'tfs', which are matrices of size (Number of frequency lines x number of sources x number of receivers).</p> <p>More information can be found here https://github.com/janniktheyssen/railrad</p> <p>This collection of databases is part of ongoing work at CHARMEC / Chalmers University of Technology, Gothenburg, Sweden (https://www.charmec.chalmers.se/). Parts of the study have been funded from the European Union's Horizon 2020 research and innovation programme in the In2Track3 project under grant agreements No 101012456. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973.</p>
DATABASE OF THE DIGITAL ELEVATION MODELS OF THE SKEIÐARÁRSANDUR KETTLE-HOLES (S ICELAND), JUNE 2022 - PART I
<p>The database concerns kettle-holes of glacial flood origin. They are located at various outwash levels of Skeiðarársandur in S Iceland. The database contains 87 digital elevation models (DEM) with a minimum resolution of 0.05 m and additional files, e.g. field measurements data, frames selected from the video, errors calculation, point cloud, 3D view. These data document the process of obtaining the material using the photogrammetric ‘Structure from Motion’ method from fieldwork conducted in June 2022 through the processing stages in free, mainly open-source software. The data is prepared in the local Cartesian system and includes relative heights, where 0 m is the lowest point of the kettle-hole. The simple technique used, based on filming the landforms with a digital camera, enables mapping of depressions up to 1250 m<sup>2</sup> in the area and a maximum depth of up to 8 m with the assumed high accuracy.</p>
Database of Uniaxial Cyclic and Tensile Coupon Tests for Structural Metallic Materials
<p><strong>Database of Uniaxial Cyclic and Tensile Coupon Tests for Structural Metallic Materials</strong></p> <p> </p> <p><strong>Background</strong></p> <p>This dataset contains data from monotonic and cyclic loading experiments on structural metallic materials. The materials are primarily structural steels and one iron-based shape memory alloy is also included. Summary files are included that provide an overview of the database and data from the individual experiments is also included.</p> <p>The files included in the database are outlined below and the format of the files is briefly described. Additional information regarding the formatting can be found through the post-processing library (https://github.com/ahartloper/rlmtp/tree/master/protocols).</p> <p><strong>Usage</strong></p> <ul> <li>The data is licensed through the Creative Commons Attribution 4.0 International.</li> <li>If you have used our data and are publishing your work, we ask that you please reference both: <ol> <li>this database through its DOI, and</li> <li>any publication that is associated with the experiments. See the Overall_Summary and Database_References files for the associated publication references.</li> </ol> </li> </ul> <p><strong>Included Files</strong></p> <ul> <li>Overall_Summary_2022-08-25_v1-0-0.csv: summarises the specimen information for all experiments in the database.</li> <li>Summarized_Mechanical_Props_Campaign_2022-08-25_v1-0-0.csv: summarises the average initial yield stress and average initial elastic modulus per campaign.</li> <li>Unreduced_Data-#_v1-0-0.zip: contain the original (not downsampled) data <ul> <li>Where # is one of: 1, 2, 3, 4, 5, 6. The unreduced data is broken into separate archives because of upload limitations to Zenodo. Together they provide all the experimental data.</li> <li>We recommend you un-zip all the folders and place them in one "Unreduced_Data" directory similar to the "Clean_Data"</li> <li>The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.</li> <li>There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the unreduced data.</li> <li>The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.</li> </ul> </li> <li>Clean_Data_v1-0-0.zip: contains all the downsampled data <ul> <li>The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.</li> <li>There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the clean data.</li> <li>The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.</li> </ul> </li> <li>Database_References_v1-0-0.bib <ul> <li>Contains a bibtex reference for many of the experiments in the database. Corresponds to the "citekey" entry in the summary files. </li> </ul> </li> </ul> <p> </p> <p><strong>File Format: Downsampled Data</strong></p> <p>These are the "LP_<N>_Specimen_<M>_processed_data.csv" files in the "Clean_Data" directory. The <N> is the load protocol designation and the <M> is the specimen number for that load protocol and material source. Each file contains the following columns:</p> <ul> <li>The header of the first column is empty: the first column corresponds to the index of the sample point in the original (unreduced) data</li> <li>Time[s]: time in seconds since the start of the test</li> <li>e_true: true strain</li> <li>Sigma_true: true stress in MPa</li> <li>(optional) Temperature[C]: the surface temperature in degC</li> </ul> <p>These data files can be easily loaded using the pandas library in Python through:</p> <pre><code class="language-python">import pandas data = pandas.read_csv(data_file, index_col=0)</code></pre> <p>The data is formatted so it can be used directly in RESSPyLab (https://github.com/AlbanoCastroSousa/RESSPyLab). Note that the column names "e_true" and "Sigma_true" were kept for backwards compatibility reasons with RESSPyLab.</p> <p> </p> <p><strong>File Format: Unreduced Data</strong></p> <p>These are the "LP_<N>_Specimen_<M>_processed_data.csv" files in the "Unreduced_Data" directory. The <N> is the load protocol designation and the <M> is the specimen number for that load protocol and material source. Each file contains the following columns:</p> <ul> <li>The first column is the index of each data point</li> <li>S/No: sample number recorded by the DAQ</li> <li>System Date: Date and time of sample</li> <li>Time[s]: time in seconds since the start of the test</li> <li>C_1_Force[kN]: load cell force</li> <li>C_1_Déform1[mm]: extensometer displacement</li> <li>C_1_Déplacement[mm]: cross-head displacement</li> <li>Eng_Stress[MPa]: engineering stress</li> <li>Eng_Strain[]: engineering strain</li> <li>e_true: true strain</li> <li>Sigma_true: true stress in MPa</li> <li>(optional) Temperature[C]: specimen surface temperature in degC</li> </ul> <p>The data can be loaded and used similarly to the downsampled data.</p> <p> </p> <p><strong>File Format: Overall_Summary</strong></p> <p>The overall summary file provides data on all the test specimens in the database. The columns include:</p> <ul> <li>hidden_index: internal reference ID</li> <li>grade: material grade</li> <li>spec: specifications for the material</li> <li>source: base material for the test specimen</li> <li>id: internal name for the specimen</li> <li>lp: load protocol</li> <li>size: type of specimen (M8, M12, M20)</li> <li>gage_length__mm_: unreduced section length in mm</li> <li>avg_reduced_dia__mm_: average measured diameter for the reduced section in mm</li> <li>avg_fractured_dia_top__mm_: average measured diameter of the top fracture surface in mm</li> <li>avg_fractured_dia_bot__mm_: average measured diameter of the bottom fracture surface in mm</li> <li>fy_n__mpa_: nominal yield stress</li> <li>fu_n__mpa_: nominal ultimate stress</li> <li>t_a__deg_c_: ambient temperature in degC</li> <li>date: date of test</li> <li>investigator: person(s) who conducted the test</li> <li>location: laboratory where test was conducted</li> <li>machine: setup used to conduct test</li> <li>pid_force_k_p, pid_force_t_i, pid_force_t_d: PID parameters for force control</li> <li>pid_disp_k_p, pid_disp_t_i, pid_disp_t_d: PID parameters for displacement control</li> <li>pid_extenso_k_p, pid_extenso_t_i, pid_extenso_t_d: PID parameters for extensometer control</li> <li>citekey: reference corresponding to the Database_References.bib file</li> <li>yield_stress__mpa_: computed yield stress in MPa</li> <li>elastic_modulus__mpa_: computed elastic modulus in MPa</li> <li>fracture_strain: computed average true strain across the fracture surface</li> <li>c,si,mn,p,s,n,cu,mo,ni,cr,v,nb,ti,al,b,zr,sn,ca,h,fe: chemical compositions in units of %mass</li> <li>file: file name of corresponding clean (downsampled) stress-strain data</li> </ul> <p> </p> <p><strong>File Format: </strong><strong>Summarized_Mechanical_Props_Campaign</strong></p> <p>Meant to be loaded in Python as a pandas DataFrame with multi-indexing, e.g.,</p> <pre><code class="language-python">tab1 = pd.read_csv('Summarized_Mechanical_Props_Campaign_' + date + version + '.csv', index_col=[0, 1, 2, 3], skipinitialspace=True, header=[0, 1], keep_default_na=False, na_values='')</code></pre> <ul> <li>citekey: reference in "Campaign_References.bib".</li> <li>Grade: material grade.</li> <li>Spec.: specifications (e.g., J2+N).</li> <li>Yield Stress [MPa]: initial yield stress in MPa <ul> <li>size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign</li> </ul> </li> <li>Elastic Modulus [MPa]: initial elastic modulus in MPa <ul> <li>size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign</li> </ul> </li> </ul> <p> </p> <p><strong>Caveats</strong></p> <ul> <li>The files in the following directories were tested before the protocol was established. Therefore, only the true stress-strain is available for each: <ul> <li>A500</li> <li>A992_Gr50</li> <li>BCP325</li> <li>BCR295</li> <li>HYP400</li> <li>S460NL</li> <li>S690QL/25mm</li> <li>S355J2_Plates/S355J2_N_25mm and S355J2_N_50mm</li> </ul> </li> </ul>
TBPos: Dataset for Large-Scale Precision Visual Localization (database files)
<p>Large-scale dataset for visual localization, provided in the format of the well-known InLoc dataset (Taira et al, 2018). Contains co-registered RGB point clouds and a script for generating the rest of the 'database' files for visual localization by the InLoc algorithm. Note: query images are provided in a separate repository.</p>
Image Databases for Computer Vision Coded for Subject Traceability
<p>This document consists of the corpus of image databases examined for traceability of dataset subjects as published in:</p> <p>Morgan Klaus Scheuerman, Katy Weathington, Tarun Mugunthan, Emily Denton, and Casey Fiesler. 2023. From Human to Data to Dataset: Mapping the Traceability of Human Subjects in Computer Vision Datasets. Proc. ACM Hum.-Comput. Interact. 7, CSCW1, Article 55 (April 2023), 33 pages. https://doi.org/10.1145/3579488</p>
Dataset: A database of near-field head-related transfer functions based on measurements with a laser spark source
<p>This is a database of near-field head-related transfer functions (HRTFs) of an artificial head, measured at four distances (0.2, 0.3, 0.4 and 0.5 m), with 49 positions recorded at each distance, for a total of 196 measurement points. The HRTFs were recorded using an acoustic pulse created by a laser-induced breakdown of air (LIB), which realizes a close to ideal, massless, monopole sound source. The repository contains the original measurement data (raw_data.zip), the derived HRTFs both with (NF_LIB_HRTF_LFE.sofa) and without (NF_LIB_HRTF_measured.sofa) a low-frequency extension (LFE) applied, as well as the MATLAB code used to process the measurement data and to apply the LFE (LIB_HRTF_DB.zip). The database is made publicly available to support future research into nearby sound localization, and virtual/augmented reality applications.</p> <p>Please see the accompanying paper for further details: Marschall et al. (2023), <a href="https://doi.org/10.1016/j.apacoust.2022.109173">A database of near-field head-related transfer functions based on measurements with a laser spark source</a>, Applied Acoustics. </p>
FLOATECH WP3 Experimental Wave Database
<p>FLOATECH is a Horizon 2020 project funded under the Energy programme (<a href="https://cordis.europa.eu/programme/id/H2020_LC-SC3-RES-31-2020/en">LC-SC3-RES-31-2020 - Offshore wind basic science and balance of plant</a>). The consortium is coordinated by TU Berlin and implemented by 9 partners from 4 EU countries. The project runs from January 2021 to December 2023 and has received a budget of 4 Million € from the European Commission over these 3 years.</p> <p>FLOATECH aims at increasing the technical maturity and the cost competitiveness of floating offshore wind energy. This will be achieved by two types of actions:</p> <ul> <li> <p>The development, implementation and validation of a user-friendly and efficient <strong>design engineering tool</strong> (named QBlade-Ocean) performing simulations of floating offshore wind turbines with unseen aerodynamic and hydrodynamic fidelity. The more advanced modelling theories will lead to a reduction of the uncertainties in the design process and an increase of turbine efficiency.</p> </li> <li> <p>The development of <strong>two innovative control techniques</strong> (i.e. Active Wave-based feed-forward Control and the Active Wake Mixing) for Floating Wind Turbines and floaters, combining wave prediction and anticipation of induced platform motions. This is expected to reduce the wake effects in floating wind farms, leading to a net increase in the annual energy production of the farm.</p> </li> </ul> <p>The Work Package 3 of FLOATECH focuses on the advanced feed-forward wave-based control strategies for floating offshore wind turbines (FOWTs). This includes a prediction of the hydrodynamic force acting on the FOWT’s platform to mitigate the response of the structure while enhancing its performance.</p> <p>The experimental work carried out in Centrale Nantes in the context of this work package contains three experimental campaigns, listed below:</p> <p> C1: Measurement of wave fields, used to perform a wave elevation prediction at the turbine’s position;</p> <p> C2: FOWT in operations, development and validation of the 1-component aerodynamic force actuator, including force feedback loop and control to reproduce accurately the target aerodynamic thrust. The considered FOWT is the DTU 10 MW wind turbine supported by a spar platform designed at Centrale Nantes (Arnal, 2020);</p> <p> C3: same FOWT in operations, including the feed-forward wave-based control with both a 1-component and a 6-component aerodynamic force actuators.</p> <p>This database presents the results of the campaign C1 as well as the prediction of the free surface elevation acting on the FOWT’s platform.</p>
Database on Certified Reference Materials measured with PAT tools for validation and verification purposes
<p>The H2020 PAT4Nano project aims to develop and demonstrate Process Analytical Technologies (PAT) tools for nanosuspension characterization which have sufficiently high resolution, accuracy, and speed, for real-time industrial process monitoring and control. Real time monitoring is desired for example to obtain: small, high precision, specialty batch of materials, processing monitoring of nucleation/growth/milling of materials at different scales (lab, pilot, production), and for producing feedback loops (adapt T, pH, etc.,) needed for process control.<br> Laser diffraction (LD), Spatially Resolved Dynamic Light Scattering (SR-DLS), Cross-Correlation Dynamic Light Scattering (CC-DLS), Ultrasound Nanoparticle Sizer (UNPS), Raman, and Transmission Electron Microscopy (TEM) are the main PAT tools used in this project. For validation and verification purposes of these measurement techniques, polystyrene and silica samples (200 and 1000 nm particle size) were selected as (Certified) Reference Materials ((C))RMs) by the consortium partners. The results described in this database are particle size measurements using PAT methods in an offline mode. The particle size and particle size distribution data are presented as the D10, D50 and D90 and PDI/span measured with each PAT tool.<br> Raman spectra of the CRMs are presented as well. Here, particle size data was extracted by using chemometric software. Lastly, TEM images of the CRMs are included in the database to cross-correlate and cross-validate the results of the spectroscopic and scattering PAT tools.</p>
GeoDAR-TopoCat: Drainage topology and catchment database (TopoCat) for Georeferenced global Dams And Reservoirs (GeoDAR)
<p><strong>Contact</strong>: Md Safat Sikder (msikder@ksu.edu), Jida Wang (jidawang@ksu.edu; gdbruins@ucla.edu)</p> <p> </p> <p><strong>Data description</strong></p> <p>This data can be considered a supplement to the Georeferenced global Dams And Reservoirs (GeoDAR) dataset (doi:10.5281/zenodo.6163413). </p> <p>Here in GeoDAR-TopoCat, the method of TopoCat (doi:10.5281/zenodo.7420810) has been applied on GeoDAR reservoirs in order to construct the drainage topology and catchments for global reservoirs.</p> <p>To avoid ambiguity, please refer to this version of GeoDAR-TopoCat as “<strong>GeoDAR-TopoCat v1.1-1.0</strong>”, where “1.1” specifies the version of GeoDAR reservoirs, whose drainage topology and catchments are constructed using the method in version “1.0” of TopoCat.</p> <p> </p> <p><strong>Relevant datasets</strong></p> <ul> <li>The original GeoDAR v1.1 dataset without topology can be accessed here: doi:10.5281/zenodo.6163413.</li> <li>The TopoCat v1.0 dataset, originally developed based on HydroLAKES v1.0, can be accessed here: doi:10.5281/zenodo.7420810.</li> </ul> <p> </p> <p><strong>Attribute description</strong></p> <p>Description of the attributes of GeoDAR-TopoCat is the same as those of TopoCat v1.0. The unique ID of each GeoDAR reservoir is specified in “id_v11” (consistent with the GeoDAR dataset). Please refer to the attributes of TopoCat and GeoDAR for more details.</p> <p> </p> <p><strong>Data and code availability</strong></p> <p>All datasets are available under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license (<a href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</a>).</p> <p>Please refer to GeoDAR and TopoCat datasets for other details and disclaimers.</p> <p> </p> <p><strong>Citation</strong></p> <p>We request anyone who uses GeoDAR-TopoCat to cite <strong>both GeoDAR and TopoCat papers</strong>:</p> <p>Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869-1899, 2022, <a href="https://doi.org/10.5194/essd-14-1869-2022">https://doi.org/10.5194/essd-14-1869-2022</a>.</p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. Earth System Science Data Discussion, in review, <a href="https://doi.org/10.5194/essd-2022-433">https://doi.org/10.5194/essd-2022-433</a>.</p>
The Mixoplankton Database (MDB)
<p>Database for marine protist mixoplankton.</p> <p>The Mixoplankton Database (MDB) is associated with the following manuscript:</p> <p>Title: The Mixoplankton Database – diversity of photo-phago-trophic plankton in form, function and distribution across the global ocean</p> <p>Authors: Aditee Mitra*, David A Caron, Emile Faure, Kevin J Flynn, Suzana Gonçalves Leles, Per J Hansen, George B McManus, Fabrice Not, Helga do Rosario Gomes, Luciana Santoferrara, Diane K Stoecker, Urban Tillmann</p> <p>Journal: Journal of Eukaryotic Microbiology, e12972. Available from: https://doi.org/10.1111/jeu.12972</p>
Co-Creation Database
<p>The Co-Creation Database groups scientific references on co-creation.</p> <p>It mainly contains the title, abstract, DOI, and authors.</p> <p>Two versions are available:</p> <ul> <li><strong>Version 1.5</strong> includes 13,501 references, from PubMed, ProQuest and CINAHL, from January 1970 to November 2021. Available in RIS (Research Information Systems) format and CSV (CSV UTF-8). <em>Quality metrics: 9.38% false negatives; 20.35% false positives.</em></li> <li><strong>Version 2.0 </strong>is an update from a classification model trained with version 1.5. It includes references from Scopus and Web of Science from January 1970 to March 2023, with an update of the previous databases used for version 1.5 from December 2021 to March 2023. Two CSV (CSV UTF-8) files are available. The "Co-Creation Database v2.0 - full.csv" combines the last version, 1.5 and the update, with 52,821 references. The file "Co-Creation Database v2.0 - adding.csv" has only the update, with 39,219 references. <em>Quality metrics: 13.98% false negatives; 36.43% false positives.</em></li> </ul> <p><strong>To perform your search:</strong> we recommend you extend your search to the title and abstract since some data are initially missing. The RIS file can be uploaded to any references manager (e.g., Zotero, Mendeley, etc.), where you will have the feature to search. For example, here is the link for advanced search instructions in Zotero: <a href="https://www.zotero.org/support/searching">https://www.zotero.org/support/searching</a>. Additionally, You can run a Boolean search for CSV files using a Python script.</p> <p><strong>To improve the database in further updates: </strong>we make available an online form to submit any irrelevant references you may find or to submit any relevant reference not inside the last version. The form is available at the following link: <a href="https://forms.office.com/e/6vu9X0kBcw">https://forms.office.com/e/6vu9X0kBcw</a></p> <p>It was produced as part of Health CASCADE, a Marie Skłodowska-Curie Innovative Training Network funded by the European Union's Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement n° 956501.</p> <p>The work is made available under the terms of the license CC-BY-NC-4.0 (<a href="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode">Creative Common Attribution - NonCommercial - NoDerivatives 4.0 International</a>).</p>
Quantum-Chemical Bonding Database (Unprocessed data : Part 4)
<p>This dataset is published as part of our publication: <a href="https://www.nature.com/articles/s41597-023-02477-5">A Quantum-Chemical Bonding Database for Solid-State Materials. </a>Details about the data generation, validation, and metadata description can be found in our publication.</p><p>Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID)</p>
Toluene/Water Partition Coefficient Database
<p>In recent years the use of partition systems other than the widely used biphasic <em>n</em>-octanol/water has received increased attention to gain insight into the molecular features that dictate the lipophilicity of compounds. Thus, the difference between <em>n</em>-octanol/water and toluene/water partition coefficients has proven to be a valuable descriptor to study the propensity of molecules to form intramolecular hydrogen bonds and exhibit chameleon-like properties that modulate solubility and permeability. In this context, a dataset of <strong>252 unique molecules</strong> with experimentally determined <strong>toluene/water partition coefficient</strong> is deposited here. </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.