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4,283 results for “Database”
Cadenza Challenge (CAD2): databases for rebalancing classical music task
<h1>Cadenza</h1> <p>Please, cite CadenzaWoodwind as</p> <blockquote> <p><strong>Gerardo Roa-Dabike , Trevor J. Cox , Alex J. Miller , Bruno M. Fazenda , Simone Graetzer , Rebecca R. Vos , Michael A. Akeroyd , Jennifer Firth , William M. Whitmer , Scott Bannister , Alinka Greasley , Jon P. Barker , The Cadenza Woodwind Dataset: Synthesised Quartets for Music Information Retrieval and Machine Learning, Data in Brief (2024), doi: https://doi.org/10.1016/j.dib.2024.111199</strong></p> </blockquote> <p>This is the training and validation data for the rebalancing classic music task from the <a href="https://cadenzachallenge.org/">Second Cadenza Machine Learning Challenge (CAD2).</a></p> <p>The Cadenza Challenges are improving music production and processing for people with a hearing loss. According to The World Health Organization, 430 million people worldwide have a disabling hearing loss. Hearing aid users report several issues when listening to music, including distortion in the bass, difficulties in perceiving the full range of the music, especially high-frequency pitches, and a tendency to miss the impact of quieter parts of compositions [1]. In a pilot study, we found giving listeners sliders to allow them to rebalance different instruments in a classical music ensemble was desirable.</p> <p>Overview of files:</p> <ol> <li>CadenzaWoodwind. Synthesized dataset of small ensembles of woodwind instruments for training and validation.</li> <li>EnsembleSet_Mix_1. A subset of the synthesised <a href="../records/6519024">EnsembleSet [7]</a> for training and validation (Mix_1 render).</li> <li>Real Data for Tuning: <a href="../api/records/12664932/draft/files/Stereo_Reverb_Real_Data_For_Tuning.zip/content" target="_blank" rel="noopener noreferrer">Stereo_Reverb_Real_Data_For_Tuning.zip</a>.</li> <li>metadata.zip contains audiograms, scene details, target gains and compressor settings.</li> </ol> <p>The audio files are in FLAC format in the .zip archives. The json files contain metadata.</p> <p>More details below.</p> <p> </p>
USFWS Red Bluff Diversion Dam Rotary Screw Trap Juvenile Fish Monitoring Database
The United States Fish and Wildlife Service (USFWS) has conducted direct monitoring of juvenile Chinook Salmon Oncorhynchus tshawytscha passage at the Red Bluff Diversion Dam (RBDD), river kilometer (RKM) 391 on the Sacramento River, in Northern California since 1994 (Johnson and Martin 1997). Martin et al. (2001) developed quantitative methodologies for indexing juvenile Chinook passage using rotary-screw traps (RST) to assess the impacts of the United States Bureau of Reclamation’s (USBR) RBDD Research Pumping Plant. Absolute abundance (passage and production) estimates were needed to determine the level of impact from the entrainment of salmonids and other fish community populations through RBDD’s experimental ‘fish friendly’ Archimedes and internal helical pumps (Borthwick and Corwin 2001). The original project objectives were met by 2000 and funding of the project was discontinued. From 2001 to 2008, funding was secured through a CALFED Bay-Delta Program grant for annual monitoring operations to determine the effects of restoration activities in the upper Sacramento River aimed primarily at winter Chinook Salmon*. The USBR, the primary proponent of the Central Valley Project (CVP), has funded this project since 2010 due to regulatory requirements contained within the National Marine Fisheries Service’s (NMFS) Biological Opinion for the Long-term Operations of the CVP and State Water Project (NMFS 2009 and 2019). The project began sampling in 1994 with (4) 2.4-m diameter RST’s which sampled through March of 2020. From March 25, 2020 through June 25, 2020, in order to protect employee health and safety during the Coronavirus global pandemic (COVID-19), sampling ceased. Just prior to resuming sampling operations in July of 2020, (4) 1.5-m diameter and one 2.4-m diameter RSTs were re-installed across the transect at the RBDD site. This new five-trap configuration provides a solution to sampling a location that has become shallower since the RBDD gates were permanen
GRiMeDB: a comprehensive global database of methane concentrations and fluxes in fluvial ecosystems with supporting physical and chemical information
The Global River Methane Database (GriMeDB) is a compilation of measurements of CH4 concentrations and fluxes for flowing water environments derived from publications, reports, data repositories, and other outlets between 1973 and 2021. Assembly of GRiMeDB was motivated by the goal of having a centralized, standardized resource to facilitate further studies of CH4 pattern and process in flowing water systems, upscaling efforts, and identification of tendencies in when, where, and how CH4 has been sampled in streams and rivers across the world. Thus, CH4 data are supported by concurrent observations (as available) of aquatic CO2, N2O, temperature, conductivity, pH, dissolved oxygen, nitrogen, phosphorus, organic carbon, and discharge, along with site data (latitude, longitude, elevation, and [as available]: stream order, elevation, channel slope, catchment size, and codes for distinct or disturbed channel types). GRiMeDB includes over 24,000 records of CH4 concentration and greater than 8,000 flux measurements from over 5,000 unique sites, most of which are resolved to the daily time scale.
Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of two parts. The first part of the data set is measurements for six different specimens with wave type impact – short sweep signal with duration 0.05 s. The second part is the measurements during splice connection degradation of one of the specimens with short impulse. The degradation of a connection is presented by four different states of joints. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>Used materials, methods and results for the second part of the data set is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Database of measurements for damage detection of steel beam splice connections by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of measurements for six different specimens with two types of impact – sweep signal with duration 0.5 s and short impulse, during degradation of the splice connections realised by unbolting the bolts in the connections. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>This database is a continuation of the database Kurtenoks, V., Buka-Vaivade, K., Serdjuks, D., Lapkovskis, V., Mironovs, V., & Podkoritovs, A. (2023). Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10077332<br>Suggested by authors data post-processing is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Mining and Metallurgical Residue Database
<p><span>The dataset includes 44 relevant data attributes from 64 mining and metallurgical sites in 27 countries. </span></p>
XAlkeneDB: A database illuminating the electronic ground and excited state quantum chemical features of ethene, propene and butene
<div> <div> <div> <p>The dataset associated with this research has been published in <a href="https://pubs.rsc.org/en/content/articlelanding/2024/sc/d4sc04164j" target="_blank" rel="noopener"> Chem. Sci., 2024,15, 15880-15890.</a> Please cite this journal article when using the data.</p> </div> </div> </div>
Database of 3D Concrete Printed Buildings
<p>This dataset contains all 3D concrete printed buildings known to the authors built between 2013 and 2023. This dataset is part of a publication and was used to research different fabrication strategies. The Excel database developed for this purpose is divided into 22 categories and filled in as far as possible. The sources are also indicated in the database. For a more detailed description of the categories and the results of the study, please refer to the corresponding publication. We would be happy if the data are used and expanded for future research into 3D concrete printing.</p>
glenglat: Global englacial temperature database
<p>Open-access database of englacial temperature measurements compiled from data submissions and published literature. It is developed on <a href="https://github.com/mjacqu/glenglat">GitHub</a> and published to <a href="https://doi.org/10.5281/zenodo.11516611">Zenodo</a>. This version (1.0.0) of the dataset is described in the following publication:</p> <blockquote> <p>Mylène Jacquemart, Ethan Welty, Marcus Gastaldello, and Guillem Carcanade (2025). glenglat: A database of global englacial temperatures. Earth System Science Data 17(4): 1627–1666. <a href="https://doi.org/10.5194/essd-17-1627-2025">https://doi.org/10.5194/essd-17-1627-2025</a></p> </blockquote> <h2>Dataset structure</h2> <p>The dataset adheres to the Frictionless Data <a href="https://specs.frictionlessdata.io/tabular-data-package">Tabular Data Package</a> specification. The metadata in <code>datapackage.json</code> describes, in detail, the contents of the tabular data files in the <code>data</code> folder:</p> <ul> <li><code>source.csv</code>: Description of each data source (either a personal communication or the reference to a published study).</li> <li><code>borehole.csv</code>: Description of each borehole (location, elevation, etc), linked to <code>source.csv</code> via <code>source_id</code> and less formally via source identifiers in <code>notes</code>.</li> <li><code>profile.csv</code>: Description of each profile (date, etc), linked to <code>borehole.csv</code> via <code>borehole_id</code> and to <code>source.csv</code> via <code>source_id</code> and less formally via source identifiers in <code>notes</code>.</li> <li><code>measurement.csv</code>: Description of each measurement (depth and temperature), linked to <code>profile.csv</code> via <code>borehole_id</code> and <code>profile_id</code>.</li> </ul> <p>For boreholes with many profiles (e.g. from automated loggers), pairs of <code>profile.csv</code> and <code>measurement.csv</code> are stored separately in subfolders of <code>data</code> named <code>{source.id}-{glacier}</code>, where <code>glacier</code> is a simplified and kebab-cased version of the glacier name (e.g. <code>flowers2022-little-kluane</code>).</p> <h3>Supporting information</h3> <p>The folder <code>sources</code>, available on <a href="https://github.com/mjacqu/glenglat">GitHub</a> but omitted from dataset releases on <a href="https://doi.org/10.5281/zenodo.11516611">Zenodo</a>, contains subfolders (with names matching column <code>source.id</code>) with files that document how and from where the data was extracted.</p> <h2>Tables</h2> <p>Jump to: <a href="#source"><code>source</code></a> · <a href="#borehole"><code>borehole</code></a> · <a href="#profile"><code>profile</code></a> · <a href="#measurement"><code>measurement</code></a></p> <h3><a name="source"></a><code>source</code></h3> <p>Sources of information considered in the compilation of this database. Column names and categorical values closely follow the Citation Style Language (CSL) 1.0.2 specification. Names of people in non-Latin scripts are followed by a latinization in square brackets (e.g. В. С. Загороднов [V. S. Zagorodnov]) and non-English titles are followed by a translation in square brackets. The family name of Latin-script names is wrapped in curly braces when it is not the last word of the name (e.g. Emmanuel {Le Meur}, e.g. {Duan} Keqin) or the name ends in two or more unabbreviated words (e.g. Jon Ove {Hagen}). The family name of a Chinese name (and of the latinization) is wrapped in curly braces when it is not the first character.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>id</code> (required)</td> <td>string</td> <td>Unique identifier constructed from the first author's lowercase, latinized, family name and the publication year, followed as needed by a lowercase letter to ensure uniqueness (e.g. Загороднов 1981 → zagorodnov1981a).</td> </tr> <tr> <td><code>author</code></td> <td>string</td> <td>Author names (optionally followed by their ORCID or contact email in parentheses) as a pipe-delimited list.</td> </tr> <tr> <td><code>year</code> (required)</td> <td>year</td> <td>Year of publication.</td> </tr> <tr> <td><code>type</code> (required)</td> <td>string</td> <td>Item type.<br>- article-journal: Journal article<br>- book: Book (if the entire book is relevant)<br>- chapter: Book section<br>- document: Document not fitting into any other category<br>- dataset: Collection of data<br>- map: Geographic map<br>- paper-conference: Paper published in conference proceedings<br>- personal-communication: Personal communication between individuals<br>- speech: Presentation (talk, poster) at a conference<br>- report: Report distributed by an institution<br>- thesis-phd: Doctor of Philosophy (PhD) thesis<br>- thesis-msc: Master of Science (MSc) thesis<br>- webpage: Website or page on a website</td> </tr> <tr> <td><code>title</code> (required)</td> <td>string</td> <td>Item title.</td> </tr> <tr> <td><code>url</code></td> <td>string</td> <td>URL (DOI if available).</td> </tr> <tr> <td><code>language</code> (required)</td> <td>string</td> <td>Language as ISO 639-1 two-letter language code.<br>- da: Danish<br>- de: German<br>- en: English<br>- es: Spanish<br>- fr: French<br>- ja: Japanese<br>- ko: Korean<br>- ru: Russian<br>- sv: Swedish<br>- zh: Chinese</td> </tr> <tr> <td><code>container_title</code></td> <td>string</td> <td>Title of the container (e.g. journal, book).</td> </tr> <tr> <td><code>volume</code></td> <td>integer</td> <td>Volume number of the item or container.</td> </tr> <tr> <td><code>issue</code></td> <td>string</td> <td>Issue number (e.g. 1) or range (e.g. 1-2) of the item or container, with an optional letter prefix (e.g. F1) or part number (e.g. 75pt2).</td> </tr> <tr> <td><code>page</code></td> <td>string</td> <td>Page number (e.g. 1) or range (e.g. 1-2) of the item in the container, with an optional letter prefix (e.g. S1).</td> </tr> <tr> <td><code>version</code></td> <td>string</td> <td>Version number (e.g. 1.0) of the item.</td> </tr> <tr> <td><code>editor</code></td> <td>string</td> <td>Editor names (e.g. of the containing book) as a pipe-delimited list.</td> </tr> <tr> <td><code>collection_title</code></td> <td>string</td> <td>Title of the collection (e.g. book series).</td> </tr> <tr> <td><code>collection_number</code></td> <td>string</td> <td>Number (e.g. 1) or range (e.g. 1-2) in the collection (e.g. book series volume).</td> </tr> <tr> <td><code>publisher</code></td> <td>string</td> <td>Publisher name.</td> </tr> </tbody> </table> <h3><a name="borehole"></a><code>borehole</code></h3> <p>Metadata about each borehole.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>id</code> (required)</td> <td>integer</td> <td>Unique identifier.</td> </tr> <tr> <td><code>source_id</code> (required)</td> <td>string</td> <td>Identifier of the source of the earliest temperature measurements. This is also the source of the borehole attributes unless otherwise stated in <code>notes</code>.</td> </tr> <tr> <td><code>glacier_name</code> (required)</td> <td>string</td> <td>Glacier or ice cap name (as reported).</td> </tr> <tr> <td><code>glims_id</code></td> <td>string</td> <td>Global Land Ice Measurements from Space (GLIMS) glacier identifier.</td> </tr> <tr> <td><code>location_origin</code> (required)</td> <td>string</td> <td>Origin of location (<code>latitude</code>, <code>longitude</code>).<br>- submitted: Provided in data submission<br>- published: Reported as coordinates in original publication<br>- digitized: Digitized from published map with complete axes<br>- estimated: Estimated from published plot by comparing to a map (e.g. Google Maps, CalTopo)<br>- guessed: Estimated with difficulty, for example by comparing <code>elevation</code> to a map (e.g. Google Maps, CalTopo)</td> </tr> <tr> <td><code>latitude</code> (required)</td> <td>number [degree]</td> <td>Latitude (EPSG 4326).</td> </tr> <tr> <td><code>longitude</code> (required)</td> <td>number [degree]</td> <td>Longitude (EPSG 4326).</td> </tr> <tr> <td><code>elevation_origin</code> (required)</td> <td>string</td> <td>Origin of elevation (<code>elevation</code>).<br>- submitted: Provided in data submission<br>- published: Reported as number in original publication<br>- digitized: Digitized from published plot with complete axes<br>- estimated: Estimated from elevation contours in published map<br>- guessed: Estimated with difficulty, for example by comparing location (<code>latitude</code>, <code>longitude</code>) to a map of contemporary elevations (e.g. CalTopo, Google Maps)</td> </tr> <tr> <td><code>elevation</code> (required)</td> <td>number [m]</td> <td>Elevation above sea level.</td> </tr> <tr> <td><code>mass_balance_area</code></td> <td>string</td> <td>Mass balance area.<br>- ablation: Ablation area<br>- equilibrium: Near the equilibrium line<br>- accumulation: Accumulation area</td> </tr> <tr> <td><code>label</code></td> <td>string</td> <td>Borehole name (e.g. as labeled on a plot).</td> </tr> <tr> <td><code>date_min</code></td> <td>date (%Y-%m-%d)</td> <td>Begin date of drilling, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).</td> </tr> <tr> <td><code>date_max</code></td> <td>date (%Y-%m-%d)</td> <td>End date of drilling, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).</td> </tr> <tr> <td><code>drill_method</code></td> <td>string</td> <td>Drilling method.<br>- mechanical: Push, percussion, rotary<br>- thermal: Hot point, electrothermal, steam<br>- combined: Mechanical and thermal</td> </tr> <tr> <td><code>ice_depth</code></td> <td>number [m]</td> <td>Starting depth of continuous ice. Infinity (INF) indicates that only snow, firn, or intermittent ice was reached.</td> </tr> <tr> <td><code>depth</code></td> <td>number [m]</td> <td>Total borehole depth (not including drilling in the underlying bed).</td> </tr> <tr> <td><code>to_bed</code></td> <td>boolean</td> <td>Whether the borehole reached the glacier bed.</td> </tr> <tr> <td><code>temperature_uncertainty</code></td> <td>number [°C]</td> <td>Estimated temperature uncertainty (as reported).</td> </tr> <tr> <td><code>notes</code></td> <td>string</td> <td>Additional remarks about the study site, the borehole, or the measurements therein as a pipe-delimited list. Sources are referenced by <code>source.id</code>. Quality concerns are prefixed with '[flag]'.</td> </tr> <tr> <td><code>curator</code></td> <td>string</td> <td>Names of people who added the data to the database, as a pipe-delimited list.</td> </tr> <tr> <td><code>investigators</code></td> <td>string</td> <td>Names of people and/or agencies who performed the work, as a pipe-delimited list. Each entry is in the format 'person (agency; ...) {notes}', where only person or one agency is required. Person and agency may contain a latinized form in square brackets.</td> </tr> <tr> <td><code>funding</code></td> <td>string</td> <td>Funding sources as a pipe-delimited list. Each entry is in the format 'funder [rorid] > award [number] url', where only funder is required and rorid is the funder's ROR (https://ror.org) ID (e.g. 01jtrvx49).</td> </tr> </tbody> </table> <h3><a name="profile"></a><code>profile</code></h3> <p>Date and time of each measurement profile.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>borehole_id</code> (required)</td> <td>integer</td> <td>Borehole identifier.</td> </tr> <tr> <td><code>id</code> (required)</td> <td>integer</td> <td>Borehole profile identifier (starting from 1 for each borehole).</td> </tr> <tr> <td><code>source_id</code> (required)</td> <td>string</td> <td>Source identifier.</td> </tr> <tr> <td><code>measurement_origin</code> (required)</td> <td>string</td> <td>Origin of measurements (<code>measurement.depth</code>, <code>measurement.temperature</code>).<br>- submitted: Provided as numbers in data submission<br>- published: Numbers read from original publication<br>- digitized-discrete: Digitized with Plot Digitizer from discrete points of depth versus temperature<br>- digitized-continuous: Digitized with Plot Digitizer from a continuous data source (e.g. line plot of depth versus temperature)</td> </tr> <tr> <td><code>date_min</code></td> <td>date (%Y-%m-%d)</td> <td>Measurement date, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).</td> </tr> <tr> <td><code>date_max</code> (required)</td> <td>date (%Y-%m-%d)</td> <td>Measurement date, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).</td> </tr> <tr> <td><code>time</code></td> <td>time (%H:%M:%S)</td> <td>Measurement time.</td> </tr> <tr> <td><code>utc_offset</code></td> <td>number [h]</td> <td>Time offset relative to Coordinated Universal Time (UTC).</td> </tr> <tr> <td><code>equilibrium</code></td> <td>string</td> <td>Whether and how reported temperatures equilibrated following drilling.<br>- true: Equilibrium was measured<br>- estimated: Equilibrium was estimated (typically by extrapolation)<br>- false: Equilibrium was not reached</td> </tr> <tr> <td><code>notes</code></td> <td>string</td> <td>Additional remarks about the profile or the measurements therein as a pipe-delimited list. Sources are referenced by <code>source.id</code>. Quality concerns are prefixed with '[flag]'.</td> </tr> </tbody> </table> <h3><a name="measurement"></a><code>measurement</code></h3> <p>Temperature measurements with depth.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>borehole_id</code> (required)</td> <td>integer</td> <td>Borehole identifier.</td> </tr> <tr> <td><code>profile_id</code> (required)</td> <td>integer</td> <td>Borehole profile identifier.</td> </tr> <tr> <td><code>depth</code> (required)</td> <td>number [m]</td> <td>Depth below the glacier surface.</td> </tr> <tr> <td><code>temperature</code> (required)</td> <td>number [°C]</td> <td>Temperature.</td> </tr> </tbody> </table>
Harvard Forest Flora Database from 1908 to Present
We conducted a floristic inventory of Harvard Forest, in order to: (1) document the current vascular flora of Harvard Forest; (2) evaluate the extent to which the flora has changed over the past century.
Harvard Forest Herbarium Database from 1908 to Present
As a part of the broader Harvard Forest Flora project (see data set HF116), we prepared a database of all specimens located in the Harvard Forest herbarium.
Naturalistic Neuroimaging Database
Open the record for dataset details and reuse information.
Database of Annotated Core Arguments: English, Lao and Russian
<p>This database contains corpus examples of transitive clauses with annotated core arguments realized as syntactic subjects and objects (A and P) in English, Lao and Russian. The coding scheme was developed together with Alena Witzlack-Makarevich</p>
Storm Database Files for CLIMK–WINDS: A New Database of Extreme European Winter Windstorms
<p>This database is comprised of the four netCDF files containing the 50 most extreme European winter windstorms identified within the four input sources, with one netCDF file per source: ERA5 reanalysis, CCLM_ERA5_EUR-11 regional climate model simulation, COSMO-REA6 reanalysis, and CCLM_ERA5_CEU-3 regional climate model. This database was created by Clare Marie Flynn and its creation is described in the following paper: Flynn, C. M., Moemken, J., Pinto, J., Schutte, M., and Messori, G.: CLIMK–WINDS: A New Database of Extreme European Winter Windstorms, under review for final submission, Earth System Science Data, 2025.</p>
SWOT River Database (SWORD)
<p><strong>VERSION NOTES:</strong></p> <p><strong>v17 versus v17b</strong></p> <ul> <li>"Type" change for 1662 reaches and associated nodes globally. Please reference the Product Description Document for the "Type" identifier definition. </li> <li>Updates to reach and node lengths and distance-from-outlet variable to correct a bug in the node length calculation in select reaches (<2% of reaches were impacted globally).</li> <li>SWORD v17b is the official version for SWOT <strong>Version D</strong> <a href="https://podaac.jpl.nasa.gov/SWOT?tab=datasets-information&sections=about"><strong>RiverSP Vector Products</strong></a>.</li> </ul> <p>The project and public versions of SWORD were kept separate while algorithms were being developed in preparation for SWOT's launch in 2022. Now that the SWOT mission is here, the project version of SWORD is published as the public version which is why the version numbers jump after v2. The primary difference between the project and public versions of SWORD are extra "filler" variables in the NetCDF format that will be used for calculating discharge. For details on the filler variables please reference the Product Description Document provided with the downloads. </p> <p>If you use the SWORD Database in your work, please cite: Altenau et al., (2021) The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A Global River Network for Satellite Data Products. <em>Water Resources Research</em>. <a href="https://doi.org/10.1029/2021WR030054">https://doi.org/10.1029/2021WR030054</a></p> <p>You can also visit <a href="http://www.swordexplorer.com"><strong>www.swordexplorer.com</strong></a> to explore the current version of SWORD before downloading. </p> <p><strong>1. Summary:</strong></p> <p>The Surface Water and Ocean Topography (SWOT) satellite mission vastly expands observations of river water surface elevation (WSE), width, and slope. In order to facilitate a wide range of new analyses with flexibility, the SWOT mission provides a range of relevant data products. One product the SWOT mission provides are river vector products stored in shapefile format for each SWOT overpass (JPL Internal Document, 2020b). The <strong>SWO</strong>t <strong>R</strong>iver <strong>D</strong>atabase (<strong>SWORD</strong>) combines multiple global river- and satellite-related datasets to define the nodes and reaches that constitute SWOT river vector data products. SWORD provides high-resolution river nodes (200 m) and reaches (~10 km) in shapefile and netCDF formats with attached hydrologic variables (WSE, width, slope, etc.) as well as a consistent topological system for global rivers 30 m wide and greater.</p> <p><strong>2. Data Formats:</strong></p> <p>The SWORD database is provided in netCDF, geopackage, and shapefile formats. All files start with a two-digit continent identifier ("af" – Africa, "as" – Asia / Siberia, "eu" – Europe / Middle East, "na" – North America, "oc" – Oceania, "sa" – South America). File syntax denotes the regional information for each file and varies slightly between netCDF and shapefile formats.</p> <p>NetCDF files are structured in 3 groups: centerlines, nodes, and reaches. The centerline group contains location information and associated reach and node ids along the original GRWL 30 m centerlines (Allen and Pavelsky, 2018). Node and reach groups contain hydrologic attributes at the ~200 m node and ~10 km reach locations (see description of attributes below). NetCDFs are distributed at continental scales with a filename convention as follows: [continent]_sword_v17.nc (<em>i.e. na_sword_v17.nc</em>).</p> <p>SWORD shapefiles consist of four main files (.dbf, .prj, .shp, .shx). There are separate shapefiles for nodes and reaches, where nodes are represented as ~200 m spaced points and reaches are represented as polylines. All shapefiles are in geographic (latitude/longitude) projection, referenced to datum WGS84. Shapefiles are split into HydroBASINS (Lehner and Grill, 2013) Pfafstetter level 2 basins (hbXX) for each continent with a naming convention as follows: [continent]_sword_[nodes/reaches]_hb[XX]_v17.shp (<em>i.e. na_sword_nodes_hb74_v17.shp; na_sword_reaches_hb74_v17.shp</em>).</p> <p>SWORD geopackage files are split into two files for nodes and reaches per continental region, where nodes are represented as 200 m spaced points and reaches are represented as polylines. All geopackage files are in geographic (latitude/longitude) projection, referenced to datum WGS84. Geopackage file names are distributed at continental scales and are defined by a two-digit identifier (Table 2): [continent]_sword_[nodes/reaches]_v17.gpkg (i.e. na_sword_nodes_v17.gpkg; na_sword_reaches_v17.gpkg).</p> <p><strong>3. Attribute Description:</strong></p> <p>This list contains the primary attributes contained in the SWORD database.</p> <ul> <li><strong>x:</strong> Longitude of the node or reach ranging from 180°E to 180°W (units: decimal degrees).</li> <li><strong>y:</strong> Latitude of the node or reach ranging from 90°S to 90°N (units: decimal degrees).</li> <li><strong>node_id:</strong> ID of each node. The format of the id is as follows: CBBBBBRRRRNNNT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin code up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream), N = Node number (assigned sequentially within a reach starting at the downstream end working upstream), T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost node).</li> <li><strong>node_length </strong><em>(node files only</em>): Node length measured along the GRWL centerline points (units: meters).</li> <li><strong>reach_id:</strong> ID of each reach. The format of the id is as follows: CBBBBBRRRRT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin codes up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream, T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach).</li> <li><strong>reach_length </strong>(<em>reach files only</em>): Reach length measured along the GRWL centerline points (units: meters).</li> <li><strong>wse:</strong> Average water surface elevation (WSE) value for a node or reach. WSEs are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) and referenced to the EGM96 geoid (units: meters).</li> <li><strong>wse_var:</strong> WSE variance along the GRWL centerline points used to calculate the average WSE for each node or reach (units: square meters).</li> <li><strong>width:</strong> Average width for a node or reach (units: meters).</li> <li><strong>width_var:</strong> Width variance along the GRWL centerline points used to calculate the average width for each node or reach (units: square meters).</li> <li><strong>max_width: </strong>Maximum width value across the channel for each node or reach that includes island and bar areas (units: meters).</li> <li><strong>facc: </strong>Maximum flow accumulation value for a node or reach. Flow accumulation values are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) (units: square kilometers).</li> <li><strong>n_chan_max:</strong> Maximum number of channels for each node or reach.</li> <li><strong>n_chan_mod:</strong> Mode of the number of channels for each node or reach.</li> <li><strong>obstr_type: </strong>Type of obstruction for each node or reach based on the Globale Obstruction Database (GROD, Whittemore et al., 2020) and HydroFALLS data (http://wp.geog.mcgill.ca/hydrolab/hydrofalls). Obstr_type values: 0 - No Dam, 1 - Dam, 2 - Channel Dam, 3 - Lock, 4 - Low Permeable Dam, 5 - Waterfall.</li> <li><strong>grod_id:</strong> The unique GROD ID for each node or reach with obstr_type values 1-4.</li> <li><strong>hfalls_id:</strong> The unique HydroFALLS ID for each node or reach with obstr_type value 5.</li> <li><strong>dist_out:</strong> Distance from the river outlet for each node or reach (units: meters).</li> <li><strong>type:</strong> Type identifier for a node or reach: 1 – river, 2 – lake off river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach/node.</li> <li><strong>lakeflag</strong>: GRWL water body identifier for each reach: 0 – river, 1 – lake/reservoir, 2 – canal, 3 – tidally influenced river.</li> <li><strong>manual_add </strong>(<em>node files only</em>): Binary flag indicating whether the node was manually added to the public GRWL centerlines (Allen and Pavelsky, 2018). These nodes were originally given a width = 1, but have since been updated to have the reach width values.</li> <li><strong>meand_len </strong>(<em>node files only</em>): Length of the meander that a node belongs to, measured from beginning of the meander to its end in meters. For nodes longer than one meander, the meander length will represent the average length of all meanders belonging to the node (units: meters).</li> <li><strong>sinuosity </strong>(<em>node files only</em>): The total reach length the node belongs to divided by the Euclidean distance between the reach end points.</li> <li><strong>slope </strong>(<em>reach files only</em>): Reach average slope calculated along the GRWL centerline points. Slopes are calculated using a linear regression (units: meters/kilometer).</li> <li><strong>n_nodes</strong> (<em>reach files only</em>): Number of nodes associated with each reach.</li> <li><strong>n_rch_up</strong> (<em>reach files only</em>): Number of upstream reaches for each reach.</li> <li><strong>n_rch_down</strong> (<em>reach files only</em>): Number of downstream reaches for each reach.</li> <li><strong>rch_id_up</strong> (<em>reach files only</em>): Reach IDs of the upstream neighboring reaches.</li> <li><strong>rch_id_dn</strong> (<em>reach files only</em>): Reach IDs of the downstream neighboring reaches.</li> <li><strong>swot_obs </strong>(<em>reach files only</em>): The maximum number of SWOT passes to intersect each reach during the 21 day orbit cycle.</li> <li><strong>swot_orbits </strong>(<em>reach files only</em>): A list of the SWOT orbit tracks that intersect each reach during the 21 day orbit cycle.</li> <li><strong>river_name:</strong> All river names associated with a node or reach. If there are multiple names for a node or reach they are listed in alphabetical order and separated by a semicolon.</li> <li><strong>edit_flag:</strong> Numerical flag indicating the type of update applied to SWORD nodes or reaches from the previous version. Flag descriptions are listed in the Product Description Documentation included with the file downloads.</li> <li><strong>trib_flag: </strong>Binary flag indicating if a large tributary not represented in SWORD is entering a node or reach. 0 - no tributary, 1 - tributary.</li> </ul> <p><strong>4. References:</strong></p> <p>Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. <em>Science</em>, 361(6402), 585-588.</p> <p>Altenau, E. H., Pavelsky, T. M., Durand, M. T., Yang X., Frasson, R. P. d. M., & Bendezu, L. (2021). The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A global river network for satellite data products". Water Resources Research.</p> <p>Biancamaria, S., Lettenmaier, D. P., & Pavelsky, T. M. (2016). The SWOT mission and its capabilities for land hydrology. In Remote Sensing and Water Resources (pp. 117-147). Springer, Cham.</p> <p>JPL Internal Document (2020b). Surface Water and Ocean Topography Mission Level 2 KaRIn high rate river single pass vector product, JPL D-56413, Rev. A, https://podaac-tools.jpl.nasa.gov/drive/files/misc/web/misc/swot_mission_docs/pdd/D-56413_SWOT_Product_Description_L2_HR_RiverSP_20200825a.pdf</p> <p>Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org.</p> <p>Tessler, Z. D., Vörösmarty, C. J., Grossberg, M., Gladkova, I., Aizenman, H., Syvitski, J. P. M., & Foufoula-Georgiou, E. (2015). Profiling risk and sustainability in coastal deltas of the world. Science, 349(6248), 638-643.</p> <p>Whittemore, A., Ross, M. R., Dolan, W., Langhorst, T., Yang, X., Pawar, S., Jorissen, M., Lawton, E., Januchowski-Hartley, S., & Pavelsky, T. (2020). A Participatory Science Approach to Expanding Instream Infrastructure Inventories. <em>Earth's Future</em>, <em>8</em>(11), e2020EF001558.</p> <p>Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G., & Pavelsky, T. (2019). MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets. Water Resources Research. <a href="https://doi.org/10.1029/2019WR024873">https://doi.org/10.1029/2019WR024873</a>.</p> <p>Yang, X., Pavelsky, T. M., Allen, G. H. (2019). The past and future of global river ice. Nature.</p> <p>SWOT Orbits: https://www.aviso.altimetry.fr/en/missions/future-missions/swot/orbit.html</p> <p>HydroFALLS: <a href="http://wp.geog.mcgill.ca/hydrolab/hydrofalls/">http://wp.geog.mcgill.ca/hydrolab/hydrofalls/</a></p>
Chemobrionics Database
<p>An online live version of this database can be found in the following webpage: https://cpimentelguerra.com/chemobrionics/</p> <p> </p> <p> </p> <p><em>Acknowledgements</em></p> <p>The authors would like to acknowledge the contribution of the European COST Action CA17120 supported by the EUFramework Programme Horizon 2020.</p> <p>Carlos Pimentel has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 101021894 [CARS-CO2]. (02/2022-01/2024)</p>
AusTraits: a curated plant trait database for the Australian flora
<p>AusTraits is a transformative database, containing measurements on the traits of Australia's plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 300 distinct sources, describing > 500 plant traits and > 34,000 taxa.</p> <p>To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project's GitHub repository, <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Further information on the project is available at the project website <a href="https://austraits.org">austraits.org</a> and in the associated publication (see below).</p> <p><strong>CONTRIBUTORS</strong></p> <p>The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University.</p> <p>We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment Land Water and Planning Victoria and the Royal Botanic Gardens Victoria.</p> <p>AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their "Transformative data collections" (https://doi.org/10.47486/TD044) and "Data Partnerships" (https://doi.org/10.47486/DP720, https://doi.org/10.47486/DP720A) programs; and grants from the Australian Research Council (FT160100113, DE170100208, FT100100910) and Macquarie University, The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS).</p> <p><strong>ACCESSING AND USE OF DATA</strong></p> <p>The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community.</p> <p>A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors:</p> <blockquote> <p>Falster, Gallagher et al (2021) <em>AusTraits, a curated plant trait database for the Australian flora</em>. Scientific Data 8: 254, <a href="https://doi.org/10.1038/s41597-021-01006-6">https://doi.org/10.1038/s41597-021-01006-6</a></p> </blockquote> <p>In addition, we encourage users you to cite the original data sources, wherever possible.</p> <p>Note that under the license data may be redistributed, provided the attribution is maintained.</p> <p>The downloads below provide the data in two formats:</p> <ul> <li>austraits-X.X.X.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python.</li> <li>austraits-X.X.X.rds: data as compressed R object. Suitable for users of R (see below).</li> <li> <div>austraits-X.X.X-flattened.rds: contains a flattened version of the dataset for direct loading in R; all data tables are joined into a wider format</div> </li> <li> <div>austraits-X.X.X-flattened.parquet: contains a flattened version of the dataset in parquet format; all data tables are joined into a wider format </div> </li> </ul> <p>For R users, access and manipulation of data is assisted with the <a href="http://github.com/traitecoevo/austraits">austraits R package</a>. The package can both download data and provides examples and functions for running queries.<br><br><strong>STRUCTURE OF AUSTRAITS</strong></p> <p>The compiled AusTraits database contains a series of relational tables and files. These elements include all the data, contextual information submitted with each contributed datasets, database schema, and trait definitions. The file dictionary.html provides the same information in textual format. Similar information is available at <a href="https://traitecoevo.github.io/traits.build-book/">https://traitecoevo.github.io/traits.build-book/</a>.</p> <p><strong>CONTRIBUTING</strong></p> <p>We envision AusTraits as an on-going collaborative community resource that:</p> <ol> <li>Increases our collective understanding the Australian flora;</li> <li>Facilitates accumulation and sharing of trait data;</li> <li>Builds a sense of community among contributors and users; and</li> <li>Aspires to fully transparent and reproducible research of the highest standard.</li> </ol> <p>As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Here are some of the ways you can contribute:</p> <p><strong>Reporting Errors</strong>: If you notice a possible error in AusTraits, please <a href="https://github.com/traitecoevo/austraits.build/issues">post an issue on GitHub</a>.</p> <p><strong>Refining documentation:</strong> We welcome additions and edits that make using the existing data or adding new data easier for the community.</p> <p><strong>Contributing new data</strong>: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p>
Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from various reanalysis datasets
<h1>Dataset Description</h1> <p>Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from several reanalysis datasets. This dataset is the result of an extension of the Jenkinson-Collison circulation type classification to the entire globe, including a modification of its original formulation for the southern hemisphere.</p> <p>A modified version of the IPCC-AR6 Reference Regions that excludes the intertropical range where the method is not applicable is also included, as used in the reference paper for global assessment.</p> <p>Further details in <a href="https://doi.org/10.1007/s00382-022-06658-7" target="_blank" rel="noopener">https://doi.org/10.1007/s00382-022-06658-7 </a></p> <h2>Note for version 1.1.0</h2> <p>This version corrects an issue in the previous release, which was incorrectly labeled as <em>version 0.1</em>. That version was incomplete due to the omission of previously existing files, and should be considered <strong>incomplete</strong>. Version 1.1.0 restores all original files alongside the newly added one, ensuring the dataset is now complete and consistent. We apologize for any inconvenience this may have caused and appreciate your understanding.</p>
Database of measurements for damage detection of T-type timber structural joint by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated joint between two timber beams connected at an angle of 90⁰. Presented data related to seven different states of joints, five load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p>
Database of measurements for damage detection of panel-to-panel moment joints in timber structures by Coaxial Correlation Method
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned in two different ways on either side of the investigated panel-to-panel connection. Presented data related to ten different states of joints, two load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds with frequency range from 10 Hz to 2000 Hz). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>Used materials, methods and results for the case of static load equal to 151.8 kg with sweep-type input signal, and T2 scheme of sensors placement is described in Kurtenoks, V.; Kurajevs, A.; Buka-Vaivade, K.; Serdjuks, D.; Lapkovskis, V.; Mironovs, V.; Podkoritovs, A.; Vilnitis, M. The Quality Assessment of Timber Structural Joints Using the Coaxial Correlation Method. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1929. https://doi.org/10.3390/buildings13081929</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.