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Shipboard Conductivity–Temperature–Depth (CTD) and dissolved oxygen profile data collected during hypoxia surveys along six hydrographic sampling lines within Olympic Coast National Marine Sanctuary, 2004–2015
<p>This data set includes Conductivity-Temperature-Depth (CTD) and dissolved oxygen profile data that were collected along Washington State’s outer coast within Olympic Coast National Marine Sanctuary (OCNMS). Measurements were made along six cross-shelf hydrographic sampling lines during a series of hypoxia survey cruises from 2004 – 2015. The 398 CTD profiles were acquired using Sea-Bird Scientific 19 SeaCAT or 19plus SeaCAT CTD profilers with associated SBE-43 (Sea-Bird Electronics) or Beckman or YSI-type (Yellow Springs Instruments) dissolved oxygen sensors. The data were processed via Sea-Bird Scientific’s SBE Data Processing application using six of the modules in the following order: Data Conversion, Filter, Align CTD, Loop Edit, Derive, and Bin Average. These processing steps and associated methods are the same as those used to process CTD data collected during OCNMS mooring maintenance cruises (<a href="https://www.sciencedirect.com/science/article/pii/S2352340924001422">Risien et al., 2024</a>) and along the Newport Hydrographic Line (<a href="https://www.sciencedirect.com/science/article/pii/S2352340922001342">Risien et al., 2022</a>) located off the central Oregon coast.</p> <table> <tbody> <tr> <td><strong>Station Name </strong></td> <td><strong>Latitude</strong></td> <td><strong>Longitude</strong></td> <td><strong>Water Depth (m, MLLW)</strong></td> </tr> <tr> <td><strong>Cape Alava (CA)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>CA010</td> <td>48.1661oN</td> <td>124.7540oW</td> <td>10</td> </tr> <tr> <td>CA020</td> <td>48.1661oN</td> <td>124.7598oW</td> <td>20</td> </tr> <tr> <td>CA030</td> <td>48.1659oN</td> <td>124.7783oW</td> <td>30</td> </tr> <tr> <td>CA040</td> <td>48.1659oN</td> <td>124.7852oW</td> <td>40</td> </tr> <tr> <td>CA045</td> <td>48.1659oN</td> <td>124.8335oW</td> <td>45</td> </tr> <tr> <td>CA050</td> <td>48.1658oN</td> <td>124.8578oW</td> <td>50</td> </tr> <tr> <td>CA060</td> <td>48.1659oN</td> <td>124.8843oW</td> <td>60</td> </tr> <tr> <td>CA070</td> <td>48.1655oN</td> <td>124.9011oW</td> <td>70</td> </tr> <tr> <td>CA080</td> <td>48.1657oN</td> <td>124.9141oW</td> <td>80</td> </tr> <tr> <td>CA090</td> <td>48.1659oN</td> <td>124.9247oW</td> <td>90</td> </tr> <tr> <td>CA100</td> <td>48.1658oN</td> <td>124.9319oW</td> <td>100</td> </tr> <tr> <td><strong>Teahwhit Head (TH)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>TH030</td> <td>47.8759oN</td> <td>124.6481oW</td> <td>30</td> </tr> <tr> <td>TH035</td> <td>47.8761oN</td> <td>124.7024oW</td> <td>35</td> </tr> <tr> <td>TH040</td> <td>47.8760oN</td> <td>124.7281oW</td> <td>40</td> </tr> <tr> <td>TH050</td> <td>47.8761oN</td> <td>124.7567oW</td> <td>50</td> </tr> <tr> <td>TH060</td> <td>47.8765oN</td> <td>124.7822oW</td> <td>60</td> </tr> <tr> <td>TH070</td> <td>47.8765oN</td> <td>124.8084oW</td> <td>70</td> </tr> <tr> <td>TH080</td> <td>47.8768oN</td> <td>124.8415oW</td> <td>80</td> </tr> <tr> <td>TH090</td> <td>47.8769oN</td> <td>124.8868oW</td> <td>90</td> </tr> <tr> <td>TH100</td> <td>47.8769oN</td> <td>124.9182oW</td> <td>100</td> </tr> <tr> <td><strong>Hoh Head (HH)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>HH025</td> <td>47.7688oN</td> <td>124.5605oW</td> <td>25</td> </tr> <tr> <td>HH042</td> <td>47.7688oN</td> <td>124.6428oW</td> <td>42</td> </tr> <tr> <td>HH065</td> <td>47.7688oN</td> <td>124.7401oW</td> <td>65</td> </tr> <tr> <td><strong>Raft River (RR)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>RR015</td> <td>47.4632oN</td> <td>124.3748oW</td> <td>15</td> </tr> <tr> <td>RR020</td> <td>47.4644oN</td> <td>124.4510oW</td> <td>20</td> </tr> <tr> <td>RR042</td> <td>47.4632oN</td> <td>124.5199oW</td> <td>42</td> </tr> <tr> <td>RR065</td> <td>47.4629oN</td> <td>124.6074oW</td> <td>65</td> </tr> <tr> <td><strong>Cape Elizabeth (CE)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>CE010</td> <td>47.3541oN</td> <td>124.3347oW</td> <td>10</td> </tr> <tr> <td>CE020</td> <td>47.354oN</td> <td>124.3608oW</td> <td>20</td> </tr> <tr> <td>CE030</td> <td>47.3538oN</td> <td>124.3913oW</td> <td>30</td> </tr> <tr> <td>CE040</td> <td>47.3534oN</td> <td>124.4678oW</td> <td>40</td> </tr> <tr> <td>CE050</td> <td>47.3532oN</td> <td>124.5064oW</td> <td>50</td> </tr> <tr> <td>CE060</td> <td>47.3529oN</td> <td>124.5510oW</td> <td>60</td> </tr> <tr> <td>CE070</td> <td>47.3528oN</td> <td>124.5823oW</td> <td>70</td> </tr> <tr> <td>CE080</td> <td>47.3527oN</td> <td>124.6158oW</td> <td>80</td> </tr> <tr> <td>CE090</td> <td>47.3526oN</td> <td>124.6491oW</td> <td>90</td> </tr> <tr> <td>CE100</td> <td>47.3522oN</td> <td>124.6754oW</td> <td>100</td> </tr> <tr> <td><strong>Moclips (MO)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>MO010</td> <td>47.2214oN</td> <td>124.2394oW</td> <td>10</td> </tr> <tr> <td>MO015</td> <td>47.2214oN</td> <td>124.2599oW</td> <td>15</td> </tr> <tr> <td>MO020</td> <td>47.2214oN</td> <td>124.2791oW</td> <td>20</td> </tr> <tr> <td>MO030</td> <td>47.2195oN</td> <td>124.3347oW</td> <td>30</td> </tr> <tr> <td>MO042</td> <td>47.2195oN</td> <td>124.3958oW</td> <td>42</td> </tr> </tbody> </table>
Dataset collected by the s-Nautilus profiler at "La Isleta" yacht club in the Mar Menor
<p>This dataset presents a collection of environmental measurements taken by the s-Nautilus profiler at "La Isleta" yacht club in the Mar Menor. The recorded variables include:</p> <ul> <li><strong>Time (yyyy-mm-dd hh:mm:ss)</strong>: The timestamp indicating the date and time of each measurement.</li> <li><strong>Depth (m)</strong>: The depth at which the measurements were taken.</li> <li><strong>Dissolved Oxygen (DO) </strong>: The concentration of dissolved oxygen, essential for assessing water quality and the health of the marine ecosystem.</li> <li><strong>Electrical Conductivity (EC)</strong>: A parameter indicating the ability of the water to conduct electricity, directly related to salinity and the presence of ions.</li> <li><strong>Temperature (T) (ºC)</strong>: The water temperature, a critical factor influencing many chemical and biological processes.</li> <li><strong>Battery Voltage (Batt3)</strong>: The voltage of the batteries powering the electronics of the profiler, providing insight into its autonomy.</li> </ul> <p>Measurements were taken every 6 hours during the ascent of the profiler.</p>
Statistical analysis and dataset for: Invasive ants fed spinosad collectively recruit to known food faster yet individually abandon food earlier
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.20.599949).</p> <p><em><strong>Abstract</strong></em></p> <p>Current management strategies applied to invasive ants rely on slow-acting insecticides which aim to delay the ant’s ability to detect the poison until its effects are noticeable. Despite this, most control efforts are unsuccessful, likely due to bait abandonment and insufficient sustained consumption. Conditioned taste aversion, a learned avoidance of a particular taste, is a crucial survival mechanism which prevents animals from repeatedly ingesting toxic substances. However, whether ants are capable of this delayed association between food taste and subsequent illness remains largely unexplored. Here, we exposed colonies of the highly invasive Argentine ant, <em>Linepithema humile</em>, to a sublethal dose of the slow-acting insecticide spinosad. We combined measurements of individual-level feeding patterns with quantification of collective preferences and foraging dynamics to investigate the potential effects of the toxicant on behaviour. Collectively, ants preferred an odour associated with a previously experienced food, even if this contained spinosad, over a novel one. However, at the individual-level, previous exposure to spinosad resulted in reduced food consumption, as a consequence of earlier food abandonment. Moreover, while control-treated colonies recruited slower to a food source which tasted like a previously experienced one, spinosad-exposed colonies recruited equally fast to both novel and familiar foods. Although it appears that ants are unable to develop a conditioned taste aversion to sublethal doses of spinosad, ingestion of even small amounts of the toxicant strongly influences foraging behaviour. Understanding the subtle effects of slow-acting pesticides on ant cognition and behaviour can ultimately inspire the development of more efficient control methodologies.</p>
Exemple of a valorization network based on a 10% collection rate from potential biowaste in the Grand Lyon territory (France).
<p>This study, conducted in the frame of the H2020 DECISIVE project, aims at developing a method to design a decentralized and small-scale AD (mAD) network in urban and peri-urban areas. A mixed integer linear program (MILP) was set up based on the proposed system. It aims at minimizing the impacts of the biowaste and the digestate transportation by minimizing their payload-distances while taking into account notably the technical constraints of the newly developed micro-AD. A Geographic Information System (GIS) based methodology was developed to feed the MILP model with very fine-scale data required to optimized a proximity treatment system. The method allows to locate and estimate the biowaste generation and to locate the digestate outlets, the agricultural areas, and to estimate the maximal amount of digestate usable. The candidate sites for mAD are identified with a GIS multi-criteria analysis that includes the environmental regulations, some urban planning rules and the site accessibility and heat outlet valorization. The method developed is successfully applied in the territory of The Grand Lyon Metropole (534 km²), located in France. The MILP model succeeds at providing a solution even with the very large problem studied (≥10<sup>8</sup> possible combination). Different scenarios can be easily tested to meet the potential needs of the stakeholder: the quantity of biowaste to treat, the type of sources to target, the synergy with the current treatment solution, etc.</p> <p>The data are provided through the open, non-proprietary GeoPackage files (GPKG) commonly recognized in GIS tools. A complementary xml file describe the metadata in compliance with the Inspire directive. A complementary pdf file describe the fields of the datasets.</p> <p> </p>
Bicycle trips collected using Cyclists Geo-C geo-game
<p>This is an experimental dataset for the bicycle trips recorded using and geo-game called "Cyclist Geo-C". It contains the geometry of the trips recorded by 60 participants from three European Cities: Münster, Germany; Castelló, Spain; Valletta, Malta. This dataset was collected and analysed for the PhD Thesis "Mobile Services for Green Living" part of the European Joint Doctorate in Geoinformatics and the <a href="http://geo-c.eu/">Geo-C </a>Project. </p> <p>The dataset is composed of three subsets.</p> <ol> <li>There is a point dataset called "<em><strong>trips_od.geojson</strong></em>" which contained the point geometries where each trip started and ended with attributes for latitude, longitude, altitude, and precision coordinates. Each point also had the timestamp which indicates the time when the user started or ended the trip.</li> <li>There is a line dataset called "<em><strong>segments.geojson</strong></em>" which contained the geometries of the straight lines connecting two locations of the participant. Each segment started from an initial point "p<sub>i</sub>" recorded at a "t<sub>i</sub>” and ended at the next point recorded by the user "p<sub>f</sub>” at time “t<sub>f</sub>”. The time difference between "t<sub>i</sub>” and “t<sub>f</sub>” was at most five minutes while the length of the segment was at most one kilometre. Each segment also had the participant and trip identifier, and the segment's sequence number within the trip For each of the trip segments, we calculated the distance and speed using the recorded coordinates and timestamps from "p<sub>i</sub>" and "p<sub>f</sub>" points. <span class="math-tex">\(trip\_segment = f(p_i,p_f)\)</span> and <span class="math-tex">\(segment\_speed = \frac{distance(p_i,p_f)}{\Delta time(p_i,p_f)}\)</span>. Then we classified the segments according to the calculated distance as: “<em>walking segment</em>” when the calculated speed was less than 5 km/h; “<em>cycling segment</em>” when the calculated speed was between 5 and 50 km/h; or “<em>non-cycling segment</em>” when the calculated speed was more than 50 Km/h.</li> <li>There was another line dataset called “<em><strong>trips_tags.geojson </strong></em>” which contained the geometries of each of the trip paths. A trip was a line (also called polyline by GIS users) defined by the ordered sequence of trip segments. It started from origin point "p<sub>i</sub>" of the trip’s first segment and ended at the destination point "p<sub>f</sub>" of the trip's last segment. Each trip also had the participant's identification, trip's identification, the number of segments, start and end times.</li> </ol> <p>In addition to the experimental dataset recorded by participants, our analysis used a secondary dataset to define a comparable framework for the three cities. The secondary dataset consisted of the existing bicycle paths in the cities of Münster and Castelló as well as the planned bicycle paths around Valletta. For the city of Münster, the source of the bicycle paths was the <a href="http://www.openstreetmap.org">OpenStreetMap</a> (we downloaded the line elements with the tags “<em>bicycle=yes</em>” and "<em>cycleway=yes</em>”). For the city of Castelló, we obtained the bicycle paths from the city transport authority, including the city of Valletta, we created a digital version of the national bicycle network plan.</p> <p>We estimated the number of trips "<em><strong>bikepaths_trips.geojson</strong></em>" and the number of segments "<em><strong>bikepaths_segments</strong></em><em><strong>.</strong></em><em><strong>geojson</strong></em>" at each bike path. Also, we provide the areas where participants faced frictions during the experiment which corresponded to low cycling speeds "frictions.geojson".</p> <p>Finally, we provide a visual reference of the dataset in "<em><strong>frictions_cities.pdf</strong></em>".</p>
The Red Queen in the Repository: metadata quality in an ever-changing environment (preprint of paper, presentation slides and dataset collection with validation schemas to IDCC2019 conference paper)
<p>This fileset contains a preprint version of the conference paper (.pdf), presentation slides (as .pptx) and the dataset(s) and validation schema(s) for the IDCC 2019 (Melbourne) conference paper: <em>The Red Queen in the Repository: metadata quality in an ever-changing environment. </em>Datasets and schemas are in .xml, .xsd , Excel (.xlsx) and .csv (two files representing two different sheets in the .xslx -file). The <em>validationSchemas.zip</em> holds the additional validation schemas (.xsd), that were not found in the schemaLocations of the metadata xml-files to be validated. The schemas must all be placed in the same folder, and are to be used for validating the Dataverse <em>dcterms</em> records (with <em>metadataDCT.xsd</em>) and the Zenodo <em>oai_datacite</em> feeds respectively (<em>schema.datacite.org_oai_oai-1.0_oai.xsd</em>). In the latter case, a simpler way of doing it might be to replace the incorrect URL "<em>http://schema.datacite.org/oai/oai-1.0/ oai_datacite.xsd</em>" in the <em>schemaLocation </em>of these xml-files by the CORRECT: <em>schemaLocation="http://schema.datacite.org/oai/oai-1.0/ http://schema.datacite.org/oai/oai-1.0/oai.xsd"</em> as has been done already in the sample files here. The sample file folders <em>testDVNcoll.zip </em>(Dataverse), <em>testFigColl.zip </em>(Figshare)<em> </em>and <em>testZenColl.zip </em>(Zenodo)<em> </em>contain all the metadata files tested and validated that are registered in the spreadsheet with objectIDs.<br> In the case of Zenodo, one original file feed,<br> <em>zen2018oai_datacite3orig-https%20_zenodo.org_oai2d%20verb=ListRecords%26metadata<br> Prefix=oai_datacite%26from=2018-11-29%26until=2018-11-30.xml</em> ,<br> is also supplied to show what was necessary to change in order to perform validation as indicated in the paper.</p> <p>For Dataverse, a corrected version of a file,<br> <em>dvn2014ddi-27595<strong>Corr</strong>_https%20_dataverse.harvard.edu_api_datasets_export%20<br> exporter=ddi%26persistentId=doi%253A10.7910_DVN_27595<strong>Corr</strong>.xml</em> ,<br> is also supplied in order to show the changes it would take to make the file validate without error.</p>
S43 | NEUROTOXINS | Neurotoxicants Collection from Public Resources
<p>This is the collection associated with list S43 NEUROTOXINS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S43</p> <p>NEUROTOXINS</p> <p><strong>Neurotoxicants Collection from Public Resources</strong></p> <p>NEUROTOXINS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/NEUROTOXINS_14022019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/NEUROTOXINS_14022019.csv">CSV</a> (14/02/2019)<br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/neurotoxins">NEUROTOXINS List</a></p> <p>NEUROTOXINS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/NEUROTOXINS_InChIKeys_14022019.txt">InChIKeys</a> (14/02/2019)</p> <p>A list of neurotoxicants compiled from public resources, details on CompTox and Schymanski <em>et al. </em>(submitted). </p>
S44 | STATINS | Statins Collection from Public Resources
<p>This is the collection associated with list S44 STATINS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S44</p> <p>STATINS</p> <p><strong>S</strong><strong>tatins Collection from Public Resources</strong></p> <p>STATINS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/STATINS_14022019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/STATINS_14022019.csv">CSV</a> (14/02/2019)<br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/statins">STATINS List</a></p> <p>STATINS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/STATINS_InChIKeys_14022019.txt">InChIKeys</a> (14/02/2019)</p> <p>A list of statins (lipid-lowering medications) compiled from public resources, details on CompTox. </p>
Biogenic silicate concentration in sea water samples, collected from the CTD in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>Biogenic Silicate (Bsi) concentration (µmol/L) in seawater data. Water samples were collected from CTD rosette deployments, filtered on board and then analysed by flow injection following appropriate digestion.</p> <p>This data supports chemical and biological oceanography studies conducted during the Antarctic Circumnavigation Expedition.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_biogenic_silicate_concentration_in_seawater_ctd.csv, data file, comma-separated values</li> <li>ace_biogenic_silicate_concentration_in_seawater_ctd_visual_summary.png, metadata, portable network graphics</li> <li>data_file_header.txt, metadata, text format</li> <li>README.md, metadata, text format</li> </ul> <p>All missing values where no data point exists from lack of sample, have been set to NaN.</p> <p><strong>Dataset license</strong></p> <p>This biogenic silica concentration dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
RSOI: Sea ice properties collected during the detection of oil on-in-and-under ice experiment
<p>Data collected during the detection of oil on-in-and-under ice oil experiment lead at CRREL in 2014/2015.<br> - Sea ice core properties (salinity and temperature)</p> <p>- Sea ice porosity and permeability field, derived from salinity and temperature</p> <p>- Oil volumes, in the lens derived from underwater acoustic measurement, are included in the RSOI-data-*.xlsx spreadsheet.</p>
Uncorrected inertial navigation dataset collected on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>A HYDRINS Inertial Navigation System (INS) was installed on the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as it circumnavigated Antarctica as part of the Antarctic Circumnavigation Expedition (ACE).</p> <p>This dataset is uncorrected and as-was recorded and covers the period from November 2016 – April 2017. Data files are provided in text format which can be used in many software packages. Heading, attitude, position and speed data are provided.</p> <p>This dataset can be used alongside, for example, mulitbeam survey data and to give high positional accuracy.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACE_place-X_Y.txt, data file, comma-separated values format</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This inertial navigation dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Quality-checked meteorological data from the Southern Ocean collected during the Antarctic Circumnavigation Expedition from December 2016 to April 2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains quality-checked meteorological observations of air temperature, relative humidity, dew point, barometric pressure and observations of downwelling solar radiation and ultraviolet radiation. Further it contains the wind speed and direction relative to the ship but not corrected for air-flow distortion, and translated into the earth reference frame. For each of these variables observations are available from a portside and starboard side sensor. The dataset also contains, cloud base height and sky cover at three levels measured with a Ceilometer.</p> <p>As additional information the solar azimuth and altitude angle have been calculated for the ship’s position every five minutes and have been added as a one-minute time series using the nearest value. The ship’s position, heading, course and speed over ground are also provided.</p> <p>The wind speed measurements were made at a height of approximately 30.5 meters above sea level. The measurement height of the temperature and humidity probes is 23.7 meters above sea level. The barometric pressure was measured at 20 meters above sea level.</p> <p>The observations have been screened for implausible values and on some occasions despiking based on visual inspection and a rolling interquartile range filter have been applied. Solar radiation measurements are affected by shadowing of the ship, and the air temperature and humidity by the heating of air that passes over the ship. Masks are provided to flag affected observations. The wind speed readings are affected by airflow distortion and should be used with consideration until a dataset of corrected wind speeds is published. More details on airflow distortion can be requested from the contact person.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACE_filtered_meteorological_data_1min.csv, data file, comma-separated values</li> <li>diff_TA1_TA3_WDR2_5min_1.png, metadata, portable network graphics</li> <li>ratio_SR1_SR3_solangle_5min_1.png, metadata, portable network graphics</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>ace_filtered_meteorological_data_change_log.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - The range check for skycover (SC) and cloudlevel (CL) was added to the quality-checking routines. 53 data points violated the range check for these variables: these have now been marked as NaN.</p> <p><strong>v1.0</strong> - Initial release of verified meteorological data.</p> <p><strong>Dataset license</strong></p> <p>This meteorological dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/</p>
Medley-solos-DB: a cross-collection dataset for musical instrument recognition
<p>Medley-solos-DB<br> =============<br> Version 1.2 March 2019.<br> </p> <p> </p> <p>Created By<br> --------------</p> <p>Vincent Lostanlen (1), Carmine-Emanuele Cella (2), Rachel Bittner (3), Slim Essid (4).<br> <br> (1): New York University<br> (2): UC Berkeley<br> (3): Spotify, Inc.<br> (4): Télécom ParisTech</p> <p> </p> <p><br> Description<br> ---------------</p> <p> </p> <p>Medley-solos-DB is a cross-collection dataset for automatic musical instrument recognition in solo recordings. It consists of a training set of 3-second audio clips, which are extracted from the MedleyDB dataset of Bittner et al. (ISMIR 2014) as well as a test set set of 3-second clips, which are extracted from the solosDB dataset of Essid et al. (IEEE TASLP 2009). Each of these clips contains a single instrument among a taxonomy of eight: clarinet, distorted electric guitar, female singer, flute, piano, tenor saxophone, trumpet, and violin.</p> <p>The Medley-solos-DB dataset is the dataset that is used in the benchmarks of musical instrument recognition in the publications of Lostanlen and Cella (ISMIR 2016) and Andén et al. (IEEE TSP 2019).</p> <p> </p> <p>[1] V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016.</p> <p>[2] J. Andén, V. Lostanlen, and S. Mallat. Joint time-frequency scattering. IEEE Transactions in Signal Processing, vol. 67, no. 14, pp. 3704-3718, 2019. doi: 10.1109/TSP.2019.2918992</p> <p> </p> <p><br> Data Files<br> --------------</p> <p>The Medley-solos-DB contains 21571 audio clips as WAV files, sampled at 44.1 kHz, with a single channel (mono), at a bit depth of 32. Every audio clip has a fixed duration of 2972 milliseconds, that is, 65536 discrete-time samples.</p> <p>Every audio file has a name of the form:</p> <p>Medley-solos-DB_SUBSET-INSTRUMENTID_UUID.wav</p> <p> </p> <p>For example:</p> <p>Medley-solos-DB_test-0_0a282672-c22c-59ff-faaa-ff9eb73fc8e6.wav</p> <p>corresponds to the snippet whose universally unique identifier (UUID) is 0a282672-c22c-59ff-faaa-ff9eb73fc8e6, contains clarinet sounds (clarinet has instrument id equal to 0), and belongs to the test set.</p> <p> </p> <p><br> Metadata Files<br> -------------------</p> <p>The Medley-solos-DB_metadata is a CSV file containing 21572 rows (one for each audio clip) and five columns:</p> <p>1. subset: either "training", "validation", or "test"</p> <p>2. instrument: tag in Medley-DB taxonomy, such as "clarinet", "distorted electric guitar", etc.</p> <p>3. instrument id: integer from 0 to 7. There is a one-to-one between "instrument" (string format) and "instrument id" (integer). We provide both for convenience.</p> <p>4. song id: integer from 0 to 226. The track and artist names are anonymized.</p> <p>5. UUID4: universally unique identifier. Assigned and random, and different for every row.</p> <p> </p> <p>The list of instrument classes is:</p> <p>0. clarinet</p> <p>1. distorted electric guitar</p> <p>2. female singer</p> <p>3. flute</p> <p>4. piano</p> <p>5. tenor saxophone</p> <p>6. trumpet</p> <p>7. violin</p> <p> </p> <p><br> Please acknowledge Medley-solos-DB in academic research<br> ---------------------------------------------------------------------------------</p> <p>When Medley-solos-DB is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016.</p> <p>The creation of this dataset was supported by ERC InvariantClass grant 320959.</p> <p> </p> <p><br> Conditions of Use<br> ------------------------</p> <p>Dataset created by Vincent Lostanlen, Rachel Bittner, and Slim Essid, as a derivative work of Medley-DB and solos-Db.</p> <p>The Medley-solos-DB dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, the authors are not liable for, and expressly exclude all liability for, loss or damage however and whenever caused to anyone by any use of the Medley-solos-DB dataset or any part of it.</p> <p> </p> <p><br> Feedback<br> -------------</p> <p>Please help us improve Medley-solos-DB by sending your feedback to:<br> vincent.lostanlen@nyu.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p> </p> <p>Acknowledgement<br> -------------------------<br> We thank all artists, recording engineers, curators, and annotators of both MedleyDB and solosDb.</p>
Raw multibeam bathymetry data collected around the Candlemas Islands, part of the South Sandwich Islands chain in the Southern Ocean on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around the Candlemas Islands, part of the South Sandwich Islands chain in the Southern Ocean in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>lineYYYYDDmonHHMMSS.ssv, data file, ASCII</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Raw multibeam bathymetry data collected around Siple Island in Marie Byrd Land, Antarctica on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected Siple Island in Marie Byrd Land, Antarctica in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>lineYYYYDDmonHHMMSS.ssv, data file, ASCII</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Raw multibeam bathymetry data collected around Scott Island in the Southern Ocean on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around Scott Island in the Southern Ocean in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p> <p> </p>
Raw multibeam bathymetry data collected around the sub-Antarctic Prince Edward Islands on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around the sub-Antarctic Prince Edward Islands in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>*.txt, ancillary file, ASCII</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Raw multibeam bathymetry data collected in the wider polynya area around the Mertz glacier, East Antarctica on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected in the wider polynya area around the Mertz glacier, East Antarctica in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/l</p>
Raw multibeam bathymetry data collected around the Mertz glacier, East Antarcica on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around the Mertz glacier, East Antarcica in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Raw multibeam bathymetry data collected around the Balleny Islands, Antarctica on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around the Balleny Islands, Antarctica in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset is made available the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
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