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Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference grade performances - dataset
<p>This repository contains the data used for the analysis of the paper "Characterisation and calibration of PM sensors at high temporal resolution to reference grade performances" submitted to Heliyon and available as a pre-print:</p> <p>Bulot, Florentin M. J. and Ossont, Steven J. and Morris, Andrew and Basford, Philip J. and Easton, Natasha H. C. and Mitchell, Hazel L. and Foster, Gavin L. and Cox, Simon J. and Loxham, Matthew, Characterisation and Calibration of Low-Cost Pm Sensors at High Temporal Resolution to Reference-Grade Performance. Available at SSRN: <a href="https://ssrn.com/abstract=4360707">https://ssrn.com/abstract=4360707</a> or <a href="http://dx.doi.org/10.2139/ssrn.4360707">http://dx.doi.org/10.2139/ssrn.4360707</a></p> <p> </p> <p>The code used to conduct the data analysis is available at <a href="https://doi.org/10.5281/zenodo.7261417">https://doi.org/10.5281/zenodo.7261417</a></p> <p> </p> <p>.</p> <p> </p> <p>The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used<br> during this study, please refer to the methods section of the paper.</p> <p>Description of the files.</p> <p>202007_to_202107_nocs - contains the data from the low-cost sensors</p> <p>It contains the following headers:<br> - "sensor" - sensor id<br> - "site" - name of the air quality monitor hosting the sensor<br> - "median_PM1" - PM1 mass concentration (ug/m3)<br> - "median_PM10" - PM10 mass concentration (ug/m3)<br> - "median_PM25" - PM25 mass concentration (ug/m3)<br> - "median_PM4" - PM4 mass concentration (ug/m3) (only available for SPS30)<br> - "median_n05" - particle number concentration (SPS30) of particles between 0.3um and 0.5um<br> - "median_n1" - particle number concentration (SPS30) of particles between 0.3um and 1um<br> - "median_n10" - particle number concentration (SPS30) of particles between 0.3um and 10um<br> - "median_n25" - particle number concentration (SPS30) of particles between 0.3um and 2.5um<br> - "median_n4" - particle number concentration (SPS30) of particles between 0.3um and 4um<br> - "median_gr03um" - particle number concentration (PMS5003) of particles >0.3um<br> - "median_gr05um" - particle number concentration (PMS5003) of particles >0.5um<br> - "median_gr100um" - particle number concentration (PMS5003) of particles >10um<br> - "median_gr10um" - particle number concentration (PMS5003) of particles >1um<br> - "median_gr25um" - particle number concentration (PMS5003) of particles >2.5um<br> - "median_gr50um" - particle number concentration (PMS5003) of particles >5um<br> - "median_pm100_cf1" - PM10 mass concentration with cf1 calibration for PMS5003<br> - "median_pm10_cf1" - PM1 mass concentration with cf1 calibration for PMS5003<br> - "median_pm25_cf1" - PM25 mass concentration with cf1 calibration for PMS5003<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_pm_2min - contains the PM mass concentration data from the Fidas 200S.</p> <p>It contains the following headers:<br> - "PM2.5" - PM2.5 mass concentration (ug/m3) Fidas 200S<br> - "PM10" - PM10 mass concentration (ug/m3) Fidas 200S<br> - "PMtot" - PM total mass concentration (ug/m3) Fidas 200S<br> - "PM1" - PM1 mass concentration (ug/m3) Fidas 200S<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_weather_2min - contains the weather data from the Fidas 200S</p> <p>It contains the following headers:<br> - "rh" - relative humidity (%)<br> - "dew_point_temperature" - dew point temperature (Celsius)<br> - "air_pressure" - Air pressure (hPa)<br> - "temperature" - temperature (Celsius)<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"</p>
Reference Data Set: Electricity, Heat, and Gas Sector Data for Modeling the German System
<p>This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector).</p> <p>While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references).</p> <p>The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D.</p>
Reference list related to Carl Paul Caspari
<p>This is a reference list of content related to Carl Paul Caspari. It contains references to articles and books written by and about Caspari. This was collected by researcher Oskar Skarsaune about his work with a biography about Caspari with the title «En Lærd af Guds Naade»: Carl Paul Caspari 1814–1892: En påbegynt biografi som bidrag til norsk teologi- og lærdomshistorie Oskar Skarsaune Oslo 1989/.<br><br>It includes bibliographical tabular data in txt format (text data). </p>
Reference site conditions for floating wind arrays: dataset of reference sites
<h1>IEA Task 49: Integrated Design of Floating Wind Arrays</h1> <h3>This dataset contains atmospheric and oceanographic data of 11 locations around the globe of future floating offshore wind farms.<br>Each dataset was used to perform a metocean analysis for preliminary design, published in the Work Package 1 Report of IEA Task 49.</h3> <div> <div> <div><a name="_msocom_1"></a></div> </div> </div> <table> <tbody> <tr> <td><strong>4COffshore ID</strong></td> <td><strong>Name</strong></td> <td><strong>Latitude [deg]</strong></td> <td><strong>Longitude [deg]</strong></td> <td><strong>Water Depth [m] (GEBCO)</strong></td> <td><strong>Distance from shore [m]</strong></td> <td><strong>Country</strong></td> <td><strong>Dataset curated by</strong></td> </tr> <tr> <td>IT95 </td> <td>Hannibal </td> <td> <div>37.842</div> </td> <td> <div>12.0722</div> </td> <td> <div> -353</div> </td> <td> <div> 35</div> </td> <td>Italy </td> <td>RSE</td> </tr> <tr> <td>US0W </td> <td> Humboldt</td> <td> <div> 40.928</div> </td> <td> <div>-124.708</div> </td> <td> <div> -707</div> </td> <td> <div> 43.8</div> </td> <td>USA</td> <td>NREL</td> </tr> <tr> <td>KR0R </td> <td>Ulsan </td> <td> <div> 35.449</div> </td> <td> <div> 129.949</div> </td> <td> <div> -188</div> </td> <td> <div> 32</div> </td> <td>South Korea </td> <td>UOU</td> </tr> <tr> <td>IE34 </td> <td>Moneypoint One</td> <td> <div> 52.519</div> </td> <td> <div>-10.276</div> </td> <td> <div> -1<a>02</a> </div> </td> <td> <div> 23.4</div> </td> <td>Ireland </td> <td>GDG</td> </tr> <tr> <td>UK6L </td> <td>Havbredey </td> <td> <div>58.862</div> </td> <td> <div> <div> <p>-5.54</p> </div> </div> </td> <td> <div> <div> <p> -91</p> </div> </div> </td> <td> <div> <div> <p> 41.6</p> </div> </div> </td> <td>Scotland </td> <td>DHI</td> </tr> <tr> <td>JP06 </td> <td>Fukushima</td> <td> <div>37.311</div> </td> <td> <div>141.251</div> </td> <td> <div> -90</div> </td> <td> <div> 19.4</div> </td> <td>Japan </td> <td>AIT</td> </tr> <tr> <td>NO44 </td> <td>Utsira nord<strong>*</strong></td> <td> <div>59.276</div> </td> <td> <div>4.541</div> </td> <td> <div> -273</div> </td> <td> <div> 42.4</div> </td> <td>Norway </td> <td>4subsea / UiS,UiB*</td> </tr> <tr> <td>USZ3 </td> <td>Gulf of Maine</td> <td> <div>43.25</div> </td> <td> <div>-69.5</div> </td> <td> <div> -148</div> </td> <td> <div> 138</div> </td> <td>USA </td> <td>NREL</td> </tr> <tr> <td>KR88 </td> <td>Geomundo<strong>**</strong></td> <td> <div>34.039</div> </td> <td> <div>126.901</div> </td> <td> <div> -70</div> </td> <td> <div> 47</div> </td> <td>South Korea </td> <td>IAE**</td> </tr> <tr> <td>FR87 </td> <td>Sud de la Bretagne II</td> <td> <div>47.325</div> </td> <td> <div>-3.659</div> </td> <td> <div> -94</div> </td> <td> <div> 30.7</div> </td> <td>France </td> <td>UiB</td> </tr> <tr> <td>NO66</td> <td>Sørlige Nordsjø II<strong>***</strong></td> <td> <div> 56.78 </div> </td> <td> <div> 4.92 </div> </td> <td> <div> -60</div> </td> <td> <div> 180</div> </td> <td>Norway</td> <td>4subsea / UiS,UiB***</td> </tr> </tbody> </table> <p>* Suplementary dataset published at: https://doi.org/10.5281/zenodo.10048048</p> <p>** Dataset is confidential, for details of usage reach out to the contact person.</p> <p>*** Suplementary dataset published at: https://doi.org/10.5281/zenodo.7057407</p>
Curated reference files for GCAP (WES)
<p>Provides big reference files or extra datasets/models for (in) GCAP project. </p> <p>The reference files are adapted from https://github.com/Wedge-lab/battenberg, more specifically, https://ora.ox.ac.uk/objects/uuid:08e24957-7e76-438a-bd38-66c48008cf52.</p> <p>News:</p> <ul> <li>Removed the correction files marked with 'update', which does not work for the latest version of ASCAT v3.</li> </ul>
Development of ferret immune repertoire reference resources and single-cell-based high- throughput profiling assays
<p>We performed long read transcriptome sequencing of ferret splenocyte and lymph node samples full-length, non-chimeric circular consensus sequencing (CCS) reads to obtain over 120,000 high-quality immunoglobin (Ig) and T cell receptor (TCR) transcripts.</p>
Dataset for the comparison of performance of two leak detectors using hydrogen reference leaks (supplement to paper "Advancing Hydrogen Leak Detection: Design and Calibration of Reference Leaks")
<p>Excel file containing some measurements made in December 2023, using three hydrogen reference leaks, to assess the performance of two distinct leak detectors, one portable and made specifically for hydrogen and one MSLD in hydrogen-mode.</p>
Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards
<h1>Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards</h1> <p>In this community effort, we compared measurements between 34 laboratories from 19 countries, utilizing mixtures of labelled authentic synthetic standards, to quantify by mass spectrometry four clinically used ceramide species in the NIST (National Institute of Standards and Technology) human blood plasma Standard Reference Material (SRM) 1950, as well as new suite of candidate plasma reference materials (RM 8231). Participants either utilized a provided validated method (SOP) and/or their method of choice (OTHER). Mean concentration values, and intra- and inter-laboratory coefficients of variation (CV) were calculated using single-point and multi-point calibrations, respectively.</p> <p>The attached file "ILS-Ceramide-Ring-Trial-Datasets.csv" and the table below map the lab number (LabNum) used in the manuscript in all plots to the originally assigned submission Id (LabId) used in <a href="https://github.com/lifs-tools/ils-ceramide-ring-trial/tree/main/data/original-reports" target="_blank" rel="noopener">the anonymized reports </a>containing the peak areas submitted by each lab for their SOP (Standard) and / or OTHER (Preferred) workflow. The table below provides further information on the separation used (LC), the mass analyzer type (QQQ=Triple Quads and Traps, Orbitrap, TOF) and the associated mass analyzer resolution (LowRes, HighRes), and links each LabNum to the corresponding dataset name and Zenodo DOI, if available. In order to retain the anonymity of all participating labs w.r.t. the submitted datasets, only the converted mzML files are provided in the linked submissions. Please note that labs were free to choose whether they wanted to disclose their MS data or not. Thus, missing datasets indicate that the corresponding lab did not provide their raw / mzML data. </p> <p>All reports together with the code for analysis and visualization, reproducing the figures in the manuscript, are available under the following doi: <a href="../doi/10.5281/zenodo.10081970" target="_blank" rel="noopener">https://zenodo.org/doi/10.5281/zenodo.10081970</a>. This links to releases of the following GitHub repository: <a href="https://github.com/lifs-tools/ils-ceramide-ring-trial">https://github.com/lifs-tools/ils-ceramide-ring-trial</a>. </p> <h2>Ring Trial mzML Datasets</h2> <table> <tbody> <tr> <th>LabNum</th> <th>LabId</th> <th>Protocol</th> <th>LC</th> <th>MassAnalyzerType</th> <th>MassAnalyzerResolution</th> <th>DatasetName</th> <th>DOI</th> </tr> <tr> <td>1</td> <td>02b</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_01_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134264" target="_blank" rel="noopener">10.5281/zenodo.13134264</a></td> </tr> <tr> <td>2</td> <td>3</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_02_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13145059" target="_blank" rel="noopener">10.5281/zenodo.13145059</a></td> </tr> <tr> <td>3</td> <td>4</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_03_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134437" target="_blank" rel="noopener">10.5281/zenodo.13134437</a></td> </tr> <tr> <td>4</td> <td>5</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_04_OTHER</td> <td> </td> </tr> <tr> <td>5</td> <td>7</td> <td>OTHER</td> <td>RP</td> <td>Orbitrap</td> <td>HighRes</td> <td>Lab_05_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134439" target="_blank" rel="noopener">10.5281/zenodo.13134439</a></td> </tr> <tr> <td>6</td> <td>9</td> <td>OTHER</td> <td>FIA</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_06_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134441" target="_blank" rel="noopener">10.5281/zenodo.13134441</a></td> </tr> <tr> <td>7</td> <td>10a</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_07_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134445" target="_blank" rel="noopener">10.5281/zenodo.13134445</a></td> </tr> <tr> <td>7</td> <td>10b</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_07_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134443" target="_blank" rel="noopener">10.5281/zenodo.13134443</a></td> </tr> <tr> <td>8</td> <td>12</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_08_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134447" target="_blank" rel="noopener">10.5281/zenodo.13134447</a></td> </tr> <tr> <td>9</td> <td>13</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_09_OTHER</td> <td> </td> </tr> <tr> <td>10</td> <td>14</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_10_SOP</td> <td> </td> </tr> <tr> <td>11</td> <td>15</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_11_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134449" target="_blank" rel="noopener">10.5281/zenodo.13134449</a></td> </tr> <tr> <td>12</td> <td>16</td> <td>OTHER</td> <td>RP</td> <td>Orbitrap</td> <td>HighRes</td> <td>Lab_12_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134451" target="_blank" rel="noopener">10.5281/zenodo.13134451</a></td> </tr> <tr> <td>13</td> <td>17a</td> <td>OTHER</td> <td>RP</td> <td>TOF</td> <td>HighRes</td> <td>Lab_13_OTHER</td> <td> </td> </tr> <tr> <td>14</td> <td>18a</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_14_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135617" target="_blank" rel="noopener">10.5281/zenodo.13135617</a></td> </tr> <tr> <td>14</td> <td>18b</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_14_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135617" target="_blank" rel="noopener">10.5281/zenodo.13135617</a></td> </tr> <tr> <td>15</td> <td>19</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_15_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135625" target="_blank" rel="noopener">10.5281/zenodo.13135625</a></td> </tr> <tr> <td>16</td> <td>20a</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_16_SOP</td> <td> </td> </tr> <tr> <td>16</td> <td>20b</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_16_OTHER</td> <td> </td> </tr> <tr> <td>17</td> <td>21</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_17_SOP</td> <td> </td> </tr> <tr> <td>18</td> <td>22</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_18_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135627" target="_blank" rel="noopener">10.5281/zenodo.13135627</a></td> </tr> <tr> <td>19</td> <td>23</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_19_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135629" target="_blank" rel="noopener">10.5281/zenodo.13135629</a></td> </tr> <tr> <td>20</td> <td>24</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_20_SOP</td> <td> </td> </tr> <tr> <td>21</td> <td>25</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_21_SOP</td> <td><a href="../doi/10.5281/zenodo.13244007" target="_blank" rel="noopener">10.5281/zenodo.13244007</a></td> </tr> <tr> <td>22</td> <td>26a</td> <td>OTHER</td> <td>FIA</td> <td>Orbitrap</td> <td>HighRes</td> <td>Lab_22_OTHER</td> <td> </td> </tr> <tr> <td>22</td> <td>26b</td> <td>OTHER</td> <td>FIA</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_22_OTHER</td> <td> </td> </tr> <tr> <td>23</td> <td>27</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_23_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135632" target="_blank" rel="noopener">10.5281/zenodo.13135632</a></td> </tr> <tr> <td>24</td> <td>28</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_24_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135634" target="_blank" rel="noopener">10.5281/zenodo.13135634</a></td> </tr> <tr> <td>25</td> <td>29a</td> <td>SOP</td> <td>RP</td> <td>TOF</td> <td>HighRes</td> <td>Lab_25_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13136167" target="_blank" rel="noopener">10.5281/zenodo.13136167</a></td> </tr> <tr> <td>25</td> <td>29b</td> <td>OTHER</td> <td>SFC</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_25_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13135638" target="_blank" rel="noopener">10.5281/zenodo.13135638</a></td> </tr> <tr> <td>26</td> <td>30</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_26_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13136171" target="_blank" rel="noopener">10.5281/zenodo.13136171</a></td> </tr> <tr> <td>27</td> <td>31</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_27_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13136176" target="_blank" rel="noopener">10.5281/zenodo.13136176</a></td> </tr> <tr> <td>28</td> <td>32</td> <td>OTHER</td> <td>RP</td> <td>TOF</td> <td>HighRes</td> <td>Lab_28_OTHER</td> <td> </td> </tr> <tr> <td>29</td> <td>33</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_29_OTHER</td> <td> </td> </tr> <tr> <td>30</td> <td>34</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_30_SOP</td> <td> </td> </tr> <tr> <td>31</td> <td>35</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_31_OTHER</td> <td> </td> </tr> <tr> <td>32</td> <td>36</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_32_SOP</td> <td><a href="../doi/10.5281/zenodo.13166454" target="_blank" rel="noopener">10.5281/zenodo.13166454</a></td> </tr> <tr> <td>33</td> <td>37</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_33_OTHER</td> <td><a href="../doi/10.5281/zenodo.13732469" target="_blank" rel="noopener">10.5281/zenodo.13732469</a></td> </tr> <tr> <td>34</td> <td>38</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_34_SOP</td> <td><a href="../doi/10.5281/zenodo.13324807" target="_blank" rel="noopener">10.5281/zenodo.13324807</a></td> </tr> </tbody> </table>
Deep Reference Mining from Scholarly Literature in the Arts and Humanities - Pre-trained word embeddings
<p>Pre-trained word vectors of dimensionality 100 and 300 for the publication: Deep Reference Mining from Scholarly Literature in the Arts and Humanities, submitted to Frontiers in Digital Humanities.</p> <p>The corpus of scholarly publications from which these vectors were trained is under copyright, therefore we publish these vectors for reproducibility. Please refer to the publication's repository for further details: <a href="https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing">https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing</a>.</p> <p>These vectors were trained using Gensim 3.1.0. The corpus was preprocessed as follows:</p> <ol> <li>word tokenization with NLTK word_punct tokenizer.</li> <li>digits were converted into the $NUM$ token</li> <li>words less frequent than 5 times, for every document, were converted to the $UNK$ token</li> <li>vectors were trained using the function: Word2Vec(window=5, min_count=5, sg=1)</li> </ol>
InTeReC: In-text Reference Corpus - Single References Dataset
<p>This dataset contains a set of sentences extracted from articles published by the Public Library of Science (PLOS) up to September 2013. Information is given on the position of the sentences relative to the article and the section in which they appear, the section type with respect to the four main types of the IMRaD structure, as well as verb phrases that occur in the sentence. Each sentence contains one single in-text reference.</p> <p>The dataset is in the CSV format. Size: 314023 sentences.</p> <p>Column list:</p> <ul> <li><em>journal</em>: journal title</li> <li><em>doi</em>: DOI of the article from which the sentence was extracted</li> <li><em>article-length</em>: size of the article, as number of sentences</li> <li><em>article-pos</em>: position of the sentence in the article, as number of sentences from the beginning of the article</li> <li><em>section-length</em>: size of the section, as number of sentences</li> <li><em>section-pos</em>: position of the sentence in the section, as number of sentences from the beginning of the section</li> <li><em>section-type</em>: section type (see below)</li> <li><em>sentence-text</em>: full text of the sentence</li> <li><em>verb-phrases</em>: a list of verb phrases that occur in the sentence, comma separated</li> </ul> <p>Possible section types are:</p> <ul> <li>I: Introduction</li> <li>M: Methods</li> <li>R: Results</li> <li>D: Discussion</li> <li>MR: Methods and Results</li> <li>RD: Results and Discussion</li> </ul> <p> </p> <p>Full description of the construction of the dataset is published in:</p> <p>Marc Bertin and Iana Atanassova (2018) InTeReC : an In-text Reference corpus for applying Natural Language Processing to Bibliometrics. Bibliometric-enhanced Information Retrieval: 7th International BIR workshop (7th BIR workshop) at the 40th European Conference on Information Retrieval (ECIR).</p>
Reference Workloads for Traffic Generation
<p>Collection of traffic generator profiles, implemented for the needs of Superfluidity 5G project.</p> <p>Used in the context of the performance characterization (WP4) and validation (WP7) activities.</p>
SCOPE Climate: Penman-Monteith reference evapotranspiration
<p>SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset) is a 25-member ensemble of 142-year high-resolution reconstructions of precipitation, temperature and Penmann-Monteith reference evapotranspiration over France, from 1 January 1871 to 29 December 2012. SCOPE Climate results from the statical downscaling of the global extended reanalysis 20CR V2 with the SCOPE method (Caillouet et al., 2016, 2017). SCOPE Climate provides an ensemble of 25 equally-plausible spatially-coherent gridded multivariate time series. Data are available at a daily time step on a 8 km grid over France as 25 files in NetCDF format. Reconstructed values cover grid cells located only within metropolitan France national borders (including Corsica). The SCOPE Climate dataset is fully described by Caillouet et al. (2019).</p> <p>This dataset provides reconstructions of Penman-Monteith reference evapotranspiration as a 25-member ensemble of gridded time series. Reconstructed evapotranspiration values from member 1 should be used with reconstructed precipitation/temperature values from member 1 (and so on). The corresponding precipitation dataset can be found at http://dx.doi.org/10.5281/zenodo.1299760, and the temperature dataset at http://dx.doi.org/10.5281/zenodo.1299712.</p>
Preliminary data collected by 2 prototype Surface Velocity Platform drifters with Barometer and Reference Sensor for Temperature (SVP-BRST)
<p>The SVP-BRST drifter was developed to serve calibration and validation of Sentinel satellite SST retrievals. Two prototypes were deployed in the Mediterranean Sea end of April 2018. Preliminary data collected then until 11 June 2018 are published in this dataset. The drifters were developed and deployed under funding from the European Union's Copernicus Programme. The data are transmitted from the buoy to shore using data format #091 (see References).</p>
Exported Definitions, References, Document Structure from EU Legislations
<p>A sample corpus of definitions, references, and document structures extracted from the EUR-LEX corpus of EU Legislations.</p>
Updates of listings of published versions and references relating to Rubáiyát of Omar Khayyám
<p>These files provide updates of listings of published versions of the <em>Rubáiyát </em>of Omar Khayyám and references relating to <em>Rubáiyát</em>. They form part of an archive of research data relating to the spread and influence of the <em>Rubáiyát</em> of Omar Khayyám. The data have been compiled by independent researchers W H (Bill) Martin and Sandra Mason; their contact details are in the README file. The files comprise a number of searchable listings relating to the poem and its different manifestations. </p> <p>This section of the archive contains two database tables updating earlier material to September 2018<em>.</em> The first of these, ROKlist2018, provides a listing of over 1750 editions or reprints of the poem, in different languages and translations, which we know either to exist or to have been identified by other compilers of <em>Rubáiyát </em>bibliographies. The second database ROKref2018 lists more than 500 books, articles and other material relating to the <em>Rubáiyát of Omar Khayyám</em>, which we have identified in the course of our research. Further details of sources, and of the fields and codings used in the databases, are given in the accompanying README document.</p>
References relating to Rubáiyát of Omar Khayyám
<p>These files cover references relating to Rubáiyát of Omar Khayyám. They form part of an archive of research data relating to the spread and influence of the <em>Rubáiyát</em> of Omar Khayyám. The data have been compiled by independent researchers W H (Bill) Martin and Sandra Mason; their contact details are in the README file. The files comprise a number of searchable listings relating to the poem and its different manifestations. </p> <p>This section of the archive contains the database ROKref2011. It lists some 400 books, articles and other material relating to the <em>Rubáiyát of Omar Khayyám</em>, which we have identified in the course of our research. Further details of the types of material covered, of the sources and of the fields and coding used in the database are given in the accompanying README document.</p>
High quality white matter reference tracts
<p><strong>Overview</strong></p> <p>This dataset contains segmentations of 72 white matter tracts obtained from 105 subjects included in the Human Connectome Project (HCP) young adult dataset (https://www.humanconnectome.org/study/hcp-young-adult). The folder names correspond to the ID of the HCP subjects. This dataset only contains the tracts. It does not contain the original DWI data. This has to be downloaded from the HCP website (it is free, but you have to register to get access).</p> <p>The data is part of the following publication: <a href="https://doi.org/10.1016/j.neuroimage.2018.07.070">Wasserthal et al., TractSeg - Fast and accurate white matter bundle segmentation. NeuroImage (2018)</a>. If you use the data please cite the paper.</p> <p> </p> <p><strong>Details for generating corresponding whole brain tractograms</strong></p> <p>The tracts were extracted semi-automatically from whole-brain tractograms. For a detailed description of the tract segmentation process please refer to the paper. The following MRtrix (http://www.mrtrix.org/) commands were used to obtain the whole-brain tractograms:</p> <p>5ttgen fsl T1w_acpc_dc_restore_brain.nii.gz 5TT.mif -premasked<br> dwi2response msmt_5tt Diffusion.nii.gz 5TT.mif RF_WM.txt RF_GM.txt RF_CSF.txt -voxels RF_voxels.mif -fslgrad Diffusion.bvecs Diffusion.bvals<br> dwi2fod msmt_csd Diffusion.nii.gz RF_WM.txt WM_FODs.mif RF_GM.txt GM.mif RF_CSF.txt CSF.mif -mask nodif_brain_mask.nii.gz -fslgrad Diffusion.bvecs Diffusion.bvals<br> tckgen -algorithm iFOD2 WM_FODs.mif output.tck -act 5TT.mif -backtrack -crop_at_gmwmi -seed_image nodif_brain_mask.nii.gz -maxlength 250 -minlength 40 -number 10M -cutoff 0.06 -maxnum 0</p> <p>For "CA", "IFO_left", "IFO_right", "UF_left", "UF_right" we used tracking without anatomical constraints:</p> <p>tckgen -algorithm iFOD2 WM_FODs.mif output.tck -seed_image nodif_brain_mask.nii.gz -maxlength 250 -minlength 40 -number 10M -cutoff 0.06 -maxnum 0</p> <p>Due to their enormous size, the whole brain tractograms corresponding to the segmented tracts are not included this dataset. Please contact the author of the paper if you are interested in these tractograms.</p> <p> </p> <p><strong>Included tracts</strong></p> <p>1: AF_left (Arcuate fascicle)<br> 2: AF_right<br> 3: ATR_left (Anterior Thalamic Radiation)<br> 4: ATR_right<br> 5: CA (Commissure Anterior)<br> 6: CC_1 (Rostrum)<br> 7: CC_2 (Genu)<br> 8: CC_3 (Rostral body (Premotor))<br> 9: CC_4 (Anterior midbody (Primary Motor))<br> 10: CC_5 (Posterior midbody (Primary Somatosensory))<br> 11: CC_6 (Isthmus)<br> 12: CC_7 (Splenium)<br> 13: CG_left (Cingulum left)<br> 14: CG_right <br> 15: CST_left (Corticospinal tract<br> 16: CST_right <br> 17: MLF_left (Middle longitudinal fascicle)<br> 18: MLF_right<br> 19: FPT_left (Fronto-pontine tract)<br> 20: FPT_right <br> 21: FX_left (Fornix)<br> 22: FX_right<br> 23: ICP_left (Inferior cerebellar peduncle)<br> 24: ICP_right <br> 25: IFO_left (Inferior occipito-frontal fascicle) <br> 26: IFO_right<br> 27: ILF_left (Inferior longitudinal fascicle) <br> 28: ILF_right <br> 29: MCP (Middle cerebellar peduncle)<br> 30: OR_left (Optic radiation) <br> 31: OR_right<br> 32: POPT_left (Parieto‐occipital pontine)<br> 33: POPT_right <br> 34: SCP_left (Superior cerebellar peduncle)<br> 35: SCP_right <br> 36: SLF_I_left (Superior longitudinal fascicle I)<br> 37: SLF_I_right <br> 38: SLF_II_left (Superior longitudinal fascicle II)<br> 39: SLF_II_right<br> 40: SLF_III_left (Superior longitudinal fascicle III)<br> 41: SLF_III_right <br> 42: STR_left (Superior Thalamic Radiation)<br> 43: STR_right <br> 44: UF_left (Uncinate fascicle) <br> 45: UF_right <br> 46: CC (Corpus Callosum - all)<br> 47: T_PREF_left (Thalamo-prefrontal)<br> 48: T_PREF_right <br> 49: T_PREM_left (Thalamo-premotor)<br> 50: T_PREM_right <br> 51: T_PREC_left (Thalamo-precentral)<br> 52: T_PREC_right <br> 53: T_POSTC_left (Thalamo-postcentral)<br> 54: T_POSTC_right <br> 55: T_PAR_left (Thalamo-parietal)<br> 56: T_PAR_right <br> 57: T_OCC_left (Thalamo-occipital)<br> 58: T_OCC_right <br> 59: ST_FO_left (Striato-fronto-orbital)<br> 60: ST_FO_right <br> 61: ST_PREF_left (Striato-prefrontal)<br> 62: ST_PREF_right <br> 63: ST_PREM_left (Striato-premotor)<br> 64: ST_PREM_right <br> 65: ST_PREC_left (Striato-precentral)<br> 66: ST_PREC_right <br> 67: ST_POSTC_left (Striato-postcentral)<br> 68: ST_POSTC_right<br> 69: ST_PAR_left (Striato-parietal)<br> 70: ST_PAR_right <br> 71: ST_OCC_left (Striato-occipital)<br> 72: ST_OCC_right<br> </p> <p><strong>Cross-validation data splits</strong></p> <p>The following data splits were used for cross-validation in the TractSeg paper:</p> <pre><code class="language-python">fold1 = ['992774', '991267', '987983', '984472', '983773', '979984', '978578', '965771', '965367', '959574', '958976', '957974', '951457', '932554', '930449', '922854', '917255', '912447', '910241', '907656', '904044'] fold2 = ['901442', '901139', '901038', '899885', '898176', '896879', '896778', '894673', '889579', '887373', '877269', '877168', '872764', '872158', '871964', '871762', '865363', '861456', '859671', '857263', '856766'] fold3 = ['849971', '845458', '837964', '837560', '833249', '833148', '826454', '826353', '816653', '814649', '802844', '792766', '792564', '789373', '786569', '784565', '782561', '779370', '771354', '770352', '765056'] fold4 = ['761957', '759869', '756055', '753251', '751348', '749361', '748662', '748258', '742549', '734045', '732243', '729557', '729254', '715647', '715041', '709551', '705341', '704238', '702133', '695768', '690152'] fold5 = ['687163', '685058', '683256', '680957', '679568', '677968', '673455', '672756', '665254', '654754', '645551', '644044', '638049', '627549', '623844', '622236', '620434', '613538', '601127', '599671', '599469']</code></pre> <p>Hyperparameters were optimized using fold 1-3 for training and fold 4 for validation.</p> <p>The final 5-fold cross-validation (results reported in the TractSeg paper) was done by always training on 3 folds, selecting the best epoch by evaluating on the fourth fold and then reporting the final results (of the model from the best epoch) on the fifth fold.</p> <p>The pretrained TractSeg model which will automatically be used when you download TractSeg was trained on fold1+fold2+fold3.</p> <p>Please use the same data splits to make your work comparable.</p> <p> </p> <p><strong>Data format</strong></p> <p>From version 1.2.0 of this dataset onwards it uses the newest trackvis (trk) standard (using nibabel.streamlines API). Streamlines are saved in native voxel space and when loaded are transformed to coordinate space using the affine stored in the trk file header. In the previous versions of the dataset the older nibabel.trackvis API was used (streamlines are saved in real coordinate space and no affine is applied when loading them).</p>
Top Quark Tagging Reference Dataset
<p>A set of MC simulated training/testing events for the evaluation of top quark tagging architectures.</p> <p>In total 1.2M training events, 400k validation events and 400k test events. Use “train” for training, “val” for validation during the training and “test” for final testing and reporting results.</p> <p><strong>Description</strong></p> <ul> <li> <p>14 TeV, hadronic tops for signal, qcd diets background, Delphes ATLAS detector card with Pythia8</p> </li> <li> <p>No MPI/pile-up included</p> </li> <li> <p>Clustering of particle-flow entries (produced by Delphes E-flow) into anti-kT 0.8 jets in the pT range [550,650] GeV</p> </li> <li> <p>All top jets are matched to a parton-level top within ∆R = 0.8, and to all top decay partons within 0.8</p> </li> <li> <p>Jets are required to have |eta| < 2</p> </li> <li> <p>The leading 200 jet constituent four-momenta are stored, with zero-padding for jets with fewer than 200</p> </li> <li> <p>Constituents are sorted by pT, with the highest pT one first</p> </li> <li> <p>The truth top four-momentum is stored as truth_px etc.</p> </li> <li> <p>A flag (1 for top, 0 for QCD) is kept for each jet. It is called is_signal_new</p> </li> <li> <p>The variable "ttv" (= test/train/validation) is kept for each jet. It indicates to which dataset the jet belongs. It is redundant as the different sets are already distributed as different files.</p> </li> </ul>
CVD2014 - A database for evaluating no-reference video quality assessment algorithms
<p>The CVD video database is developed to provide an useful tool for researchers in the validation and developing processes of no-reference (NR) objective video quality assessment (VQA) algorithms. It consists of 234 videos from five different scenes captured by 78 different cameras (mobile phones, compact camera, video camera, SLR). The subjective experiments are conducted following the Single-Stimulus (SS) procedure to collect ratings of video quality.</p> <p><strong>Setup</strong></p> <p>We implement our experiments according to the Single Stimulus methodology using VQone MATLAB toolboxon high quality monitors (Eizo ColorEdge CG241W) with 1920x1200 pixel resolution in a dark room (ambient light < 20 lux). Video stimuli were displayed at their original size of VGA (640 x 480) or HD (1280 x 720). The subjects viewing distance (80 cm) was controlled by a string hanging from a ceiling and they were instructed to keep their head steady next to it. The monitors were calibrated to according to sRGB (target values were: 6500 K, 80 lux, and gamma 2.2) using EyeOne Pro calibrator (X-rite co.). The laboratory setup is showed in the figure below.</p> <p><strong>Subjects</strong></p> <p>Subjects (n = 30, 30, 28, 33, 30, 32 and 27 for Tests 1 - 7 respectively) were naïve in a sense that they did not study or work with image quality or related fields. They were recruited through student mailing lists consisting mainly humanities and behavioral science students. Subjects’ vision was controlled for the near visual acuity, near contrast vision (near F.A.C.T.) and color vision (Farnsworth D15) before the participation. They received movie tickets as a reward.</p> <p><strong>Procedure</strong></p> <p>Subjects evaluated one video sample at a time and all video samples of one scene were presented in a row. The order of video samples and scenes was randomized. Subjects had the option to view video samples again as many times as they wanted.</p> <p><strong>Data</strong></p> <p>The results are processed and reported in the form of Mean Opinion Score (MOS) for the tested video samples. In addition, we provide the whole raw data from the subjective experiments instead of just pre-calculated mean opinion scores from each video sample. This allows further analyses to be made by those who wish to use this database and gives them better opportunity to utilize the data to its full potential.</p> <p>Realignment study (test 7) contains the data from the additional study in which the mappings from the test and scene specific quality scales (test 1-6) to the global quality scale were formed. The global scale is valuable when studying and developing VQA algorithms. With the global scale, all of the samples (234 video samples in the case of the CVD2014) are in the same scale, and the performance analysis for algorithms can be conducted with a high number of samples.</p> <p><strong>If you use this database in your research, we kindly ask that you follow The Copyright notice below and cite the following paper:</strong></p> <p> </p> <p>M. Nuutinen, T. Virtanen, M. Vaahteranoksa, T. Vuori, P. Oittinen and J. Häkkinen, "CVD2014—A Database for Evaluating No-Reference Video Quality Assessment Algorithms," in <em>IEEE Transactions on Image Processing</em>, vol. 25, no. 7, pp. 3073-3086, July 2016. doi: 10.1109/TIP.2016.2562513</p> <p> </p> <p> </p> <p>-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------</p> <p>Copyright (c) 2014 The University of Helsinki<br> All rights reserved.</p> <p>Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the videos, the images, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirely appear in all copies of this database, and the original source of this database,Visual Cognition research group (www.helsinki.fi/psychology/groups/visualcognition/index.htm) and the Institute of Behavioral Science (www.helsinki.fi/ibs/index.html) at the University of Helsinki (www.helsinki.fi/university/), is acknowledged in any publication that reports research using this database. Individual videos and images may not be used outside the scope of this database (e.g. in marketing purposes) without prior permission.</p> <p>The database and our paper are to be cited in the bibliography as: M. Nuutinen, T. Virtanen, M. Vaahteranoksa, T. Vuori, P. Oittinen and J. Häkkinen, "CVD2014—A Database for Evaluating No-Reference Video Quality Assessment Algorithms," in <em>IEEE Transactions on Image Processing</em>, vol. 25, no. 7, pp. 3073-3086, July 2016.<br> doi: 10.1109/TIP.2016.2562513</p> <p>-----------------------------------------------------------------------------</p> <p>LIMITATION OF LIABILITY</p> <p>UNIVERSITY OF HELSINKI SHALL IN NO CASE BE LIABLE IN CONTRACT, TORT OR OTHERWISE FOR ANY LOSS OF REVENUE, PROFIT, BUSINESS OR GOODWILL OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL OR PUNITIVE COST, DAMAGES OR EXPENSE OF ANY KIND HOWEVER CAUSED OR HOWEVER ARISING UNDER OR IN CONNECTION WITH THE USE OF THIS DATABASE.</p> <p>THE UNIVERSITY OF HELSINKI SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF HELSINKI HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.</p> <p>THIS AGREEMENT SHALL BE CONSTRUED AND INTERPRETED IN ACCORDANCE WITH THE LAWS OF FINLAND, EXCLUDING ITS RULES FOR CHOICE OF LAW.</p> <p>-----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------</p>
Kinematically collected reference fingerprint map (RFM) with the high precision tracking system for feature-based indoor positioning
<p>The offline referencing phase, one of the core phases of the fingerprinting-based indoor positioning system (FIPS), is the key stage for deploying the positioning system. The reference fingerprint map (RFM) is acquired for representing the relationship between location-relevant features and the corresponding locations and used for inferring the user’s location at the online stage. The kinematically collecting the RFM using the mobile device with the help of high precision tracking system is contributed to the community for benchmarking comparison of the indoor positioning performance. The detailed description of the data is cooming soon.<br> </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.