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12,237 results for “2019”
YSI Data from Chalgrove Lake; 2019-05-30 to 2019-06-12
<p># General Metadata for Chalgrove Lake YSI Data </p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> chalgrove_lake_DDMonthYYYY_metadata.txt<br> <br> Where DDMonthYYY is the date that the deployment ended.</p> <p>## File Created</p> <p> * 2019-04-12 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a YSI EXO2 multiparameter sonde deployed at approximatly 0.7 m in Chalgrove Lake (37.242983 N, -78.464116 W) in approximately 4 m of water.</p> <p>## Sonde Specifics</p> <p> The specific sensors and SNs deployed are on the metadata specific to each deployment but generally the sonde has:<br> <br> * Temperature<br> * Conductivity<br> * pH<br> * ODO<br> * Total Algae<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * date.time = the combined date and time of the measurement (YYYY-MM-DD HH:MM:SS)<br> * date = the sampling date reported by the YSI (M/D/YYYY)<br> * time = the sampling time reported by the YSI (HH:MM:SS)<br> * fract_sec = the smapling fractions of a second reported by the YSI<br> * site_name = the site name on the YSI deployment (left blank)<br> * cond = the conductivity (uS/cm)<br> * nLF_cond = the nLF conductivity (uS/cm)<br> * perc_ODO = the percent oxygen saturation<br> * perc_local_ODO = the percent local oxygen saturation<br> * ODO_conc = the dissolved oxygen concentration (mg/L)<br> * sal = the salinity (psu)<br> * sp_cond = the specific conductivity (uS/cm)<br> * TDS = the total dissolved solids (mg/L)<br> * pH = the pH<br> * pH mV = the millivolts of the pH sensor<br> * temp = the temperature (dC)<br> * battery = the battery voltage<br> * cable_pwr = the cable power<br> </p>
TESS-JUNE-2019
<p>Measurements taken by the European Photometer Network (Project STARS4ALL). June 2019</p>
TESS-MAY-2019
<p>Measurements taken by the European Photometer Network (Project STARS4ALL). May 2019</p>
Tweet IDs for the #brexit tweet dataset collected for the Helsinki Digital Humanities Hackathon 2019
<p>This dataset contains lists of tweet ids for the tweets used as material by the "Brexit in Transna­tional So­cial Me­dia" group in the <a href="http://heldig.fi/dhh19/">Helsinki Digital Humanities Hackathon 2019</a>.</p> <p>Due to restrictions in Twitter's terms of service, the full tweet dataset cannot be made public. However, Twitter allows the publication of tweet ids, from which the dataset can be reconstituted, <em>with the exception of deleted tweets</em>.</p> <p>The dataset was gathered as follows: Between 2019-01-22T09:19Z and 2019-04-15T14:42Z, a <a href="https://github.com/DocNow/twarc">Twarc</a> version 1.6.1 script was called hourly to retrieve and archive tweets from Twitter matching the #brexit hashtag using the Twitter search API. All 5,547,585 tweet IDs returned by this run are listed in the file <code>original_ids.txt.gz</code>.</p> <p>However, upon further inspection, problems were identified in the archiving. For an unidentified reason, gathering did not occur between 2019-02-13T06:17Z and 2019-02-26T09:24Z. In addition, the Twarc script had been run without the <code>--extended</code> argument, so the tweet data contained only truncated contents for many tweets.</p> <p>Due to this, a decision was taken to rehydrate a new dataset of tweets falling between 2019-02-26T09:24Z and 2019-04-15T14:42Z. Of the original 4,197,059 tweets gathered for this time period (listed in <code>continuous_ids.txt</code>), 3,941,653 could be rehydrated (i.e., they had not been deleted in the interim). The ids of tweets in this dataset are listed in the file <code>continuous_rehydrated_ids.txt</code>.</p> <p>Finally, out of these rehydrated tweets, a subset was derived that filtered out all tweets that were pure retweets. This subset consisted of 1,104,514 tweets, whose ids are listed in the file <code>continuous_rehydrated_no_retweets_ids.txt</code>.</p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2019 Week 27
<p>These data were collected from robot controller during solar tanks welding operation.</p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2019 Week 28
<p>These data were collected from robot controller during solar tanks welding operation.</p>
YSI Data from Chalgrove Lake 2019-08-28 to 2019-09-11
<p># General Metadata for Chalgrove Lake YSI Data </p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> chalgrove_lake_DDMonthYYYY_metadata.txt<br> <br> Where DDMonthYYY is the date that the deployment ended.</p> <p>## File Created</p> <p> * 2019-04-12 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a YSI EXO2 multiparameter sonde deployed at approximatly 0.7 m in Chalgrove Lake (37.242983 N, -78.464116 W) in approximately 4 m of water.</p> <p>## Sonde Specifics</p> <p> The specific sensors and SNs deployed are on the metadata specific to each deployment but generally the sonde has:<br> <br> * Temperature<br> * Conductivity<br> * pH<br> * ODO<br> * Total Algae<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * date.time = the combined date and time of the measurement (YYYY-MM-DD HH:MM:SS)<br> * date = the sampling date reported by the YSI (M/D/YYYY)<br> * time = the sampling time reported by the YSI (HH:MM:SS)<br> * fract_sec = the smapling fractions of a second reported by the YSI<br> * site_name = the site name on the YSI deployment (left blank)<br> * cond = the conductivity (uS/cm)<br> * nLF_cond = the nLF conductivity (uS/cm)<br> * perc_ODO = the percent oxygen saturation<br> * perc_local_ODO = the percent local oxygen saturation<br> * ODO_conc = the dissolved oxygen concentration (mg/L)<br> * sal = the salinity (psu)<br> * sp_cond = the specific conductivity (uS/cm)<br> * TDS = the total dissolved solids (mg/L)<br> * pH = the pH<br> * pH mV = the millivolts of the pH sensor<br> * temp = the temperature (dC)<br> * battery = the battery voltage<br> * cable_pwr = the cable power<br> <br> </p>
YSI Data from Chalgrove Lake 2019-08-08 to 2019-08-28
<p># General Metadata for Chalgrove Lake YSI Data </p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> chalgrove_lake_DDMonthYYYY_metadata.txt<br> <br> Where DDMonthYYY is the date that the deployment ended.</p> <p>## File Created</p> <p> * 2019-04-12 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a YSI EXO2 multiparameter sonde deployed at approximatly 0.7 m in Chalgrove Lake (37.242983 N, -78.464116 W) in approximately 4 m of water.</p> <p>## Sonde Specifics</p> <p> The specific sensors and SNs deployed are on the metadata specific to each deployment but generally the sonde has:<br> <br> * Temperature<br> * Conductivity<br> * pH<br> * ODO<br> * Total Algae<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * date.time = the combined date and time of the measurement (YYYY-MM-DD HH:MM:SS)<br> * date = the sampling date reported by the YSI (M/D/YYYY)<br> * time = the sampling time reported by the YSI (HH:MM:SS)<br> * fract_sec = the smapling fractions of a second reported by the YSI<br> * site_name = the site name on the YSI deployment (left blank)<br> * cond = the conductivity (uS/cm)<br> * nLF_cond = the nLF conductivity (uS/cm)<br> * perc_ODO = the percent oxygen saturation<br> * perc_local_ODO = the percent local oxygen saturation<br> * ODO_conc = the dissolved oxygen concentration (mg/L)<br> * sal = the salinity (psu)<br> * sp_cond = the specific conductivity (uS/cm)<br> * TDS = the total dissolved solids (mg/L)<br> * pH = the pH<br> * pH mV = the millivolts of the pH sensor<br> * temp = the temperature (dC)<br> * battery = the battery voltage<br> * cable_pwr = the cable power<br> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 36
<p>These data were collected from FIDIA machine tool controller during milling operation.</p>
Consumer 'Smart Home' Technology Predictions For 2019
<p>Source: <a href="https://www.forbes.com/sites/moorinsights/2019/01/09/consumer-smart-home-technology-predictions-for-2019/#">https://www.forbes.com/sites/moorinsights/2019/01/09/consumer-smart-home-technology-predictions-for-2019</a></p>
Dataset for IJCAI 2019 paper, Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks
<p>The dataset and pre-trained model for IJCAI 2019 paper "Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks"</p> <ul> <li>Pre-trained model(Pytorch): DSPNet_G_200_epochs.pth</li> <li>Dataset (generated from coco dataset): starry_night_coco.zip</li> <li>Github: https://github.com/jinningli/DSP-Net</li> </ul> <p>Please cite our paper if you are using this dataset:</p> <p><em>Jinning Li, and Yexiang Xue. Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks. In International Joint Conference on Artificial Intelligence (IJCAI) 2019</em></p> <p>Abstract:</p> <p><em>We propose the Dual Scribble-to-Painting Network (DSP-Net), which is able to produce artistic paintings based on user-generated scribbles. In scribble-to-painting transformation, a neural net has to infer additional details of the image, given relatively sparse information contained in the outlines of the scribble. Therefore, it is more challenging than classical image style transfer, in which the information content is reduced from photos to paintings. Inspired by the human cognitive process, we propose a multi-task generative adversarial network, which consists of two jointly trained neural nets -- one for generating artistic images and the other one for semantic segmentation. We demonstrate that joint training on these two tasks brings in additional benefit. Experimental result shows that DSP-Net outperforms state-of-the-art models both visually and quantitatively. In addition, we publish a large dataset for scribble-to-painting transformation.</em></p>
Dataset for Leslie, Peredo & Pyenson 2019
<p>Supplemental dataset for Leslie et al. (2019)</p> <p>The data included in this Zenodo archive are primarily the derived 3D models (STL or PLY files) for the main osteological elements of USNM 10300: the skull, the left and right periotics, and the right tympanic bulla. One DICOM data file includes all CT data for right tympanic bulla and both periotics.</p> <p>The files in this archive include materials published with the original article in <em>PeerJ</em> (Leslie et al., 2019). </p> <p>The type specimen itself (USNM 10300) is housed in the Department of Paleobiology at the Smithsonian Institution's National Museum of Natural History in Washington, D.C.</p> <p>Skull:</p> <p>- 3D model created from 3D laser scan data. (PLY, 138.9 MB)</p> <p>right tympanic bulla:</p> <p>- 3D model created from CT scan data. (STL, 31 MB)</p> <p>- within DICOM data set. (578.1 MB)</p> <p>right periotic</p> <p>- 3D model created from CT scan data. (STL, 17.5 MB)</p> <p>- within DICOM data set. (578.1 MB)</p> <p>left periotic</p> <p>- 3D model created from CT scan data. (STL, 19.1 MB)</p> <p>- within DICOM data set. (578.1 MB)</p>
Seismic anisotropy dataset from Illsley-Kemp et al., G3, 2019 (10.1029/2019GC008529)
<p>The datasets provided here are the seismic anisotropy results for the four seperate regions of New Zealand, reported and discussed in Illsley-Kemp et al., <em>Geochemistry, Geophysics, Geosystems, </em>2019 (10.1029/2019GC008529). </p> <p>If you use this date, please cite the following paper:</p> <p>Illsley-Kemp, F., Savage, M. K., Wilson, C. J. N., & Bannister, S., 2019, 10.1029/2019GC008529. Mapping Stress and Structure from Subducting Slab to Magmatic Rift: Crustal Seismic Anisotropy of the North Island, New Zealand. <em>Geochemistry, Geophysics, Geosystems.</em></p> <p>The data are csv files in the same format as MFAST output (http://mfast-package.geo.vuw.ac.nz), with each column corresponding to the following:</p> <p>1: Result ID</p> <p>2: Station code</p> <p>3: Station latitude</p> <p>4: Station longitude</p> <p>5: Earthquake ID (after GeoNet)</p> <p>6: Year</p> <p>7: Julian day on which the event occurred, with decimal digits giving the fraction of the day</p> <p>8: Earthquake latitude</p> <p>9: Earthquake longitude</p> <p>10: Earthquake-station distance (km)</p> <p>11: Earthquake depth (km)</p> <p>12: Earthquake magnitude</p> <p>13: Back azimuthal angle</p> <p>14: Initial polarisation of the shear wave in degrees</p> <p>15: Error of Spol in degrees, one standard deviation</p> <p>16: Start time of the selected measurement window in seconds, relative to the start of the seismogram at t = 0</p> <p>17: End time of the selected measurement window in seconds, relative to the start of the seismogram at t = 0</p> <p>18: Not used</p> <p>19: Not used</p> <p>20: Signal to noise ratio</p> <p>21: Delay time (δt) between fast and slow shear wave in seconds</p> <p>22: Error of δt in degrees, one standard deviation</p> <p>23: Angle of the orientation of the fast shear wave (φ), in degrees from North</p> <p>24: Error of φ in degrees, one standard deviation</p> <p>25: Angle of incidence at the station, measured against a horizontal plane in degrees, where 0 means vertical incidence</p> <p>26: Not used</p> <p>27: Type of measurement. This field contains the measurement code that is used, the number of measurement window start times and the number of window end times</p> <p>28: Not used</p> <p>29: Not used</p> <p>30: Nyquist frequency of the event in Hz</p> <p>31: Evaluation of the measurement quality</p> <p>32: Lower corner frequency of the bandpass filter in Hz</p> <p>33: Higher corner frequency of the bandpass filter in Hz</p> <p>34: Angle between the initial polarisation and the fast orientation in degrees</p> <p>35: Not used</p> <p>36: Not used</p> <p>37: The maximum value of the eigenvalue of the corrected covariance matrix</p> <p>38: The number of degrees of freedom in the measurement</p> <p>39: The minimum value of the eigenvalue of the covariance matrix before it was scaled to have the 95% confidence level set to 1</p> <p>40: The S-wave travel time between the earthquake and the station</p> <p>41: The dominant frequency in the S wave, determined from the frequency at the maximum spectral amplitude</p>
Festival of Frequency Measurement 1 October 2019
<p>Festival of Frequency Measurement 1 October 2019</p> <p>J. Kommers KN6CYU</p> <p>Latitude: 34.0556 / 34° 3' 20" N Longitude: -118.443 / 118° 26' 34" W</p> <p>Grid: <strong>DM04sb</strong></p> <p>Files starting 30 Sep 2019 at 18:42 PDT through 1 Oct 2019 at 4:18 PDT</p> <p>70 ft random wire antenna, SDR Play RSPduo tuned to 4.999 MHz with diversity reception - screenshots included for configuration details</p>
WWV Centennial Trial Run Recordings of WWV during August 1st, 2019 UTC
<p>Trial run recording on August 1st 2019 in preparation for the Festival of Frequency Measurement, a component of the celebrations of WWV's 100th anniversary.</p> <p> </p> <p>The Festival of Frequency Measurement is a citizen-science component of WWV's 100th anniversary celebration. This data set was collected on August 1st, 2019 UTC as a trial run for the centennial celebrations.</p>
Festival of Frequency Measurement 1 October 2019
<p>Steve VK3ZAZ</p> <p>Hamilton Victoria Australia</p> <p>QF12ag 37:30 South 142:01 East</p> <p>Sunset October 1 700pm local 0900 UTC</p> <p>Yaesu FTDX5000MP with OCXO Delta Loop east west oriented</p> <p>WWV Centennial 08:00 apprx 5.000 MHZ</p>
Fig. 5 in The genus Parbatthorax Tanasevitch, 2019 (Araneae, Linyphiidae) new to China, with a new species from the Gaoligong Mountains
Fig. 5. Parbatthorax proiectus sp. nov., one of the paratypes collected with the holotype (CGY107). A. Epigyne, ventral view. B. Internal genitalia, dorsal view. Scale bars: 0.1 mm.
Fig. 2 in The genus Parbatthorax Tanasevitch, 2019 (Araneae, Linyphiidae) new to China, with a new species from the Gaoligong Mountains
Fig. 2. Parbatthorax proiectus sp. nov., holotype, male palp (CGY107). A. Prolateral view. B. Retrolateral view. C. Ventral view. D. Dorsal view. Scale bars: 0.1 mm.
Fig. 4 in The genus Parbatthorax Tanasevitch, 2019 (Araneae, Linyphiidae) new to China, with a new species from the Gaoligong Mountains
Fig. 4. Parbatthorax proiectus sp. nov., holotype, male palp (CGY107). A. Prolateral view. B. Retrolateral view. Scale bars: 0.1 mm.
Fig. 6 in The genus Parbatthorax Tanasevitch, 2019 (Araneae, Linyphiidae) new to China, with a new species from the Gaoligong Mountains
Fig. 6. Parbatthorax proiectus sp. nov., one of the paratypes collected with the holotype (CGY107). A. Epigyne, ventral view. B. Epigyne cleared, ventral view. C. Internal genitalia, dorsal view. Scale bars: 0.1 mm.
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