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586 results for “March”
Marsh water table height, logging data from the Typha marsh site on the upper Parker River for March-November 2013.
Measurements of water table height in the upper Parker River Typha sp. marsh. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the Typha site, MAR-PR-Wtable-T, for Mar - November 2013
Rapid Outer Radiation Belt Flux Dropouts and Fast Acceleration during the March 2015 and 2013 Storms: The Role of ULF Wave Ttansport From a Dynamic Outer Boundary
<p>Duplicate copy of the electron phase space density provided for the Geospace Environment Modeling (GEM) challenge event in March 2013 selected by the <em>Quantitative Assessment of Radiation Belt Modeling</em> focus group. The original copy of the data is available from <a href="https://drive.google.com/drive/u/0/folders/0ByNhSbWkAgdfaGt6TnJMcElhUTg">https://drive.google.com/drive/u/0/folders/0ByNhSbWkAgdfaGt6TnJMcElhUTg</a></p> <p> </p> <p>Data Providers:<br> Michael G. Henderson (LANL; mghenderson@lanl.gov)<br> Steven K. Morley (LANL; smorley@lanl.gov)</p> <p>This data product provides electron phase space density from the Van Allen Probes<br> ECT suite of instruments. The data are calculated similarly to the method described<br> in Morley et al. (2013), with some differences that are noted below.</p> <p>The files are provided in HDF5 format, so the files are self-describing and contain<br> ISTP-style metadata. The files should be directly readable with:<br> - SpacePy (http://sourceforge.net/p/spacepy)<br> - import the spacepy.datamodel module, use the function fromHDF5 to read the data<br> - Autoplot (http://autoplot.org)<br> - MatLab and IDL provide convience routines for reading HDF5</p> <p>Method<br> ------<br> Starting with directional differential flux data from HOPE, MagEIS and REPT, we<br> calculate the PSD as a function of energy, pitch angle, position and time.<br> Following the same basic method given by Morley et al., we transform this to phase <br> space density as a function of the three adiabatic invariants (M, K, L*); note that<br> where Morley et al. used a relativistic Maxwellian fit to the flux spectrum, these<br> data use a smoothing spline fit so that more complex spectral shapes can be<br> represented. Note also that Morley et al. only used REPT, where these files represent<br> the energy ranges of MagEIS and REPT, but also use HOPE to constrain the fit at low<br> energies.</p> <p>While the pitch angles are determined using the EMFISIS data, all three adiabatic <br> invariants are derived from a magnetic field model. These PSD data files use the<br> Tsyganenko and Sitnov (2005) model (aka TS04, T05 or TS05). The models were run using<br> the "definitive" Qin-Denton data files provided by the RBSP ECT-SOC. These files<br> should be made available through the QARBM google drive. </p> <p><br> Caveats<br> -------<br> These data should be considered preliminary. They have undergone a limited amount of<br> verification and prior to publication the data providers should be contacted. New<br> versions of these data may be generated at some point - we do not expect noticeable <br> changes to the data present.<br> Some gaps may be present in the files that are due to calculation of the adiabatic <br> invariants failing. The issues causing these gaps have been resolved in the underlying <br> software, but the data have not yet been regenerated.</p> <p><br> References<br> ----------<br> Morley, S. K., M. G. Henderson, G. D. Reeves, R. H. W. Friedel, and D. N. Baker (2013), <br> Phase Space Density matching of relativistic electrons using the Van Allen Probes: REPT results,<br> Geophys. Res. Lett., 40, 4798-4802, doi:10.1002/grl.50909.</p> <p>Tsyganenko, N. A., and M. I. Sitnov (2005), <br> Modeling the dynamics of the inner magnetosphere during strong geomagnetic storms, <br> J. Geophys. Res., 110, A03208, doi:10.1029/2004JA010798.</p> <p> </p> <p>Also included is the copy of the LANLgeoMag software used in the paper provided on <a href="https://github.com/drsteve/LANLGeoMag">https://github.com/drsteve/LANLGeoMag</a> </p> <p>Copyright (c) 2014, Los Alamos National Security, LLC All rights reserved. Copyright 2014. Los Alamos National Security, LLC. This software was produced under U.S. Government contract DE-AC52-06NA25396 for Los Alamos National Laboratory (LANL), which is operated by Los Alamos National Security, LLC for the U.S. Department of Energy. The U.S. Government has rights to use, reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR LOS ALAMOS NATIONAL SECURITY, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is modified to produce derivative works, such modified software should be clearly marked, so as not to confuse it with the version available from LANL.</p> <p>Additionally, redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of Los Alamos National Security, LLC, Los Alamos National Laboratory, LANL, the U.S. Government, nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY LOS ALAMOS NATIONAL SECURITY, LLC AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL LOS ALAMOS NATIONAL SECURITY, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</p> <p><br> </p>
Dataset with Curated Coronavirus-related R&D Outputs (1970 - March 2020)
<p><strong>The zip file includes metadata about R&D outputs related to all </strong><a href="https://www.niaid.nih.gov/diseases-conditions/coronaviruses"><strong>Coronaviruses</strong></a>.</p> <p>The two main data sources used for this work include:</p> <ul> <li> <p><a href="https://docs.microsoft.com/en-us/academic-services/project-academic-knowledge/introduction"><strong>Microsoft Academic Graph (MSA) API</strong></a>: Subset of 10.000+ coronavirus R&D outputs, with data from 1970, including patents and scientific publications.</p> </li> <li> <p><a href="https://www.grid.ac/"><strong>The Global Research Identifier Database (GRID)</strong></a><strong>:</strong> Used to enrich the organization data extracted from MSA.</p> </li> </ul> <p>After cleaning and enriching the data we extracted a total of <strong>1.100+ organization and</strong> <strong>26.700+ researchers</strong> spread across <strong>90+ countries</strong> and <strong>700+ cities</strong>.</p> <p>Inside the zip file you will find: </p> <ul> <li>documents.csv: Full list of documents</li> <li>topics.csv: list of topics connected to documents</li> <li>terms.csv: list of terms connected to documents</li> <li>people.csv: list of people (authors) connected to documents</li> <li>orgsplus.csv: list of organisations (incl their locations) connected to documents</li> </ul> <p>More information is available at <a href="https://app.gitbook.com/@dataverz/s/coronavirus-r-and-d/">https://app.gitbook.com/@dataverz/s/coronavirus-r-and-d/</a> </p>
Twitter historical dataset: March 21, 2006 (first tweet) to July 31, 2009 (3 years, 1.5 billion tweets)
<p><strong>Disclaimer: </strong>This dataset is distributed by Daniel Gayo-Avello, an associate professor at the Department of Computer Science in the University of Oviedo, for the sole purpose of non-commercial research and it just includes tweet ids.</p> <p>The dataset contains tweet IDs for <strong>all the published tweets</strong> (in any language) <strong>bettween March 21, 2006 and July 31, 2009</strong> thus comprising the first whole three years of Twitter from its creation, that is, about <strong>1.5 billion tweets</strong> (see file <em>Twitter-historical-20060321-20090731.zip</em>).</p> <p>It covers several defining issues in Twitter, such as the invention of hashtags, retweets and trending topics, and it includes tweets related to the 2008 US Presidential Elections, the first Obama’s inauguration speech or the 2009 Iran Election protests (one of the so-called Twitter Revolutions).</p> <p>Finally, it does contain tweets in many major languages (mainly English, Portuguese, Japanese, Spanish, German and French) so it should be possible–at least in theory–to analyze international events from different cultural perspectives.</p> <p>The dataset was completed in November 2016 and, therefore, the tweet IDs it contains were publicly available at that moment. This means that there could be tweets public during that period that do not appear in the dataset and also that a substantial part of tweets in the dataset has been deleted (or locked) since 2016.</p> <p>To make easier to understand the decay of tweet IDs in the dataset a number of representative samples (99% confidence level and 0.5 confidence interval) are provided.</p> <p>In general terms, <strong>85.5% ±0.5 of the historical tweets are available as of May 19, 2020</strong> (see file <em>Twitter-historical-20060321-20090731-sample.txt</em>). However, since the amount of tweets vary greatly throughout the period of three years covered in the dataset, additional representative samples are provided for 90-day intervals (see the file <em>90-day-samples.zip</em>).</p> <p>In that regard, the ratio of publicly available tweets (as of May 19, 2020) is as follows:</p> <ul> <li>March 21, 2006 to June 18, 2006: 88.4% ±0.5 (from 5,512 tweets).</li> <li>June 18, 2006 to September 16, 2006: 82.7% ±0.5 (from 14,820 tweets).</li> <li>September 16, 2006 to December 15, 2006: 85.7% ±0.5 (from 107,975 tweets).</li> <li>December 15, 2006 to March 15, 2007: 88.2% ±0.5 (from 852,463 tweets).</li> <li>March 15, 2007 to June 13, 2007: 89.6% ±0.5 (from 6,341,665 tweets).</li> <li>June 13, 2007 to September 11, 2007: 88.6% ±0.5 (from 11,171,090 tweets).</li> <li>September 11, 2007 to December 10, 2007: 87.9% ±0.5 (from 15,545,532 tweets).</li> <li>December 10, 2007 to March 9, 2008: 89.0% ±0.5 (from 23,164,663 tweets).</li> <li>March 9, 2008 to June 7, 2008: 66.5% ±0.5 (from 56,416,772 tweets; see below for more details on this).</li> <li>June 7, 2008 to September 5, 2008: 78.3% ±0.5 (from 62,868,189 tweets; see below for more details on this).</li> <li>September 5, 2008 to December 4, 2008: 87.3% ±0.5 (from 89,947,498 tweets).</li> <li>December 4, 2008 to March 4, 2009: 86.9% ±0.5 (from 169,762,425 tweets).</li> <li>March 4, 2009 to June 2, 2009: 86.4% ±0.5 (from 474,581,170 tweets).</li> <li>June 2, 2009 to July 31, 2009: 85.7% ±0.5 (from 589,116,341 tweets).</li> </ul> <p>The apparent drop in available tweets from March 9, 2008 to September 5, 2008 has an easy, although embarrassing, explanation.</p> <p>At the moment of cleaning all the data to publish this dataset there seemed to be a gap between April 1, 2008 to July 7, 2008 (actually, the data was not missing but in a different backup). Since tweet IDs are easy to regenerate for that Twitter era (source code is provided in <em>generate-ids.m</em>) I simply produced all those that were created between those two dates. All those tweets actually existed but a number of them were obviously private and not crawlable. For those regenerated IDs the actual ratio of public tweets (as of May 19, 2020) is 62.3% ±0.5.</p> <p>In other words, what you see in that period (April to July, 2008) is not actually a huge number of tweets having been deleted but the combination of deleted *and* non-public tweets (whose IDs should not be in the dataset for performance purposes when rehydrating the dataset).</p> <p>Additionally, given that not everybody will need the whole period of time the earliest tweet ID for each date is provided in the file <em>date-tweet-id.tsv</em>.</p> <p>For additional details regarding this dataset please see: <strong>Gayo-Avello, Daniel. "How I Stopped Worrying about the Twitter Archive at the Library of Congress and Learned to Build a Little One for Myself." <em>arXiv preprint arXiv:1611.08144</em> (2016)</strong>.</p> <p><strong>If you use this dataset in any way please cite that preprint </strong>(in addition to the dataset itself).</p> <p>If you need to contact me you can find me as <a href="https://twitter.com/pfcdgayo">@PFCdgayo</a> in Twitter.</p>
Surface cyclone mask for the Antarctic Circumnavigation Expedition from December 2016 – March 2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains a surface cyclone mask, which records the presence of a surface cyclone along the track of the Antarctic Circumnavigation expedition (ACE). The surface cyclones are calculated applying a 2D cyclone identification algorithm (Wernli and Schwierz, 2006; Sprenger et al., 2017) using global operational analysis data of the European Centre for Medium Range Weather Forecasts.</p> <p><strong>Dataset contents</strong></p> <p>- cyclone_mask_1h.csv, data file, comma-separated values<br> - data_file_header.txt, metadata, text<br> - README.txt, metadata, text</p> <p><strong>Dataset license</strong></p> <p>This surface cyclone mask dataset from ACE 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>
Supplement to : Accelerated Snow Melt in the Russian Caucasus Mountains After the Saharan Dust Outbreak in March 2018
<p>These datasets contains all the data used in Accelerated Snow Melt in the Russian Caucasus Mountains After the Saharan Dust Outbreak in March 2018 by Dumont et al., in Journal of Geophysical Research.<br> This dataset includes : Sentinel-2 cloud masks, snow depth measurements, snow surface impurity content estimated from Sentinel-2, Sentinel-2 surface reflectances and digital elevation models.</p> <p>Dumont, M., Tuzet, F., Gascoin, S., Picard, G., Kutuzov, S., Lafaysse, M., et al. (2020). Accelerated snow melt in the Russian Caucasus mountains after the Saharan dust outbreak in March 2018. Journal of Geophysical Research: Earth Surface, 125, e2020JF005641. <a href="https://doi.org/10.1029/2020JF005641">https://doi.org/10.1029/2020JF005641</a></p>
Cloud radar, micro rain radar, parsivel and pluvio measurements at Ny-Ålesund for 7 Feb 2018, 16 March 2018 and 16 April 2018
<p>This data set contains netcdf files of the University of Cologne's cloud radar MiRAC-A, micro rain radar, parsivel and pluvio installed at the Arctic research site AWPIEV at Ny-Ålesund. Data are available for 3 days: 7 Feb 2018, 16 March 2018 and 16 April 2018.</p> <p>The MiRAC-A hourly files (“mirac-a_nya_compact_*_P01_ZEN.nc” include (among other variables):</p> <p>float Ze(time, range) ;</p> <p>Ze:long_name = "Equivalent radar reflectivity factor Ze" ;</p> <p>Ze:units = "mm^6/m^3" ;</p> <p>float vm(time, range) ;</p> <p>vm:long_name = "Mean Doppler velocity" ;</p> <p>vm:units = "m/s" ;</p> <p>vm:comment = "negative values indicate falling particles towards the radar" ;</p> <p>float sigma(time, range) ;</p> <p>sigma:long_name = "Spectral width of Doppler velocity spectrum" ;</p> <p>sigma:units = "m/s" ;</p> <p> </p> <p>The Micro Rain Radar daily files (“*_nya_mrr_improtoo_0-101.nc”) include (among other variables):</p> <p>float Ze(time, range) ;</p> <p>Ze:description = "reflectivity of the most significant peak" ;</p> <p>Ze:units = "dBz" ;</p> <p> </p> <p>The parsivel daily files (“sups_nya_dm00_l1_any_v00_*.nc”) include (among other variables):</p> <p>float N(dclasses, time) ;</p> <p>N:fill_value = NaN ;</p> <p>N:units = "log10(m-3 mm-1)" ;</p> <p>N:long_name = "particle concentration per diameter class" ;</p> <p>float dclasses(dclasses) ;</p> <p>dclasses:units = "mm" ;</p> <p>dclasses:long_name = "volume equivalent diameter class center" ;</p> <p> </p> <p>The pluvio daily files (“pluvio_nya_*.nc”) include (among other variables):</p> <p>r_accum_NRT(dim) ;</p> <p>r_accum_NRT:description = "accumulated precipitation NRT" ;</p> <p>r_accum_NRT:units = "mm" ;</p> <p> </p> <p>All available variables are listed and described in the header of the corresponding netcdf files.</p>
Lowpoly Marching Band Hat (Red)
A shako (Marching Band Hat) is a tall, cylindrical military cap, usually with a visor, and sometimes tapered at the top. It is usually adorned with some kind of ornamental plate or badge on the front, metallic or otherwise, and often has a feather, plume, or pompom attached at the top. *1 model (low poly) with textures and materials. Source: Objaverse 1.0 / Sketchfab
Grange avec Marche-à-terre animé en 3D
Marche-à-terre, 19e siècle Fabrication artisanale Bois, métal Diamètre extérieur 180,20 cm Collection Musée POP, 1983.11190 Acheté par Robert-Lionel Séguin en 1963, cet instrument mécanique peu commun, à engrenage, de forme conique, servait de point de départ à un procédé original de mouture du grain. Il s'agit d'une fabrication spécialisée, très rare au Québec, réplique de modèles similaires utilisés en Angleterre et aux États-Unis au milieu du 18e siècle. Le système est actionné par des bêtes qui servaient de force motrice. Cette pièce maîtresse a inspiré l'architecte du Musée qui a dessiné, pour la mettre en valeur, une rotonde dans le grand hall. Source: Objaverse 1.0 / Sketchfab
Figure 23. from New subgenus and new species of Oriental Omophorus (Coleoptera, Curculionidae, Metatygini) - ZooKeys 85: 41-59 (11 March 2011) https://doi.org/10.3897/zookeys.85.973
Figure 23. - Omophorus (Sinomophorus) rongshu sp. n., distribution map. Red dot is Xīnpíng.
Figure 22. from New subgenus and new species of Oriental Omophorus (Coleoptera, Curculionidae, Metatygini) - ZooKeys 85: 41-59 (11 March 2011) https://doi.org/10.3897/zookeys.85.973
Figure 22. - Omophorus (Sinomophorus) rongshu sp. n., female genitalia. Scale: 1 mm.
Figure 1. from Euastacus morgani sp. n., a new spiny crayfish (Crustacea, Decapoda, Parastacidae) from the highland rainforests of eastern New South Wales, Australia - ZooKeys 85: 17-26 (11 March 2011) https://doi.org/10.3897/zookeys.85.1237
Figure 1. - Euastacus morgani sp. n. Female specimen, ACP 1103.
Figure 11. from The genus Hygrocrates Deeleman-Reinhold, 1988 (Aranei, Dysderidae) in Turkey - ZooKeys 85: 1-16 (11 March 2011) https://doi.org/10.3897/zookeys.85.927
Figure 11. - Cheliceral teeth of Hygrocrates deelemanus sp. n.
Figure 2. from The genus Hygrocrates Deeleman-Reinhold, 1988 (Aranei, Dysderidae) in Turkey - ZooKeys 85: 1-16 (11 March 2011) https://doi.org/10.3897/zookeys.85.927
Figure 2. - Hygrocrates deelemanus sp. n. (Holotype ♂); scale line: 1 mm.
Supplementary file pwt: Female length of Tylorida striata per taxonomic reference, examined syntypes and specimens from India from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Female length of Tylorida striata per taxonomic reference, examined syntypes and specimens from India
Figure 3. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 3. - Tyloridastriata, vulva, dorsal view, specimen from India (BNHS Sp. 139) CD- copulatory duct, FD- fertilization duct, S- spermatheca (Scale=0.2 mm).
Figure 4. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 4. - Map showing distribution of Tylorida species with T.striata-like globose abdomen.
Figure 1d. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 1d. - Metastriata, female syntype.Figure 1a.Habitus (detached cephalothorax and abdomen placed together), dorsal view (Scale=5 mm)Figure 1b.Abdomen of same syntype, lateral view (Scale=3 mm)Figure 1c.Vulva, ventral view (Scale=0.3 mm)Figure 1d.Labels in syntype vial <br> Labels in syntype vial
Figure 1a. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 1a. - Metastriata, female syntype.Figure 1a.Habitus (detached cephalothorax and abdomen placed together), dorsal view (Scale=5 mm)Figure 1b.Abdomen of same syntype, lateral view (Scale=3 mm)Figure 1c.Vulva, ventral view (Scale=0.3 mm)Figure 1d.Labels in syntype vial <br> Habitus (detached cephalothorax and abdomen placed together), dorsal view (Scale=5 mm)
Figure 1c. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 1c. - Metastriata, female syntype.Figure 1a.Habitus (detached cephalothorax and abdomen placed together), dorsal view (Scale=5 mm)Figure 1b.Abdomen of same syntype, lateral view (Scale=3 mm)Figure 1c.Vulva, ventral view (Scale=0.3 mm)Figure 1d.Labels in syntype vial <br> Vulva, ventral view (Scale=0.3 mm)
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.