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672 results for “August”
Changes in gene expression of Pinus armandii seed kernels during the harvesting period (August to September)
GEO Series GSE271040. Pinus armandii. 6 samples. Type: Expression profiling by high throughput sequencing.
AIS data prepared by Statsat AS for August 2020
<p>AIS data prepared and provided by Statsat AS (Norway) in the framework of the T-SAR project (<em>IKTPLUSS programme on reducing digital vulnerabilities, </em><strong>10 MNOK from the Research Council of Norway, Norway</strong>).</p> <p>The dataset contains AIS data (satellite + other) on a global coverage for 2020. There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day.</p> <p> </p> <p>The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020.</p> <p>The csv files have one header line:</p> <p>mmsi;lon;lat;date_time_utc;sog;cog;true_heading;nav_status;rot;message_nr;source</p> <p>where:</p> <table> <tbody> <tr> <td> <p><strong>mmii</strong></p> </td> <td> <p>integer</p> </td> <td> <p>MMSI number of the vessel (AIS identifier). All records belonging to the same vessel will have the same identifier.</p> </td> </tr> <tr> <td> <p><strong>lon</strong></p> </td> <td> <p>float</p> </td> <td> <p>Geographical longitude (WGS84) between -180 to 180</p> </td> </tr> <tr> <td> <p><strong>lat</strong></p> </td> <td> <p>float</p> </td> <td> <p>Geographical latitude (WGS84) between -90 to 90</p> </td> </tr> <tr> <td> <p><strong>date_time_utc</strong></p> </td> <td> <p>datetime</p> </td> <td> <p>Date and Time (in UTC) when position was recorded by AIS. It is represented as: YYYY-MM-DD HH:MM:SS (for instance 2020-01-01 00:00:00).</p> </td> </tr> <tr> <td> <p><strong>sog</strong></p> </td> <td> <p>float</p> </td> <td> <p>Speed over ground (knots)</p> </td> </tr> <tr> <td> <p><strong>cog</strong></p> </td> <td> <p>float</p> </td> <td> <p>Course over ground (degrees)</p> </td> </tr> <tr> <td> <p><strong>true_heading</strong></p> </td> <td> <p>integer</p> </td> <td> <p>Heading (degrees) of the vessel's hull. A value of 511 indicates there is no heading data.</p> </td> </tr> <tr> <td> <p><strong>nav_status</strong></p> </td> <td> <p>integer</p> </td> <td> <p>Navigation status according to <a href="https://api.vtexplorer.com/docs/ref-navstat.html"><strong>AIS Specification</strong></a></p> </td> </tr> <tr> <td> <p><strong>rot</strong></p> </td> <td> <p>integer</p> </td> <td> <p>rate of turn</p> </td> </tr> <tr> <td> <p><strong>message_nr</strong></p> </td> <td> <p>integer</p> </td> <td> <p>message number</p> </td> </tr> <tr> <td> <p><strong>source</strong></p> </td> <td> <p>integer</p> </td> <td> <p>source is the source of AIS data ('g' for ground or 's' for satellite).</p> </td> </tr> </tbody> </table> <p>One row in the CSV file corresponds to one message.</p>
Ludwig Institute/UCSD ENCODE project August 2007 Release
GEO Series GSE8887. Homo sapiens. 15 samples. Type: Genome binding/occupancy profiling by genome tiling array.
TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 4, January 2021 - August 2022)
<p><strong><a href="https://data.gesis.org/tweetscov19/">TweetsCOV19</a></strong><strong> </strong>is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of <a href="https://data.gesis.org/tweetskb">TweetsKB</a> and aims at capturing online discourse about various aspects of the pandemic and its societal impact. <strong>Metadata</strong> information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong>, and <strong>resolved URLs </strong>are exposed in RDF using established RDF/S vocabularies*.</p> <p>We also provide a <em><strong>tab-separated values (tsv)</strong></em> version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices:</p> <ol> <li>Tweet Id: Long.</li> <li>Username: String. Encrypted for privacy issues*.</li> <li>Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ).</li> <li>#Followers: Integer.</li> <li>#Friends: Integer.</li> <li>#Retweets: Integer.</li> <li>#Favorites: Integer.</li> <li>Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from <a href="https://github.com/yahoo/FEL">FEL</a> library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;".</li> <li>Sentiment: String. <a href="http://sentistrength.wlv.ac.uk/">SentiStrength</a> produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1").</li> <li>Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;".</li> <li>Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;".</li> <li>URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;"</li> </ol> <p>To extract the dataset from <a href="https://data.gesis.org/tweetskb">TweetsKB</a>, we compiled a seed list of 268 COVID-19-related <a href="https://data.gesis.org/tweetscov19/keywords_v1.1.txt">keywords</a>.</p> <p><em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em></p>
TweetsKB (Part 11, January 2022 - August 2022)
<p><strong>TweetsKB</strong><strong> </strong>is a public RDF corpus of anonymized data for a large collection of <strong>annotated </strong>tweets. The dataset currently contains data for more than <strong>2 billion tweets</strong>, spanning more than 7 years (February 2013 - August 2022). Metadata information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong> and <strong>URLs </strong>are exposed in RDF using established RDF/S vocabularies*. Example queries and more information are available through TweetsKB's home page: <a href="https://data.gesis.org/tweetskb/">https://data.gesis.org/tweetskb/</a>.</p> <p> </p> <p><em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em></p>
Data and generating files for the manuscript "A Multi-Model Analysis of Solute Plume Behavior in a Synthetic Braided-River Deposit", submitted to Water Resources Research, August 2018.
<p>Data and generating files for the manuscript "A Multi-Model Analysis of Solute Plume Behavior in a Synthetic Braided-River Deposit", submitted to Water Resources Research, August 2018.</p> <p>This zipped folder contains the codes and data generated for the manuscript. The main routine for generating the ensembles is fidelity/run_fidelity.py. The folders 'dtgeostats', 'flowtrans', and 'hyvr' contain utilities for generating parameter fields and running flow-and-transport simulations. Note that the codes and data are provided as-is and relative pathways, etc. in the code may not function correctly. The data can be found in the fidelity/runfiles/braid005 directory and includes the outputs for the synthetic virtual reality (fidelity/runfiles/braid005/braid_vr) and the following model ensembles: object-based with no conditioning (fidelity/runfiles/braid005/braid_1a), object-based with soft conditioning (fidelity/runfiles/braid005/braid_1b), MPS (fidelity/runfiles/braid005/braid_2a/), isotropic multi-Gaussian (fidelity/runfiles/braid005/braid_3/isn/), and anisotropic multi-Gaussian (fidelity/runfiles/braid005/braid_3/ann/) .</p>
Total OH reactivity measured around the Arabian Peninsula July-August 2017
<p>Total OH reactivity measured by the Comparative Reactivity Method in July and August 2017 during the Air Quality and Climate Change in the Arabian Basin (AQABA) Campaign. The data are available upon request to all scientists agreeing to the AQABA protocol after August 2019 (eva.pfannerstill@mpic.de). Data is in 5 min resolution and measurements influenced by own ship exhaust are excluded.</p>
Facebook, Instagram and Twitter datasets of Sinn Féin, Fine Gael and Fianna Fáil, August 2019-February 2020
<p>Datasets for social media metrics for Sinn Féin, Fine Gael and Fianna Fáil for a period of six months prior to the 2020 general election in Ireland. The information was used to discern which party had the strongest online following and whether this party used tactics to achieve this. </p>
Auguste and Ferro et al. Fixed Tissue Raw Images
<p>Within each compressed ZIP file are the raw fixed tissue images used for the majority of data presented in figures 1-3. The image nomenclature is in a blinded state and a key to the blinding can be found within each dataset. </p> <p>Also, find attached the in-house ImageJ macro used throughout the paper. </p>
Monthly Chlorophyll-a and total suspended solids concentrations during August 2002 and July 2020 in the Pearl River Estuary, China
<p>Monthly Chlorophyll-a (Chl-a) and total suspended solids (TSS) concentrations during August 2002 and July 2020 in the Pearl River Estuary, China. Chl-a and TSS were retrieved based on a machine learning algorithm developed by <em>Ma et al.</em> [2022] using satellite imagery from MODerate-resolution Imaging Spectroradiometer (MODIS) onboard Aqua. </p> <p>Reference</p> <p>Ma, C., J. Zhao, B. Ai, S. Sun, and Z. Yang (2022), Machine Learning Based Long‐Term Water Quality in the Turbid Pearl River Estuary, China, <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(1).</p>
Experten Interview Transkript - Videostreaming Industrie - 2023 August
<p>Experten Interview Transkript - Videostreaming Industrie - 2023 August</p>
Experten Interview Transkript - Video Playout - 2023 August
<p>Experten Interview Transkript - Video Playout - 2023 August</p>
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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)
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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.