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Dataset results
14 results for “SGD”
SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Products (2013-2019)
<p><strong>If you used our dataset, please cite our reference:</strong></p> <p><strong>Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021.</strong></p> <p><strong>Description:</strong></p> <ul> <li>A <strong>seamless global daily</strong> (<strong>SGD</strong>) AMSR2 soil moisture long-term (2013-2019) dataset is generated through the proposed model. This daily products include <strong>2553</strong> global soil moisture NetCDF4 files, starting from Jan 01, 2013 to Dec 31, 2019 (about <strong>20GB</strong> memory after uncompressing this zip file).</li> <li>To further validate the effectiveness of these products, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. More validation results can be viewed at <strong><a href="https://qzhang95.github.io/Projects/Global-Daily-Seamless-AMSR2">SGD-SM</a></strong>.</li> <li>An example Python code of extracting this dataset is also available at <strong><a href="https://github.com/qzhang95/SGD-SM">https://github.com/qzhang95/SGD-SM</a></strong>.</li> <li>Official LPRM AMSR2 Descending L3 soil moisture products indeed only have 28 daily files in May 2013 (missing data files in date May 11, May 12, and May 13).</li> <li>This soil moisture dataset is comprised of netCDF4 (*.nc) files. Therefore, users need to install <strong>netCDF4</strong> toolkit before reading the data: <pre><code class="language-python">pip install netCDF4 pip install numpy</code></pre> <p> </p> </li> <li>It should be noted that the original and reconstructed soil moisture data are both recorded in one NC file. User can read the original data, reconstructed data, and mask data as follows:</li> <li> <pre><code class="language-python">Data = nc.Dataset(NC_file_position) Ori_data = Data.variables['original_sm_c1'] Rec_data = Data.variables['reconstructed_sm_c1'] Ori = Ori_data[0:720, 0:1440] Rec = Rec_data[0:720, 0:1440] Mask_ori = np.ma.getmask(Ori)</code></pre> <p> </p> </li> </ul>
Dataset: Safe and Green Development Corporation (SGD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fast Byzantine SGD (video)
Full video presentation of the paper: Fast Byzantine SGD.<br><br>Appears in Session 2 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
SGD-SM 2.0
<p>To overcome the disadvantages of previous <strong><a href="https://essd.copernicus.org/articles/13/1385/2021/">SGD-SM 1.0</a></strong>, we develop an improved seamless global daily soil moisture (<strong>SGD-SM 2.0</strong>) dataset for the years 2002-2022. The main improvements and contributions of <strong>SGD-SM 2.0</strong> are listed as follows:</p> <ul> <li><strong>Compared with SGD-SM 1.0 (from 2013 to 2019), the application scope of SGD-SM 2.0 can be enlarged from 2002 to 2022.</strong></li> <li><strong>SGD-SM 2.0 assimilates auxiliary precipitation information, to consider the sudden extreme weather condition.</strong></li> <li><strong>SGD-SM 2.0 develops an integrated LSTM-CNN to fill the gaps and missing regions in global daily products.</strong></li> </ul> <p>More Descriptions:</p> <ul> <li><strong>This is An improved seamless global daily soil moisture long-term (2002-2022) dataset.</strong></li> <li><strong>These daily products include 7115 global soil moisture NetCDF4 files (Both original and reconstructed data), starting from Jun 23, 2002 to Feb 05, 2022.</strong></li> <li><strong>Application Codes: <a href="https://github.com/qzhang95/SGD-SM">https://github.com/qzhang95/SGD-SM</a></strong></li> <li><strong>Homepage: <a href="https://qzhang95.github.io">https://qzhang95.github.io</a></strong></li> <li><strong>Processed by Qiang Zhang (qzhang95@dlmu.edu.cn)</strong></li> </ul>
PG-SGD Animations
<p>The two GIFs show a snapshot after each epoch of the Path-Guided Stochastic Gradient Descent (PG-SGD) algorithm which is described in https://github.com/pangenome/sorting-paper.<br> <br> `1D_PG-SGD.gif` shows the progression of the 1D algorithm of applied to a pangenome graph with 12 ALT sequences of the HLA-DRB1 gene from the GRCh38 reference genome. The layout and snapshots were created using `odgi sort` and each snapshot was visualized with `odgi viz`.</p> <p>`2D_PG-SGD.gif`shows the progression of the 2D algorithm of applied to a pangenome graph with 12 ALT sequences of the HLA-DRB1 gene from the GRCh38 reference genome. The layout and snapshots were created using `odgi layout` and each snapshot was visualized with `odgi draw`.</p>
AA-Pruebadeposito-SGD
<p>Conjunto de datos.</p>
RNAseq dataset of murine NVU cells post ischemic stroke isolated using the SGD method from the same starting material.
GEO Series GSE163752. Mus musculus. 48 samples. Type: Expression profiling by high throughput sequencing.
SGD-SST: Seamless Global Daily Sea Surface Temperature Products
<p><strong>Seamless Global Daily SNPP-VIIRS Sea Surface Temperature </strong><strong>Products from 2020 to 2024</strong></p>
SGD-SM 2.0
<p>SGD-SM 2.0</p>
SGD-SST (2016-2022)
<p>SGD-SST</p>
SGD-SST (2012-2015)
<p>SGD-SST (2012-2015)</p>
SGD-SST (To Visual)
<p>SGD-SST (To Visual)</p>
A Comparative Study Between Laryseal Pro Supra Glottic Device (SGD) With Proseal Laryngeal Mask Airway
ClinicalTrials.gov study NCT05901753. IPD Sharing: NO. Countries: 1. Publications: 0.
Global SGD and CO2 data
<p>This dataset is a global compilation of 40 coastal ecosystems across 12 countries. It consists of continuous time series for carbon dioxide and radon. Other parameters included are latitude, longitude, depth, temperature, salinity, and dissolved oxygen. Timestamps of each measurement correspond to the local time zone. Carbon dioxide and radon were measured using Licor and RAD7, respectively. Depth, temperature, salinity, and dissolved oxygen were measured using probes. This dataset was used to interpret the impact of SGD on CO2 dynamics in globally distributed coastal ecosystems. </p> <p>Data sources are mentioned in the sheet References. </p> <p>Column names in the "Raw Data" sheet: </p> <ul> <li>Location: Name of the location of measurement</li> <li>Location ID: ID of the location </li> <li>Country: Country of location </li> <li>Ecosystem Type: categorical ecosystem type </li> <li>Latitude: Latitudinal coordinate of the location (DD)</li> <li>Longitude: Longitudinal coordinate of the location (DD)</li> <li>Date (DD/MM/YYYY): Date when the measurement was taken (DD/MM/YYYY)</li> <li>Time since start (Days): Duration of the continuous measurement since the first measurement was started (days)</li> <li>Periods of Day: Part of the diurnal cycle when the measurement was taken (Day/Night)</li> <li>Tide: Part of the Tidal cycle when the measurement was taken (High/Ebb/Low/Flood)</li> <li>Depth (m): Depth at which the measurement was taken (m)</li> <li>Temperature (°C): Temperature at the point of radon/carbon dioxide measurement</li> <li>Salinity: Salinity of water at the point of radon/carbon dioxide measurement</li> <li>DO (%): Dissolved oxygen in the water at the point where radon measurement was taken (%)</li> <li>Radon (dpm/L): Radon measurement in water (dpm/L). Measured using Durridge RAD7 </li> <li>CO2 (µatm) at sst: measurement of Carbon Dioxide dissolved in water at sea surface temperature (µatm). Measured using LiCor. </li> </ul>
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.