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1,118 results for “Time series”
Shallow Slow Slip Events Identified Offshore the Osa Peninsula in Southern Costa Rica from GNSS Time Series
<p>Using new continuous GNSS time series from the region, we identify 5 distinct shallow SSEs that occurred immediately offshore and beneath the Osa peninsula: an event detected by one site in 2013, and two events occurring in close succession in both 2018 and 2022 detected by multiple stations, indicating a preliminary recurrence interval of ~4-5 years. While SSEs have been observed to the north at Nicoya, this is their first documentation in southern Costa Rica. Modeled slip distributions of the 2018 and 2022 events indicate that they ruptured the same or overlapping patches of the plate interface, nearly at the trench, updip of historic earthquakes including the 1983 Mw 7.4 Osa event. Immediately offshore, the estimated cumulative slip from the 2018 and 2022 events is sufficient to close the slip deficit from tectonic loading over the recurrence interval, likely influencing the magnitude and spatial slip distribution of future large ruptures.</p>
Unlabeled Sentinel 2 time series dataset (training, T30TXT): Self-supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p> <strong> T30TXT unlabeled S2 dataset </strong></p> <p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TXT</strong> are available. To download the full pretraining dataset, see : <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table> <p> </p>
GNSS time series from "Subdaily slow fault slip dynamics captured by low-frequency earthquakes" (Mouchon et al., 2023)
<p>GNSS raw time series from "Subdaily slow fault slip dynamics captured by low-frequency earthquakes" (Mouchon et al., 2023)</p> <p>The raw GNSS time series are available for each three GNSS stations we focus on in this study (COYU, IGUA and MECZ), as well as the three distant stations use to correct from common mode (INEG, TAMP and UXAL), at both 24-hour and 6-hour sampling rates.<br> Each file contains the date, East, North and Vertical displacement and associated errors in meters (format: Decimal year, E (m), N (m), U (m), dE (m), dN (m), dU (m)).</p>
CESNET-USTS23: a benchmark dataset of Unevenly spaced time series from network traffic
<p>This dataset was created to evaluate characteristics of <em>Unevenly sampled time series from network traffic (USTS)</em> for the paper <em>Unevenly Spaced Time Series from Network Traffic</em>.</p> <p>The file named <code>time_series.tar.gz</code> contains a folder with time series CSV files as raw data of the experiment. In the folder are the following files:</p> <ul> <li><code>fts.csv</code> -- contains 2.6 million <em>Flow time series (FTS)</em> created from 259 million IP flows,</li> <li><code>pts.csv</code> -- contains 19 million <em>Packet time series (PTS)</em> created from 110 million network packets,</li> <li><code>sfts.csv</code> -- contains 15 million <em>Single flow time series (SFTS)</em> created from 160 million network packets.</li> </ul> <p>Traffic was captured on the national CESNET2 network from February 2023 to April 2023. All IP addresses in the dataset were anonymized.</p> <p>The <code>fts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Flow time series. (real IP address was anonimized by replacing with a random IP address)</li> <li>N_FLOWS -- Number of flows in time series, i.e., number of data points.</li> <li>N_PACKETS -- Number of packets in time series, i.e., the sum of metric PACKETS.</li> <li>N_BYTES -- Number of bytes in time series, i.e., the sum of metric PACKETS.</li> <li>PACKETS -- The array containing the time series metric number of packets in the IP flow.</li> <li>BYTES -- The array containing the time series metric number of bytes in the IP flow.</li> <li>START_TIMES -- The array containing the time series time axis of the flows starts.</li> <li>END_TIMES -- The array containing the time series time axis of the flows ends.</li> </ul> <p>The <code>pts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Packet time series. (real IP address was anonymized by replacing with a random IP address)</li> <li>BYTES -- The array containing the time series metric payload length of the network packet.</li> <li>TIMES -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The <code>sfts.csv</code> has the following format:</p> <ul> <li>SRC_IP -- Source IP address. (real IP address was anonimized by replacing with a random IP address)</li> <li>SRC_PORT -- Source port.</li> <li>DST_IP -- Destination IP address (real IP address was anonymized by replacing with a random IP address)</li> <li>DST_PORT -- Destination port.</li> <li>bytes -- The array containing the time series metric payload length of the network packet.</li> <li>time -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The file named <code>characteristics.tar.gz</code> contains a folder with characteristics gained by experiments from time series files. In the folder are the following files:</p> <ul> <li><code>fts.characteristics.csv</code> -- Characteristics about Flow time series from the fts.csv.</li> <li><code>pts.characteristics.csv</code> -- Characteristics about Packet time series from the pts.csv.</li> <li><code>sfts.characteristics.csv</code> -- Characteristics about Single flow time series from the sfts.csv.</li> </ul> <p>The <code>fts.characteristics.csv</code> has the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H_BYTES -- Hurst exponent of the source time series metric BYTES.</li> <li>STATIONARITY_PACKETS -- Stationarity of the source time series metric PACKETS.</li> <li>STATIONARITY_BYTES -- Stationarity of the source time series metric BYTES.</li> <li>OVERALL_STATIONARITY -- Overal stationarity created by merging STATIONARITY_PACKETS and STATIONARITY_BYTES.</li> </ul> <p>The <code>pts.characteristics.csv</code> and <code>sfts.characteristics.csv</code> have the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H -- Hurst exponent of the source time series.</li> <li>STATIONARITY -- Stationarity of the source time series.</li> </ul> <p>We provide the samples of all zipped files for a quick lookup: <code>fts.characteristics.sample.csv</code>, <code>fts.sample.csv</code>, <code>pts.characteristics.sample.csv</code>, <code>pts.sample.csv</code>, <code>sfts.characteristics.sample.csv</code>, <code>sfts.sample.csv</code></p> <p> </p>
Unlabeled Sentinel 2 time series dataset (training, T30TYS): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TYS</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Shallow Slow Slip Events Identified Offshore the Osa Peninsula in Southern Costa Rica from GNSS Time Series
<p>Using new continuous GNSS time series from the region, we identify 5 distinct shallow SSEs that occurred immediately offshore and beneath the Osa peninsula: an event detected by one site in 2013, and two events occurring in close succession in both 2018 and 2022 detected by multiple stations, indicating a preliminary recurrence interval of ~4-5 years. While SSEs have been observed to the north at Nicoya, this is their first documentation in southern Costa Rica. Modeled slip distributions of the 2018 and 2022 events indicate that they ruptured the same or overlapping patches of the plate interface, nearly at the trench, updip of historic earthquakes including the 1983 Mw 7.4 Osa event. Immediately offshore, the estimated cumulative slip from the 2018 and 2022 events is sufficient to close the slip deficit from tectonic loading over the recurrence interval, likely influencing the magnitude and spatial slip distribution of future large ruptures.</p>
Dataset: Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture
<p>Dataset for Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture manuscript submitted to Plant Phenomics. The dataset contains raw and processed root architecture images, RhizoVision trait outputs, and the associated R scripts for statistical analysis and model parameterization.</p>
Transport and decomposition time series
<p>Transport and decomposition annual time series.</p>
CLIMBER-X time series output of simulated Dansgaard-Oeschger events
<p>CLIMBER-X time series output of simulations showing Dansgaard-Oeschger -like events.</p> <p>The amoc_* files contain the time series of the maximum Atlantic overturning streamfunction for the simulations with different ice sheets and atmospheric CO2 concentrations as specified in the file name.</p> <p>The ocn_ts_* files contain the time series of the maximum Atlantic overturning streamfunction and the buoyancy flux integrated north of 55°N in the North Atlantic for the simulations with different ice sheets and gradually decreasing or increasing atmospheric CO2 concentration as specified in the file name. File names ending with *_noise.nc are for simulations with noise and those ending with *_nonoise.nc are for simulations without applied noise.</p> <p> </p>
EOT modeling time series data
<p>EOT modeling time series outputs (NorESM-L)</p>
Dataset for 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations'
<p> </p> <p>This dataset is associated with the research entitled: 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’. It contains raw data from six low-cost sensor loggers and one anemometer, collected during an experiment consisting of a series of controlled releases conducted in October 2019 at the TADI (TotalEnergies Anomaly Detection Initiative) platform.</p> <p><strong>Dataset Structure:</strong></p> <p>- `time`: Timestamp, marking the exact time the data was collected.<br> - `CH4`: Methane concentration measured by the reference instrument in parts per million (ppm).<br> - `2611C`: Voltage variation measured by the Figaro TGS 2611C-00 MOS sensor in volts (V).<br> - `2600`: Voltage variation measured by the Figaro TGS 2600 MOS sensor in volts (V).<br> - `2611E`: Voltage variation measured by the Figaro TGS 2611E-00 MOS sensor in volts (V).<br> - `RH_DHT22`: Relative humidity measured by the DHT22 sensor in percentage (%).<br> - `RH_SHT75`: Relative humidity measured by the SHT75 sensor in percentage (%).<br> - `T_DHT22`: Air temperature measured by the DHT22 sensor in degrees Celsius (°C).<br> - `T_SHT75`: Air temperature measured by the SHT75 sensor in degrees Celsius (°C).<br> - `T_BMP180`: Air temperature measured by the BMP180 sensor in degrees Celsius (°C).<br> - `T_BMP280`: Air temperature measured by the BMP280 sensor in degrees Celsius (°C).<br> - `P_BMP180`: Atmospheric pressure measured by the BMP180 sensor in pascals (Pa).<br> - `P_BMP280`: Atmospheric pressure measured by the BMP280 sensor in pascals (Pa).<br> - `Release`: Number of the controlled release.</p> <p><strong>Acknowledgment:</strong></p> <p>When using this dataset, please reference the original research paper titled ‘Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’.</p> <p><strong>Contact Information:</strong></p> <p>Olivier Laurent (olivier.laurent@lsce.ipsl.fr)</p> <p> </p>
Unsupervised segmentation and clustering time series approach to Southern Africa rainfall regime changes.
<p>The datasets were sourced from Southern Africa countries namely Malawi, Mozambique, South Africa, and Zimbabwe where recorded historical rainfall data was available. In each country different meteorological stations were selected and were then categorized into coastal, sub-humid and semi-arid zones based on agro-ecological regions. The following information was collected for each meteorological service station; country, meteorological service station name, agroecological region, year , recorded rainfall, and stations geographical coordinates. </p>
Temperature time series at 25m depth in the North pass of the Great Reef of Toliara (Madagascar) from May 6th 2019 to September 4th 2020
<p>Temperature data collected using two temperature loggers (HOBO Pendant, Temperature/Light data logger) that were successively deployed in the North pass of the Great Reef of Toliara (Madagascar), at 25m depth (S23°21.010', E43°36.837'). The first one was set to log a measurement every four hours, from May 6th till November 20th, 2019, and the second one was set to log a measurement every two hours, from November 20th, 2019 till September 4th, 2020.</p>
Bermuda Atlantic Time-Series Study (BATS) Pigment Data Validation
<p>This dataset is published on Zenodo by the Simons CMAP curators for long-term care. All credits go to the data producers at the Bermuda Atlantic Time-series Study (BATS): https://bats.bios.asu.edu/bats-data/ </p><p>The BATS (Bermuda Atlantic Time-series Study) discrete HPLC pigment validation dataset is time-series spanning from 1996 to 2022. The dataset contains the 21 separate in-situ pigment measurements along with sampling depth and the BATS Cruise ID.</p><p>This description has been reproduced using https://www.dropbox.com/s/6ajl545hyua8ot8/bval_pigments.txt?dl=0</p>
Evaluating a Community-Based Exercise Intervention With Adults Living With HIV: An Interrupted Time Series Study
ClinicalTrials.gov study NCT02794415. IPD Sharing: YES. Countries: 1. Publications: 5.
Transforming Hypertension Treatment in Nigeria Using a Type II Hybrid, Interrupted Time Series Design
ClinicalTrials.gov study NCT04158154. IPD Sharing: YES. Countries: 1. Publications: 9.
Data from: Using time series analysis to characterize evolutionary and plastic responses to environmental change: a case study of a shift toward earlier migration date in sockeye salmon
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Nonlinear time series analysis of the interaction between the citrus whitefly and the whitefly-specialist ladybird
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Data from: Biodiversity-ecosystem functioning relationships in long-term time series and palaeoecological records: deep sea as a test bed
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Data from: Multi-decadal time series of remotely sensed vegetation improves prediction of soil carbon in a subtropical grassland
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.