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1,118 results for “Time series”
Time series data and codes from: Quantifying social media predictors of violence during the 6 January US Capitol insurrection using Granger causality
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Data for Concurrent Time-Series Selections Using Deep Learning and Dimension Reduction
<p>This data set has the ground truth labels for the publication "Concurrent Time-Series Selections Using Deep Learning and Dimension Reduction".</p> <p>The raw tri-axial accelerometry data is available as a separate data set:<br> dataset doi: 10.5281/zenodo.5500402<br> original paper for that data set doi: 10.3354/esr00084</p> <p>The data set here (available at the doi: 10.5281/zenodo.5503031) has the ground truth labels in 4 columns.</p> <p>The first and second columns are the start and end of the label in normalized coordinates [-1,1] representing the x-axis mouse clicks that created the label.<br> The third and fourth columns are transformed back to the data x-axis [0,173255].</p> <p>1D_1 are the labels for surfacing feature<br> 1D_2 are the labels for diving feature</p> <p>We also provide a recording of one of the participants in the user study.</p>
NDVI time series - early warning signals
<p>Many freshwater forested wetlands along the southeastern U.S. coastline are rapidly transitioning from forest to marsh or open water, due to climate change related disturbances, such as saltwater intrusion and increasing flooding frequency. These changes in wetland state are considered a regime shift, and the timing and trajectory of change are not well understood. Recent studies have found early warning signals (EWS) of regime shifts in other ecosystems, but it is unclear if these can be detected for coastal wetlands.</p> <p>In this study, we examined the ability to detect EWS of regime shifts in coastal wetlands within the Albemarle Pamlico peninsula, North Carolina, U.S.A. We used 35 years of the Landsat record to examine trends and variance of normalized difference vegetation index (NDVI) time series for selected areas known to have undergone regime shifts.</p> <p>We found that NDVI time series trends combined with changes in standard deviation of NDVI allowed us to identify four scenarios of change for coastal wetlands: 1) unstable transitioning; 2) gradual transition (declining); 3) unstable re-vegetated (recovering); and 4) stable vegetated. At the landscape scale within the Albemarle Pamlico peninsula, we found that approximately 114,294 ha (40%) of natural wetlands are considered stable, while 77,732 ha (27%) are areas are re-vegetating following a disturbance (mostly fires). We also found that 39,828 ha (14%) experienced a regime shift with an abrupt change, while 24,092 ha (8.5%) are forests gradually shifting to marshes.</p> <p><i>Syntheses and applications: </i>Our results suggest that the transition from a forest to a marsh can occur both rapidly and slowly, and remote sensing of NDVI time series can help identify ecosystem trajectories. Remote sensing provides the ability to measure and monitor the resilience of ecosystems, identify trajectories of change, and opens windows of opportunity for intervention, and prioritization of conservation/restoration of coastlines, all of which will become more important in the face of climate change and sea level rise.</p>
Data from: Decadal Time-Series Depletion of Dissolved Oxygen at Abyssal Depths in the Northeast Pacific
<p><strong>Decadal Time-Series Depletion of Dissolved Oxygen at Abyssal Depths in the Northeast Pacific </strong></p> <p>K.L. Smith Jr., M. Messié, T.P. Connolly, and C.L. Huffard</p> <p>10.1029/2022GL101018</p> <p><strong>Abstract</strong></p> <p>Dissolved oxygen depletion in the global ocean is well documented over several decades from the surface ocean to abyssal depths. This decline is especially prevalent in the Northeast Pacific. A significant decline in dissolved oxygen has been measured over 30 years at 4000-4100 m depth (Station M) beneath the California Current off central California. Three principal hypotheses examined the relationship of declining oxygen with biological and physical factors over the 30-year time series. Annual resolution revealed Ekman pumping, coastal upwelling, particulate matter flux, and sediment community oxygen consumption having significant correlations with bottom water dissolved oxygen concentration. Coastal upwelling accounted for 65% of the annual variation in bottom water oxygen concentration. Stepwise regression yielded descriptive models of bottom water dissolved oxygen using coastal upwelling, wind stress and primary production variables. Is continued oxygen depletion in the Northeast Pacific indicative of abyssal regions in the world ocean?</p> <p> </p> <p><strong>Dataset description:</strong></p> <p>This datafile contains two data tabs- monthly and yearly summaries of parameters measured near the seafloor and overlying surface waters and climate conditions. </p> <p>See details of data sources in <a href="http://10.1029/2022GL101018">Smith et al. Geophysical Research Letters</a></p> <p> </p> <p> </p>
Time series of the correlation coefficient at seismic stations in the Mexican subduction zone
<p>The directory structure is as follows:<br> correlation_coefficient/[STATION]/[YYMMDD]</p> <p>Each file contains the time series of the correlation coefficient every 10 seconds for the data YYMMDD.</p>
Datasets used in Detecting Plumes in Mobile Air Quality Monitoring Time Series with Density-based Spatial Clustering of Applications with Noise v01
<p>This repository contains the following data sets related to Detecting Plumes in Mobile Air Quality Monitoring Time Series with DBSCAN published in . Please cite the following: .</p> <p>Validated_Data.csv: A .csv file containing the validation set used in the study. Column headings are the following:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been manually flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p> <p>Labeled_DBSCAN_Anomalies.csv: A .csv file containing points labeled as anomalies by the DBSCAN algorithm described in the manuscript. Columns are the following.</p> <p>"BC": Black carbon measurement (ng/m^3)<br> "CO2": Carbon dioxide measurement (ppm)<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2 (ppb)<br> "UFP": Ultrafine particle count (p/cc)<br> "Anomaly": Whether the DBSCAN algorithm has labeled this point as "Anomaly" (2) or "Normal" (1)<br> "Uniq_Fac": Factor spanning from 1-277 grouping measurements taken on separate days by car. E.g. all measurements with Uniq_Fac=1 were grouped and analyzed together.<br> "LST": Timestamp (US/Central)<br> "Road_Class": TigerLINE census road class designation for the given point. Possible road classes are S1100 - Primary Road, S1200 - Secondary Road, S1400 - Local Road, S1630 - Ramps, S1640 - Service Drives, S1730 - Private Roads<br> "X": Universal Transverse Mercator Easting for Zone 15N (m).<br> "Y": Universal Transverse Mercator Northing for Zone 15N (m).</p> <p>*_To_Be_Validated.csv: A series of files where * denotes the following.</p> <p>"DB": DBSCAN Algorithm<br> "QOR": QOR Algorithm<br> "QAND": QAND Algorithm<br> "Drew": Drewnick Algorithm</p> <p>Each file contains the following columns:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p>
S&P500 Mean Correlation Time Series (1992-2012)
<p>Time Series of the S&P 500 mean market correlations evaluated in windows of length T trading days.</p> <ul> <li>the used companies for the correlations are given as tickers in the file Companies_Tickers.txt</li> <li>Financial_Time_Series_Centered_Interval.csv uses correlations calculated over a window of T = 42 trading days and the window is shfited by 1 trading day</li> <li>Financial_Time_Series_Centered_Interval__weekly.csv uses windows of T = 5 trading days (i.e. one trading week) and shifts the window by 5 days for each new interval (i.e. disjoint intervals)</li> </ul> <p>Data is gathered via yfinance in Python and spans the whole time from 1.1.1992 to 31.12.2012</p> <p> </p>
Bermuda Atlantic Time-Series Study (BATS) Primary Production
<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) primary production rates dataset is time-series spanning from 1988 to 2022. The dataset contains six primary production bottle estimates, along with CTD temperature and salinity measurements.</p><p>This description has been reproduced using https://www.dropbox.com/sh/8dbumf8tx3uidjv/AADlDuNqqGVOQ08TLyjQ7nxAa/bats_primary_production_v002.txt?dl=0</p>
Bermuda Atlantic Time-Series Study (BATS) Sediment Trap - Flux
<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) sediment trap - flux dataset is time-series spanning from 1988 to 2022. The dataset contains multiple nutrient flux measurements.</p><p>This dataset description has been reproduced using: https://www.dropbox.com/sh/3jl8pq7fvy7iejl/AAC8c91nuNa5aanxLtNxau2aa?dl=0&preview=bats_flux_v002.txt</p>
One-Tooth One-Time (1T1T) A Straightforward Approach to Replace Missing Teeth in the Posterior Region: a Case Series
ClinicalTrials.gov study NCT02898311. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Characteristics and Treatment Experiences of Individuals Using Injectable Semaglutide for Weight Management, a Cross-sectional Time-series Analysis, Multi-country Study.
ClinicalTrials.gov study NCT07154238. IPD Sharing: YES. Countries: 1. Publications: 0.
The Association Between Post-resuscitation Time Series Management in the Emergency Department and Short-term Outcomes for Out-of-hospital Cardiac Arrest Patients
ClinicalTrials.gov study NCT06165081. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Interrupted Time-Series Study for In-hospital Fall Reduction
ClinicalTrials.gov study NCT03003663. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The WikiTrauma Interrupted Time Series Protocol
ClinicalTrials.gov study NCT04035772. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Antibiotic Stewardship Program in Pancreatic Surgery: a Multicenter Time Series Analysis (BIOSTEPS).
ClinicalTrials.gov study NCT04199494. IPD Sharing: NO. Countries: 1. Publications: 0.
Machine Learning Enabled Time Series Analysis in Medicine
ClinicalTrials.gov study NCT05802563. IPD Sharing: YES. Countries: 1. Publications: 0.
Quantifying Nitrous Oxide Effect on Depth of Anaesthesia Using Theoretically Based Time Series Modelling
ClinicalTrials.gov study NCT00226837. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Artificial Intelligence Prognostic Model for Sepsis Based on Time Series Analysis
ClinicalTrials.gov study NCT06724120. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Time-series of transcriptome analysis of Bacillus subtilis response to 25 mM potassium acetate and 0.85 µM CCCP
GEO Series GSE55051. Bacillus subtilis. 12 samples. Type: Expression profiling by array.
Time-series DNA occupancy during the yeast respiratory cycle
GEO Series GSE60112. Saccharomyces cerevisiae. 35 samples. Type: Genome variation profiling by genome tiling array.
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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.