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1,118 results for “Time series”
Time-series drinking water metagenomes: Assemblies & MAGs
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GNSS uplift time series and ice surface elevation changes
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Time-series of groundwater recharge, Tiber Riber Basin, Italy from 801 CE to the present day
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Acoustic Doppler Profiler (ADP) Time Series Measurements at Hog Island Bay at the Virginia Coast Reserve 2002-2004
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Automated Greenland Glacier Termini Position Time Series, Version 1
This data set contains shapefiles of termini traces from 294 Greenland glaciers, derived using a deep learning algorithm (AutoTerm) applied to satellite imagery. The model functions as a pipeline, imputing publicly availably satellite imagery from Google Earth Engine (GEE) and outputting shapefiles of glacial termini positions for each image. Also available are supplementary data, including temporal coverage of termini traces, time series data of termini variations, and updated land, ocean, and ice masks derived from the <a href="https://nsidc.org/data/nsidc-0714/versions/1">Greenland Ice Sheet Mapping Project (GrIMP) ice masks</a>.
Elevation change time series, Kjer and Hayes glacier drainage basins, NW Greenland
<p>This data set includes ice sheet elevation time series reconstructed at 19 locations in the Kjer and Hayes glacier drainage basins. Time series were derived from Digital Elevation Models (DEMs) and altimetry data collected from 1993 to 2015 by using the SERAC approach (Schenk and Csatho, 2012).</p> <p>Reference: Schenk, T. & Csatho, B. A New Methodology for Detecting Ice Sheet Surface Elevation Changes From Laser Altimetry Data. <em>Geoscience and Remote Sensing, IEEE Transactions on</em> <strong>50,</strong> 3302–3316 (2012).</p>
Time series data of rotational angular velocity of Typhoon Lan (2017) derived by Tsukada and Horinouchi (2020)
<p>Time series data of rotational angular velocity in the eye of Typhoon Lan (2017) on October 21, 2017, derived by Tsukada and Horinouchi (2020) using the data observed by Himawari-8 satellite. More information is also available at <a href="http://wwwoa.ees.hokudai.ac.jp/people/horinouchi-lab/TC/data_en.html">http://wwwoa.ees.hokudai.ac.jp/people/horinouchi-lab/TC/data_en.html</a>.</p>
Figure S1 - Time-series and trends of VIs, DIN-output and S-input for all catchments
<p>Figure S1: Trends (dotted lines) in long-term standard scores for measured sulfur deposition (S-input) and stream water total dissolved inorganic nitrogen export (DIN-output) during the observation period 1994-2016 and trends in vegetation indices extracted from Landsat satellite data for the observation period 1984-2016 for all catchments</p> <p> </p> <p>Table S1: Linear regression coefficients of trend lines in Figure S1</p>
High-resolution time series of leaf length and leaf thickness of strawberry and tomato measured in a growth chamber
<p>This dataset captures leaf thickness and leaf length of two strawberry plants and one tomato seeding. Leaf thickness measurements are only available of the strawberry plants. Additional data included light intensity data, soil water content (of one plant), relative humidity and temperature data. All sensors were read every second over a period of two weeks.</p>
Rotating globe – time series of temperature
<p>Rotating globe animation created by using data from the CMIP6 next generation archive from ETH Zurich (DOI: 10.5281/zenodo.3734128). It shows the temperature anomaly of the SSP5-8.5 scenario, relative to the mean of the years 1980-2014, using all available CMIP6 models over a 251 years time period (1850-2100).</p>
Pre-eruption InSAR time-series at Kīlauea (Hawai`i, USA): Sentinel Ascending and Descending 2018
<p>InSAR time-series data for Kīlauea (Hawai`i, USA) derived from InSAR, between Jan 2017 and May 2018 . Data were obtained by processing Sentinel-1 ascending and descending SAR data (tracks 124 and 87, respectively). Data were processed using the JPL-developed InSAR Scientific Computing Environment (<code>ISCE</code>) open-source software package, and further time-series analysis was performed using the <code>MintPy</code> software toolbox (<a href="https://github.com/insarlab/MintPy">Miami INsar Time-series software in PYthon</a>), developed at the University of Miami. </p> <p>Three files are available in Hierarchical Data Format:</p> <ol> <li><code>geo_timeseries_tropHgt_demErr_SenAT124.h5</code>: Ascending Track timeseries file. Dates available: <code>['timeseries-20170107', 'timeseries-20170131', 'timeseries-20170212', 'timeseries-20170224', 'timeseries-20170308', 'timeseries-20170320', 'timeseries-20170401', 'timeseries-20170425', 'timeseries-20170501', 'timeseries-20170507', 'timeseries-20170519', 'timeseries-20170525', 'timeseries-20170531', 'timeseries-20170606', 'timeseries-20170612', 'timeseries-20170618', 'timeseries-20170624', 'timeseries-20170630', 'timeseries-20170706', 'timeseries-20170712', 'timeseries-20170718', 'timeseries-20170724', 'timeseries-20170730', 'timeseries-20170805', 'timeseries-20170811', 'timeseries-20170817', 'timeseries-20170823', 'timeseries-20170829', 'timeseries-20170904', 'timeseries-20170910', 'timeseries-20170916', 'timeseries-20170922', 'timeseries-20170928', 'timeseries-20171004', 'timeseries-20171010', 'timeseries-20171016', 'timeseries-20171022', 'timeseries-20171028', 'timeseries-20171103', 'timeseries-20171109', 'timeseries-20171115', 'timeseries-20171121', 'timeseries-20171127', 'timeseries-20171203', 'timeseries-20171209', 'timeseries-20171215', 'timeseries-20171221', 'timeseries-20171227', 'timeseries-20180102', 'timeseries-20180108', 'timeseries-20180114', 'timeseries-20180120', 'timeseries-20180126', 'timeseries-20180201', 'timeseries-20180207', 'timeseries-20180213', 'timeseries-20180219', 'timeseries-20180225', 'timeseries-20180303', 'timeseries-20180309', 'timeseries-20180315', 'timeseries-20180327', 'timeseries-20180408', 'timeseries-20180420', 'timeseries-20180502']</code></li> <li><code>geo_timeseries_tropHgt_demErr_SenDT87.h5</code>: Descending Track timeseries file. Dates available:<code>['timeseries-20170104', 'timeseries-20170116', 'timeseries-20170128', 'timeseries-20170209', 'timeseries-20170221', 'timeseries-20170305', 'timeseries-20170317', 'timeseries-20170329', 'timeseries-20170410', 'timeseries-20170422', 'timeseries-20170428', 'timeseries-20170504', 'timeseries-20170510', 'timeseries-20170522', 'timeseries-20170603', 'timeseries-20170615', 'timeseries-20170627', 'timeseries-20170709', 'timeseries-20170721', 'timeseries-20170814', 'timeseries-20170826', 'timeseries-20170907', 'timeseries-20170919', 'timeseries-20171001', 'timeseries-20171013', 'timeseries-20171025', 'timeseries-20171106', 'timeseries-20171118', 'timeseries-20171130', 'timeseries-20171212', 'timeseries-20171224', 'timeseries-20180105', 'timeseries-20180117', 'timeseries-20180129', 'timeseries-20180210', 'timeseries-20180222', 'timeseries-20180306', 'timeseries-20180318', 'timeseries-20180330', 'timeseries-20180411', 'timeseries-20180423', 'timeseries-20180505', 'timeseries-20180511', 'timeseries-20180517', 'timeseries-20180523', 'timeseries-20180529']</code></li> <li><code>up_20180315_20180423.h5</code>: Vertical displacement file between 20180315 and 20180423, created using <code>timeseries2velocity.py</code> (from <code>MintPy</code>).</li> </ol> <p>These data are supplemental to: Farquharson, J. I. and Amelung, F. [2020], "<em>Extreme rainfall triggered the 2018 rift eruption at Kīlauea Volcano.</em>" <a href="https://doi.org/10.1038/s41586-020-2172-5">https://doi.org/10.1038/s41586-020-2172-5</a></p>
A time-series of strains in response to wind measured on 19 trees in Danum Valley, Malaysia
<p>Strain data measured on the trunks of 19 trees in the Danum Valley, Malaysia. It also contains wind speed data measured using anemometers attached to emergent trees nearby. This data can be used to extract the fundamental swaying frequencies of these trees, and to quantify the relationship between wind speed and bending strain and so estimate wind damage risk. This data would be useful for anyone studying the interaction between wind and trees. The tallest tree in this data set is approximately 51 m tall. The nearby field centre is located at (4°57'53.0"N 117°48'18.3"E) and the measured trees are part of the 50ha Smithsonian plot and the 1ha intensive carbon monitoring plot 1. </p>
Time Series Charactersitics of Libra and existing Forecasting Competitions
<p>We calculate different time series characteristics for our data set (libra) and the time series competitions M1, M3, M4, NN3, NN5, NNGC1, and Tourism.</p>
Additional information for manuscript entiteld "Host-parasitoid associations in marine planktonic time series: can metabarcoding help reveal them?" (PONE-D-20-17825R1)
<p><strong>Description:</strong></p> <p>This repository contains material to reproduce metabarcoding analyses based on the q-zip pipeline (https://github.com/PyoneerO/qzip). Raw fastq files can be downloaded from https://www.ebi.ac.uk/ena/browser/view/PRJEB37135. The used reference file can be downloaded from https://github.com/pr2database/pr2database/releases/tag/4.11.1. Please select the files created for the classifier implemented in mothur.</p> <p>The dockerfile in this repository can be used to set up the environment which inludes the installation of the needed versions of the needed tools.</p> <p>Twelve different analyses had been conducted. For each analysis one zip file had been created which contains the following files:</p> <p>- q-zip_commands.sh: the shell script to launch the pipeline</p> <p>- q-zip_parameters.txt: pipeline parameter file as input of the shell script</p> <p>- q-zip_workflow.log: log file containing stdout and sdterr</p> <p>- q-zip_seq_of_coms.txt: file containing each command executed during the pipeline run (minimal set of command to reproduce the results)</p> <p>- seq_number_stats.txt: file containing the sequence numbers at each filtering step</p> <p>- OTU tables in tsv and biom format (sequences and taxonomic annotation included)</p> <p>- Meta data map (here only including the raw file names)</p> <p>- swarm sequences in fasta format</p> <p> </p> <p><strong>The following analyses had been conducted:</strong></p> <p>- otu formation at swarm distance 1; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 2; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 3; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 5; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 10; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 1; relaxt settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 2; relaxt settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 3; relaxt settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 1; strict settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 2; strict settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 3; strict settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 1; very strict settings settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p><strong>Settings into more detail:</strong></p> <p>relaxt settings:</p> <ul> <li>trimmomatic filtering: sliding window length of 3 bp - threshold of average quality within of 5</li> <li>vsearch paired-end merging: length of minimum overlap of 25 bp - number of mismatches allowed of 5 bp</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 75% - percentage mismatches allowed of 20%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 1 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.6</li> </ul> <p>default settings (used for the manuscript):</p> <ul> <li>trimmomatic filtering: sliding window length of 3 bp - threshold of average quality within of 8</li> <li>vsearch paired-end merging: length of minimum overlap of 50 bp - number of mismatches allowed of 5</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 90% - percentage mismatches allowed of 10%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 0.25 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.8</li> </ul> <p>strict settings:</p> <ul> <li>trimmomatic filtering: sliding window length of 1 bp - threshold of average quality within of 15</li> <li>vsearch paired-end merging: length of minimum overlap of 50 bp - number of mismatches allowed of 0</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 90% - percentage mismatches allowed of 10%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 0.1 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.9</li> </ul> <ul> </ul> <p>very strict settings:</p> <ul> <li>trimmomatic filtering: sliding window length of 1 bp - threshold of average quality within of 15</li> <li>vsearch paired-end merging: length of minimum overlap of 50 bp - number of mismatches allowed of 0</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 100% - percentage mismatches allowed of 0%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 0.1 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.9</li> </ul>
Data from: Uncovering the genetic signature of quantitative trait evolution with replicated time series data
The genetic architecture of adaptation in natural populations has not yet been resolved: it is not clear to what extent the spread of beneficial mutations (selective sweeps) or the response of many quantitative trait loci drive adaptation to environmental changes. Although much attention has been given to the genomic footprint of selective sweeps, the importance of selection on quantitative traits is still not well studied, as the associated genomic signature is extremely difficult to detect. We propose 'Evolve and Resequence' as a promising tool, to study polygenic adaptation of quantitative traits in evolving populations. Simulating replicated time series data we show that adaptation to a new intermediate trait optimum has three characteristic phases that are reflected on the genomic level: (1) directional frequency changes towards the new trait optimum, (2) plateauing of allele frequencies when the new trait optimum has been reached and (3) subsequent divergence between replicated trajectories ultimately leading to the loss or fixation of alleles while the trait value does not change. We explore these 3 phase characteristics for relevant population genetic parameters to provide expectations for various experimental evolution designs. Remarkably, over a broad range of parameters the trajectories of selected alleles display a pattern across replicates, which differs both from neutrality and directional selection. We conclude that replicated time series data from experimental evolution studies provide a promising framework to study polygenic adaptation from whole-genome population genetics data.
Data from: Increasing compliance with low tidal volume ventilation in the ICU with two nudge-based interventions: evaluation through intervention time-series analyses
Objectives: Low tidal volume (TVe) ventilation improves outcomes for ventilated patients, and the majority of clinicians state they implement it. Unfortunately, most patients never receive low TVes. 'Nudges' influence decision-making with subtle cognitive mechanisms and are effective in many contexts. There have been few studies examining their impact on clinical decision-making. We investigated the impact of 2 interventions designed using principles from behavioural science on the deployment of low TVe ventilation in the intensive care unit (ICU). Setting: University Hospitals Bristol, a tertiary, mixed medical and surgical ICU with 20 beds, admitting over 1300 patients per year. Participants: Data were collected from 2144 consecutive patients receiving controlled mechanical ventilation for more than 1 hour between October 2010 and September 2014. Patients on controlled mechanical ventilation for more than 20 hours were included in the final analysis. Interventions: (1) Default ventilator settings were adjusted to comply with low TVe targets from the initiation of ventilation unless actively changed by a clinician. (2) A large dashboard was deployed displaying TVes in the format mL/kg ideal body weight (IBW) with alerts when TVes were excessive. Primary outcome measure: TVe in mL/kg IBW. Findings: TVe was significantly lower in the defaults group. In the dashboard intervention, TVe fell more quickly and by a greater amount after a TVe of 8 mL/kg IBW was breached when compared with controls. This effect improved in each subsequent year for 3 years. Conclusions: This study has demonstrated that adjustment of default ventilator settings and a dashboard with alerts for excessive TVe can significantly influence clinical decision-making. This offers a promising strategy to improve compliance with low TVe ventilation, and suggests that using insights from behavioural science has potential to improve the translation of evidence into practice.
Data from: Bayesian inference of selection in a heterogeneous environment from genetic time-series data
Evolutionary geneticists have sought to characterize the causes and molecular targets of selection in natural populations for many years. Although this research program has been somewhat successful, most statistical methods employed were designed to detect consistent, weak to moderate selection. In contrast, phenotypic studies in nature show that selection varies in time and that individual bouts of selection can be strong. Measurements of the genomic consequences of such fluctuating selection could help test and refine hypotheses concerning the causes of ecological specialization and the maintenance of genetic variation in populations. Herein, I proposed a Bayesian non-homogenous hidden Markov model to estimate effective population sizes and quantify variable selection in heterogeneous environments from genetic time-series data. The model is described and then evaluated using a series of simulated data, including cases where selection occurs on a trait with a simple or polygenic molecular basis. The proposed method accurately distinguished neutral loci from non-neutral loci under strong selection, but not from those under weak selection. Selection coefficients were accurately estimated when selection was constant or when the fitness values of genotypes varied linearly with the environment, but these estimates were less accurate when fitness was polygenic or the relationship between the environment and the fitness of genotypes was non-linear. Past studies of temporal evolutionary dynamics in lab populations have been remarkably successful. The proposed method makes similar analyses of genetic time-series data from natural populations more feasible, and thereby could help answer fun damental questions about the causes and consequences of evolution in the wild.
Data from: Novel Fourier quadrature transforms and analytic signal representations for nonlinear and non-stationary time series analysis
The Hilbert transform (HT) and associated Gabor analytic signal (GAS) representation are well-known and widely used mathematical formulations for modeling and analysis of signals in various applications. In this study, like the HT, to obtain quadrature component of a signal, we propose novel discrete Fourier cosine quadrature transforms (FCQTs) and discrete Fourier sine quadrature transforms (FSQTs), designated as Fourier quadrature transforms (FQTs). Using these FQTs, we propose sixteen Fourier quadrature analytic signal (FQAS) representations with following properties: (1) real part of eight FQAS representations is the original signal and imaginary part of each representation is FCQT of real part, (2) imaginary part of eight FQAS representations is the original signal and real part of each representation is FSQT of imaginary part, (3) like the GAS, Fourier spectrum of the all FQAS representations has only positive frequencies, however unlike the GAS, real and imaginary parts of FQAS representations are not orthogonal. The Fourier decomposition method (FDM) is an adaptive data analysis approach to decompose a signal into a set Fourier intrinsic band functions. This study also proposes new formulations of the FDM using discrete cosine transform with GAS and FQAS representations, and demonstrate its efficacy for improved time-frequency-energy representation and analysis of many real-life nonlinear and non-stationary signals.
Campi Flegrei cGPS Weekly Positions Time Series
<p>Weekly positions time series for the 21 Campi Flegrei cGPS stations from January 2000 to October 2023.</p><p>A full description of cGPS network and time series analysis is reported in:</p><ul><li>De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</li></ul><p>Please cite this when using the dataset</p>
Long time-series (2020-2100) high-resolution (1km) multi-scenario and multi-depth soil organic carbon dataset in China
<p>unit: kg C m-2 (soil oganic carbon density)</p><p>0100: denote 0-100 cm</p><p>020: denote 0-20 cm</p><p>Example 2020: 2020-2024 (five years mean soc)</p>
ScienceDex guides
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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)
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.