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327 results for “Time-Series”
Time-series of groundwater recharge, Tiber Riber Basin, Italy from 801 CE to the present day
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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>
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>
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.
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>
Detecting periodicities in time-series (for exoplanet detection)
<p>Recording of the presentation given at the Summer School</p>
Association Between Short-term Exposure to Air Pollutants and Emergency Attendance for Upper Gastrointestinal Bleeding in Hong Kong: A Time-series Study
<p>Related data and DLNM code for generating figures and results.</p>
Bermuda Atlantic Time-Series Study (BATS) Bottle 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 Bottle Validation (bval_bottle) dataset is a collection of discrete bottle samples from 1991-2022, including in-situ measurements of CO2, nutrients, carbon, oxygen, temperature, and Prochlorococcus and Synechococcus abundance. Note all bottle fires are included that do not have any BATS core parameters so as to be of use for other ancillary measurements. No data was provided for Particulate Biogenic Silica or Particulate Lithogenic Silica, so those variables were not included in this dataset. </p><p>This description has been reproduced using https://www.dropbox.com/s/2vutjrt5ce9t5qx/bval_bottle.txt?dl=0</p>
Bermuda Atlantic Time-Series Study (BATS) Bottle
<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 Bottle dataset is a collection of discrete bottle samples from 1988-2022, including in-situ measurements of CO2, nutrients, carbon, oxygen, temperature, and Prochlorococcus and Synechococcus abundance. Note all bottle fires are included that do not have any BATS core parameters so as to be of use for other ancillary measurements.</p><p>This description has been reproduced using https://www.dropbox.com/s/4sdk2tewx46cla8/bats_bottle.txt?dl=0</p>
Data from: Bayesian inference of selection in a heterogeneous environment from genetic time-series data
Open the record for dataset details and reuse information.
Data from: Increasing compliance with low tidal volume ventilation in the ICU with two nudge-based interventions: evaluation through intervention time-series analyses
Open the record for dataset details and reuse information.
A time-series of strains in response to wind measured on 19 trees in Danum Valley, Malaysia
Open the record for dataset details and reuse information.
Time-series transcriptomic analysis of murine colon undergoing AOM-DSS tumorigenesis with and without LXR activation
GEO Series GSE180078. Mus musculus. 26 samples. Type: Expression profiling by high throughput sequencing.
Genome-Wide Mapping Of Time-Series ChIP-Seq Data For Human ERα Breast Cancer Cell Line (MCF-7)
GEO Series GSE35109. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
RNA-Seq time-series of Adventitious root development in Populus ussuriensis
GEO Series GSE135804. Populus ussuriensis. 18 samples. Type: Expression profiling by high throughput sequencing.
Effect of Bmal1 pre-natal knockout on liver gene expression: time-series
GEO Series GSE70499. Mus musculus. 36 samples. Type: Expression profiling by high throughput sequencing.
LASSIM -a network inference toolbox for genome-wide mechanistic modeling [Time-series polarization: CD4+ T cells toward Th2]
GEO Series GSE60681. Homo sapiens. 15 samples. Type: Expression profiling by array.
Expression time-series from Streptomyces coelicolor germination
GEO Series GSE44415. Streptomyces coelicolor A3(2). 37 samples. Type: Expression profiling by array.
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.