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327 results for “Time-Series”
DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data
GEO Series GSE255888. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Photoperiodic time-series mRNASeq assays of the sheep pars tuberalis
GEO Series GSE144677. Ovis aries. 60 samples. Type: Expression profiling by high throughput sequencing.
SPEI and SSI time-series datasets for the Indus Basin of Pakistan
<p>The file contains 2 datasets namely:<br> 1-Standard Precipitation and Evapotranspiration Index (SPEI) for four catchments (Chenab,Jhelum, Indus and Kabul) of the Indus Basin<br> 2-Standard Streamflow Index (SSI) for four catchments of the Indus Basin.</p> <p>SPEI:<br> The Standard Precipitation and Evapotranspiration Index (SPEI) (Vicente-Serrano et al., 2010a) is the indicator used <br> for quantification and monitoring of meteorological droughts.<br> SPEI is calculated for the four catchments (Chenab,Jhelum, Indus and Kabul) at each catchment level using gridded climate data from the CRU data set (Harris et al., 2020). <br> For catchment level SPEI calculations, gridded precipitation and Potential Evapotranspiration values extracted from CRU dataset are averaged over the catchment. A map(study_area) delineating the four catchments is provided.</p> <p>SSI:<br> The Standard Streamflow Index (SSI) (Nalbantis and Tsakiris, 2009; Modarres, 2007) is used (also called the Streamflow<br> Drought Index and the Streamflow Runoff Index) to quantify and analyze hydrological droughts.SSIs are computed at catchment outlets as shown in the attached map (study_area). Streamflow station names are Marala(for Chenab),Mangla (Jhelum), Tarbela(for Indus) and Nowshera(for Kabul).</p> <p><br> Units: Both the datasets are dimensionless </p> <p><br> References:</p> <p>Harris, I. C. and Jones, P. D.: CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018), https://doi.org/10.5285/10d3e3640f004c578403419aac167d82, university of East Anglia Climatic Research Unit; Centre for Environmental Data Analysis, 2020.</p> <p>Modarres, R.: Streamflow drought time series forecasting, Stochastic Environmental Research and Risk Assessment, 21, 223–233, https://doi.org/10.1007/s00477-006-0058-1, 2007.</p> <p>Nalbantis, I. and Tsakiris, G.: Assessment of hydrological drought revisited, Water Resources Management, 23, 881–897,<br> https://doi.org/10.1007/s11269-008-9305-1, 2009.</p> <p>Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to GlobalWarming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, https://doi.org/10.1175/2009JCLI2909.1, 2010a.</p>
Data from: Trends in malaria in Odisha, India—an analysis of the 2003-2013 time-series data from the National Vector Borne Disease Control Program
Background: Although Odisha is the largest contributor to the malaria burden in India, no systematic study has examined its malaria trends. Hence, the spatio-temporal trends in malaria in Odisha were assessed against the backdrop of the various anti-malaria strategies implemented in the state. Methods: Using the district-wise malaria incidence and blood examination data (2003-2013) from the National Vector Borne Disease Control Program, blood examination-adjusted time-trends in malaria incidence were estimated and predicted for 2003-2013 and 2014-2016, respectively. An interrupted time series analysis using segmented regression was conducted to compare the disease trends between the pre (2003-2007) and post-intensification (2009-2013) periods. Key-informant interviews of state stakeholders were used to collect the information on the various anti-malaria strategies adopted in the state. Results: The state annual malaria incidence declined from 10.82/1000 to 5.28/1000 during 2003-2013 (adjusted annual decline: -0.54/1000, 95% CI: -0.78 to -0.30). However, the annual blood examination rate remained almost unchanged from 11.25% to 11.77%. The key-informants revealed that intensification of anti-malaria activities in 2008 led to a more rapid decline in malaria incidence during 2009-2013 as compared to that in 2003-2007 [adjusted decline: -0.83 (-1.30 to -0.37) and -0.27 (-0.41 to -0.13), respectively]. There was a significant difference in the two temporal slopes, i.e., -0.054 (-0.10 to -0.002, p=0.04) per 1000 population per month, between these two periods, indicating almost a 200% greater decline in the post-intensification period. Although, the seven southern high-burden districts registered the highest decline, they continued to remain in that zone, thereby, making the achievement of malaria elimination (incidence <1/1000) unlikely by 2017. Conclusion: The anti-malaria strategies in Odisha, especially their intensification since 2008, have helped improve its malaria situation in recent years. These successful measures need to be sustained and perhaps intensified further for eliminating malaria from Odisha.
Long time-series (1980-2020) high-resolution (1km) 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 1980: 1980-1984 (five years mean soc)</p><p> </p><p> </p><p> </p>
Data for the preprint of "Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems"
<p>README: description of the files. </p> <p>This Zenodo repository contains all the data and original code necessary to reproduce the results of the paper https://doi.org/10.48550/arXiv.2402.07786. The code (continuously mantained and updated) is available open-source as a Python package at https://pypi.org/project/onion-clustering/ and on GitHub (https://github.com/matteobecchi/timeseries_analysis). </p> <p>The repository contains the folders "Fig1", "Fig2" etc, which contain the corresponding Datasets, together with the code to reproduce the figures. Additionally, the folder "FigS1 contains code and data for FigS1. </p> <p>The repository also contains the Supplementary Movies S1 to S4, in .mp4 format. </p>
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>
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>
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>
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.
Interrupted Time-Series Study for In-hospital Fall Reduction
ClinicalTrials.gov study NCT03003663. IPD Sharing: Not stated. 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.
Integrated time-series analysis and high-content CRISPR screening delineate the dynamics of macrophage immune regulation [RNA-seq]
GEO Series GSE263759. Mus musculus. 64 samples. Type: Expression profiling by high throughput sequencing.
Fine scale time-series RNA-Seq of shoot and root responses to Nitrogen supply
GEO Series GSE97500. Arabidopsis thaliana. 114 samples. Type: Expression profiling by high throughput sequencing.
MYC drives temporal evolution of small cell lung cancer subtypes by reprogramming neuroendocrine fate [Single Cell RNA seq on RPM time-series cells and 4 RPM bulk tumors]
GEO Series GSE149179. Mus musculus. 11 samples. Type: Expression profiling by high throughput sequencing.
Data from: Trends in malaria in Odisha, India—an analysis of the 2003-2013 time-series data from the National Vector Borne Disease Control Program
Open the record for dataset details and reuse information.
Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis
GEO Series GSE224396. Homo sapiens. 40 samples. Type: Other.
Circadian time-series RNA-sequencing of SHEP and SKNAS neuroblastoma cells with inducible N-MYC-ER
GEO Series GSE221103. Homo sapiens. 83 samples. Type: Expression profiling by high throughput sequencing.
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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.