Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
61
datasets available to search
ShareScore release 0.9.0
Dataset results
61 results for “Time-series data”
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: 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.
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.
Inferring population dynamics from single-cell RNA-sequencing time-series data
GEO Series GSE126579. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
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.
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.
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>
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.
The time-series gene expression data in PMA stimulated THP-1
GEO Series GSE15528. Homo sapiens. 10 samples. Type: Expression profiling by RT-PCR.
Time-series transcription microarray data of Streptomyces coelicolor M145
GEO Series GSE53562. Streptomyces coelicolor. 7 samples. Type: Expression profiling by array.
Hypoxia transcriptomic time-series data in three different cancer cell lines
GEO Series GSE29641. Homo sapiens. 24 samples. Type: Expression profiling by array.
Hypoxia transcriptomic time-series data in three different cancer cell lines
GEO Series GSE41491. Homo sapiens. 24 samples. Type: Expression profiling by array.
High-time-resolution time-series transcriptome data of Pseudomonas aeruginosa PAO1 under two inverted oxygen-availability transitions
GEO Series GSE52445. Pseudomonas aeruginosa PAO1; Pseudomonas aeruginosa. 28 samples. Type: Expression profiling by array.
ZGA-Timer: a framework for the identification of key zygotic genome activation genes from time-series RNA-seq data
GEO Series GSE174530. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Time-series RNA-seq data of Geobacillus thermoglucosidasius DSM2542
GEO Series GSE166135. Parageobacillus thermoglucosidasius. 6 samples. Type: Expression profiling by high throughput sequencing.
TimeChange: Topology inspired methods for the detection of differential dynamics in time-series data
GEO Series GSE193991. Drosophila melanogaster. 60 samples. Type: Expression profiling by high throughput sequencing.
Time-series transcription microarray data of Streptomyces coelicolor M145-OA
GEO Series GSE100343. Streptomyces coelicolor. 7 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.