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Dataset results
1,118 results for “Time series”
Hela Mitotic Selection - gene expression time series in early G1 phase of the human cell cycle
GEO Series GSE12473. Homo sapiens. 17 samples. Type: Expression profiling by array.
Causal network inference from gene transcriptional time-series response to glucocorticoids
GEO Series GSE144663. Homo sapiens. 199 samples. Type: Expression profiling by high throughput sequencing.
Time series integrative analysis of RNA-Seq and miRNA expression data reveals key biologic pathways during keloid formation [miRNA]
GEO Series GSE113620. synthetic construct; Homo sapiens. 27 samples. Type: Non-coding RNA profiling by array.
Time series analysis of the transition from dark to light growth of Dinoroseobacter shibae DFL12
GEO Series GSE25579. Dinoroseobacter shibae DFL 12 = DSM 16493. 26 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.
A time series of the pituitary gland transcriptome during development and sexual maturation in the model fish medaka
GEO Series GSE179598. Oryzias latipes. 84 samples. Type: Expression profiling by high throughput sequencing.
Time-series spatial transcriptomic analysis of murine colon undergoing AOM-DSS tumorigenesis with and without LXR activation
GEO Series GSE227598. Mus musculus. 5 samples. Type: Other.
Extraction of periodic signals in GNSS vertical coordinate time series using adaptive Ensemble Empirical Modal Decomposition method
Open the record for dataset details and reuse information.
Area averaged Sentinel 2 vegetation Indices time series on NBS and control sites (Panaro River). Further details can be found in D4.5 of the OPERANDUM project.
<p>NBS interventions in OAL-IT (Panaro) aimed at the establishment of a dense deep rooted perennial vegetation on river embankments with the goal to improve the strength of the vadose zone and to reduce erosivity of the soil cover. Monitoring the vegetation cover at the NBS location over time shall provide information about vegetation growth and structure past the completion of the intervention and could give useful insights on the status and effectiveness of the NBS.</p> <p>Dataset includes area averaged Vegetation Indices calculated by a time series of Sentinel-2 multispectral data (10-20 m spatial resolution). Normalized Difference Vegetation Index, Inverted Red-Edge Chlorophyll Index and Red Position Index were calculated for the cloud free images available in the time period between January 2019 and December 2021 providing a total of 71 images covering the NBS site and a control site close to it. The analysis was focused on the evaluation of the temporal profile of Vegetation Indices that can provide metrics of the vigor and health of the vegetation in both the NSB and the standard vegetation (control area).</p>
Pressure time series for waves on shelf
<p>Time: in UTC<br>Position 28.9192 -90.7744<br>Platform CSI 10A<br>Station ioos:station:WAVCIS:CSI10A<br>Description SS91 <br>Units: Pressure (dbar); Sea Pressure (dbar); depth (m)<br>Instrument: RBRvirtuoso pressure sensor<br>Data type: 4Hz ASCII data directly converted from the raw data with no additional user processing.</p>
Pressure time series for waves on shelf
<p>Time: in UTC<br>Station near ioos:station:WAVCIS:CSI06<br>Description South Timbalier Block 52, LA<br>Position 28.8667 -90.4833<br>Units: Pressure (dbar); Sea Pressure (dbar); depth (m)<br>Instrument: RBRvirtuoso pressure sensor<br>Data type: 4Hz ASCII data directly converted from the raw data with no additional user processing.</p> <p> </p>
Pressure time series for waves on shelf
<p>Time: in UTC<br>Station ioos:station:WAVCIS:CSI06<br>Description South Timbalier Block 52, LA<br>Position 28.8667 -90.4833<br>Units: Pressure (dbar); Sea Pressure (dbar); depth (m)<br>Instrument: RBRvirtuoso pressure sensor<br>Data type: 4Hz ASCII data directly converted from the raw data with no additional user processing.</p>
Calculating Lifetimes of Discrete States From Discrete Time Series
<p>Data set containing artificial discrete trajectories to test different methods for calculating lifetimes of discrete states from discrete time series. Additionally, the data set contains the results of applying the different tested lifetime methods to the artificial trajectories and plots that compare the different methods.</p>
DO UNCERTAINTIES IN US AFFECT BITCOIN RETURNS? EVIDENCE FROM TIME SERIES ANALYSIS.
<p>Data set used for a research paper</p>
matric suction time series in ground anchors and landslide scar at OAL-UK
<p>Dataset containing time series of matric suction collected with 10 IR tensiometers deployed on an NBS intervention (i.e. live ground anchors) and on landslide scar. The dataset is raw and unprocessed, so only the voltage signal retrieved from the tensiometers is given in the data set. Temporal resolution: 15 min </p>
Time Series from Smart Meters
<h1> </h1> <h1><strong>This file contains technical problems that make it insuitable for public use. Please use <a href="https://zenodo.org/records/7362094" target="_blank" rel="noopener">this dataset</a></strong><strong> instead.</strong></h1> <p> </p> <p> </p> <p> </p> <ul> <li><strong>Name</strong>: Time Series from Smart Meters</li> <li><strong>Summary</strong>: The dataset contains: (1) raw and cleaned time series of smart meters from Spanish electric cooperatives, and (2) feature values and metadata extracted from residential load profiles, both from Spanish electric cooperatives and publicly available datasets.</li> <li><strong>License</strong>: CC BY-NC-SA</li> <li><strong>Acknowledge</strong>: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li><strong>Disclaimer</strong>: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European Commission (EC). EASME or the EC are not responsible for any use that may be made of the information contained therein.</li> <li><strong>Collection Date</strong>: The time series on Spanish electric cooperatives were collected between 2014 and 2021. Data on feature values and metadata were extracted between 2020 and 2021.</li> <li><strong>Publication Date</strong>:</li> <li><strong>DOI</strong>: 10.5281/zenodo.4455198</li> <li><strong>Other repositories</strong>:</li> <li><strong>Author</strong>: University of Deusto</li> <li><strong>Objective of collection</strong>: This data is collected originally for invoice of the electrical consumption. In this project will be used to segment the households.</li> <li><strong>Description</strong>: The dataset contains a CSV file with one entry for both each load profile of the Spanish electric cooperatives and publicly available load profiles. The fields that can be found for each entry are (1) metadata such as the originating dataset to which they belong, the start and end dates, number of days that have been imputed and/or extended, provenance (country, administrative division, municipality, zip code, origin identifier (households, businesses, industry, etc. ), classification of socioeconomic conditions, and tariffs; and (2) extracted features, grouped by types such as statistical moments, quantiles, lag <em>d</em>-day autocorrelations, seasonal aggregates, peak and off-peak periods, load factors, energy consumed, features obtained using the R package "<em>tsfeatures</em>", and <em>Catch-22</em> features. In addition, the dataset also contains a rData file for each entry of the Spanish electric cooperatives. These files contain the original time series (timestamped values), a field indicating whether the signal has been imputed and/or extended and another field indicating the date of the extension.</li> <li><strong>5 star</strong>: ⭐⭐⭐</li> <li><strong>Preprocessing steps</strong>: anonymization, data fusion, imputation of gaps, extension of time series shorter than 800 days, computation of features.</li> <li><strong>Reuse</strong>: NA</li> <li><strong>Update policy</strong>: The data will be updated throughout 2021.</li> <li><strong>Ethics and legal aspects</strong>: Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has been carried out to substitute the CUPS by a MD5 hash.</li> <li><strong>Technical aspects</strong>: Decompressed data is quite large (big data).</li> <li><strong>Other</strong>:</li> </ul>
Azaspiracid_jurkat_time series experiment
GEO Series GSE5346. Homo sapiens. 6 samples. Type: Expression profiling by array.
Time-series single-cell transcriptomic profiling of luteal-phase endometrium uncovers dynamic characteristics and its dysregulation in recurrent implantation failures
GEO Series GSE250130. Homo sapiens. 28 samples. Type: Expression profiling by high throughput sequencing.
Maximum Likelihood Estimate of Payoffs from Time Series
Open the record for dataset details and reuse information.
Time-series toxicogenomics analysis of N-nitroso compound exposure in Caco-2 cells
GEO Series GSE20993. Homo sapiens. 36 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.