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.
677
datasets available to search
ShareScore release 0.9.0
Dataset results
677 results for “time of use”
RNA-sequencing time course of Human Intestinal Epithelial Cells (HIECs) following knockdown of miR-30bcd using complementary locked nucleic acids
GEO Series GSE79923. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
COSMOS: A Platform for real-time morphology-based, label-free cell sorting using deep learning (targeted RNA-Seq)
GEO Series GSE241835. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Disentangling pro-mitotic signaling during cell cycle progression using time-resolved single-cell imaging
GEO Series GSE147259. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Learning causal networks using inducible transcription factors and transcriptome-wide time series
GEO Series GSE142864. Saccharomyces cerevisiae. 1768 samples. Type: Expression profiling by array.
Time-course tracking of physical proximity and its effect on gene expression for elucidating single NK and cancer cell interactions using a microfluidic platform
GEO Series GSE181591. Homo sapiens. 340 samples. Type: Expression profiling by high throughput sequencing.
Reversal of MECP2 duplication syndrome using genetic rescue and antisense oligonucleotides [ASO time point 1]
GEO Series GSE71233. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
COSMOS: A Platform for real-time morphology-based, label-free cell sorting using deep learning (bulk RNA-Seq)
GEO Series GSE241833. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
Long-read transcriptome profiling of the rat hippocampal neurons in primary culture at different time points after activation using Nanopore sequencing
GEO Series GSE218694. Rattus norvegicus. 20 samples. Type: Expression profiling by high throughput sequencing.
On the use of real-time mortality data in modelling and analysis during an epidemic outbreak – extended data
<p>This project contains extended data related to the journal article "On the use of real-time mortality data in modelling and analysis during an epidemic outbreak" by Per Liljenberg</p> <p>This project contains the following extended data:</p> <ul> <li>Liljenberg2020_OGR_Appendix.pdf (Appendix to the main article)</li> <li>swedish_covid_deaths_OGR.R. (R-script to generate graphs and nowcasts in the paper.)</li> <li>MDAR author checklist.pdf (Completed MDAR reporting checklist)</li> </ul>
Table 1 : Norms for clinical use of CXM, a real-time marker of height velocity
<p>Table 1 data set</p>
Real-Time Early Detection and Monitoring of Flooding Using Low-Cost Highly Sensitive Ultrasound Sensing of Water Level
<p>Flooding poses safety hazards to motorists, emergency and maintenance crews and may cause costly damage to transportation infrastructure and its operation. Flash flooding, in particular, causes the most flood-related deaths According to NOAA, in 2017 alone, flash flooding also caused $60.7 billion worth of economic damage. Low-water crossings are among the first places where deaths and significant damages to vehicles occur during flooding. With flash flooding, when a critical corridor is blocked by a high level of water, it affects the safety of the general public. To keep the critical corridors open as long as possible, and to minimize losses from flooding, accurate early detection of the rising water level is essential. The flood level detection system has to have flood stage maps in the geographic information system for the street-, roadway-, and critical-freight corridors. This area encompasses public roads in urbanized areas that provide access and connection to the primary roads for ports, public transportation or other transportation facilities. The main goal of this project is to develop cost-effective and high efficient solar-powered water level detection units and implement real-time water level monitoring for water both pavement and river stream. The project was performed with three objectives: to develop the low cost reliable real-time data of the ultrasound water level detection system, increasing its data reliability and resolution; 2) to improve an energy-saving processing system; and to deploy the ultrasound water level detection system and real-time monitoring system for water both pavement and river stream for safety measures. The obtained results and findings imply the developed monitoring system can present reliable water level data with efficient power and data transmission system.</p>
Data from: Quantifying habitat use of migratory fish across riverscapes using space-time isotope models
1.Migratory animals pose difficult challenges to conservation and management because identifying critical habitats used throughout their lives is rarely possible. Endogenous tracers (e.g., isotope ratios) recorded in sequentially growing biogenic tissues, however, represent a potential source of unique insights at the more elusive temporal and spatial scales central to understanding the ecology of mobile species. To this end, a general probabilistic framework has emerged that quantitatively compares predictive models of isotopic variation across landscapes (called isoscapes) to the isotopic composition recorded in a biological tissue to determine the provenance and movements of animals throughout their lives. 2.Although this spatially continuous approach to isotope‐based geographic assignment is becoming more common across taxa and ecosystems, adopting this framework to take advantage of serial isotope records stored within sequentially growing biogenic tissues (e.g., teeth or otoliths) is less common. 3.Here, we construct a novel space‐time isotope model of provenance (STIMP) that determines the habitat use through time of migratory fish across river basins. To do so, this model integrates: strontium isotope (87Sr/86Sr) ratios across a riverscape, the serial records of 87Sr/86Sr within otoliths, habitat geomorphology, and the directional movement patterns of fish through river networks. 4.To illustrate an application of the model, we applied it to a published dataset from Chinook salmon (Oncorhynchus tshawytscha) harvested in 2011 during a coastal fishery in Bristol Bay, Alaska, U.S.A. Using this model, we show how individuals exploit an array of habitat types to achieve their juvenile growth prior to ocean migration, and that the intensity of habitat use among habitat types across the basin shifts spatially over the course of freshwater residence (e.g., from headwaters to migration corridors). The STIMP presented here integrates diverse information sources to reveal the cryptic juvenile movement patterns of a highly migratory species, providing new insights critical to their conservation. This general framework is applicable to any migratory taxa that use isotopically heterogeneous landscapes during their lives and record such variation in sequentially growing biogenic tissues.
Data from: Classifying three imaginary states of the same upper extremity using time-domain features
Brain-computer interface (BCI) allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG) signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area. The proposed method extracts time-domain features and classifies them using a support vector machine (SVM) with a radial basis kernel function (RBF). An average accuracy of 74.2% was obtained when using the proposed method on a dataset collected, prior to this study, from 12 healthy individuals. This accuracy was higher than that obtained when other widely used methods, such as common spatial patterns (CSP), filter bank CSP (FBCSP), and band power methods, were used on the same dataset. These results are encouraging and the proposed method could potentially be used in future applications including BCI-driven robotic devices, such as a portable exoskeleton for the arm, to assist individuals with impaired upper extremity functions in performing daily tasks.
urbisphere_gb-london_UR-2: Activities profiles derived from UK Time Use Survey data for modelling
<h2>Files in this archive </h2> <ul> <li>UK_TUS2014-15_activity_profiles_190324.zip <ul> <li>Activity profile dataset derived from the UK Time Use Survey (2014/15; *.csv) </li> </ul> </li> <li>auxiliary_data.zip <ul> <li>Auxiliary datasets created for processing</li> </ul> </li> <li>AppliancePowerManufacturer.zip <ul> <li>Information used to assigned values</li> </ul> </li> <li>code.zip <ul> <li>Code to produce the dataset</li> <li>Python3.9 Jupyter notebook</li> </ul> </li> <li>urbisphere_gb-london_UR-2.pdf <ul> <li>Documentation</li> </ul> </li> </ul> <h2>Data purpose </h2> <p>People’s activities change through the day and between days. Information about human behaviour and the changing locations where activities occur are used in neighbourhood scale agent-based models of anthropogenic heat fluxes and building scale energy modelling to give realistic occupancy and activity-based energy-use timings.</p> <h3>Linked with</h3> <ul> <li>Hertwig et al. 2024a: urbisphere_presentations_UR-1: Modelling anthropogenic heat emissions from residential buildings-comparison between Berlin and London. EMS Annual Meeting 2023 [Poster]. Zenodo. https://doi.org/10.5281/zenodo.10889863</li> <li>Hertwig et al. 2024b: urbisphere_presentations_UR-2: Connecting physical and socio-economic spaces for urban agent-based modelling, EMS Annual Meeting 2023 [Poster]. Zenodo. https://doi.org/10.5281/zenodo.10889885</li> <li>McGrory et al. 2024: urbisphere_presentations_UR-3: Dynamic Anthropogenic actiVities and feedback to Emissions (DAVE): An agent-based model for heat and exposure to other anthropogenic emissions. EMS Annual Meeting 2023 [Presentation]. Zenodo. https://doi.org/10.5281/zenodo.10889900</li> </ul>
Backward in time particle-tracking simulation in the Kuroshio Extension re-circulation gyre in 2019 using GLORYS12
<p>Tracers were released at the targeted mesoscale eddy in September 03 2019, and their surface transport was modeled for the previous three months. Units are expressed as the number of tracer particles in each glid of 1/12° horizontal resolution. The color shades indicate the number of particles. The particle number 50 indicates that the number of particles in a grid is 50 or more. </p>
Video results of the study "Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"
<p># Video Results of the Study "Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"</p> <p>This repository contains the result videos from the study titled *"Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"*. These videos showcase the performance of the algorithm on two datasets: the **Singapore Maritime Dataset (Onboard Segment)** and the **Buoy Dataset**. Please note that the original datasets are not included in this repository but are publicly available elsewhere.</p> <p>## Contents</p> <p>### 1. Singapore Maritime Dataset (Onboard Segment)<br>The videos labeled with **MVI** belong to the Singapore Maritime Dataset. These result videos demonstrate the performance of the horizon line detection algorithm on onboard maritime footage. For example:<br>- **MVI_0792_VIS_OB.avi** - Original input video (available in the Singapore Maritime Dataset)<br>- **MVI_0792_VIS_OB_R.avi** - The output video showing the results of the algorithm</p> <p>### 2. Buoy Dataset<br>The videos labeled with **buoyGT** belong to the Buoy Dataset. These results show the performance of the algorithm near maritime buoys. For example:<br>- **buoyGT_2_5_3_0.avi** - Original input video (available in the Buoy Dataset)<br>- **buoyGT_2_5_3_0_R.avi** - The output video showing the results of the algorithm</p> <p>## Video Naming Convention<br>Each result video follows a consistent naming convention related to the original input videos:<br>- **Original Video Filename**: [dataset]_[video details].avi<br>- **Result Video Filename**: [original video filename]_R.avi</p> <p>For instance:<br>- **buoyGT_2_5_3_0.avi** corresponds to **buoyGT_2_5_3_0_R.avi**<br>- **MVI_0792_VIS_OB.avi** corresponds to **MVI_0792_VIS_OB_R.avi**</p> <p>The **_R** suffix in the result videos indicates that the video contains the output of the horizon line detection algorithm.</p> <p>## How to Use<br>- To view the results for a specific video, locate the original input video in the corresponding public dataset and find the matching result video in this repository.<br>- For example, the result for the video "buoyGT_2_5_3_0.avi" can be found as "buoyGT_2_5_3_0_R.avi".</p> <p>## Datasets<br>The original datasets used in this study are not included in this repository. They are publicly available from the following sources:<br>1. **Singapore Maritime Dataset** (Onboard Segment)<br>2. **Buoy Dataset**</p> <p>Please refer to the respective dataset repositories for the original video files.</p> <p>## Citation<br>If you use these videos or the method presented in this study in your work, please cite the following:</p> <p>*Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis*.</p>
Dataset for the paper 'Debris-flow return times and pre-event terrains: a novel framework using copula functions and quantile-based approaches'
<p>here attached you can find the dataset we used for our paper. While in the first two columns, you have X, and Y coordinates of the domain's pixels, the other five columns are DEMs' elevations (2006, 2010, 2011, 2015, and 2021). See in the paper's 'Methodology' Section how to employ these data. For any questions, please reach us at massimiliano.schiavo@unipd.it. Thanks!</p>
Time series data and codes from: Quantifying social media predictors of violence during the 6 January US Capitol insurrection using Granger causality
Open the record for dataset details and reuse information.
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>
Datasets used in Detecting Plumes in Mobile Air Quality Monitoring Time Series with Density-based Spatial Clustering of Applications with Noise v01
<p>This repository contains the following data sets related to Detecting Plumes in Mobile Air Quality Monitoring Time Series with DBSCAN published in . Please cite the following: .</p> <p>Validated_Data.csv: A .csv file containing the validation set used in the study. Column headings are the following:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been manually flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p> <p>Labeled_DBSCAN_Anomalies.csv: A .csv file containing points labeled as anomalies by the DBSCAN algorithm described in the manuscript. Columns are the following.</p> <p>"BC": Black carbon measurement (ng/m^3)<br> "CO2": Carbon dioxide measurement (ppm)<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2 (ppb)<br> "UFP": Ultrafine particle count (p/cc)<br> "Anomaly": Whether the DBSCAN algorithm has labeled this point as "Anomaly" (2) or "Normal" (1)<br> "Uniq_Fac": Factor spanning from 1-277 grouping measurements taken on separate days by car. E.g. all measurements with Uniq_Fac=1 were grouped and analyzed together.<br> "LST": Timestamp (US/Central)<br> "Road_Class": TigerLINE census road class designation for the given point. Possible road classes are S1100 - Primary Road, S1200 - Secondary Road, S1400 - Local Road, S1630 - Ramps, S1640 - Service Drives, S1730 - Private Roads<br> "X": Universal Transverse Mercator Easting for Zone 15N (m).<br> "Y": Universal Transverse Mercator Northing for Zone 15N (m).</p> <p>*_To_Be_Validated.csv: A series of files where * denotes the following.</p> <p>"DB": DBSCAN Algorithm<br> "QOR": QOR Algorithm<br> "QAND": QAND Algorithm<br> "Drew": Drewnick Algorithm</p> <p>Each file contains the following columns:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p>
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.