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.
230
datasets available to search
ShareScore release 0.7.1
Dataset results
230 results for “time series data”
PB preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Preprocessed PB data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of solar power data collected in Alabama by <a href="https://www.nrel.gov/grid/solar-power-data.html">C</a><a href="https://pems.dot.ca.gov/">alTrans</a>.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=325 representing 325 traffic detectors ordered like in <a href="https://www.nrel.gov/grid/solar-power-data.html">C</a><a href="https://pems.dot.ca.gov/">alTrans</a>).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :<br> Type: .npz<br> Size: 114.23MB<br> *** Key: 'data_TR_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325]</p> <p>*** Key: 'data_VAL_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325]</p> <p>*** Key: 'data_TE_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325]</p> <p>*** Key: 'data_TR'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325]</p> <p>*** Key: 'data_VAL'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325]</p> <p>*** Key: 'data_TE'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325]</p> <p>*** Key: 'position_TR_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TR'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p>
AL preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Preprocessed AL data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of solar power data collected in Alabama by <a href="https://www.nrel.gov/grid/solar-power-data.html">NREL</a>.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=137 representing 137 solar plants ordered like in <a href="https://www.nrel.gov/grid/solar-power-data.html">NREL</a>).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :<br> Type: .npz<br> Size: 34.48MB<br> *** Key: 'data_TR_AL'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_VAL_AL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TE_AL'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TR'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_VAL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TE'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'position_TR_AL'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL_AL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE_AL'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TR'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p>
Time Series Measurement Data of Office Building in Jakarta Indonesia - Incoming Transformer of 20 kV | 0.4 kV
<p>Time Series Measurement Data of Office Buildings in Jakarta Indonesia - Incoming Three Transformers of 2 MVA rating (20 kV | 0.4 kV)<br> The data was taken by Standardized Power Quality Analyzer within minutes span data during eight days in 2016. <br> The data consist of Voltage, Current, Active/Reactive/Apparent Power, Power Factor, Total Harmonic Distortion (THD) within three-phase measurement. Related data was also included from with it i.e., calculated data for active power losses, Unbalanced Voltage per phase, efficiency etc</p>
Time Series Measurement Data of Medium Voltage | Low Voltage [MV|LV] Feeders in Jabodetabek Regions Indonesia – District Incoming Transformers of 630 kVA & 400 kVA (20 kV | 0.4 kV)
<p>Time Series Measurement Data of Medium Voltage | Low Voltage (MV|LV] Feeders in Jabodetabek Regions Indonesia – District Incoming Transformers of 630 kVA & 400 kVA (20 kV | 0.4 kV)<br> Standardized Power Quality Analyzer took the data within 1- and 5 minutes span data during seven days in 2016. <br> The data consist of Voltage, Current, Active/Reactive/Apparent Power, Power Factor, and Total Harmonic Distortion (THD) within three-phase measurement. Related data was also included with it i.e., calculated data for active power losses, Unbalanced Voltage per phase, efficiency etc</p>
Time Series Measurement Data of Residentials | Jabodetabek Regions Indonesia – 220|230 Volt (1300|2200|3300|4400|5500) VA
<p>Time Series Measurement Data of Residentials | Jabodetabek Regions Indonesia – Voltage rating from 220|230|400 Volt within Power Contract/Limiter of (1300|2200|3300|4400|5500) VA.<br> The data was collected with Standardized EDMI Read Head Optical Read Head for Data Communication (FLAG IEC-62056-21) within 5, 10, and 15 minutes. The data were then levelized into 1 minute for computation needs through the interpolation method.</p> <p>The data consist of Apparent Power (S, kVA) and Time (in minutes) within one-phase measurement, representing the behavioral patterns and social practices around load consumption in the rural, semi-urban, and urban areas of Jabodetabek.</p>
Fluorescence correlation spectroscopy time-series data with and without peak artifacts - simulated data
<p>This is a dataset of FCS time-series with and without peak artifacts. It was created by 2D Monte Carlo simulations of diffusing particles. The provenance of the data is recorded in <a href="https://github.com/aseltmann/fluotracify/blob/data/data/exp-201231-clustsim/LabBook-exp-201231-clustsim.org">this file</a> (see a rendered version <a href="https://aseltmann.github.io/fluotracify/data/LabBook-all.html#sec-2-2">here</a>). This parent project (<a href="https://github.com/aseltmann/fluotracify">https://github.com/aseltmann/fluotracify</a>]) also contains examples of how to use this data and related Python code to load it.</p> <p>The following connected paper is currently under review and should be cited together with this dataset: Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)</p> Data structure inside each .csv file <table><tbody> <tr> <td><header></td> <td> <p>10 to 12 lines, contains metadata</p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>...</td> <td>...</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><source_1></td> <td><target_1a></td> <td><target_1b></td> <td><source_2></td> <td><target_2a></td> <td><target_2b></td> <td>...</td> </tr> <tr> <td> <p>FCS time-series with artifact</p> </td> <td>Artifact time series</td> <td> <p>FCS time series without artifact</p> </td> <td> <p>FCS time series with artifact</p> </td> <td>Artifact time series</td> <td> <p>FCS time-series without artifact</p> </td> <td>...</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p> </p>
InTheMED WP2 Data Archive - Groundwater Level Annual Time Series
<p>The data archive InTheMED_WP2_DS_GWLevelAnnualTimeSeries is part of Task 2.2 “Review and collect the available groundwater quantity and quality data sets in the MED region” and contains the groundwater level time series in the InTheMED study countries,<br> Greece, Portugal, Spain, Tunisia, Turkey, Italy, and also France.</p>
Geodetic displacement data from Airborne-LiDAR data and Time Series InSAR: Baton Rouge Case Study.
<p>This repository the results produced by Hurtado-Pulido, Amer, Ebinger, and Holcomb “Variations in subsidence patterns in the Gulf of Mexico passive margin from Airborne-LiDAR data and Time Series InSAR: Baton Rouge Case Study”.</p> <p>This repository presents data sets for figures 4, 5, 6, 7 and 8. Processing methods are described in the paper. The READme file contains details about each file. Please address any questions about this dataset to Hurtado-Pulido.</p> <ul> <li>LiDAR data from 1999 is stored and distributed by the Atlas: The Louisiana Statewide GIS (<a href="https://maps.ga.lsu.edu/lidar2000/">https://maps.ga.lsu.edu/lidar2000/</a>). LiDAR data from 2018 is stored and distributed by the USGS Server through The National Map Download Manager (<a href="https://apps.nationalmap.gov/downloader/">https://apps.nationalmap.gov/downloader/</a>).</li> <li>EnviSAT SAR images were retrieved from the Earth Observation Catalogue (<a href="https://eocat.esa.int/sec/#data-services-area">https://eocat.esa.int/sec/#data-services-area</a>). Sentinel-1 SAR images from the Copernicus Open Access Hub (<a href="https://scihub.copernicus.eu/dhus/#/home">https://scihub.copernicus.eu/dhus/#/home</a>). Both property of the European Space Agency.</li> <li>GNSS information was processed by the Nevada Geodetic Laboratory (Blewitt et al., 2018; <a href="http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html">http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html</a>).</li> <li>Data from water, injection, and extraction wells is stored in the Strategic Online Natural Resources Information System property of the Louisiana Department of Natural Resources (<a href="http://sonris-www.dnr.state.la.us/gis/agsweb/IE/JSViewer/index.html?TemplateID=181">http://sonris-www.dnr.state.la.us/gis/agsweb/IE/JSViewer/index.html?TemplateID=181</a>).</li> </ul>
Data from: A user-friendly guide to using distance measures to compare time series in ecology
<p>Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g. assigning species to individual bird calls); clustering (e.g. clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g. accuracy of model predictions to original time series data); and anomaly detection (e.g. detecting possible catastrophic events from population time series). These common tasks in ecological research rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored.</p> <p>Here we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology.</p> <p>Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends.</p> <p>The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series, and help us answer new ecological questions.</p>
Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
Open the record for dataset details and reuse information.
Data from: Time series of bird abundances, land cover and temperature from standardized breeding bird monitoring schemes (line transects and point count routes) from Norway, Sweden and Finland, for 1975-2016
Open the record for dataset details and reuse information.
Data from: A user-friendly guide to using distance measures to compare time series in ecology
Open the record for dataset details and reuse information.
Data from: Soil chemical variation along a four-decade time-series of reclaimed water amendments in northern Idaho forests
Open the record for dataset details and reuse information.
InSAR Time-series of Jakobshavn and Petermann from Sentinel-1 Data
<p>Dataset 1: Sentinel-1 ascending track 90, descending track 127</p> <p>Study areas: Jakobshavn glacier in Greenland. We separate Jakobshavn into three individual areas (N, NE, and S) based on different reference locations.</p> <p>Date: Ascending: April 2016 to March 2020; Descending: July 2017 to April 2020.</p> <p>Processor: ISCE/topsStack + MintPy</p> <p>Dataset 2: Sentinel-1 ascending track 90, descending track 26</p> <p>Study areas: Petermann glacier in Greenland. </p> <p>Date: Ascending: April 2017 to April 2020; Descending: January 2017 to April 2020.</p> <p>Processor: ISCE/topsStack + MintPy</p>
Data from: Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis
<p><strong>Background</strong>. We evaluated the impact of a pharmacist-led Safety Medication dASHboard (SMASH) intervention on medication safety in primary care.<br> <strong>Methods and findings</strong>. SMASH comprised: (1) training of clinical pharmacists to deliver the intervention; (2) a web-based dashboard providing actionable, patient-level feedback; and (3) pharmacists reviewing individual at-risk patients, and initiating remedial actions or advising general practitioners on doing so. It was implemented in forty-three general practices covering a population of 235,595 people in Salford (Greater Manchester), UK. All practices started receiving the intervention between 18 April 2016 and 26 September 2017. We used an interrupted time series analysis of rates of potentially hazardous prescribing and inadequate blood-test monitoring, comparing observed rates post-intervention to extrapolations from a 24-month pre-intervention trend. The number of people registered to participating practices and having one or more risk factors for being exposed to hazardous prescribing or inadequate blood-test monitoring at the start of the intervention was 47,413 (males: 23,073 [48.7%]; mean age: 60 [standard deviation: 21]). At baseline, 95% of practices had rates of potentially hazardous prescribing (composite of 10 indicators) between 0.88% and 6.19%. The prevalence of potentially hazardous prescribing reduced by 27.9% (95% confidence interval [CI], 20.3% to 36.8%) at 24 weeks and by 40.7% (95% CI, 29.1% to 54.2%) at twelve months after introduction of SMASH. The rate of inadequate blood-test monitoring (composite of 2 indicators) reduced by 22.0% (95% CI, 0.2% to 50.7%) at 24 weeks and by 23.5% (95% CI, -4.5% to 61.6%) at 12 months. After 12 months, 95% of practices had rates of potentially hazardous prescribing between 0.74% and 3.02%. We did not randomise practices but enrolled them in a naturalistic fashion. All our measurements were based on routinely kept electronic health records.<br> <strong>Conclusions</strong>. The SMASH intervention was associated with reduced rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practices. This reduction was sustained over 12 months after start of the intervention for prescribing but not for monitoring of medication. There was a marked reduction in the variation in rates of high-risk prescribing between practices.</p>
Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series
<p>The datasets contain extreme time series obtained from the post-processed 3D wave fields simulated using HOS-Ocean, a high-order spectral model (HOSM) that solves the deterministic propagation of nonlinear wave fields in deep water (Ducrozet et al., 2016).</p> <p>Voermans. (2020). Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4028014</p> <p> </p>
Data from: Disturbance detection in Landsat time series is influenced by tree mortality agent and severity, not by prior disturbance
<p><span>Landsat time series (LTS) and associated change detection algorithms are useful for monitoring the effects of global change on Earth's ecosystems. Because LTS algorithms can be easily applied across broad areas, they are commonly used to map changes in forest structure due to wildfire, insect attack, and other important drivers of tree mortality. But factors such as initial forest density, tree mortality agent, and disturbance severity (i.e., percent tree mortality) influence patterns of surface reflectance and may influence the accuracy of LTS algorithms. And while LTS algorithms are widely used in areas with a history of multiple disturbance events during the Landsat record, the effectiveness of LTS algorithms in these conditions is not well understood. We compared products from the LTS algorithm LandTrendr (<span>Landsat-based Detection of Trends in Disturbance and Recovery) with</span> a unique field dataset from a landscape heavily influenced by both wildfire and spruce beetles (<i>Dendroctonus rufipennis</i>) since c. 2000. We also compared LandTrendr to other common methods of mapping fire- and spruce beetle-affected areas. We found that LandTrendr more accurately detected wildfire than spruce beetle-induced tree mortality, and both mortality agents were more easily detected when they occurred at high severity. Surprisingly, prior spruce beetle outbreaks did not influence the detectability of subsequent wildfire. Compared to alternative disturbance mapping approaches, LandTrendr predicted a c. 40% lower area affected by wildfire or spruce beetle outbreaks. <span>Our findings indicate that disturbance type- and severity-specific differences in omission error may have broad implications for disturbance mapping efforts that utilize Landsat data. Gradual, low-severity disturbances (e.g., background tree mortality and non-stand replacing disturbance) are pervasive in forest ecosystems, yet they can be difficult to detect using automated LTS algorithms. Whenever possible, methods to account for these biases should be incorporated in LTS-based mapping efforts, including the use of multispectral ensembles and ancillary spatial data to refine predictions. However, our findings also indicate that LTS algorithms appear to be robust in areas with multiple disturbance events, which is important because these areas will increase as new acquisitions extend the length of the Landsat record. </span></span></p>
High-frequency measurements of aeolian saltation flux: time series data
<p>High-frequency (25-50 Hz) coupled observations of wind speed and aeolian saltation flux (i.e, the wind-blown movement of sand) were measured at three field sites: Jericoacoara, Brazil; Rancho Guadalupe, California; and Oceano, California. The dataset provided here contains the full record of raw and processed time series of saltation flux and wind speed measured at multiple heights above the sediment surface.</p>
Scripts and data for "The adequacy of time-series reduction for renewable energy systems"
<p>This upload provides the scripts and data used for the computations in the aforementioned working paper. To run these files, you will need to adjust the directory in the files 'testTimeSeries.jl' and 'calli.bat' to your local directory.</p> <p>The subfolder 'reduceTimeSeries' contains all data and the script 'reduceTimeSeries.jl' to reduce the full time-series. Reduction using the 'Gerbaulet' method unfortunately requires a GAMS installation. The results of the reduction are already provided in the folder 'output'.</p> <p>The subfolder 'testTimeSeries' contains all data and the script 'testTimeSeries.jl' to test the reduced time-series with a capacity expansion model. The 'comment' and ‘source’ columns in the AnyMOD.jl input files provide further documentation on the used input parameters. The labels 'lowDem' and 'newDem' relate to what was referred to conventional demand and demand with sector integration in the paper, respectively.</p>
InSAR time series analysis results of ALOS-2/PALSAR-2 data for the post-eruptive displacement of the 2015 phreatic eruption of Hakone volcano, Japan
<p>This repository contains the InSAR products used in Doke et al., GRL (submitted).</p> <p> </p> <p><strong>Dataset 1</strong>: Surface velocity data estimated by InSAR time series analysis with NetCDF grid format.</p> <ol> <li>surface_velocity_p126.nc</li> <li>surface_velocity_p18.nc</li> </ol> <p> </p> <p><strong>Dataset 2</strong>: Time-series of LOS displacements in selected locations with text format.</p> <ol> <li>time_series_p126.txt</li> <li>time_series_p18.txt</li> </ol> <p> </p> <p><strong>Dataset 3</strong>: Inputs and results of model inversion with shapefile.</p> <p>Subsampled observation data, modeled (simulated) displacements, and other parameters are shown in attribute tables in shapefiles. Shapefiles that show the location of the estimated models are also included in ZIP files.</p> <ol> <li>point_source_deflation.zip</li> <li>sill_deflation.zip</li> </ol>
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.