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66 results for “Timeseries”
30 Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel using Landsat Timeseries
<p>30m resolution historically consistent land cover and cover fraction maps over the Sudano-Sahel for the period 1986-2015. These land cover / cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification / regression model, while historical consistency is achieved using the Hidden Markov Model.</p> <p>Validated land cover / cover fraction maps covering the full Sudano-Sahel are provided for 2015 (2015_Sahel.zip), while historical maps are available for four focus areas. The extent of the areas are displayed in 11_study_area.jpeg</p> <p>Each of the zip files contains 14 GeoTIFF files for the respective period and area:</p> <ul> <li>Landsat_LC30_epochYYYY_AREA_bare-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_crops-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_DataDensityIndicator.tif [# overpasses that are used as input for the creation of the maps for this region / epoch]</li> <li>Landsat_LC30_epochYYYY_AREA_discrete-classification-HMM.tif [temporally cleaned discrete classification map using the Hidden Markov Model; legend see below] </li> <li>Landsat_LC30_epochYYYY_AREA_discrete-classification.tif [original discrete classification map; legend see below]</li> <li>Landsat_LC30_epochYYYY_AREA_forest-type-layer.tif [legend see below]</li> <li>Landsat_LC30_epochYYYY_AREA_grass-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_moss-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_shrub-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_snow-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_tree-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_urban-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_water-permanent-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_water-seasonal-coverfraction-layer.tif [0-100%]</li> </ul> <p>Discrete classification legend:</p> <ul> <li>0: Unknown. No or not enough satellite data available.</li> <li>20: Shrubs. Woody perennial plants with persistent and woody stems and without any defined main stem being less than 5 m tall. The shrub foliage can be either evergreen or deciduous.</li> <li>30: Herbaceous vegetation. Plants without persistent stem or shoots above ground and lacking definite firm structure. Tree and shrub cover is less than 10 %.</li> <li>40: Cultivated and managed vegetation / agriculture. Lands covered with temporary crops followed by harvest and a bare soil period (e.g., single and multiple cropping systems). Note that perennial woody crops will be classified as the appropriate forest or shrub land cover type.</li> <li>50: Urban / built up. Land covered by buildings and other man-made structures.</li> <li>60: Bare / sparse vegetation. Lands with exposed soil, sand, or rocks and never has more than 10 % vegetated cover during any time of the year.</li> <li>70: Snow and ice. Lands under snow or ice cover throughout the year.</li> <li>80: Permanent water bodies. Lakes, reservoirs, and rivers. Can be either fresh or salt-water bodies.</li> <li>90: Herbaceous wetland. Lands with a permanent mixture of water and herbaceous or woody vegetation. The vegetation can be present in either salt, brackish, or fresh water.</li> <li>100: Moss and lichen.</li> <li>111: Closed forest, evergreen needle leaf. Tree canopy >70 %, almost all needle leaf trees remain green all year. Canopy is never without green foliage.</li> <li>112: Closed forest, evergreen broad leaf. Tree canopy >70 %, almost all broadleaf trees remain green year round. Canopy is never without green foliage.</li> <li>113: Closed forest, deciduous needle leaf. Tree canopy >70 %, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>114: Closed forest, deciduous broad leaf. Tree canopy >70 %, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>115: Closed forest, mixed.</li> <li>116: Closed forest, not matching any of the other definitions.</li> <li>121: Open forest, evergreen needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all needle leaf trees remain green all year. Canopy is never without green foliage.</li> <li>122:Open forest, evergreen broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all broadleaf trees remain green year round. Canopy is never without green foliage.</li> <li>123: Open forest, deciduous needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>124: Open forest, deciduous broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>125: Open forest, mixed.</li> <li>126: Open forest, not matching any of the other definitions.</li> <li>200: Oceans, seas. Can be either fresh or salt-water bodies.</li> </ul> <p>Forest type legend:</p> <ul> <li>0: Unknown</li> <li>1: Evergreen needle leaf</li> <li>2: Evergreen broad leaf</li> <li>3: Deciduous needle leaf</li> <li>4: Deciduous broad leaf</li> <li>5: Mix of forest types</li> </ul> <p>More detail on the classification algorithm and the resulting maps can be found in the accompanying paper: </p> <p>Souverijns, N.; Buchhorn, M.; Horion, S.; Fensholt, R.; Verbeeck, H.; Verbesselt, J.; Herold, M.; Tsendbazar, N.-E.; Bernardino, P.N.; Somers, B.; Van De Kerchove, R. Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series. <em>Remote Sens.</em> <strong>2020</strong>, <em>12</em>, 3817. https://doi.org/10.3390/rs12223817</p> <p>Please note that a quality layer is available for each of the historical areas / periods (Landsat_LC30_epochYYYY_AREA_DataDensityIndicator.tif). In case a value of 4 or lower is achieved here, the discrete land cover classification / cover fraction for this period / area is highly uncertain. Take this into account when analysing the maps. Furthermore, take note that there is a large difference between the temporally cleaned (Landsat_LC30_epochYYYY_AREA_discrete-classification-HMM.tif) and original discrete land cover classification (Landsat_LC30_epochYYYY_AREA_discrete-classification.tif). We recommend to use the temporally cleaned version in combination with the quality layer.</p>
1 km Monthly Maximum Temperature Dataset for China from 1952 to 2019 (ChinaClim_timeseries)
<p>ChinaClim_timeseries is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
1 km Monthly Precipitation Dataset for China from 1952 to 2019 (ChinaClim_timeseries)
<p>ChinaClim_timeseries is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
Mapping Deformation Processes using InSAR PS+DS Timeseries Estimation in Northern California, U.S.
Open the record for dataset details and reuse information.
Annotated timeseries from yeast cell lifespans - Training and Test sets - DetecDiv (id03)
<p>This dataset represents timeseries lables of cell divisions, to train & test a classifier to detect cell-cycle slowdown.<br> It has been generated by manual annotation from yeast cells lifespans using the DetecDiv software (see below).</p> <p>It is made of 1 file containing :</p> <ul> <li>Groundtruth Input data (Xdata): 250 timeseries of classes "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog" which are outputs from the <a href="https://doi.org/10.5281/zenodo.5553862">doi.org/10.5281/zenodo.5553862</a> network</li> <li>Groundtruth outputdata (Ydata): 250 timeseries of classes "1. pre-slowdown", "2. post-slowdown", which have been annotated manually.</li> </ul> <p>The indexes from the Xdata correspond to that of the Ydata. Timeseries from 1->200 were used as training while 201->250 were used as validation.</p> <p>It is related to the trained network <a href="https://https://doi.org/10.5281/zenodo.5553829">doi.org/10.5281/zenodo.5553829</a> from the software DetecDiv: <a href="https://github.com/gcharvin/DetecDiv">github.com/gcharvin/DetecDiv</a></p> <p><a href="http://biorxiv.org/content/10.1101/2021.10.05.463175v1">biorxiv.org/content/10.1101/2021.10.05.463175v1</a></p> <p> </p> <p><strong>Data type</strong>: Vector timeseries (.mat) (250xF with F the number of frames, between 700 and 1000).</p> <p><strong>File format</strong>: .mat</p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p> <p> </p>
Ischia cGPS Daily Timeseries (2001-2019)
<p>Daily positions time series for the 7 Ischia cGPS stations from January 2001 to December 2019.</p> <p>A full description of cGPS time series analysis is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset.</p>
Campi Flegrei cGPS Daily Timeseries (2000-2019)
<p>Daily positions time series for the 21 Campi Flegrei cGPS stations from January 2000 to December 2019.</p> <p>A full description of cGPS network and time series analysis is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset.</p>
Vesuvio cGPS Daily Timeseries (2001-2019)
<p>Daily positions time series for the 9 Somma-Vesuvio cGPS stations from January 2001 to December 2019.</p> <p>A full description of cGPS network and time series analysis is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset.</p>
Kristineberg Seagrass CH4 & CO2 Timeseries Data
<p>This dataset comprises raw data obtained from measurements conducted within a seagrass meadow located at Kristineberg, Sweden. The dataset originates from continuous timeseries observations across four seasons: late summer, autumn, spring, and early summer. It includes measurements of methane concentration and carbon dioxide partial pressure within the water column. Additional parameters are depth, temperature, salinity, dissolved oxygen, pH, wind speed, and radon decay. The timestamps correspond to the Central European Time zone (CET). This dataset was used to resolve diel and seasonal dynamics of methane and carbon dioxide water-air fluxes over a cold-temperate seagrass meadow. </p> <p>Dataset is found in "Timeseries" sheet. </p> <p>Units, parameter and instrument description are found in "Metadata" sheet. </p>
GPS timeseries raw and filtered
<p>The data files include the raw and ICA filtered GPS timeseries for the five-minute (early postseismic of the five six days), and daily timeseries (the first two years).</p>
ACCESS-ESM-1.5 net-zero emissions 1000-year long simulations: Seasonal temperature and precipitation gridded data with area-average timeseries of temperature, precipitation and sea ice data
<p>NetCDF files used in analysis shown in King et al. (2024) https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2961/.</p> <p>Seasonal-average mapped 1.5 metre air temperature temperature data are in NetCDFs with "fld_s03i236" in their name. These files are for boreal summer "JJA" or boreal winter "DJF". The "B20__" represents the start year of the net-zero emissions simulation from which these data are drawn. These files each have 1000 time-steps and the data are in native resolution of ACCESS-ESM-1.5: 1.875 degrees longitude by 1.25 degrees latitude.</p> <p>Seasonally-averaged precipitation rate (kg/m2/s) data are in NetCDFs with "fld_s05i216" in their name. Seasons and simulation information follow the same format as the temperature files.</p> <p>The file "ts_gwl.nc" includes several timeseries used in the paper including global-average SST ("TEMP_SURFACE"), global mean ocean temperature ("TEMP_GLOBAL"), and March/September Arctic ("ICEN") and Antarctic ("ICES") sea ice extent.</p> <p>The scripts used to make the Figures are included and run using IDL version 9.0. </p> <p>For any advice on using these files please feel free to contact me: andrew.king@unimelb.edu.au.</p>
Timeseries for UK parliamentary petititions
<p>This dataset contains a timeseries with the number of people who have signed petitions to UK parliament at many timepoints from July 2016 until November 2022. It is not comprehensive -- at each timepoint only recently popular petititions are captured, from https://petition.parliament.uk/petitions.json?state=open .</p> <p>The original data source is https://petition.parliament.uk/, licensed under the Open Government Licence v3.0. Duplication of data here is to allow analysis of temporal trends which are not available from the original source.</p>
Virtual Sensor Benchmark training - timeseries set 1
<p>Timeseries data in .csv format with semicolon (;) as delimiter.</p>
Project BioDyn: compilation of long-term (>20yrs) community timeseries data from terrestrial and freshwater realms
<p>The given dataset was compiled for n=2668 communities (242 study IDs in total, see the metadata file) across terrestrial and freshwater realms (terrestrial birds: n=1259, invertebrates: n=124, plants: n=435, and mammals: n=136, freshwater fish: n=587, invertebrates: n=112, and phytoplankton: n=15). Community timeseries include species-level abundance (or biomass in 15% of cases) and a minimum of 20 years sampled. We have compiled this data and used it for the first time to analyze how community stability and its drivers (species richness, overall synchrony, and tail-dependent synchrony) would differ across realms.</p>
Validation of Fracture Caging to Contain Hydraulic Fractures: Timeseries, Videos, and Model Script
<p>The data file include an Excel spreadsheet and two videos for each experimental test.</p> <p>You can start with reading the ReadMeFirst.txt file to understand the whole structure of the dataset.</p> <p>The caging_model.txt file includes python codes to calculate critical flow rates and uncaged fracture radius according to the theory that the authors developed and will be published soon.</p>
Temperature timeseries and idealized simulations of internal solitary waves colliding with the Grande-Anse wharf, Saguenay Fjord, Canada
Open the record for dataset details and reuse information.
California steelhead abundance and environmental conditions timeseries
Open the record for dataset details and reuse information.
Dataset: timeseries of temperatures and anomalies for the city of Paris (France) for Climate 101 Galaxy training
<p>Dataset is originally downloaded from <a href="https://knmi-ecad-assets-prd.s3.amazonaws.com/ensembles/data/Grid_0.1deg_reg_ensemble/tg_ens_mean_0.1deg_reg_v20.0e.nc">https://knmi-ecad-assets-prd.s3.amazonaws.com/ensembles/data/Grid_0.1deg_reg_ensemble/tg_ens_mean_0.1deg_reg_v20.0e.nc</a> </p> <p>Then 3 single locations have been extracted: </p> <ul> <li>Paris (France): latitude=48.85341,longitude=2.3488</li> <li>Freiburg (Germany): latitude=47.996894,longitude=7.841431</li> <li>Oslo (Norway): latitude=59.911491,longitude=10.75793</li> </ul> <p>Climatologies and anomalies have been computed using <a href="https://code.mpimet.mpg.de/">cdo</a></p> <p>This dataset is meant to be used for teaching purposes only.</p>
TimeSeries_Clustering_ML_data
<p>extended database upload for Time Series Clustering Machine Learning framework</p> <p>(this version includes all the initial large dataset files >10 GB that were created with Time Series Clustering Machine Learning)</p> <p> </p> <p> </p>
MOD09A1_TimeSeries_NDVI_00_22_2sm_SG_9_2_INT1_CLIP
<p>MOD09A1 Time Series has been calculates for the Iberican Península.</p>
ScienceDex guides
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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.