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1,118 results for “Time series”
High-rate GNSS data in seismic moment tensor inversion. The study of anthropogenic earthquakes - GNSS displacement time series
<p>The dataset of GNSS displacement time series with duration of 30 seconds before and 90 seconds after the origin time of the mining tremors. The dataset was used in the research on High-rate GNSS data in seismic moment tensor inversion. The study of anthropogenic earthquakes.</p> <p>Further description of the HR-GNSS processing can be found in the paper by Kudlacik et al. (2021) and in the research paper "High-rate GNSS data in seismic moment tensor inversion. The study of anthropogenic earthquakes".</p>
FM95-14 Fetal Myoblast Differentiation Time Series
GEO Series GSE3780. Homo sapiens. 60 samples. Type: Expression profiling by array.
Transcription profiling by array of mouse hepatocytes following treatment with growth factor beta 1 in a time series
GEO Series GSE261215. Homo sapiens; Mus musculus. 15 samples. Type: Expression profiling by array.
Time-series analyses of Monterey Bay coastal microbial picoplankton using a “genome proxy” microarray
GEO Series GSE21502. uncultured marine alpha proteobacterium; uncultured Pseudomonadota bacterium; uncultured crenarchaeote 74A4; uncultured marine bacterium EB000_55B11; uncultured marine bacterium 440; uncultured marine bacterium 583; marine metagenome; uncultured marine group II euryarchaeote EF100_57A08; uncultured crenarchaeote 4B7; Prochlorococcus marinus subsp. pastoris str. CCMP1986; uncultured marine gamma proteobacterium EBAC31A08; uncultured marine group II euryarchaeote 37F11; uncultured marine bacterium EB0_41B09; uncultured proteobacterium 60D04; unidentified; uncultured marine bacterium 577; uncultured gamma proteobacterium eBACHOT4E07; uncultured proteobacterium 65D09. 58 samples. Type: Other.
CASSINI SATURN UVIS SOLAR STELLAR BRIGHTNESS TIME SERIES 1.1
Photometric observations of stellar occultations by Saturnian rings, satellites, atmospheres, and Jovian atmosphere.
CASSINI SATURN UVIS SOLAR STELLAR BRIGHTNESS TIME SERIES 1.4
Photometric observations of stellar occultations by Saturnian rings, satellites, atmospheres, and Jovian atmosphere.
CASSINI SATURN UVIS SOLAR STELLAR BRIGHTNESS TIME SERIES 1.0
Photometric observations of stellar occultations by Saturnian rings, satellites, atmospheres, and Jovian atmosphere.
CASSINI N/A UVIS SOLAR STELLAR BRIGHTNESS TIME SERIES 1.0
Photometric observations of stellar occultations by Saturnian rings, satellites, atmospheres, and Jovian atmosphere.
CASSINI JUP UVIS SOLAR STELLAR BRIGHTNESS TIME SERIES 1.0
Photometric observations of stellar occultations by Saturnian rings, satellites, atmospheres, and Jovian atmosphere.
CASSINI SATURN UVIS SOLAR STELLAR BRIGHTNESS TIME SERIES 1.2
Photometric observations of stellar occultations by Saturnian rings, satellites, atmospheres, and Jovian atmosphere.
Weekly time series of total precipitation for Europe at 1 km resolution (2016 - 2020) derived from ERA5-Land data
<p> </p> <p> </p> <p><strong>Data have been moved to</strong>: <a href="https://doi.org/10.5281/zenodo.6559048">https://doi.org/10.5281/zenodo.6559048</a></p> <p> </p> <p> </p>
Coastal Groundwater Time Series
<p>These are the time series observation dataset from the southern coast of Rhode Island</p>
Time Series Data Set for Evaluation Telescope
<p>The evaluation data set consisting of 64 time series for the hybrid forecasting method Telescope.</p>
Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat 8 time series for the years 2017 - 2020
<p>Grasslands provide a wide range of important ecosystem services. Mapping and assessing the status and use intensity of grasslands is thus important for environmental monitoring. We here provide maps with detected mowing events, as a proxy for grassland use intensity, for grassland areas across Germany for the years 2017 to 2020.</p> <p>The algorithm used to derive the maps is described in Schwieder, et al. (accepted) and is available as a user-defined function for the FORCE (Frantz, D., 2019) environment (https://github.com/davidfrantz/force-udf/tree/main/python/ts/mowingDetection). The here provided GeoTiffs contain a band with the number of detected mowing events per pixel for the repsective year. In the products, only stable grassland areas that were consistently classified as grassland within three years (2017 - 2019) were considered, based on crop maps provided by Blickensdörfer et al. (2021). Note that grassland uses (pasture, mowed, mixed) were not separated prior to analysis. The maps for 2018, 2019, and 2020 were validated in different regions of Germany, with accuracies - in terms of Mean Absolute Percentage Error - ranging from 35% to 40% (for more details see Schwieder et al. accepted). The maps may thus give an indication of extensively or intensively grassland use.</p> <p>Please contact the authors, if you are interested in additional products e.g., regarding the estimated mowing dates.</p> <p>All satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; https://force-eo.readthedocs.io/en/latest/ last accessed: 15. October 2021).</p> <p> </p> <p>References:</p> <p>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P.. (2021). National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2017, 2018 and 2019) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5153047 </p> <p>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</p> <p>Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795.</p> <p><a href="https://zenodo.org/record/5571613">Grassland mowing events across Germany</a> © 2022 by Schwieder, Marcel; Wesemeyer, Maximilian; Frantz, David; Pfoch, Kira; Erasmi, Stefan; Pickert, Jürgen; Nendel, Claas; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a></p>
Raw time series of GNSS sites used in the manuscript submitted to JGR-solid earth (2022JB025725)
<p>These are the raw GNSS time series files (in .pos format) we used to derive the velocity solution in the manuscript submitted to JGR-solid earth (2022JB025725). These data are not allowed to use without permission.</p>
Raw time series of GNSS sites used in the manuscript 'South China plate motion modified by 2008 Mw7.9 great Wenchuan earthquake'
<p>The *.pre.*.pos files are raw position time series before the Wenchuan earthquake (from July 2001 to December 2004) and the *.aft.*.pos files are the raw postition time sereis after the Wenchuan earthquake (from July 2014 to December 2017). The file 'breaks' contains the epoches of breaks due to equipment changes or that are indentified visually.</p>
LP MOON MAG LEVEL 4 LUNAR MAGNETIC FIELD TIME SERIES V1.0
not applicable
Mapping of Inundation Extent in the Amazon Basin 2014-2017 with ALOS-2 PALSAR-2 ScanSAR Time-Series Data
<p>Figures and Tables </p>
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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.