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1,118 results for “Time series”
National-scale tree species/genera map for Poland from Sentinel-2 time series
<p>Map of 16 dominant tree species/genera in Poland based on classification of time series of Sentinel-2 imagery. This dataset is associated with the article by Grabska-Szwagrzyk et al. (2024)<em>: <a href="https://essd.copernicus.org/articles/16/2877/2024/">Map of forest tree species for Poland based on Sentinel-2 data.</a></em></p> <p>The map is provided as GeoTiff file. In addition, training and test data is provided in shapefile format. The map can be explored online in a <a href="https://ee-aweaksbarg.projects.earthengine.app/view/speciesmappl">webviewer</a>.</p> <p> </p>
Bermuda Atlantic Time-Series Study (BATS) Zooplankton Biomass
<p>The BATS (Bermuda Atlantic Time-series Study) zooplankton biomass dataset is time-series spanning from 1994 to 2022. The dataset contains zooplankton biomass measurements.</p><p>Due to an ambiguity with depth a subset of the dataset been removed as part of the Simons CMAP curation process. The original dataset is also included here:</p><ul><li>Curated by Simons CMAP: BATS_Zooplankton_Biomass_CMAP.xlsx </li><li>Original version as text file: BATS_zooplankton.xlsx</li><li>Original version as excel file: BATS_zooplankton.xlsx </li></ul><p>The following dates were impacted by the depth ambiguity: 1994 (4/6 – 12/12); 1995 (1/11 – 4/27, 8/22); 2000 (2/28); 2001 (1/30, 8/7-8/8, 9/12, 10/16); 2004 (2/14, 3/23, 4/7, 7/14-15, 8/16-17); 2005 (1/27); 2006 (5/11, 6/26, 9/4-9/5); 2007 (7/18, 8/9, 10/6); 2008 (6/22); 2009 (2/10, 4/1, 4/15, 5/16, 5/19, 10/10); 2017 (5/9); 2018: (8/13); 2022: (6/29).</p><p>This description has been reproduced using https://www.dropbox.com/sh/xo6c72qaeznyv05/AACCiijqHcd2chjbwrqNcxica?dl=0&preview=BATS_zooplankton.txt</p>
The reconstructed surface water area time series (2000-2019) dataset for lakes >1 km2 in China
<p>This repository contains the <strong>revised version</strong> of the supplementary data for the paper: <strong>Reconstruction of long-term high-resolution lake variability: Algorithm improvement and applications in China </strong>(https://www.sciencedirect.com/science/article/pii/S0034425723003267?via%3Dihub). </p> <p>Specifically, this dataset documents the reconstructed surface water area time series for all studied lakes in China during the period of 2000-2019. In the prior version of the dataset, there was an erroneous assignment of IDs to each lake. This issue has been rectified in the revised version, ensuring that the updated IDs now accurately correspond to the actual GLAKES_ID for each of the GLAKES lake polygons.</p> <p>For more detailed information of the dataset, please refer to the README file.</p>
Displacement time series from GNSS stations in the Alto Tiberina Fault area (Central Italy)
<p>The files report the position time-series of GNSS stations deployed in the Alto Tiberina Fault area (Central Italy). <br>Columns are: Time, E, N, Se, Sn, Ren, U, Su, Reu, Rnu, site, long, lati, representing, respectively, epoch (in decimal years), displacement in the East component (in mm), displacement in the North component (in mm), uncertainty (one standard deviation) of the East component (in mm), uncertainty (one standard deviation) of the North component (in mm), correlation between the East and North components, displacement in the Up component (in mm), uncertainty (one standard deviation) of the Up component (in mm), correlation between the East and Up components, correlation between the North and Up components, Station ID (four letters), Longitude of the station (°), Latitude of the station (°).</p>
Daily GNSS time series of La Palma 2021 eruption
<p>Time series form GNSS station in La Palma (Canary Island, Spain) during 2021 eruption (19/09/2021-21/01/2022). The daily neu time series have been computed in a Double Diference method using Bernese v.5.2 software as describe in the references.</p> <p> </p>
AutoML Applied to Time Series Analysis Tasks in Production Engineering
<p>The dataset is accompanying the paper "AutoML Applied to Time Series Analysis Tasks in Production<br>Engineering" (<a href="https://doi.org/10.1016/j.procs.2024.01.085" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2024.01.085</a>). It contains the experimental data referred to in the paper as "KIOptiPack".</p>
Dataset for: A 31-year time series of at-sea counts shows a non-significant decline of Marbled Murrelets at Laskeek Bay, Haida Gwaii, 1990-2020
<p>This dataset includes counts of Marbled Murrelets (<em>Brachyramphus marmoratus</em>) conducted from a small boat near Laskeek Bay on the east coast of Louise Island, and north of Lyell Island in Haida Gwaii, from 1990 to 2020. The dataset includes counts of Marbled Murrelets observed on the water or flying by. Information is also included on the date, time of day, survey width, and spatial coordinates of the transects where the observation took place.</p>
High-Resolution Beach Profile Time Series: Morecambe Bay, 2007-2022
<p><strong>Description:</strong> This dataset provides a comprehensive collection of beach profile measurements for Morecambe Bay, covering a 16-year period from 2007 to 2022. The data was collected and shared by Sefton Council (Sefton MBC), UK, as part of the coastal monitoring programme. Captured biannually during spring and autumn, the data offers valuable insights into the bay's coastal morphology and dynamics.</p> <p><strong>Data Description:</strong></p> <ul> <li><strong>Beach Transects:</strong> Detailed profiles are provided for various locations along the Morecambe Bay coastline. Station numbers are used to identify each measurement point on a dedicated map (included within the data).</li> <li><strong>Temporal Coverage:</strong> The dataset encompasses measurements conducted biannually, capturing seasonal variations in beach profiles.</li> </ul>
Data from: Decadal-scale time series highlight the role of chronic disturbances in driving ecosystem collapse in the Anthropocene
<p>These data support a publication in the Journal Ecology that describes 37 years of change on the coral reefs of St. John, US Virgin Islands. In this paper, four decades of surveys from two coral reefs (9 and 14 m depth) off St. John, US Virgin Islands, are used to quantify the associations of acute and chronic events with the changes in benthic community structure. These reefs profoundly changed over 36 years, with coral death altering species assemblages to depress abundances of the ecologically important coral <em>Orbicella</em> spp. and elevating the coverage of macroalgae and crustose coralline algae/turf/bare space (CTB). Linear mixed models revealed the prominent role of chronic variation in temperature in accounting for changes in coverage of corals, macroalgae, and CTB, with rising temperature associated with increases in coral cover on the deep reef, and declines on the shallow reef. Hurricanes were also associated with declines in coral cover on the shallow reef, and increases on the deep reef. Multivariate analyses revealed strong associations between community structure and temperature, but weaker associations with hurricanes, bleaching, and diseases. These results highlight the overwhelming importance of chronically increasing temperature in altering the benthic community structure of Caribbean reefs.</p>
The raw GNSS position time series (raw_time_series.rar) and TDEFNODE models (TDEFNODE_models.rar) related to the manuscript authored by Rui Xu, D. S. Stamps and C. A. Williams
<p>This repository saves the raw GNSS position time series (raw_time_series.rar) and TDEFNODE models (TDEFNODE_models.rar) related to the manuscript authored by Rui Xu, D. S. Stamps and C. A. Williams. For more details, please refer to the NOTES files in each .rar archive.</p>
Acute pseudo-landmarking and Constellation homologies: A generalized workflow to identify and track segmented structures in plant time series images
<p>Assessing plant phenotypes throughout the lifecycle is integral to exploring the development, genetics, and evolution of morphology, and can be critical for agronomic and basic research studies. Although various automated or semi-automated phenomic approaches have been developed, it has been challenging to analyze differential growth because of difficulties in segmenting and annotating specific structures or positions in the plant body and maintaining their identities throughout time-series data. To address this gap, we have developed a generalized workflow linking our previously published function, <i>Acute</i>, with a companion homology workflow, <i>Constellation</i>, in the PlantCV environment. <i>Acute</i> identifies acute shapes (pseudo-landmarks) in the plant body, most often corresponding to leaf tips and ligular regions. <i>Constellation</i> uses a strategy of dimensionality reduction via <i>starscape</i> followed by hierarchical clustering through <i>constella </i>to identify 'constellations' of segments in eigenspace that represent the same landmark in consecutive images of a time-series. We devised a quality control function, <i>constellaQC</i>, to test the accuracy of the clustering approach, and use it to show that the approach appropriately clusters the pseudo-landmarks derived from <i>Acute</i>, with 80-90% accuracy. We discuss the reasons for and consequences of this lack of 100% accuracy in automated workflows and suggest how to develop these functions for other phenomics datasets that may vary in dimensional complexity.</p>
Replication Package for the Paper: Transfer Learning with Time Series Data: A Systematic Mapping Study
<p>This is a replication package for the paper "Transfer Learning with Time Series Data: A Systematic Mapping Study".</p> <p>It provides</p> <ul> <li>a documentation of the conducted electronic literature search,</li> <li>exports of the search results from each literature database,</li> <li>and an excel file on the included literature and extracted data.</li> </ul>
GPS Time Series Kamchatka 2013
<p>GPS time series presented in the article: "Transient slab plunge prior to the Mw 8.3 2013 Okhotsk deep-focus earthquake"</p> <p>The columns of the files correspond to </p> <p>Year ; Month ; Day ; Hour ; Minute ; Second ; East position (mm) ; North position (mm) ; Up position (mm) ; East uncertainty (mm) ; North uncertainty (mm) ; Up uncertainty (mm) ;</p>
Analysis of the root diameter distribution from time series images of real and simulated Cassava root systems
<p>The data was collected, simulated and analyzed in the framework of the CassavaStore project (a collaboration between IBG-2, Forschungszentrum Jülich, Germany and different institution from Thailand; for details see <a href="https://www.international-bioeconomy.org/cassavastore_eng">https://www.international-bioeconomy.org/cassavastore_eng</a>). Aim of this project is to get a better understanding of storage root development in cassava (<em>Manihot esculenta</em> Crantz) in order to optimize cassava growth with respect to variety breeding and growth management. The storage root is one of the main providers of starch in Thailand and therefore of high economic importance. Monitoring the formation of storage roots over time via quantification of the root diameter distribution of excavated root systems was one of the key aspects addressed in this project. To measure the diameters a software was developed that identifies roots in RGB images and analyzes the diameters along each identified root automatically. The published data contains 1) analyzed images from cassava roots that were acquired in a video box; 2) simulated virtual root model images with known root diameter distributions that were used to validate the analysis approach; 3) a description of the data and the folder structure.</p>
1 km Monthly Precipitation Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series 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 Maximum Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series 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 Average Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series 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 Minimum Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series 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>
Meteorological time series recorded at the station implemented in OAL-Austria
<p>Time series of meteorological variables recorded at the station implemented in OAL-Austria with the following senors:</p> <p>Variable | Unit | Sensors | Measurement<br> AT | degC | Judd, HMP45 | 60 min Avg<br> RH | % | HMP45 | 60 min Avg<br> SR | W/m2 | Pyranometer SP-Lite | 60 min Avg<br> WS | m/s | Wind wheel A100R | 60 min Avg + Max<br> JD | cm | Judd Ultraschall | 60 min burst<br> VSWD | cm | Yard stick+DCIM | daily<br> SLT | degC | TDR-315L in 15, 30 and 50cm depth | 60 min burst - Avg<br> SLVW | % | TDR-315L in 15, 30 and 50cm depth | 60 min burst - Avg<br> P | mm/m2 | ARG100 (during summer season) | 60 min Total</p> <p> </p> <p>Location: 11.596024 E, 47.267723 N, elevation: 1090m a.s.l.</p>
Global temperature time series from IPCC AR6
<p>Annual global mean temperature time series used in IPCC Sixth Assessment Report (AR6). Includes both consolidated mulit-dataset mean and individual component data sets. Further documentation is available in AR6 (Working Group I, section 2.3.1). </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.