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327 results for “Time-Series”
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
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Data from: Soil chemical variation along a four-decade time-series of reclaimed water amendments in northern Idaho forests
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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>
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>
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>
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>
Sentinel-1 InSAR time-series and velocity map over the Bay Area (Descending track 42, 2015-2020)
<p>Supplemental material for <em>"Spatiotemporal variations of surface deformation, shallow creep rate, and slip partitioning between the San Andreas and southern Calaveras Fault"</em> at JGR-Solid Earth</p> <p>Citation: <strong>Li, Y</strong>., Bürgmann, R., & Taira, T. (2023). Spatiotemporal variations of surface deformation, shallow creep rate, and slip partitioning between the San Andreas and southern Calaveras Fault. <em>Journal of Geophysical Research: Solid Earth</em>, 128, e2022JB025363. <a href="https://doi.org/10.1029/2022JB025363">https://doi.org/10.1029/2022JB025363</a></p>
Data from: Girth increment changes in response to soil water availability in lowland dipterocarp forest in Borneo: an individualistic time-series analysis
<p><span>Time-series data offer a way of investigating the causes driving ecological processes as phenomena. To test for possible differences in water relations between species of different forest structural guilds at Danum (Sabah, NE Borneo), daily stem girth increments (gthi), of 18 trees across six species were regressed individually on soil moisture potential (SMP) and temperature (TEMP), accounting for temporal autocorrelation (in GLS-arima models), and compared between a wet and a dry period. The best-fitting significant variables were SMP the day before and TEMP the same day. The first resulted in a mix of positive and negative coefficients, the second largely positive ones. An adjustment for dry-period showers was applied. Interactions were stronger in dry than wet period. Negative relationships for overstorey trees can be interpreted in a reversed causal sense: fast transporting stems depleted soil water and lowered SMP. Positive relationships for understorey trees meant they took up most water at high SMP. The unexpected negative relationships for these small trees may have been due to their roots accessing deeper water supplies (if SMP was inversely related to that of the surface layer), and this was influenced by competition with larger neighbour trees. A tree-soil flux dynamics manifold may have been operating. Patterns of mean diurnal girth variation were more consistent among species, and time-series coefficients were negatively related to their maxima. Expected differences in response to SMP in the wet and dry periods did not clearly support a previous hypothesis differentiating drought and non-drought tolerant understorey guilds. Trees within species showed highly individual responses when tree size was standardized. Data on individual root systems and SMP at several depths are needed to get closer to the mechanisms that underlie the tree-soil water phenomena in these tropical forests. Neighborhood stochasticity importantly creates varying local environments experienced by individual trees.</span></p>
Towards understanding the importance of time-series features in automated algorithm performance prediction
<p><strong>merged_feature_importance.csv</strong> - CSV with feature importance values with different meta-models, forecasting algorithms, and feature importance methods computed on 30 different train/test splits.</p> <p><strong>Catch22.csv</strong> - Catch22 features (raw time-series)</p> <p><strong>Catch22Log.csv</strong> - Catch22 features (log time-series)</p> <p><strong>Catch22Diff.csv</strong> - Catch22 features (differenced time-series)</p> <p><strong>TSFresh.csv</strong> - TSFresh features (raw time-series)</p> <p><strong>TSFreshLog.csv</strong> - TSFresh features (log time-series)</p> <p><strong>TSFreshDiff.csv</strong> - TSFresh features (differenced time-series)</p> <p><strong>mape.csv</strong> - sMAPE performance for all forecasting algoirthms</p>
InSAR time-series and FEM Model of the Post-seismic Surface Deformation following the 2013 Baluchistan Earthquake
<p>Subduction zone accretionary prisms are commonly modeled as elastic structures where permanent deformation is accommodated by faulting and folding of otherwise elastic materials, yet accretionary prisms may exhibit other deformation styles over relatively short time scales. In this study, we use 6.5-year (2014-2021) Sentinel-1 InSAR time-series of post-seismic deformation in the Makran accretionary prism of southeast Pakistan to characterize non-linear viscoelastic deformation within an active accretionary prism on short timescales (months to years). We constructed a series of 3-D finite-element models of the Makran subduction zone, including an accretionary prism, and constrained the elastic thickness of the upper wedge and the flow-law parameters (power-law exponent, activation enthalpy, and pre-exponential constant) of the lower wedge through forward model fits to the InSAR time-series. Our results show that the prism is elastically thin (8-12 km) and the non-linear viscoelastic relaxation of the deep portions of the prism alone can sufficiently explain the post-seismic surface deformation. Our best fitting flow-law parameters (<em>n</em> = 3.76±0.39, <em>Q</em> = 82.2±37.73 kJ mol<sup>-1</sup>, and <em>A</em> = 10<sup>-3.36±4.69</sup>) are consistent with triggering of low temperature dislocation creep within fluid-saturated siliciclastic rocks. We believe that the fluids necessary for this weakening originate from sedimentary underplating and/or the presence the hydrocarbons. The presence of power-law rheology within the lower wedge impacts the estimated plate coupling and the stress state in the subduction system, with respect to the conventional elastic wedge model, and hence need to be considered in future earthquake cycle models.</p>
Microdata on vector abundance and IRS quality assurance (Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study)
<p>This repository contains the microdata on vector abundance and quality assurance of indoor residual spraying (IRS) that was used to estimate the impact of IRS on sandfly abundance and incidence of visceral leishmaniasis (VL) in India, as described in the paper "Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study" by Coffeng et al (<a href="https://doi.org/10.1016/S1473-3099(24)00420-1">https://doi.org/10.1016/S1473-3099(24)00420-1</a>). These data were collected as part of a BMGF-funded project led by dr. Michael Coleman at the Liverpool School for Tropical Medicine, as described in an earlier paper by Deb et al (<a href="https://doi.org/10.1371/journal.pntd.0009101">https://doi.org/10.1371/journal.pntd.0009101</a>).</p> <p>This repository does not include microdata on VL cases as these are owned by India's National Center for Vector Borne Disease Control (NCVBDC, <a href="https://ncvbdc.mohfw.gov.in/" target="_blank" rel="nofollow noreferrer noopener">https://ncvbdc.mohfw.gov.in/</a>).</p>
Dataset: Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model
<p>This dataset is a companion to the submitted WRR publication entitled ‘Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model’. The file structure is organized as follows:</p> <ul> <li><strong>ASO_50m_depth_surfaces</strong> - This folder contains the Airborne Snow Observatory lidar-derived snow depth products aggregated to 50m gridded spatial resolution. Each file is titled with a date such as ‘TB<em>YYYYMMDD</em>_SUPERsnow_depth.asc’. The coordinates are in UTM zone 11N and use the WGS84 coordinate system.</li> <li><strong>static_grids</strong> <ul> <li>Static grids are used in each of the subsequent folders and are not changed between years.</li> <li>init0000.ipw <ul> <li>Initialization file to begin the model run. Contains the digital elevation model in band 1, surface roughness raster in band 2, and zeroed images of snow properties in bands 3-7.</li> </ul> </li> <li>maxus.nc <ul> <li>netCDF file of 72 separate images of maximum upwind slope for all upwind directions from 0 (north) to 355 degrees in 5-degree increments. Derived using Adam Winstral’s Sx algorithm.</li> </ul> </li> <li>tuolx_dem_50m.ipw <ul> <li>Digital elevation model from ASO snow-free acquisition aggregated to 50m gridded spatial resolution. Same information as band 1 in the init0000.ipw file.</li> </ul> </li> <li>tuolx_hetchy_mask_50m.ipw <ul> <li>Basin mask of the Tuolumne River Basin above Hetch Hetchy Reservoir. Out-of-basin cells denoted as 0, and in-basin cells denoted as 1.</li> </ul> </li> <li>tuolx_vegheight_50m.ipw <ul> <li>Vegetation height raster in meters. Derived from NLCD dataset of vegetation type..</li> </ul> </li> <li>tuolx_vegk_50m.ipw <ul> <li>Emissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> <li>tuolx_vegnlcd_50m.ipw <ul> <li>Vegetation type from the National Land Cover Database.</li> </ul> </li> <li>tuolx_vegtau_50m.ipw <ul> <li>Fractional transmissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> </ul> </li> <li><strong>level1_raw_data</strong> <ul> <li>{Hourly data interpolated to nearest hour from downloaded raw data (CDEC/MesoWest)}</li> <li>air_temp_level1.csv</li> <li>precip_accum_level1.csv</li> <li>relative_humidity_level1.csv</li> <li>solar_radiation_level1.csv</li> <li>wind_direction_level1.csv</li> <li>wind_speed_level1.csv</li> </ul> </li> </ul> <p>The directories for each water year contain the configuration file for that year along with the vector meteorological data from measurement sites and site metadata in .csv format.</p> <ul> <li><strong>wy2013</strong></li> <li><strong>wy2014</strong></li> <li><strong>wy2015</strong></li> <li><strong>wy2016</strong> <ul> <li> <ul> <li>backup_config.ini {Initialization file used to distribute station data over a regular grid for each water year.}</li> <li>air_temp.csv</li> <li>cloud_factor.csv</li> <li>metadata.csv</li> <li>precip.csv</li> <li>vapor_pressure.csv</li> <li>wind_direction.csv</li> <li>wind_speed.csv</li> <li><strong>data/</strong> <ul> <li>[subdirectory containing all future created forcing grid files]</li> </ul> </li> <li><strong>runs/</strong> <ul> <li>[subdirectory containing all <em>iSnobal</em> output files in addition to reinitialization scripts for ASO snow depth updates]</li> </ul> </li> </ul> </li> </ul> </li> </ul>
"Toy Data Set" referenced in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (https://doi.org/10.1101/2024.08.22.609110)
<p>This data set, referenced as "toy data set" in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (<a href="Lint-to-article">https://doi.org/10.1101/2024.08.22.609110</a>), mimics a high-throughput screening data set. To demonstrate the application of our analysis framework described in the main article this toy data set was generated. It contains in total 1536000 individual transient signals, splitted in 5 batches of each 200 plates in 1536-well plate format. Five distinct signal classes were used to resemble typical shapes encountered in biological experiments. Fequency of occurrences for each class is reported in the main article.</p>
A comprehensive time-series dataset linked to cyanobacterial blooms in Lake Taihu
<p>Lake Taihu has a history of recurrent harmful cyanobacterial blooms under the pressures of climate change and human activities. Despite efforts to limit nutrient loading, there is a need to better understand the water environment of Lake Taihu in order to improve methods for controlling the cyanobacterial blooms. The spatial and temporal characteristics of the water quality, bio-optical parameters, climate, and anthropogenic data of Lake Taihu (THQBCA) could provide comprehensive information regarding cyanobacterial blooms. The THQBCA dataset contains 26 variables organized into four categories: water quality, bio-optics, climate, and anthropogenic data. The dataset spans more than 15 years (8 of which cover approximately 35 years, 4 of which cover 20 years), and the spatial resolutions of the satellite-derived data range from 30 m to 500 m. The THQBCA dataset is expected to advance research on forecasting and early warning of cyanobacterial blooms, and to support science-based management decisions for sustainable ecological development.</p>
LA-ICP-MS line scan data and time-series analysis outputs for Baltic Sea sediment core F80
<p>The datafile contains two sheets: HTM and MCA, corresponding to geochemical data from the Holocene Thermal Maximum and Medieval Climate Anomaly intervals, respectively, of a sediment core from the Baltic Sea (site F80, 58°00.00N, 19°53.81E, water depth 191m, Fårö Deep, collected during the HYPER/COMBINE cruise of R/V Aranda, May/June 2009). In each sheet, columns A-J contain Laser Ablation (LA)-ICP-MS line scan data of element ratios in resin-embedded sediment (Mo/Al, Fe/Al and Br/P) presented in the time domain (Age in years BP). Dating of the sediment core is described in the accompanying manuscript and references therein. These profiles are presented in three forms: Raw= raw data resampled to 1 year resolution; Det= detrended and normalized to unit variance; Gau; Gaussian bandpass filter at a period of 20-100 years. Columns L-S contain time-series analysis results of the detrended, normalized elemental ratios in period domain, including power spectra of each ratio (Blackman-Tukey window, columns M-O) and cross-spectral analysis (Blackman-Tukey window, bandwidth 5 years) of Mo/Al vs Br/P (columns P-Q) and Mo/Al vs Fe/Al (columns R-S), respectively. All analyses were performed in Analyseries 1.1.1 (Paillard et al., 1996). Figures containing the data have been submitted as part of a manuscript to Geophysical Research Letters (Jilbert et al., forthcoming),</p> <p> </p> <p>Paillard, D., Labeyrie, L., & Yiou, P. (1996). Macintosh program performs time‐series analysis. <em>Eos, Transactions American Geophysical Union</em>,<em> 77</em>(39), 379-379. <a href="https://doi.org/10.1029/96EO00259">https://doi.org/10.1029/96EO00259</a></p> <p>Jilbert, T., Gustafsson, B.G., Veldhuijzen, S., Reed, D.C., van Helmond, N.A.G.M., Hermans, M., & Slomp, C.P (forthcoming). Iron-phosphorus feedbacks drive multidecadal oscillations in Baltic Sea hypoxia. Submitted to <em>Geophysical Research Letters</em></p>
Synthetic soil temperature time-series
<p>The synthetic experiments are implemented to investigate the impact of different environmental conditions on the uncertainty of thermal diffusivity estimates. We generate synthetic temperature fields that represent various types of temperature gradients and fluctuations. This is achieved through forward modeling (i.e., heat-conduction process in a heterogeneous medium using an explicit finite difference method) with initial, top, and bottom boundary conditions set equal to the temperature time series observed at a monitoring site in Alaska during summer (synthetic_data_summer.csv ) and autumn (synthetic_data_autumn.csv ), and by assuming a soil column composed of three layers (i.e., top layer at 0.05–0.1 m, middle layer at 0.1–0.42 m, and bottom layer at 0.42–1.05 m). The thermal diffusivity in the three layers is assumed to be constant over time and equal to 0.16, 0.27 and 0.43 mm<sup>2</sup>s<sup>−1</sup> for the case of summer temperatures and 0.25, 0.75 and 0.6 mm<sup>2</sup>s<sup>−1</sup> for autumn.</p> <p>Each .csv file has 14 columns: first column containes information on the date and time on which temperature was recorded, the remaining 13 columns are soil temperature at 0.05, 0.10, 0.15, 0.20 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95 and 1.05 m below the ground surface.</p> <p>Impact of soil temperature trend and fluctuations on thermal diffusivity estimates is evaluated for various synthetic temperature fields including (a) summer trend and fluctuations (synthetic_data_summer.csv ), (b) detrended fluctuations (synthetic_data_summer_detrended.csv ), (c) smoothed out daily and smaller fluctuations (synthetic_data_summer_noDiurnalFluct.csv), and (d) without fluctuations (synthetic_data_summer_noFluct.csv ).</p>
Manual 4D annotations of Micro X-ray CT time-series (4D dataset)
<p>The 4D (3D+time) manual annotations of https://doi.org/10.5281/zenodo.4293394. For the annotation the SuRVoS workbench was used (https://doi.org/10.5281/10.5281/zenodo.247547) and our proposed hidden Markov model (HMM-T, https://doi.org/10.5281/zenodo.4416013 ) designed to refine 4D semantic segmentations made by a 3D semantic segmentation CNN after its applied on 4D data. Only slices 740-742 and 747-749 (refining to the first axis) are partially annotated. We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p>
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Allen Brain Atlas
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OpenNeuro
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