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32 results for “storage time”
Floodplain Sediment Storage Times in a Simulated Meandering River
<p>This dataset contains four files which enable another researcher to bypass the most computationally intensive parts of the simulations (in MATLAB) associated with the publication of my dissertation thesis. These files result as the analysis of the Simulation of the Long-Term Evolution of a Meandering River (https://doi.org/10.5281/zenodo.5651840).</p> <p>The first of these files contain the storage time and age distributions of simulated sediments from the upstream reach of the simulated river, captured after analyzing the upstream 16 mini reaches of the floodplain.</p> <p>The second file contains the same distributions from two reaches after analyzing 36 mini reaches (the downstream reach values can be found by subtraction of the data in these two files)</p> <p>The third file contains other useful parameters tracked throughout the simulation</p> <p>The fourth file contains the X & Y coordinates which mark the boundaries of each mini reach</p> <p>See the accompanying dissertation document (to be referenced once the official reference is available) and the GitHub repository which includes the code required.</p>
Three-dimensional GNSS Time Series Data for Terrestrial Water Storage Changes Inversion in Yunnan, China
<p>The dataset includes three-dimensional GNSS time series data featured in the publication "Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China", published in 'Water Resources Research'.</p> <p>Reference:<br>Zhu, H., Chen, K., Hu, S., Liu, J.,Shi, H., Wei, G., et al. (2023). Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China. Water Resources Research, 59, e2022WR033126. https:// doi.org/10.1029/2022WR033126</p> <p><br>The sitelist file lists basic information about all the utilized stations, including their names and geographic coordinates. <br>The Time.mat file contains the time vectors of the data employed. <br>The Filter_time_series_N/E/U.mat files showcase the filtered time series, which have been processed using Independent Component Analysis (ICA) for the inversion of terrestrial water storage in Yunnan, after removing the effects of outliers, steps, and non-tidal atmospheric/oceanic loading.</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Storage time and temperature affect plasma osmolality values in field-collected blood samples
<p>Data and code for a study assessing the effect of storage conditions on blood plasma samples, published in Comparative Biochemistry and Physiology in 2024.</p>
Figure 5. Sensory score and period of storage for processed cheese-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>R2 was found to be 96.5 percent of the total variation as explained by sensory scores. Period<br> of storage (days) for which the processed cheese has been in the shelf can be determined based on<br> sensory score (Fig. 5).</p>
Storage and Transit Time Data and Code
<p><br>Author: Andrew J. Felton<br>Date: 5/5/2024</p> <p>This R project contains the primary code and data (following pre-processing in python) used for data production, manipulation, visualization, and analysis and figure production for the study entitled:</p> <p>"Global estimates of the storage and transit time of water through vegetation"</p> <p>Please note that 'turnover' and 'transit' are used interchangeably in this project.</p> <p>Data information:</p> <p>The data folder contains key data sets used for analysis. In particular:</p> <p>"data/turnover_from_python/updated/annual/multi_year_average/average_annual_turnover.nc" contains a global array summarizing five year (2016-2020) averages of annual transit, storage, canopy transpiration, and number of months of data. This is the core dataset for the analysis; however, each folder has much more data, including a dataset for each year of the analysis. Data are also available is separate .csv files for each land cover type. Oterh data can be found for the minimum, monthly, and seasonal transit time found in their respective folders. These data were produced using the python code found in the "supporting_code" folder given the ease of working with .nc and EASE grid in the xarray python module. R was used primarily for data visualization purposes. The remaining files in the "data" and "data/supporting_data"" folder primarily contain ground-based estimates of storage and transit found in public databases or through a literature search, but have been extensively processed and filtered here. </p> <p>#Code information</p> <p>Python scripts can be found in the "supporting_code" folder.</p> <p>Each R script in this project has a particular function:</p> <p>01_start.R: This script loads the R packages used in the analysis, sets the<br>directory, and imports custom functions for the project. You can also load in the <br>main transit time (turnover) datasets here using the `source()` function.</p> <p>02_functions.R: This script contains the custom function for this analysis,<br> primarily to work with importing the seasonal transit data. Load this using the <br> `source()` function in the 01_start.R script. </p> <p>03_generate_data.R: This script is not necessary to run and is primarily<br>for documentation. The main role of this code was to import and wrangle<br>the data needed to calculate ground-based estimates of aboveground water storage.</p> <p>04_annual_turnover_storage_import.R: This script imports the annual turnover and<br>storage data for each landcover type. You load in these data from the 01_start.R script<br>using the `source()` function.</p> <p>05_minimum_turnover_storage_import.R: This script imports the minimum turnover and<br>storage data for each landcover type. Minimum is defined as the lowest monthly<br>estimate.You load in these data from the 01_start.R script<br>using the `source()` function.</p> <p>06_figures_tables.R: This is the main workhouse for figure/table production and <br>supporting analyses. This script generates the key figures and summary statistics <br>used in the study that then get saved in the manuscript_figures folder. Note that all<br>maps were produced using Python code found in the "supporting_code"" folder.</p>
FTIR-ATR Spectra of Pelagic Sargassum Specimens under Different Flash-Freeze Timing and Cryogenic Storage Conditions
<p><strong>About the data set origin</strong></p> <p>The dataset comprises ATR-FTIR spectral CSV files of pelagic <em>Sargassum</em> specimens, including <em>S. fluitans</em> III (labeled "flu3" in the sample ID), <em>S. natans</em> I (labeled "nat1"), and <em>S. natans</em> VII (labeled "nat8"). The specimens were collected from three sites on the south Mexican Caribbean coast: Mahahual (labeled "Ma"), Xcalak (labeled "Xk"), and Xahuayxol (labeled "Xa"). The experimental design includes a treatment condition based on the timing of flash-freezing: the fresh group (labeled d00) and the lab group (labeled d01). The fresh group was flash-frozen on the shore and stored under cryogenic conditions, while the lab samples were flash-frozen upon arrival at the laboratory facilities. The sample IDs are listed in <strong>Table 1</strong>.</p> <p><strong>About the Equipment and Data Acquisition</strong></p> <p>All ATR-FTIR analyses were conducted using a Nicolet iS5 from Thermo Scientific (USA), equipped with an iD7 accessory and a ZnSe crystal plate. The Thermo Scientific OMNIC software was used for the analysis. Background noise was removed from all spectra. The spectral acquisition range spanned from 500 to 4000 cm⁻¹, with 32 scans per sample and a spectral resolution of 4 cm⁻¹, resulting in a data spacing of 0.482 cm⁻¹. Each sample was analyzed in triplicate.</p> <p><strong>Table 1</strong>. Sample ID and size for the flash-freeze comparison dataset</p> <table> <tbody> <tr> <td> <p><strong>Site</strong></p> </td> <td> <p><strong>Morphotype</strong></p> </td> <td> <p><strong>Flash-freeze treatment</strong></p> </td> <td> <p><strong>Samples collected</strong></p> </td> <td> <p><strong>Samples read in triplicate</strong></p> </td> </tr> <tr> <td>Mahahual (Ma)</td> <td><em>S. fluitans</em> III (flu3)</td> <td>Fresh (d00)</td> <td>2</td> <td>6</td> </tr> <tr> <td> </td> <td><em> </em></td> <td>Lab (d01)</td> <td>2</td> <td>6</td> </tr> <tr> <td> </td> <td><em>S. natans</em> I (nat1)</td> <td>Fresh (d00)</td> <td>2</td> <td>6</td> </tr> <tr> <td> </td> <td> </td> <td>Lab (d01)</td> <td>2</td> <td>6</td> </tr> <tr> <td> </td> <td><em>S. natans</em> VIII (nat8)</td> <td>Fresh (d00)</td> <td>2</td> <td>6</td> </tr> <tr> <td> </td> <td> </td> <td>Lab (d01)</td> <td>2</td> <td>6</td> </tr> <tr> <td>Xahuayxol (Xa)</td> <td><em>S. fluitans</em> III (flu3)</td> <td>Fresh (d00)</td> <td>1</td> <td>3</td> </tr> <tr> <td> </td> <td><em> </em></td> <td>Lab (d01)</td> <td>2</td> <td>6</td> </tr> <tr> <td> </td> <td><em>S. natans</em> I (nat1)</td> <td>Fresh (d00)</td> <td>1</td> <td>3</td> </tr> <tr> <td> </td> <td> </td> <td>Lab (d01)</td> <td>2</td> <td>6</td> </tr> <tr> <td> </td> <td><em>S. natans</em> VIII (nat8)</td> <td>Fresh (d00)</td> <td>1</td> <td>3</td> </tr> <tr> <td> </td> <td> </td> <td>Lab (d01)</td> <td>2</td> <td>6</td> </tr> <tr> <td>Xcalak (Xk)</td> <td><em>S. fluitans</em> III (flu3)</td> <td>Fresh (d00)</td> <td>1</td> <td>3</td> </tr> <tr> <td> </td> <td> </td> <td>Lab (d01)</td> <td>2</td> <td>6</td> </tr> <tr> <td> </td> <td><em>S. natans</em> I (nat1)</td> <td>Fresh (d00)</td> <td>1</td> <td>3</td> </tr> <tr> <td> </td> <td> </td> <td>Lab (d01)</td> <td>2</td> <td>6</td> </tr> <tr> <td> </td> <td><em>S. natans</em> VIII (nat8)</td> <td>Fresh (d00)</td> <td>1</td> <td>3</td> </tr> <tr> <td> </td> <td> </td> <td>Lab (d01)</td> <td>2</td> <td>6</td> </tr> <tr> <td> </td> <td> </td> <td><strong>Total </strong></td> <td><strong>30</strong></td> <td><strong>90</strong></td> </tr> </tbody> </table>
Bucket Table Edwards et al 2021 Metabolomic Markers of Storage Temperature and Time in Pasteurized Milk
<p>NMR spectroscopic data of a set of milk samples at different temperatures and storage times. Spectra were binned using AMIX 3.9.15 (Bruker BioSpin, Rheinstetten, Germany).</p>
RRR input and output files corresponding to "Global patterns in river water storage dependent on residence time"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR input and output files that were used in the study reported in:</p> <ul> <li>Collins, E. L., C. H. David, R. Riggs, G. H. Allen, T. M. Pavelsky, P. Lin, M. Pan, D. Yamazaki, R. K. Meentemeyer, and G. M. Sanchez (2024), Global patterns in river water storage dependent on residence time.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Input datasets</strong></p> <p>The dataset herein benefited from and was built upon other datasets:</p> <ul> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> <li>GLDAS VIC Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/ZRIHVF29X43C">10.5067/ZRIHVF29X43C</a></li> <li>GLDAS Noah Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/QN80TO7ZHFJZ">10.5067/QN80TO7ZHFJZ</a></li> <li>GLDAS Catchment Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/SGSL3LNKGJWW">10.5067/SGSL3LNKGJWW</a></li> </ul> <p><strong>Known bugs and limitations in this dataset or the associated manuscript</strong></p> <p>In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, two river reaches do not have a corresponding catchment: COMID=31000001 and COMID=61000003. These two river reaches were removed from our analysis, hence bringing the total number of river reaches used from 2,938,143 to 2,938,141, the same value as the total number of catchments.</p> <p>In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, the attribute table for the river reach with COMID=67072432 contains only four upstream reaches (COMID=64072433, COMID=64072735, COMID=64073772, and COMID=64073786), while the reach actually has five upstream reaches on the map (those above and COMID=64073793). This was modified herein because all connectivity in this dataset was created by associating upstream and downstream nodes rather than using the attribute table.</p> <div> <div> <div> <p>Our monthly dataset of river gauges with 95% daily data availability for 1980–2009 and with average discharge greater than or equal to 100 m3/s contains 1,148 stations. After mapping gauges to the MERIT Hydro river network using a joint criteria of 1) within a distance of 0.05 degrees and 2) uncorrected 30-yr average simulated discharge of the river reach within one order of magnitude relative to the observed average discharge of the gauge; the number of gauges reduces to 1129. In cases of multiple gauges per reach, we selected the gauge that had the closest observed average discharge to the uncorrected 30-yr averages simulated, resulting in 1,001 gauges. The dataset was further split in calibration gauges (702) and validation gauges (299). Three specific gauges are located in places with 0 accumulation of runoff in their specific sub-basin, they are located at river reaches with ID 41003992, 41001545, and 24005996. These three gauges were removed from the overall analysis at all gauges, resulting in 998 total gauges. All three gauges are part of the 299 validation gauges, not the 702 calibration gauges. They were retained in the analysis of 299 validation gauges. </p> <p> </p> </div> </div> </div> <p><strong>Files included in this version</strong></p> <p>All monthly discharge observation files for 1148 gauges, in shapefile and CSV formats:</p> <ul> <li>sites_1980-01_2009-12_100cms_095p.zip</li> <li>timeseries_obs_1980-01_2009-12_100cms_095p_monthly.zip</li> </ul> <p>All CSV files corresponding to the hydrography of all pfaf_level_02 regions of MERIT-Hydro v0.7 Basins v1.0 (i.e. the 61 values of <em>ii</em>):</p> <ul> <li>coords_pfaf_<em>ii</em>.zip</li> <li>kfac_pfaf_<em>ii</em>_1km_hour.zip</li> <li>k_pfaf_<em>ii</em>_low.zip</li> <li>k_pfaf_<em>ii</em>_nrm.zip</li> <li>k_pfaf_<em>ii</em>_hig.zip</li> <li>rapid_catchment_pfaf_<em>ii</em>.zip</li> <li>rapid_connect_pfaf_<em>ii</em>.zip</li> <li>rapid_coupling_pfaf_<em>ii</em>_GLDAS.zip</li> <li>riv_bas_id_pfaf_<em>ii</em>_topo.zip</li> <li>sort_pfaf_<em>ii</em>_topo.zip</li> <li>xfac_pfaf_<em>ii</em>_0.1.zip</li> <li>x_pfaf_<em>ii</em>_low.zip</li> <li>x_pfaf_<em>ii</em>_nrm.zip</li> <li>x_pfaf_<em>ii</em>_hig.zip</li> </ul> <p>All netCDF4 files with monthly surface and subsurface runoff that were combined from GLDAS 2.0 and corresponding to the following land surface models for the years 1980 to 2009 (both included):</p> <ul> <li>GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_ENS_M_1980-01_2009-12_utc.zip (the 3-model ensemble average based on the three above models).</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the GLDAS data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly discharge (based on the m3_riv data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>Qout_pfaf_<em>ii</em>_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly storage (based on the Qout_pfaf_<em>ii</em>_GLDAS_ENS data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig):</p> <ul> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_hig.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above):</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_GLDAS_ENS.zip</li> </ul> <p>All monthly discharge observation files for 1148 gauges, in shapefile format, with observed monthly mean appended:</p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ.zip</li> </ul> <p>All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, in shapefile and CSV formats, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR.zip</li> <li>obs_tot_id_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> <li>Qobs_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> </ul> <p>All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, separated into calibration (CAL) and validation (VAL) gauges, in shapefile and CSV formats, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_CAL.zip</li> <li>obs_bas_id_1980-01_2009-12_100cms_095p_meanQ_CAL_pfaf_<em>ii</em>.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_VAL.zip</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for calibration (CAL) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for all correction (COR) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly storage (based on the Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig):</p> <ul> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_hig.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip data above):</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_GLDAS_COR.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) catchments that have been dissolved and of which the perimeter was extracted, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code; as well as global combined files:</p> <ul> <li>cat_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_perim.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_perim.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network retaining only those reaches that flow to the global coast, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_coast.zip</li> </ul>
Storage and Transit Time Data and Code
<p>Author: Andrew J. Felton<br>Date: 11/15/2024</p> <p>This R project contains the primary code and data (following pre-processing in python) used for data production, manipulation, visualization, and analysis, and figure production for the study entitled:</p> <p>"Global estimates of the storage and transit time of water through vegetation"</p> <p>Please note that 'turnover' and 'transit' are used interchangeably. Also please note that this R project has been updated multiple times as the analysis has updated throughout the peer review process.</p> <p>#Data information:</p> <p>The data folder contains key data sets used for analysis. In particular:</p> <p>"data/turnover_from_python/updated/august_2024_lc/" contains the core datasets used in this study including global arrays summarizing five year (2016-2020) averages of mean (annual) and minimum (monthly) transit time, storage, canopy transpiration, and number of months of data able as both an array (.nc) or data table (.csv). These data were produced in python using the python scripts found in the "supporting_code" folder. The remaining files in the "data" and "data/supporting_data" folder primarily contain ground-based estimates of storage and transit found in public databases or through a literature search, but have been extensively processed and filtered here. The "supporting_data"" folder also contains annual (2016-2020) MODIS land cover data used in the analysis and contains separate filters containing the original data (.hdf) and then the final process (filtered) data in .nc format. The resulting annual land cover distributions were used in the pre-processing of data in python.</p> <p>#Code information</p> <p>Python scripts can be found in the "supporting_code" folder. </p> <p>Each R script in this project has a role:</p> <p>"01_start.R": This script sets the working directory, loads in the tidyverse package (the remaining packages in this project are called using the `::` operator), and can run two other scripts: one that loads the customized functions (02_functions.R) and one for importing and processing the key dataset for this analysis (03_import_data.R).</p> <p>"02_functions.R": This script contains custom functions. Load this using the `source()` function in the 01_start.R script. </p> <p>"03_import_data.R": This script imports and processes the .csv transit data. It joins the mean (annual) transit time data with the minimum (monthly) transit data to generate one dataset for analysis: annual_turnover_2. Load this using the <br> `source()` function in the 01_start.R script. </p> <p>"04_figures_tables.R": This is the main workhouse for figure/table production and supporting analyses. This script generates the key figures and summary statistics used in the study that then get saved in the "manuscript_figures" folder. Note that all maps were produced using Python code found in the "supporting_code"" folder. Also note that within the "manuscript_figures" folder there is an "extended_data" folder, which contains tables of the summary statistics (e.g., quartiles and sample sizes) behind figures containing box plots or depicting regression coefficients. </p> <p>"supporting_generate_data.R": This script processes supporting data used in the analysis, primarily the varying ground-based datasets of leaf water content.</p> <p>"supporting_process_land_cover.R": This takes annual MODIS land cover distributions and processes them through a multi-step filtering process so that they can be used in preprocessing of datasets in python.</p> <p> </p> <p> </p>
Data on space-time soil moisture and modelled soil water storage, Ghana
<p>Data on spatial and temporal modelling of soil water storage from in situ soil moisture measurements in the Guinea savannah zone of Northern Ghana, West Africa.</p>
Dataset and codes used in the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data"
<p>This contains the codes and dataset for the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data". Detailed information about the dataset is described in the Readme.txt file.</p>
Data from: Temperature and storage time strongly affect the germination success of perennial Euphorbia species in Mediterranean regions
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Data from: In vitro aging behavior of dental composites considering the influence of filler content, storage media and incubation time
1. Objective: Over time dental composites age due to mechanical impacts such as chewing and chemical impacts such as saliva enzymes and food ingredients. For this research, the focus was placed on chemical degradation.The objective of this study was to simulate hydrolysis by using different food simulating liquids and to assess their impact on the mechanical parameter Vickers microhardness (MHV) and the physicochemical parameter contact angle (CA). 2. Methods: Specimen of three composites (d = 6 mm, h = 2 mm; n = 435) classified with respect to their filler content (wt%), namely low-filled, medium-filled and highly-filled, were stored for 0, 14, 30, 90 and 180 days in artificial saliva (pH 7), citric acid (pH 3; pH 5), lactic acid (pH 3; pH 5) and ethanol (40 %vol; 60 %vol) and assessed regarding to MHV and CA.Statistics: Kruskal-Wallis test, stepwise linear regression, bivariate Spearman Rank Correlation (p < 0.05). 3. Results: While stored in artificial saliva, acid and ethanol the CA decreased especially for the low- and medium-filled composites. It was shown that rising the filler content caused less surface changes in the CA. Storage in ethanol led to a significant decrease of MHV of all composites. Regression analysis showed that the effect of in vitro aging on MHV was mainly influenced by the composite material and therefore by filler content (R² = 0.67; p < 0.05). In contrast, the CA is more influenced by incubation time and filler content (R² = 0.2; p < 0.05) leading to a higher risk of plaque accumulation over time. Significance: In vitro aging showed significant changes on the mechanical and physicochemical properties of dental composites which may shorten their long-term functionality. In conclusion, it can be stated, that the type of composite material, especially rising filler content seems to improve the materials' resistance against the processes of chemical degradation.
Supplementary material 3 from: McNeill MR, Phillips CB, Robinson AP, Aalders L, Richards N, Young S, Dowsett C, James T, Bell N (2017) Defining the biosecurity risk posed by transported soil: Effects of storage time and environmental exposure on survival of soil biota. NeoBiota 32: 65-88. https://doi.org/10.3897/neobiota.32.9784
Soil chemistry of the two soil sources :
Supplementary material 2 from: McNeill MR, Phillips CB, Robinson AP, Aalders L, Richards N, Young S, Dowsett C, James T, Bell N (2017) Defining the biosecurity risk posed by transported soil: Effects of storage time and environmental exposure on survival of soil biota. NeoBiota 32: 65-88. https://doi.org/10.3897/neobiota.32.9784
Plant species common to both soil sources :
Supplementary material 1 from: McNeill MR, Phillips CB, Robinson AP, Aalders L, Richards N, Young S, Dowsett C, James T, Bell N (2017) Defining the biosecurity risk posed by transported soil: Effects of storage time and environmental exposure on survival of soil biota. NeoBiota 32: 65-88. https://doi.org/10.3897/neobiota.32.9784
Description of the data analysis methods and results :
An effective low-cost remote sensing approach to reconstruct the long-term and dense time series of area and storage variations for super-large lakes
<p>Supplementary File</p>
Data from: In vitro aging behavior of dental composites considering the influence of filler content, storage media and incubation time
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Data from: Making it last: storage time and temperature have differential impacts on metabolite profiles of airway samples from cystic fibrosis patients
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