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opencc-by-4.0Mar 2022View details →

Annual maps of cropland abandonment, land cover, and other derived data for time-series analysis of cropland abandonment

This archive contains raw annual land cover maps, cropland abandonment maps, and accompanying derived data products to support: <blockquote> Crawford C.L., Yin, H., Radeloff, V.C., and Wilcove, D.S. 2022. Rural land abandonment is too ephemeral to provide major benefits for biodiversity and climate. Science Advances <a href="https://doi.org/10.1126/sciadv.abm8999">doi.org/10.1126/sciadv.abm8999</a>. </blockquote> An archive of the analysis scripts developed for this project can be found at: <a href="https://github.com/chriscra/abandonment_trajectories">https://github.com/chriscra/abandonment_trajectories</a> (<a href="https://doi.org/10.5281/zenodo.6383127">https://doi.org/10.5281/zenodo.6383127</a>). Note that the label "_2022_02_07" in many file names refers to the date of the primary analysis. "dts” or “dt” refer to “data.tables," large .csv files that were manipulated using the data.table package in R (Dowle and Srinivasan 2021, <a href="http://r-datatable.com/">http://r-datatable.com/</a>). “Rasters” refer to “.tif” files that were processed using the raster and terra packages in R (Hijmans, 2022; <a href="https://rspatial.org/terra/">https://rspatial.org/terra/</a>; <a href="https://rspatial.org/raster">https://rspatial.org/raster</a>). Data files fall into one of four categories of data derived during our analysis of abandonment: observed, potential, maximum, or recultivation. Derived datasets also follow the same naming convention, though are aggregated across sites. These four categories are as follows (using “age_dts” for our site in Shaanxi Province, China as an example): <ol> <li>observed abandonment identified through our primary analysis, with a threshold of five years. These files do not have a specific label beyond the description of the file and the date of analysis (e.g., shaanxi_age_2022_02_07.csv);</li> <li>potential abandonment for a scenario without any recultivation, in which abandoned croplands are left abandoned from the year of initial abandonment through the end of the time series, with the label “_potential” (e.g., shaanxi_potential_age_2022_02_07.csv);</li> <li>maximum age of abandonment over the course of the time series, with the label “_max” (e.g., shaanxi_max_age_2022_02_07.csv);</li> <li>recultivation periods, corresponding to the lengths of recultivation periods following abandonment, given the label “_recult” (e.g., shaanxi_recult_age_2022_02_07.csv).</li> </ol>   This archive includes multiple .zip files, the contents of which are described below: <ul> <li>age_dts.zip - Maps of abandonment age (i.e., how long each pixel has been abandoned for, as of that year, also referred to as length, duration, etc.), for each year between 1987-2017 for all 11 sites. These maps are stored as .csv files, where each row is a pixel, the first two columns refer to the x and y coordinates (in terms of longitude and latitude), and subsequent columns contain the abandonment age values for an individual year (where years are labeled with "y" followed by the year, e.g., "y1987"). Maps are given with a latitude and longitude coordinate reference system. Folder contains observed age, potential age (“_potential”), maximum age (“_max”), and recultivation lengths (“_recult”) for all sites. Maximum age .csv files include only three columns: x, y, and the maximum length (i.e., “max age”, in years) for each pixel throughout the entire time series (1987-2017). Files were produced using the custom functions "cc_filter_abn_dt()," “cc_calc_max_age()," “cc_calc_potential_age(),” and “cc_calc_recult_age();” see "_util/_util_functions.R."</li> <li>age_rasters.zip - Maps of abandonment age (i.e., how long each pixel has been abandoned for), for each year between 1987-2017 for all 11 sites. Maps are stored as .tif files, where each band corresponds to one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Folder contains observed age, potential age (“_potential”), and maximum age (“_max”) rasters for all sites. Maximum age rasters include just one band (“layer”). These rasters match the corresponding .csv files contained in "age_dts.zip.”</li> <li>derived_data.zip - summary datasets created throughout this analysis, listed below.</li> <li>diff.zip - .csv files for each of our eleven sites containing the year-to-year lagged differences in abandonment age (i.e., length of time abandoned) for each pixel. The rows correspond to a single pixel of land, and the columns refer to the year the difference is in reference to. These rows do not have longitude or latitude values associated with them; however, rows correspond to the same rows in the .csv files in "input_data.tables.zip" and "age_dts.zip." These files were produced using the custom function "cc_diff_dt()" (much like the base R function "diff()"), contained within the custom function "cc_filter_abn_dt()" (see "_util/_util_functions.R"). Folder contains diff files for observed abandonment, potential abandonment (“_potential”), and recultivation lengths (“_recult”) for all sites.</li> <li>input_dts.zip - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 Remote Sensing of Environment (<a href="https://doi.org/10.1016/j.rse.2020.111873">https://doi.org/10.1016/j.rse.2020.111873</a>). Like “age_dts,” these maps are stored as .csv files, where each row is a pixel and the first two columns refer to x and y coordinates (in terms of longitude and latitude). Subsequent columns contain the land cover class for an individual year (e.g., "y1987"). Note that these maps were recoded from Yin et al. 2020 so that land cover classification was consistent across sites (see below). This contains two files for each site: the raw land cover maps from Yin et al. 2020 (after recoding), and a “clean” version produced by applying 5- and 8-year temporal filters to the raw input (see custom function “cc_temporal_filter_lc(),” in “_util/_util_functions.R” and “1_prep_r_to_dt.R”). These files correspond to those in "input_rasters.zip," and serve as the primary inputs for the analysis.</li> <li>input_rasters.zip - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 Remote Sensing of Environment. Maps are stored as ".tif" files, where each band corresponds one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Maps are given with a latitude and longitude coordinate reference system. Note that these maps were recoded so that land cover classes matched across sites (see below). Contains two files for each site: the raw land cover maps (after recoding), and a “clean” version that has been processed with 5- and 8-year temporal filters (see above). These files match those in "input_dts.zip."</li> <li>length.zip - .csv files containing the length (i.e., age or duration, in years) of each distinct individual period of abandonment at each site. This folder contains length files for observed and potential abandonment, as well as recultivation lengths. Produced using the custom function "cc_filter_abn_dt()" and “cc_extract_length();” see "_util/_util_functions.R."</li> </ul> derived_data.zip contains the following files: <ul> <li>"site_df.csv" - a simple .csv containing descriptive information for each of our eleven sites, along with the original land cover codes used by Yin et al. 2020 (updated so that all eleven sites in how land cover classes were coded; see below).</li> <li>Primary derived datasets for both observed abandonment (“area_dat”) and potential abandonment (“potential_area_dat”). <ul> <li>area_dat - Shows the area (in ha) in each land cover class at each site in each year (1987-2017), along with the area of cropland abandoned in each year following a five-year abandonment threshold (abandoned for >=5 years) or no threshold (abandoned for >=1 years). Produced using custom functions "cc_calc_area_per_lc_abn()" via "cc_summarize_abn_dts()". See scripts "cluster/2_analyze_abn.R" and "_util/_util_functions.R."</li> <li>persistence_dat - A .csv containing the area of cropland abandoned (ha) for a given "cohort" of abandoned cropland (i.e., a group of cropland abandoned in the same year, also called "year_abn") in a specific year. This area is also given as a proportion of the initial area abandoned in each cohort, or the area of each cohort when it was first classified as abandoned at year 5 ("initial_area_abn"). The "age" is given as the number of years since a given cohort of abandoned cropland was last actively cultivated, and "time" is marked relative to the 5th year, when our five-year definition first classifies that land as abandoned (and where the proportion of abandoned land remaining abandoned is 1). Produced using custom functions "cc_calc_persistence()" via "cc_summarize_abn_dts()". See scripts "cluster/2_analyze_abn.R" and "_util/_util_functions.R." This serves as the main input for our linear models of recultivation (“decay”) trajectories.</li> <li>turnover_dat - A .csv showing the annual gross gain, annual gross loss, and annual net change in the area (in ha) of abandoned cropland at each site in each year of the time series. Produced using custom functions "cc_calc_abn_diff()" via "cc_summarize_abn_dts()" (see "_util/_util_functions.R"), implemented in "cluster/2_analyze_abn.R." This file is only produced for observed abandonment.</li> </ul> </li> <li>Area summary files (for observed abandonment only) <ul> <li>area_summary_df - Contains a range of summary values relating to the area of cropland abandonment for each of our eleven sites. All area values are given in hectares (ha) unless stated otherwise. It contains 16 variables as columns, including 1) "site," 2) "total_site_area_ha_2017" - the total site area (ha) in 2017, 3) "cropland_area_1987" - the area in cropland in 1987 (ha), 4) "area_abn_ha_2017" - the area of cropland abandoned as of 2017 (ha), 5) "area_ever_abn_ha" - the total area of those pixels that were abandoned at least once during the time series (corresponding to the area of potential abandonment, as of 2017), 6) "total_crop_extent_ha" - the total area of those pixels that were classified as cropland at least once during the time series, 7) "total_area_abn_remaining_2017" - duplicate of "area_abn_ha_2017," the area abandoned as of 2017 (ha), taken from "area_recult_threshold," 8) "total_initial_area_abn" - the sum of the initial area of each cohort of abandonment when it is first classified as "abandoned," i.e., at the 5 year mark (note that this is cumulative, and because it counts those pixels that were abandoned more than once, it is therefore larger than "area_ever_abn_ha"), taken from "area_recult_threshold" 9) "total_area_abn_recultivated_2017" - the area of abandoned land that was recultivated as of 2017 (cumulatively, i.e., "total_initial_area_abn" - "area_abn_ha_2017"), taken from "area_recult_threshold," 10) "proportion_recultivated" - the proportion of all abandoned cropland (including multiple periods per pixel) that was recultivated by 2017, taken from "area_recult_threshold," 11) "area_2017_as_prop_site" - area abandoned as of 2017 as a proportion of the total site area, 12) "area_2017_as_prop_total_crop" - area abandoned as of 2017 as a proportion of the total crop extent, 13) "area_2017_as_prop_crop87" - area abandoned as of 2017 as a proportion of cropland area in 1987, 14) "area_ever_abn_as_prop_site" - area ever abandoned as a proportion of the total site area, 15) "area_ever_abn_as_prop_total_crop" - area ever abandoned as a proportion of the total crop extent, 16) "area_ever_abn_as_prop_crop87" - area ever abandoned as a proportion of cropland area in 1987. See script "1_summary_stats.Rmd."</li> <li>area_recult_threshold - Contains data on the proportion of observed abandoned cropland area that is recultivated by the end of our time series. This includes the area of abandoned cropland as of 2017 ("total_area_abn_remaining_2017") and the sum of the initial area of each cohort of abandonment when it is first classified as abandoned (at year 5; "total_initial_area_abn"). This "total_initial_area_abn" is cumulative, and allows for pixels that were abandoned multiple times during the time series to be counted multiple times. The difference between these two columns yields the "total_area_abn_recultivated_2017," which in turn is used to calculate the "proportion_recultivated," and the (ascending) "order" of sites based on this proportion. This file includes recultivation stats for each site for three abandonment definitions: 5, 7, and 10 years. See script "1_summary_stats.Rmd."</li> <li>abn_lc_area_2017 - Contains the number of pixels and corresponding area (in ha) of abandoned cropland in the year 2017 at each site, according to the land cover class (either woody vegetation [2], or herbaceous vegetation [4]) and the age in 2017 (5 to 30 years). See script "cluster/6_lc_of_abn.R."</li> <li>abn_prop_lc_2017 - Contains the number of pixels and corresponding area (ha) of cropland abandoned in the year 2017 in each land cover type (woody vegetation [2], or herbaceous vegetation [4]). It also shows this area as a proportion of the total area abandoned at each site (i.e., in either land cover class: 2 or 4). See script "cluster/6_lc_of_abn.R."</li> </ul> </li> <li>Carbon <ul> <li>carbon_df – contains the observed and potential carbon accumulation in abandoned croplands in each site in each year (in Mg C), for two abandonment thresholds: 5 years (our default abandonment definition) and 1 year (i.e., no threshold). Each data point corresponds to one of two scenarios (“type” column), either “observed” or “potential.” Carbon accumulation figures are for both the sum of forest and soil carbon at each site in a given year. Carbon accumulation is listed in three columns: 1) “C_up_to_20” contains the total carbon accumulated in those abandoned croplands with abandonment durations between 5 and 20 years. 2) “C_21_30” contains the total carbon accumulation in croplands with durations between 21 and 30 years, which are differentiated in order to account for non-linear carbon accumulation rates in soils over time, and 3) “total_C_Mg” contains the sum of the previous two columns, representing the total carbon accumulated across all abandoned croplands in each year.</li> <li>soc_mean – contains mean soil organic carbon accumulation rates for years 1-20 and years 21-80, derived from Sanderman et al. 2020 (in Mg C; <a href="https://doi.org/10.7910/DVN/HA17D3">https://doi.org/10.7910/DVN/HA17D3</a>). These values correspond to accumulation rates in croplands upon abandonment and regeneration to natural vegetation (Sanderman et al. 2020’s “rewilding” scenario). These mean values are calculated across those pixels identified as cropland by Sanderman et al. 2020 at each site. Mean values in year 20 and 80 are contained in columns “mean_soc_20” and “mean_soc_80” respectively, and the annualized rate over the first 20 years and the subsequent years 21 through 80 are contained in columns “mean_annual_soc_1_20” and “mean_annual_soc_21_80” respectively.</li> </ul> </li> <li>Decay model data – two R data files containing data products for our linear models of abandonment recultivation trajectories. <ul> <li>decay_endpoints_files – an R data file (.rds) containing seven data products produced as part of our common endpoint analysis, which calculated mean trajectories for each site across a range of common endpoints, ensuring that means were based on coefficient estimates derived from a consistent number of observations for each cohort. These files are: <ul> <li>common_endpoint_dat – a .csv containing subsets of “persistence_dat” for each “endpoint” (7 through 29).</li> <li>endpoint_n – a .csv describing, for each endpoint, the corresponding number of observations per cohort (“n_obs”), the number of cohorts (“n_cohorts”), the total number of observations across cohorts included (“total_obs”), and the cohorts that meet the endpoint threshold (“cohorts”).</li> <li>coef_l3_endpoints – corresponding model coefficients for our primary model (“l3”) parameterized by the range of subsets across endpoints.</li> <li>augment_endpoints – fitted values (i.e., model predictions) for linear models produced across the full range of endpoint subsets.</li> <li>fitted_endpoints – a simplified .csv containing the mean linear and log coefficients for each site at each endpoint, and the corresponding predicted proportion remaining abandoned through time (based on the “age,” or duration, of abandonment).</li> <li>time_to_endpoints – a .csv containing, for mean trajectories for each endpoint at each site, the estimated time required for a given amount of abandoned cropland in a cohort to be recultivated (deciles, 10% through 100%).</li> <li>endpoint_half_lives – a .csv containing the half-lives calculated for the mean trajectories for each endpoint at each site.</li> </ul> </li> <li>decay_mod_archive - an R data file (.rds) containing eleven data products derived from linear models of abandonment recultivation ("decay"): <ul> <li>lm_mega_lin_log_lin_l – the primary linear model produced in our analysis. This model is referred to as “lin_log_lin” (or “l3”) because the model predicts linear persistence (“lin”) as a function of a log term of time (“log”) and a linear term of time (“lin”). “mega” refers to the fact that this model is run for the full dataset, pooled across all 11 sites.</li> <li>coef_l3_mega – a .csv containing model coefficients for our primary linear model of recultivation (“lin_log_lin”, or “l3”), with a single row each for the linear term of time and the log term of time, for 26 cohorts at 11 sites.</li> <li>mean_coef_l3_mega – a data frame containing the mean coefficient values for the log and linear terms of time across cohorts at each site. This also contains the mean of the low and high coefficient estimates, based on the 95% confidence interval.</li> <li>half_lives_all_cohorts_l3 – half-lives calculated for each cohort at each site, for our primary model.</li> <li>half_life_mean_coefs_l3 – half-lives calculated based on the mean trajectory for each site (based on the mean log coefficients and mean linear coefficients across all cohorts), for our primary model.</li> <li>mod_AIC_mega – Akaike Information Criterion (AIC) values for all tested model specifications.</li> <li>fitted_combo – fitted values (i.e., model predictions) for our primary model (“l3”) and a series of alternative model specifications (“l3_trim” – excluding cohorts with fewer than 5 observations; “lin_log” – a model including only one log time term; “log2_lin” – in which the log of persistence is predicted by log and linear time terms; and “l3_no_cohort” – our primary model, predicting linear persistence as a function of log time and linear time, but without cohort-level fixed effects).</li> <li>time_to_combo – contains the estimated time required for a certain amount of abandoned cropland in a cohort to be recultivated (deciles, 10% through 100%). See script "2_decay_models.Rmd." These values are calculated for a range of alternative model specifications ("l3_trim", “lin_log”, "log2_lin", and "l3_no_cohort"; see above).</li> </ul> </li> </ul> </li> <li>Length data – includes “_distill_df” files and “mean_length_df” files for observed, potential, and recultivation. <ul> <li>length_distill_df - .csvs containing the number ("freq") of abandonment periods of a specific "length" of time (i.e., age) at each site over the course of the entire time series. Derived from the "length" files in "length.zip." See script "cluster/5_distill_lengths.R."</li> <li>mean_length_df - .csvs with the mean, median, and standard deviation, for each site, for both "all" lengths or just the "max" length per pixel, and for a range of abandonment definitions (1, 3, 5, 7, and 10 years). Derived from "length_distill_df." See script "1_summary_stats.Rmd."</li> </ul> </li> <li>Duration summary files – includes “summary_stats_all_sites” and “summary_stats_all_sites_pooled,” for observed and potential abandonment, and recultivation periods following abandonment. <ul> <li>“summary_stats_all_sites” - A simple .csv derived from "mean_length_df" files containing summary stats across the 11 sites. This includes the mean of the mean abandonment duration ("length", in years) for each of our 11 sites ("mean_of_means"), the standard deviation of these site mean abandonment lengths ("sd_of_means"), the mean of the standard deviation at each site ("mean_of_sds"), the mean median ("mean_of_medians"), and the mean number of abandonment periods ("mean_n_abn_periods"). Note that length "all" indicates that these stats account for all periods (including multiple per pixel), rather than just the max duration per pixel. See script "1_summary_stats.Rmd."</li> <li>“summary_stats_all_sites_pooled” - A summary .csv similar to "summary_stats_all_sites," but calculated by pooling all distinct periods of abandonment across all eleven sites, and then calculating the mean, median, and standard deviation of abandonment duration. See script "1_summary_stats.Rmd."</li> </ul> </li> <li>Comparing annual approach to identifying abandonment to a two-timepoint (“2yr”) approach: <ul> <li>abn_2yr_ages_df - Contains the age of former croplands identified as "abandoned" using a two-timepoint method (i.e., 2017 - 1987), where age values (as of 2017) are derived from our map of abandonment identified using the full annual time series. This includes the area in hectares (ha), in each age class (along with the number of pixels), at each of our 11 sites. This dataset is used to calculate the percent of cropland "abandonment" identified using the two-year method that is actually too "young," i.e., less than 5 years old, and therefore not truly abandonment according to our five-year abandonment definition</li> <li>abn_2yr_overestimation - Compares the area (in hectares) of cropland abandonment at each site identified with our full annual time series (and a five-year abandonment definition) and the "abandonment" identified using a two-timepoint method (2017-1987). This also includes the percent difference in area between the two methods, the Jaccard similarity of the areas identified as abandonment, and the percent of "young" (i.e., <5-year-old) "abandonment" identified by the two-timepoint method.</li> </ul> </li> </ul> Input land cover maps: As noted, the file "input_rasters.zip" contain the raw annual land cover maps for eleven sites generated by: <blockquote> Yin, H., A. Brandão, J. Buchner, D. Helmers, B. G. Iuliano, N. E. Kimambo, K. E. Lewińska, E. Razenkova, A. Rizayeva, N. Rogova, S. A. Spawn, Y. Xie, and V. C. Radeloff. 2020. Monitoring cropland abandonment with Landsat time series. Remote Sensing of Environment 246:111873. https://doi.org/10.1016/j.rse.2020.111873 </blockquote> These land cover maps served as raw inputs for this project and form the basis of the analysis. All land cover maps have a resolution of 30-m and exist for each year from 1987 through 2017. The exceptions are Nebraska / Wyoming (1986-2018) and Wisconsin (1987-2018); these additional years were excluded from our analysis of abandonment duration. Land cover categories in these maps are coded as follows: <ol> <li>Non-vegetated area (e.g., water, urban, barren land)</li> <li>Woody vegetation (e.g., forests)</li> <li>Cropland</li> <li>Herbaceous vegetation (e.g., grassland)</li> </ol> Site file names correspond to the following geographic locations: <ul> <li>belarus = Vitebsk, Belarus / Smolensk, Russia</li> <li>bosnia_herzegovina = Bosnia & Herzegovina</li> <li>chongqing = Chongqing, China</li> <li>goias = Goiás, Brazil</li> <li>iraq = Iraq</li> <li>mato_grosso = Mato Grosso, Brazil</li> <li>nebraska = Nebraska / Wyoming, USA</li> <li>orenburg = Orenburg, Russia / Uralsk, Kazakhstan</li> <li>shaanxi = Shaanxi/Shanxi, China</li> <li>volgograd = Volgograd, Russia</li> <li>wisconsin = Wisconsin, USA</li> </ul> This dataset is minimally altered from Yin et al. 2020.  However, land cover codes were updated for five sites (Iraq, Nebraska/Wyoming, Orenburg/Uralsk, Volgograd, and Wisconsin) in order to maintain consistency in how land cover was coded across all sites. The original land cover codes (matching Yin et al. 2020) are described in the file "site_df.csv" and are as follows: <ol> <li>Iraq: 1 Non-vegetated;  2 Cropland;  3 Woody;  4 Herbaceous</li> <li>Nebraska / Wyoming (USA):  1 Cropland;  2 Woody;  3 Non-vegetated;  4 Herbaceous</li> <li>Orenburg, Russia / Uralsk, Kazakhstan:  1 Non-vegetated;  2 Cropland;  3 Herbaceous;  4 Woody</li> <li>Volgograd (Russia):  1 Non-vegetated;  2 Cropland;  3 Herbaceous;  4 Woody</li> <li>Wisconsin (USA):  1 Cropland;  2 Herbaceous;  3 Woody;  4 Non-vegetated</li> </ol>

opencc-by-4.0Sep 2019View details →

Fed4Fire/CDN-X-ALL network metrics dataset for time series analysis in Media content delivery for 4G/5G networks

The following dataset was generated at VICOMTECH (https://www.vicomtech.org) under project/experiment CDN-X-ALL: "CDN edge-cloud computing for efficient cache and reliable streaming aCROSS Aggregated unicast-multicast LinkS". Project funded by Fed4FIRE+ OC5 (<a href="https://www.fed4fire.eu/">https://www.fed4fire.eu</a>) under grant 732638. The dataset provides network metrics captures across several days employing a GStreamer-based MPEG-DASH player running on an UE connected to a LTE network. Nitos LTE/OpenAirInterface (OAI) testbed (<a href="https://nitlab.inf.uth.gr/NITlab/nitos/lte">https://nitlab.inf.uth.gr/NITlab/nitos/lte</a>) was used to deploy the LTE network. CDN-like server/DASH Dataset -> Internet -> EPC/OAI -> eNodeB/OAI -> UE/DASH player The player downloads MPEG-DASH video files provided by Distributed DASH dataset (<a href="https://dash.itec.aau.at/distributed-dash-datset/">https://dash.itec.aau.at/distributed-dash-datset/</a>), a dataset for CDN-like experiments, and captures the following data: <ol> <li>Date: date when the data is collected</li> <li>Player: type of the player (in this case it is always "GStreamer")</li> <li>Num: identifier of the player</li> <li>URLVid: URL of the MPD file</li> <li>Latency: latency experienced by the player</li> <li>BW: bandwidth experienced by the player</li> <li>Quality: chosen DASH video representation</li> </ol> During the experiments, other players run in order to generate realistic media streaming traffic at the CDN-like servers. These players start playing by following Poisson or Pareto distribution. The dataset was used to train Machine Learning Time Series predictor in order to forecast network capabilities and can be used for further experimentation concerning time series analysis.

opencc-by-4.0Mar 2023View details →

InSAR Time Series Analysis (2018-2021) for Volcanic Monitoring in Northern Chile

This dataset is for the paper "First onset of unrest captured at Socompa: A Recent Geodetic Survey at Central Andean volcanoes in Northern Chile" which is published in GRL: <a href="https://doi.org/10.1029/2022GL102480">https://doi.org/10.1029/2022GL102480</a>. InSAR Data: The folder of InSAR_149A.rar stores the InSAR time series analysis dataset on ascending track 149. <ol> <li>The 'Imagedate' folder stores the empty *.rslc files to indicate the date of each SLCs.</li> <li>Data_Asc.mat stores the main InSAR time series data, which includes the UTC time of the acquisition (for accurate time calculation), the length of perpendicular baselines (unit is meter), the number of days counting from the first epoch, the unwrapped time series data (ifg), the unwrapped time series data with GACOS correction (ifg_aps), the look angles (la, unit is rad), and lat&lon.</li> <li>parms.mat stores the parameters used during the data processing by StaMPS.</li> <li>semi_fit.mat stores the results of the semi-variogram fitting of each interferogram on time series. It provides two versions for the original dataset (semi) and the GACOS-corrected dataset (semi_aps). This file is mainly used to weight the data during the time series fitting.</li> <li>runTSA.m, the main function to run the InSAR time series fitting. See more details in the Code part.</li> </ol> The folder of InSAR_156D.rar stores the same content as the InSAR_149A.rar but for descending track 156. Code: This folder contains the codes of the InSAR time series fitting for this dataset, and the GBIS software. <ol> <li>TSA_findref.m, this function is used to search the reference point of the InSAR data.</li> <li>TSA_EQ_fit.m, is the main function to perform InSAR time series fitting.</li> <li>rb_pixel_fit.m, is the robust way to fit the linear model.</li> <li>TSA_EQ_pixel.m, is the function used to plot the results.</li> </ol> To perform the InSAR time series fitting, you need to put these four functions under your Matlab path, and then run the runTSA.m function in the data folder. The GBIS folder stores the updated version of the GBIS software, which allows you to perform the pCDM, CDM, and pECM. The core functions of these models are provided by Dr. Mehdi Nikkhoo, and you could find them here: https://www.volcanodeformation.com/software GBIS_Modelling_Results: This folder stores the data of InSAR and GPS joint inversion for Socompa Uplift. <ol> <li>The folder Socompa stores the modelling results using the models of Okada(D), pECM(E), Mogi(M), pCDM(N), and Yang(Y), respectively. </li> <li>GPS_data.txt stores the cumulative displacements and the uncertainties of the SOCM station in three directions.</li> <li>Socompa.inp is the configuration file for GBIS running.</li> <li>Vol_asc.mat and Vol_asc_ds.mat stores the original and the downsampled ascending data, while Vol_dsc.mat and Vol_dsc_ds.mat store those of descending.</li> </ol> Many thanks for using our dataset and please let me know if you have any further questions!

opencc-by-4.0Jun 2023View details →

Network traffic datasets created by Single Flow Time Series Analysis

Network traffic datasets created by Single Flow Time Series Analysis Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomá&scaron; Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:   <blockquote> J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876. </blockquote> This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf   In the following table is a description of each dataset file: <table> <tbody> <tr> <td>File name</td> <td>Detection problem</td> <td>Citation of original raw dataset</td> </tr> <tr> <td>botnet_binary.csv </td> <td>Binary detection of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>botnet_multiclass.csv </td> <td>Multi-class classification of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>cryptomining_design.csv</td> <td>Binary detection of cryptomining; the design part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>cryptomining_evaluation.csv </td> <td>Binary detection of cryptomining; the evaluation part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>dns_malware.csv </td> <td>Binary detection of malware DNS </td> <td>Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. </td> </tr> <tr> <td>doh_cic.csv </td> <td>Binary detection of DoH </td> <td> Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020  </td> </tr> <tr> <td>doh_real_world.csv </td> <td>Binary detection of DoH </td> <td>Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 </td> </tr> <tr> <td>dos.csv </td> <td>Binary detection of DoS </td> <td>Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019.</td> </tr> <tr> <td>edge_iiot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.</td> </tr> <tr> <td>edge_iiot_multiclass.csv</td> <td>Multi-class classification of IoT malware</td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.</td> </tr> <tr> <td>https_brute_force.csv</td> <td>Binary detection of HTTPS Brute Force</td> <td>Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020</td> </tr> <tr> <td>ids_cic_binary.csv</td> <td>Binary detection of intrusion in IDS</td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.</td> </tr> <tr> <td>ids_cic_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>ids_unsw_nb_15_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.</td> </tr> <tr> <td>ids_unsw_nb_15_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.</td> </tr> <tr> <td>iot_23.csv </td> <td>Binary detection of IoT malware </td> <td>Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23</td> </tr> <tr> <td>ton_iot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021</td> </tr> <tr> <td>ton_iot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021</td> </tr> <tr> <td>tor_binary.csv </td> <td>Binary detection of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>tor_multiclass.csv </td> <td>Multi-class classification of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>vpn_iscx_binary.csv </td> <td>Binary detection of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_iscx_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_vnat_binary.csv </td> <td>Binary detection of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022</td> </tr> <tr> <td>vpn_vnat_multiclass.csv</td> <td>Multi-class classification of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022</td> </tr> </tbody> </table>

opencc-by-4.0Oct 2019View details →

A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis

The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..

opencc-by-4.0Apr 2022View details →

Dataset and R code: Effects of temperature and air pollution on emergency ambulance dispatches: a time series analysis in a medium-sized city in Germany

Dataset and R script to replicate results in the manuscript "Effects of temperature and air pollution on emergency ambulance dispatches: a time series analysis in a medium-sized city in Germany", currently under review.

opencc-by-4.0Aug 2024View details →

Impact of the COVID-19 pandemic on antidepressant use in eleven European regions: a comparative time series analysis 2018–2022

Data and code supporting the article: Impact of the COVID-19 pandemic on antidepressant use in eleven European regions: a comparative time series analysis 2018–2022 Prescription, prevalence and incidence data from January 2018 to December 2022 for Croatia, the Czech Republic, Finland, Germany, Slovenia, Sweden, and the United Kingdom (England, Northern Ireland, Scotland, and Wales). Data include the numbers of dispensed defined daily doses (DDDs) and packs, aggregated by country and month, and prevalence and incidence of antidepressant dispensing. For more information, see the accompanying document ReadMe.md.

dryad40/100

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

Background RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species. Results With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments. Conclusion RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

opencc-zeroJul 2021View details →

opencc-by-4.0Nov 2018View details →

The dataset for the submitted paper " Time Series Analysis of Normal Mode Energetics for Rossby Wave Breaking and Saturation using a Simple Barotropic Model".

These files are the data of the result in the submitted paper, titled "Time Series Analysis of Normal Mode Energetics for Rossby Wave Breaking and Saturation using a Simple Barotropic Model". <ul> <li>File Description</li> </ul> pv13.data   : Exp. 1 pv17.data   : Exp. 2 The raw potential vorticity (PV) data for the Exp.1 and Exp.2, respectively, used in drawing the Fig.1, 2, and the supplemental movie 1 and 2. These are the grid point value files, 72 levels for the zonal direction, 30 levels for meridional direction.  More details are described in the next ctl files.   pv13.ctl pv17.ctl Description files for pv13.data and pv17.data. This will be called from grads_pv13.gs and grads_pv17.data, respectively. grads_pv13.gs grads_pv17.gs GrADS script for mapping the PV.   energy17.txt  : Exp.2 The time series table of energy values for exp.2. One raw is identified by combination of the TIME in the experiment and zonal wave number N.

dryad40/100

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

Open the record for dataset details and reuse information.

publicJul 2021View details →

dryad36/100

Data from: Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis

Background. We evaluated the impact of a pharmacist-led Safety Medication dASHboard (SMASH) intervention on medication safety in primary care. Methods and findings. SMASH comprised: (1) training of clinical pharmacists to deliver the intervention; (2) a web-based dashboard providing actionable, patient-level feedback; and (3) pharmacists reviewing individual at-risk patients, and initiating remedial actions or advising general practitioners on doing so. It was implemented in forty-three general practices covering a population of 235,595 people in Salford (Greater Manchester), UK. All practices started receiving the intervention between 18 April 2016 and 26 September 2017. We used an interrupted time series analysis of rates of potentially hazardous prescribing and inadequate blood-test monitoring, comparing observed rates post-intervention to extrapolations from a 24-month pre-intervention trend. The number of people registered to participating practices and having one or more risk factors for being exposed to hazardous prescribing or inadequate blood-test monitoring at the start of the intervention was 47,413 (males: 23,073 [48.7%]; mean age: 60 [standard deviation: 21]). At baseline, 95% of practices had rates of potentially hazardous prescribing (composite of 10 indicators) between 0.88% and 6.19%. The prevalence of potentially hazardous prescribing reduced by 27.9% (95% confidence interval [CI], 20.3% to 36.8%) at 24 weeks and by 40.7% (95% CI, 29.1% to 54.2%) at twelve months after introduction of SMASH. The rate of inadequate blood-test monitoring (composite of 2 indicators) reduced by 22.0% (95% CI, 0.2% to 50.7%) at 24 weeks and by 23.5% (95% CI, -4.5% to 61.6%) at 12 months. After 12 months, 95% of practices had rates of potentially hazardous prescribing between 0.74% and 3.02%. We did not randomise practices but enrolled them in a naturalistic fashion. All our measurements were based on routinely kept electronic health records. Conclusions. The SMASH intervention was associated with reduced rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practices. This reduction was sustained over 12 months after start of the intervention for prescribing but not for monitoring of medication. There was a marked reduction in the variation in rates of high-risk prescribing between practices.

opencc-zeroAug 2020View details →

opencc-by-4.0Jan 2021View details →

The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings

Submitted to The ISSI 2021 Conference. The conference is organised by KU Leuven in close collaboration with the university of Antwerp under the auspices of ISSI – the International Society for Informetrics and Scientometrics (<a href="http://www.issi-society.org/">http://www.issi-society.org/</a>).  We present a forecasting analysis on the growth of scientific literature related to COVID-19 expected for 2021. Considering the paramount scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the ARIMA model and use two different data sources: the Dimensions and World Health Organization COVID-19 databases. These two sources have the particularity of including in the metadata information on the date in which papers were indexed.  We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by NLM source (PubMed and PMC), and by domain-specific repository (SSRN and MedRxiv). We conclude by discussing our findings.

opencc-by-4.0Jun 2021View details →

InSAR time series analysis results of ALOS-2/PALSAR-2 data for the post-eruptive displacement of the 2015 phreatic eruption of Hakone volcano, Japan

This repository contains the InSAR products used in Doke et al., GRL (submitted).   Dataset 1: Surface velocity data estimated by InSAR time series analysis with NetCDF grid format. <ol> <li>surface_velocity_p126.nc</li> <li>surface_velocity_p18.nc</li> </ol>   Dataset 2: Time-series of LOS displacements in selected locations with text format. <ol> <li>time_series_p126.txt</li> <li>time_series_p18.txt</li> </ol>   Dataset 3: Inputs and results of model inversion with shapefile. Subsampled observation data, modeled (simulated) displacements, and other parameters are shown in attribute tables in shapefiles. Shapefiles that show the location of the estimated models are also included in ZIP files. <ol> <li>point_source_deflation.zip</li> <li>sill_deflation.zip</li> </ol>

opencc-by-4.0Mar 2024View details →

AutoML Applied to Time Series Analysis Tasks in Production Engineering

The dataset is accompanying the paper "AutoML Applied to Time Series Analysis Tasks in Production 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".

opencc-by-4.0Jan 2022View details →

Analysis of the root diameter distribution from time series images of real and simulated Cassava root systems

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 (Manihot esculenta 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.

dryad36/100

Data from: Girth increment changes in response to soil water availability in lowland dipterocarp forest in Borneo: an individualistic time-series analysis

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.

opencc-zeroJun 2022View details →

opencc-by-4.0Apr 2024View details →

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)

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>). 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>).