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814 results for “Time Analysis”
Bayesian Analysis of Tree Distributions Across Space and Time in Eastern North America 2010-2011
The distributions of many organisms are spatially autocorrelated, but it is unclear whether including spatial terms in species distribution models (SDMs) improves projections of future species distributions. We provide the first comparative test of a purely spatial SDM, a purely non-spatial SDM, and an SDM that combines spatial and environmental information. Spatial SDMs provided better fits to the calibration data, more accurate predictions of a hold-out validation data set of modern trees, and lower false positive rates at all time periods than non-spatial SDMs. Hindcasted projection of spatial SDMs had higher variance than those of non-spatial SDMs. Overall predictive performance of non-spatial and spatial SDMs varied temporally and as a function of niche overlap. Ecological modelers should include spatial terms in SDMs used for projecting future distributions of species.
Training Datasets for Epilepsy Analysis: Preprocessing and Feature Extraction from EEG Time Series
<h2>The files include the 20 training datasets, in csv format, from 20 epileptic patients. Each set of data is described by 1080 features extracted using the sliding window technique.</h2>
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
Annual maps of cropland abandonment, land cover, and other derived data for time-series analysis of cropland abandonment
<p>This archive contains raw annual land cover maps, cropland abandonment maps, and accompanying derived data products to support:</p> <blockquote> <p>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. <em>Science Advances</em> <a href="https://doi.org/10.1126/sciadv.abm8999">doi.org/10.1126/sciadv.abm8999</a><em>.</em></p> </blockquote> <p>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>).</p> <p>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>).</p> <p>Data files fall into one of four categories of data derived during our analysis of abandonment: <strong>observed</strong>, <strong>potential</strong>, <strong>maximum</strong>, or <strong>recultivation</strong>. 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):</p> <ol> <li><strong>observed</strong> 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><strong>potential</strong> 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><strong>maximum</strong> 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><strong>recultivation </strong>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> <p> </p> <p><strong>This archive includes multiple .zip files, the contents of which are described below:</strong></p> <ul> <li><strong>age_dts.zip</strong> - 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><strong>age_rasters.zip</strong> - 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><strong>derived_data.zip</strong> - summary datasets created throughout this analysis, listed below.</li> <li><strong>diff.zip</strong> - .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><strong>input_dts.zip</strong> - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 <em>Remote Sensing of Environment </em>(<a href="https://doi.org/10.1016/j.rse.2020.111873">https://doi.org/10.1016/j.rse.2020.111873</a>)<em>. </em>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><strong>input_rasters.zip</strong> - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 <em>Remote Sensing of Environment. </em>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><strong>length.zip</strong> - .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> <p><strong>derived_data.zip</strong> contains the following files:</p> <ul> <li>"<strong>site_df.csv</strong>" - 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><strong>Primary derived datasets </strong>for both observed abandonment (“area_dat”) and potential abandonment (“potential_area_dat”). <ul> <li><strong>area_dat</strong> - 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><strong>persistence_dat</strong> - 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><strong>turnover_dat</strong> - 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><strong>Area summary files </strong>(for observed abandonment only) <ul> <li><strong>area_summary_df</strong> - 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><strong>area_recult_threshold</strong> - 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><strong>abn_lc_area_2017</strong> - 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><strong>abn_prop_lc_2017 </strong>- 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><strong>Carbon</strong> <ul> <li><strong>carbon_df </strong>– 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><strong>soc_mean</strong> – 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><strong>Decay model data</strong> – two R data files containing data products for our linear models of abandonment recultivation trajectories. <ul> <li><strong>decay_endpoints_files</strong> – 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><strong>common_endpoint_dat – </strong>a .csv containing subsets of “persistence_dat” for each “endpoint” (7 through 29).</li> <li><strong>endpoint_n – </strong>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><strong>coef_l3_endpoints – </strong>corresponding model coefficients for our primary model (“l3”) parameterized by the range of subsets across endpoints.</li> <li><strong>augment_endpoints – </strong>fitted values (i.e., model predictions) for linear models produced across the full range of endpoint subsets.</li> <li><strong>fitted_endpoints – </strong>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><strong>time_to_endpoints – </strong>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><strong>endpoint_half_lives – </strong>a .csv containing the half-lives calculated for the mean trajectories for each endpoint at each site.</li> </ul> </li> <li><strong>decay_mod_archive</strong> - an R data file (.rds) containing eleven data products derived from linear models of abandonment recultivation ("decay"): <ul> <li><strong>lm_mega_lin_log_lin_l</strong> – 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><strong>coef_l3_mega</strong> – 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><strong>mean_coef_l3_mega</strong> – 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><strong>half_lives_all_cohorts_l3</strong> – half-lives calculated for each cohort at each site, for our primary model.</li> <li><strong>half_life_mean_coefs_l3</strong> – 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><strong>mod_AIC_mega</strong> – Akaike Information Criterion (AIC) values for all tested model specifications.</li> <li><strong>fitted_combo</strong> – 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><strong>time_to_combo</strong> – 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><strong>Length data</strong> – includes “_distill_df” files and “mean_length_df” files for observed, potential, and recultivation. <ul> <li><strong>length_distill_df</strong> - .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><strong>mean_length_df</strong> - .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><strong>Duration summary files</strong> – includes “summary_stats_all_sites” and “summary_stats_all_sites_pooled,” for observed and potential abandonment, and recultivation periods following abandonment. <ul> <li><strong>“summary_stats_all_sites”</strong> - 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><strong>“summary_stats_all_sites_pooled”</strong> - 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><strong>Comparing annual approach to identifying abandonment to a two-timepoint (“2yr”) approach:</strong> <ul> <li><strong>abn_2yr_ages_df</strong> - 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><strong>abn_2yr_overestimation</strong> - 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> <p><strong>Input land cover maps:</strong></p> <p>As noted, the file "input_rasters.zip" contain the raw annual land cover maps for eleven sites generated by:</p> <blockquote> <p>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. <em>Remote Sensing of Environment</em> 246:111873. https://doi.org/10.1016/j.rse.2020.111873</p> </blockquote> <p>These land cover maps served as raw inputs for this project and form the basis of the analysis.</p> <p>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.</p> <p><strong>Land cover categories in these maps are coded as follows:</strong></p> <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> <p><strong>Site file names correspond to the following geographic locations:</strong></p> <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> <p>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:</p> <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>
Python Time Normalized Superposed Epoch Analysis (SEAnorm) Example Data Set
<p>Solar Wind Omni and SAMPEX ( Solar Anomalous and Magnetospheric Particle Explorer) datasets used in examples for <a href="https://github.com/samwalton7645/SEA_Code">SEAnorm</a>, a time normalized superposed epoch analysis package in python.</p> <p>Both data sets are stored as either a HDF5 or a compressed csv file (csv.bz2) which contain a Pandas DataFrame of either the Solar Wind Omni and SAMPEX data sets. The data sets where written with pandas.DataFrame.to_hdf() and pandas.DataFrame.to_csv() using a compression level of 9. The DataFrames can be read using pandas.DataFrame.read_hdf( ) or pandas.DataFrame.read_csv( ) depending on the file format. </p> <p>The Solar Wind Omni data sets contains solar wind velocity (V) and dynamic pressure (P), the southward interplanetary magnetic field in Geocentric Solar Ecliptic System (GSE) coordinates (B_Z_GSE), the auroral electrojet index (AE), and the Sym-H index all at 1 minute cadence. </p> <p>The SAMPEX data set contains electron flux from the Proton/Electron Telescope (PET) at two energy channels 1.5-6.0 MeV (ELO) and 2.5-14 MeV (EHI) at an approximate 6 second cadence.</p> <p> </p>
Fed4Fire/CDN-X-ALL network metrics dataset for time series analysis in Media content delivery for 4G/5G networks
<p>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".</p> <p>Project funded by Fed4FIRE+ OC5 (<a href="https://www.fed4fire.eu/">https://www.fed4fire.eu</a>) under grant 732638.</p> <p>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.</p> <p>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.</p> <p><strong>CDN-like server/DASH Dataset -> Internet -> EPC/OAI -> eNodeB/OAI -> UE/DASH player</strong></p> <p>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:</p> <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> <p>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.</p> <p>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.</p>
Data from Time since liver transplantation and immunosuppression withdrawal outcomes: a systematic review with individual patient data meta-analysis
<p>This record provides one CSV file containing anonymized individual patient data (IPD) of pre-withdrawal times (in days) of liver transplant recipients that underwent immunosuppression (IS) withdrawal. Collection and publication of anonymized data was approved by the Ethics Committee Northwest and Central Switzerland. Patients of 15 primary studies are stratified by successfully reaching the state of IS-free operational tolerance (OT) or by developing signs of immunological rejection (non-OT).</p>
InSAR Time Series Analysis (2018-2021) for Volcanic Monitoring in Northern Chile
<p>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>.</p> <p><strong>InSAR Data:</strong></p> <p>The folder of InSAR_149A.rar stores the InSAR time series analysis dataset on ascending track 149.</p> <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> <p>The folder of InSAR_156D.rar stores the same content as the InSAR_149A.rar but for descending track 156.</p> <p><strong>Code:</strong></p> <p>This folder contains the codes of the InSAR time series fitting for this dataset, and the GBIS software.</p> <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> <p>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.</p> <p>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</p> <p><strong>GBIS_Modelling_Results:</strong></p> <p>This folder stores the data of InSAR and GPS joint inversion for Socompa Uplift.</p> <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> <p>Many thanks for using our dataset and please let me know if you have any further questions!</p>
Network traffic datasets created by Single Flow Time Series Analysis
<p><strong>Network traffic datasets created by Single Flow Time Series Analysis</strong></p> <p>Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:<br> </p> <blockquote> <p>J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," <em>2023 19th International Conference on Network and Service Management (CNSM)</em>, Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.</p> </blockquote> <p>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: <em>feature_description.pdf</em></p> <p> </p> <p>In the following table is a description of each dataset file:</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Detection problem</strong></td> <td><strong>Citation of original raw dataset</strong></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> <p>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 </p> </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> <p> </p>
Effect of varying post field collection filtration times on lake water nutrient analysis for Green Lake 3 and Green Lake 4, 2025.
After field collection, filtration time on lake and stream samples can vary. To test how this affects nutrient measurements we filtered samples from Green Lakes 3 and 4 at the time of collection in the field, immediately upon returning to the lab, 24 hours, and 48 hours after collection. Samples were then frozen and analyzed for chloride, nitrate and sulfate. Chloride and nitrate were below detection limits so only sulfate is reported. There was no statistically significant loss of sulfate as time progressed, indicating current filtration methods (<48 hours after collection) are acceptable for samples being analyzed via ion chromatography.
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>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..</p>
Dataset: Analysis of timing variability in human movements by aligning parameter curves in time
<p>Supplementary Data for <em><strong>Analysis of timing variability in human movements by aligning parameter curves in time</strong></em> article</p> <p>Dataset associated with the following publication:<br> Maurer, L. K., Maurer, H., & Müller, H. (2017). Analysis of timing variability in human movements by aligning parameter curves in time.</p> <p>-------------------------------------------------------------------------------</p> <p>The data files are structured in the following way:<br> (1) Basic subject information (age, sex) can be found in the file subject_data.txt (tabulator separated text file).</p> <p>(2) The folder parameter_curves contains the angle trajectories of all trials structured in blocks of 50 trials (sometimes less than 50 because of data cleaning procedures deleating corrupted data and trials in which participants released accidentally [with zero velocity]). Each participant performed five practice days with four blocks of 50 trials, i.e. 20 blocks. File names contain subject (1,...,14), day (1,...,5), and block (1,...,4) information. Within the tabulator separated text files each column contains the angle trajectory of one trial consisting of 1000 values (sampled with 1000 Hz). Index 600 is the moment when participants released the virtual ball.</p>
Time trees and Clock genes: a Systematic Review and Comparative Analysis of Contemporary Avian Migration Genetics (Dataset)
<p>Complete dataset of <em>Clock</em> and <em>Adcyap1</em> alleles, distance matrices, and migration data used in the review and meta-analysis "<strong>Time trees and Clock genes: a Systematic Review and Comparative Analysis of Contemporary Avian Migration Genetics".</strong> </p>
Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning
<div> <div> <p>Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized.</p> <p>In the paper "Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on <strong>two experimental datasets</strong> based on silicon photomultipliers (SiPM) as main transducers:</p> <ol> <li>In the toy experiment, we collect data from a pair of SiPM sensors from a common laser source. The neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. </li> <li>In the electromagnetic calorimeter experiment, we collect data from an eight-channel calorimeter module. Several neural network models (Fully-Connected, Convolutional Neural Network and Long Short Term Memory) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. </li> </ol> <p>In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely. <strong>The dataset in this repository serves as a basis for similar researches on timing performance of SiPM-based nuclear detectors, and on application of neural networks to typical signals of nuclear radiation detectors.</strong></p> </div> </div>
Data-Driven Identification and Analysis of Waiting Times in Business Processes: A Systematic Literature Review
<p>Supplementary Material for Systematic Literature Review titled "Data-Driven Identification and Analysis of Waiting Times in<br> Business Processes: A Systematic Literature Review"</p>
Dataset for "Study of 72 pulsars discovered in the PALFA survey: Timing analysis, glitch activity, emission variability, and a pulsar in an eccentric binary"
<p>This repository includes TEMPO-readable ephemerides associated with the final timing solutions presented in Table 3 (text-readable format; .par files) of the manuscript, "<em>Study of 72 pulsars discovered in the PALFA survey: Timing analysis, glitch activity, emission variability, and a pulsar in an eccentric binary</em>" accepted to the Astrophysical Journal (October 2021).</p>
Data for figures in Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_
<p>Tar files containing gridded metrics, domain-wide metric means and confidence intervals, and rain-gauge reports used to generate figures in Kemp et al (2021).<br> <br> Citation:<br> </p> <p>Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_.</p>
Transient analysis of power loss density with time-harmonic electromagnetic waves in Debye media
<p>Due to the complex permittivity, it is difficult to directly clarify the transient mechanism between electromagnetic waves and Debye media. To overcome above problem, the temporal relationship between the electromagnetic waves and permittivity is explicitly derived by applying the Fourier inversion and introducing the remnant displacement. With the help of the Poynting theorem and energy conservation equation, the transient power loss density is derived to describe the transient dissipation of electromagnetic field and the mechanism on phase displacement has been explicitly revealed. Besides, the unique solution can be obtained by applying the time-domain analysis method rather than involving the frequency-domain characteristics. The effectiveness of transient analysis is demonstrated by giving a comparison simulation on one-dimensional example.</p>
NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model"
<p>NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model", submitted to Journal of Advances in Modelling the Earth System.</p> <p>The data are produced from an ensemble of six experiments based on the GO8p0 configuration of NEMO v4.0.1 on a global 1/4° grid, as described in the paper. The ensemble is intended to test the z~ vertical coordinate, and includes a control with the default "z-star" fixed coordinate, and five experiments with the z-tilde vertical coordinate, using a selection of values for the two z-tilde timescale parameters. The data includes time series of global mean ocean and ice fields; large-scale transports; and fields from diapycnal mixing analysis.</p> <p>The first part of each filename refers to the experiment from the ensemble ("zstar", "ztilde_5_30", "ztilde_10_30", "ztilde_20_30", ztilde_20_60" and "ztilde_40_60"); the following five-character string identifies the respective suite on the Met Office Rose system and the MASS archive system; and the rest of the name specifies the type of data contained in the file.</p>
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
<p>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.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.