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  • Rural Digital Europe
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    The River Sediment Database-Amazon (RivSed-Amazon) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the Amazon River Basin that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of SWOT River Database (SWORD, Altenau et al., 2021) centerlines (median reach length = 10 km) where high quality river water pixels were detected within each Landsat image from 1984-2018. The methods used to produce this database were initially developed in the following publications: Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. Environmental Research Letters. https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8 and Gardner et al. (2020). The color of rivers. Geophysical Research Letters. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL088946 The publication associated with RivSed-Amazon is in review. Files: 1) Metadata (rivSed_Amazon_metadata_v1.01.pdf): Description key data files associated with this repository. 2) RiverSed (RiverSed_Amazon_v1.1.txt). Table of SSC and associated data that is joinable to SWORD based on the ""reach_id". 3) Shapefile of river centerlines over South America to which the reflectance data can be attached (SWORD_SA.shp). 4) Shapefile of the reach polygons associated with SWORD_SA over the Amazon Basin. (reach_polygons_amazon.shp). 5) SSC-Landsat matchup database with extended metadata on locations and in-situ data (train_full_v1.1.csv). 6) The final training data used to build the xgboost machine learning model (train_v1.1.csv). 7) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (tssAmazon_model_v1.1.rds and .rda). The model can only be loaded and used in R at this time. 8) The correction coefficients applied to Landsat 5 and 8 to harmonized surface reflectance across Landsat 5,7,8 and over all bands to enable time series analysis.

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    ZENODO
    Dataset . 2024
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    Data sources: Datacite
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2024
      License: CC BY
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  • Authors: Aitor, Ibarguren; Javier, Gonzalez Huarte;

    Results obtained during the experiment of screw fastening on moving objects using a mobile manipulator.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
    ZENODO
    Dataset . 2024
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      ZENODO
      Dataset . 2024
      License: CC BY
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      Dataset . 2024
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  • Authors: González-Hernández, Antonio; Nieto-Lugilde, Diego; Peñas de Giles, Julio; Alba-Sánchez, Francisca;

    The presence of Abies pinsapo in the Baetic Mountain Range was verified in sampling campaigns that took place in 2012 and 2013. A total of 141 stands of natural origin were sampled, in wich the presence of the different life stages was recorded. Three datasets were arranged for a SDM analysis, based on the presence of the life stages of interest. Two of those groups consisted of stands including individuals at either extreme of the age distribution (i.e., "sapling" and "mature"), while the third included all of the stands recorded (i.e., "whole"); these were defined as follows: Sapling: stands that included the presence of young individuals whose height did not exceed 40 cm; n = 41. Mature: stands that included the presence of individuals whose diameter at 130 cm above the ground (dbh) exceeded 20 cm; n = 134. Whole: included all of the stands of natural origin, regardless of the life stages they included; n = 141. The file LeanPatternOccurrences.csv contains the following fields: POINT: Code that designates the stand with verified presence of Abies pinsapo in the Baetic Mountain Range. LON: Longitude expressed in decimal degrees (Datum WGS84). LAT: Latitude expressed in decimal degrees (Datum WGS84). X: X coordinate in UTM (zone 30S). Y: Y coordinate in UTM (zone 30S). WHO: Presence (1) or absence (0) of Abies pinsapo in the record. MAT: Presence (1) or absence (0) of mature individuals of Abies pinsapo in the record. SAP: Presence (1) or absence (0) of Abies pinsapo saplings in the record. This data set is used to explore the altitudinal shift of Abies pinsapo Boiss. in the Baetic System. We analysed the potential distribution of the realised and reproductive niches of A. pinsapo populations in the Ronda Mountains (Southern Spain) by using species distribution models (SDMs) for two life stages within the current populations. The realised and reproductive niches of A. pinsapo are different to one another, which may indicate a displacement in its altitudinal distribution.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
    ZENODO
    Dataset . 2024
    License: CC BY
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      ZENODO
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      ZENODO
      Dataset . 2024
      License: CC BY
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  • Authors: Wagner-Riddle, Claudia; Congreves, Katelyn; Brown, Shannon E; Helgason, Warren; +1 Authors

    This dataset contains the data used in the publication "Overwinter and spring thaw nitrous oxide fluxes in a northern Prairie cropland are limited but a significant proportion of annual emissions" in Global Biogeochemical Cycles. This study presented micrometeorological N2O fluxes measured using the flux-gradient method over 4 years in Saskatchewan, Canada, to evaluate the magnitude of freeze-thaw N2O emissions and investigate its driving factors. The files contain the daily average N2O emissions and supporting environmental data.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      License: CC BY
      Data sources: Datacite
      ZENODO
      Dataset . 2024
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    Authors: Islam, Md. Masudul;

    This extensive dataset presents a meticulously curated collection of low-resolution images showcasing 20 well-established rice varieties native to diverse regions of Bangladesh. The rice samples were carefully gathered from both rural areas and local marketplaces, ensuring a comprehensive and varied representation. Serving as a visual compendium, the dataset provides a thorough exploration of the distinct characteristics of these rice varieties, facilitating precise classification. #Dataset Composition# The dataset encompasses 20 distinct classes, encompassing Subol Lota, Bashmoti (Deshi), Ganjiya, Shampakatari, Sugandhi Katarivog, BR-28, BR-29, Paijam, Bashful, Lal Aush, BR-Jirashail, Gutisharna, Birui, Najirshail, Pahari Birui, Polao (Katari), Polao (Chinigura), Amon, Shorna-5, and Lal Binni. In total, the dataset comprises 4,730 original JPG images and 23,650 augmented images. #Image Capture and Dataset Organization# These images were captured using an iPhone 11 camera with a 5x zoom feature. Each image capturing these rice varieties was diligently taken between October 18 and November 29, 2023. To facilitate efficient data management and organization, the dataset is structured into two variants: Original images and Augmented images. Each variant is systematically categorized into 20 distinct sub-directories, each corresponding to a specific rice variety. #Original Image Dataset# The primary image set comprises 4,730 JPG images, uniformly sized at 853 × 853 pixels. Due to the initial low resolution, the file size was notably 268 MB. Employing compression through a zip program significantly optimized the dataset, resulting in a final size of 254 MB. #Augmented Image Dataset# To address the substantial image volume requirements of deep learning models for machine vision, data augmentation techniques were implemented. Total 23,650 images was obtained from augmentation. These augmented images, also in JPG format and uniformly sized at 512 × 512 pixels, initially amounted to 781 MB. However, post-compression, the dataset was further streamlined to 699 MB. #Dataset Storage and Access# The raw and augmented datasets are stored in two distinct zip files, namely 'Original.zip' and 'Augmented.zip'. Both zip files contain 20 sub-folders representing a unique rice variety, namely 1_Subol_Lota, 2_Bashmoti, 3_Ganjiya, 4_Shampakatari, 5_Katarivog, 6_BR28, 7_BR29, 8_Paijam, 9_Bashful, 10_Lal_Aush, 11_Jirashail, 12_Gutisharna, 13_Red_Cargo,14_Najirshail, 15_Katari_Polao, 16_Lal_Biroi, 17_Chinigura_Polao, 18_Amon, 19_Shorna5, 20_Lal_Binni. # Train and Test Data Organization To ease the experimenting process for the researchers we have balanced the data and split it in an 80:20 train-test ratio. The ‘Train_n_Test.zip’ folder contains two sub-directories: ‘1_TEST’ which contains 1125 images per class and ‘2_VALID’ which contains 225 images per class.

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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
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  • Authors: Schrenk, Dieter; Bignami, Margherita; Bodin, Laurent; Chipman, James Kevin; +22 Authors

    The file contains the raw occurrence dataset on PCNs in food and feed as extracted from EFSA DWH on 25th October 2022 and presented in the EFSA scientific opinion on the risks for animal and human health related to the presence of polychlorinated naphthalenes (PCNs) in feed and food. This dataset is compliant with EFSA SSD2 data model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the outcome of the action taken (column: outcome) to address the issue (e.g., delete record or update values in specific fields). The link to the catalogues of controlled terminologies for the updated textual description of fields values can be found under "Related works, supplemented by”. EU; eng; xlsx; FEEDCO@efsa.europa.eu

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    Dataset . 2024
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    Data sources: Datacite
    ZENODO
    Dataset . 2024
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      Dataset . 2024
      License: CC BY
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  • Authors: Burton, Chad; Rifai, Sami; Renzullo, Luigi; Van Dijk, Albert;

    AusENDVI (Australian Emprical NDVI) is a monthly, 5-km gridded estimate of NDVI across Australia from 1982-2022. It is built by calibrating and harmonising NOAA's Climate Data Record AVHRR NDVI data to MODIS MCD43A4 NDVI using a gradient boosting ensemble decision tree method. Additionally, the datasets are gapfilled using a synthetic NDVI dataset. The methods are extensively described in an Earth System Science Data (pre)-publication here. AusENDVI consists of several datasets, each dataset has a description in the attributes of the NetCDF file that describes its provenance. The naming convention is "AusENDVI___.nc". AusENDVI-clim_1982_2013. Calibrated and harmonised NOAA's Climate Data Record AVHRR NDVI data from Jan. 1982 to Dec. 2013. This version of the dataset used climate data in the calibration and harmonisation process. The dataset has not been gap filled, and extra data has been filtered/removed beyond the typical QA filtering using methods described in the publication. AusENDVI-noclim_1982_2013. Calibrated and harmonised NOAA's Climate Data Record AVHRR NDVI data from Jan. 1982 to Dec. 2013. This version of the dataset did not use climate data in the calibration and harmonisation process. The dataset has not been gap filled, and extra data has been filtered/removed beyond the typical QA filtering using methods described in the publication. AusENDVI-synthetic_1982_2022. This dataset consists of synthetic NDVI data that was built by training a model on the joined _AusENDVI-clim_ and _MODIS-MCD43A4 NDVI_ timeseries using climate, woody-cover-fraction, and atmospheric CO2 as predictors. AusENDVI-clim_gapfilled_MCD43A4_1982_2022. This dataset consists of calibrated and harmonised NOAA's Climate Data Record AVHRR NDVI data from Jan. 1982 to Feb. 2000, joined with MODIS-MCD43A4 NDVI data from Mar. 2000 to Dec. 2022. This version of the dataset _used climate data_ in the calibration and harmonisation process. The dataset has been gap filled using _AusENDVI-synthetic_ AusENDVI-noclim_MCD43A4_1982_2022. This dataset consists of calibrated and harmonised NOAA's Climate Data Record AVHRR NDVI data from Jan. 1982 to Feb. 2000, and MODIS-MCD43A4 NDVI data from Mar. 2000 to Dec. 2022. This version of the dataset _did not use climate data_ in the calibration and harmonisation process. The dataset has not been gap filled. All datasets are in "EPSG:4326" projection, and have a spatial resolution of 0.05 degrees. Geographic coordinate information is contained in the "spatial_ref" variable. A Jupyter Notebook is also provided that shows how to load, plot, QC mask, reproject, and gap-fill AusENDVI datasets. The notebook is effectively a 'readme' file. The notebook is also available to view/download here An open-source github repository details the methods used to create these datasets https://github.com/cbur24/AusENDVI

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    Dataset . 2024
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    Data sources: Datacite
    ZENODO
    Dataset . 2024
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  • Authors: Hill, Dominic;

    Traits that rapidly respond to stress in important agricultural crops have the potential to provide growers with actionable feedback. E.g., traits that respond to water-restriction could inform irrigation systems by identifying crop water status and requirements in real-time. This would be particularly useful for potato, which is extremely susceptible to drought. We conducted two pot experiments and one field experiment to evaluate the utility of two traits, canopy temperature and leaf greenness, for informing irrigation management in potatoes. We also evaluated the efficacy of Phenospex PlantEye F500 sensors for the remote sensing of leaf greenness. We found that canopy temperatures of the cvs. Maris Piper (Spring Pot Experiment, +0.8°C; Autumn Pot Experiment, +5.3°C) and Désirée (Autumn Pot Experiment, +2.5°C) increased with water-restriction and that the canopy temperatures of Maris Piper return to baseline within three days after the resumption of well-watered conditions. We also found that these responses varied between cultivars, with predictable outcomes based on reported and corroborated drought tolerance ratings. We found inconclusive evidence of leaf greenness increasing due to water-restriction (Spring Pot Experiment, +0.8°C; Autumn Pot Experiment, +5.3°C) and found no evidence that post-drought recovery periods return this trait to baseline. However, leaf greenness measurements from the Phenospex PlantEye F500 were moderately to strongly correlated with SPAD values, suggesting this tool might be useful in the screening of drought-tolerant cultivars in the future.

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    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      License: CC BY
      Data sources: Datacite
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      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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  • Authors: Qing, Ying; Poulter, Benjamin; Watts, Jennifer D.; Arndt, Kyle A.; +23 Authors

    This dataset (WetCH4) contains methane (CH4) emissions using three different wetland maps, their uncertainties, and underlying flux intensities from northern wetlands (>45° N). The dataset is a data-driven upscaling product using observations from northern eddy covariance CH4 flux sites and random forest machine learning. WetCH4 provides daily CH4 fluxes of northern wetlands at 10-km resolution from 2016 to 2022 and can be used to study regional CH4 budgets and wetland responses to climate change. The data products are provided in netCDF format files (.nc) with more details in the attributes of the files.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      Dataset . 2024
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    Authors: Greenberg, Evan; Ganti, Vamsi;

    # Data for "The Pace of Global River Meandering Set by Fluvial Sediment Supply" --- This dataset includes all underlying data used in the manuscript. This includes surface water mask files (.tif), derived channel centerline files (.csv, .pkl), bar-averaged migration, and aggregated tabular data. To work with any of the derived data, we recommend using a Python-based workflow. ## Description of the data and file structure The contents of the dataset is organized into 3 directories: ### 1) Dammed\_Rivers: This includes the underlying data for the upstream-to-downstream comparisons of river mobility across dams. It includes files for the Flint, Iowa, and Red Rivers. Files are organized by: ├── Dammed_Rivers/ ├── FlintRiver/ ├── compare.py ├── FlintRiver_WBMdata.csv ├── gpkg_shapes/ ├── masks/ ├── FlintDownstream/ ├── bar_migration/ ├── centerline/ ├── centerline_csv/ ├── mask/ ├── FlintUpstream/ ├── WBM_columns.txt ├── IowaRiver/ ├── compare.py ├── IowaRiver_WBMdata.csv ├── gpkg_shapes/ ├── masks/ ├── IowaDownstream/ ├── bar_migration/ ├── centerline/ ├── centerline_csv/ ├── mask/ ├── IowaUpstream/ ├── WBM_columns.txt ├── RedRiver/ ├── codes/ ├── data/ ├── 1995/ ├── mask/ ├── width/ ├── 2015/ ├── migration/ ├── bar/ ├── combine.py ├── DownstreamMigration.csv ├── points/ ├── UpstreamMigration.csv The structures of the Iowa and Flint River directories are roughly the same: * **compare.py**: A Python script that provides the analysis for the two rivers comparing the upstream and downstream (of the dam) portions of the reaches. * The derived **WBMsed data** along the river path. e.g. FlintRiver_WBMdata.csv. I've included a description of columns as an additional text file (WBM_columns.txt). * **gpkg_shapes/**: A directory that holds .gpkg files of Polygon shape files that cover the analyzed reaches. * **masks/**: Holds all of the geospatial and derived migration data. * **bar_migration/**: Holds the aggregated bar-scale migration data. The naming convention includes the year1 and year2 over which the migration is measured. e.g. 1990_2021 indicates the migration comparing the 1990 and 2021 year centerlines. The column descriptions are given in the directory (bar_migration_csv_column_desc.txt). * **centerline/**: Holds .pkl objects of the centerlines derived from the channel masks. The method to open and work with these pickle files is provided in the github repository: 10.5281/zenodo.8341894. * **centerline_csv/**: Holds .csv files for the channel masks. The file naming convention includes the channel mask year the centerline is derived from. e.g. FlintDownstream_1990_centerline.csv is the centerline from 1990. The column descriptions are given as a separate file in the directory (centerline_csv_column_desc.txt). * **mask/**: Contains the raster data for the channel masks used to generate the centerlines. These are provided as single band binary .tif files. The structure for the Red River directory is slightly different because this analysis was completed earlier than the other two rivers. Descriptions follow: * **codes/:** Holds a number of codes used to merge all derived centerline files, calculate the migration rates, and compare upstream and downstream portions of the reach. * **combine_widths.py**: Combines all the width csv files into a single dataframe. * **compare.py**: Statistically compares the upstream and downstream portions of the reach. * **get_migration.py**: Calculates the migration rates from the width dataframes. * **get_sinuosity.py**: Calculates the sinuosity from the width dataframes. * **Data/**: Holds all the used data for this analysis. * 1995 and 2015 are the two years compared to get the migration rate. * **mask/**: Holds all of the .tif raster files for channel water. The entire measured reach is broken down into 65 segments. * **width/**: Holds the centerline .csv files. The column descriptions are given in a separate file (red_river_width_column_desc.csv). * **migration/**: contains the migration data comparing the two timesteps. * **bar/**: Bar aggregated migration distances for each of the 65 segments. Column descriptions are given in separate file (bar_column_desc.txt). * **points/**: Point comparisons pinned to the 1995 centerline showing the migration distances. Column descriptions are given in separate file (point_column_desc.txt). * **combine.py**: Python script combining the 65 segment data into single data tables. * **DownstreamMigration.csv**: Bar-scale migration data downstream of Lake Texoma. Column descriptions are found in a separate file (migration_csv_column_desc.txt). * **UpstreamMigration.csv**: Bar-scale migration data upstream of Lake Texoma. Column descriptions are found in a separate file (migration_csv_column_desc.txt). * **RedRiver_WBMdata.csv**: Contains the WBMsed data for the Red River portion. Column descriptions are given as a separate file (red_river_wbm_column_desc.txt). ### 2) Single\_Rivers: This includes the underlying data for the individual rivers for which I estimated my own migration rates. The file structure is the same for each river. I give one example below, which follows: ├── Single_Rivers/ ├── Algeria/ ├── Algeria/ ├── bar_migration/ ├── centerline/ ├── centerline_csv/ ├── mask/ ├── Algeria.gpkg ├── ... ├── Column_Desc/ ├── bar_migration_column_desc.txt ├── centerline_csv_column_desc.txt ├── ... Descriptions of what each of the subfolders contains: * **bar_migration/**: .csv file containing bar-scale migration rates for the compared timesteps. The file naming convention contains the compared years. e.g. Algeria_1991_2021_bar_migration.csv is the migration data between 1991 and 2021. The column descriptions are provided in a separate file (Column_Desc/bar_migration_column_desc.txt). * **centerline/**: .pkl objects containing the centerline data. This data format is used by the software I use to generate the centerline data. You can find more information on this in the github repository: **TODO** * **centerline_csv/**: .csv files for the centerlines generated from the channel water masks. Column descriptions are given in a separate file (Column_Desc/centerline_csv_column_desc.txt). * **mask/**: Binary raster .tif files containing channel water. I used these to generate centerlines. ### 3) Tabular\_Data This contains all of the aggregated tabular data used in the analysis. I have here the collected primary data, collated published data, and averaged WBMsed data. Note that N/A values populate empty cells. These are missing values that are not available in the published sources or not present in the WBMsed model. ├── Single_Rivers/ ├── Column_Desc/ ├── combine_data.py ├── FullCombinedAvgData_050423.csv ├── FullCombinedData_050423.csv ├── FullWBM_data.csv ├── Primary_Data_050423.csv ├── Published_Data/ ├── bend_data/ ├── "river".csv ├── citations.txt ├── Column_Desc ├── Published_Data_050423.csv ├── PublishedBendData_050423.csv For .csv files, column descriptions are given as separate files in the Column_Desc/ directory following the pattern of "*_file_name_column_desc.txt*." There is overlap between column names. I've included enough to understand all columns in the files provided. * **combine_data.py**: A Python script used to aggregate the individual bend-scale river migrationi data. * **FullCombinedAvgData_050423.csv**: All reach-averaged data for the rivers within the dataset. * **FullCombinedData_050423.csv**: All bend-scale data for the rivers within the dataset. * **FullWBM_data.csv**: All WBMsed data for the measured rivers. * **Primary_Data_050423.csv**: Just the primary data. * **bend_data/**: Contains the published bend-scale data for each river it exists for. Note, the meander wavelength field was measured by me for this study. * **citations.txt**: Sources used for published migration rates. * **Published_Data_050423.csv**: Aggregated reach-average published data. * **PublishedBendData_050423.csv**: Aggregated bend-scale published data. ## Sharing/Access information We leverage Google Earth Engine (GEE) Landast data for the natural data. Links to the relevant datasets are: [Landsat catalog on GEE](https://developers.google.com/earth-engine/datasets/catalog/landsat) Meandering rivers move gradually across the floodplains, and this river movement presents socioeconomic risks along river corridors and regulates terrestrial biogeochemical cycles. Experimental and field studies suggest that fluvial sediment supply can exert a primary control on lateral migration rates of rivers. However, we lack an understanding of the relative importance of environmental boundary conditions, such as floodplain vegetation and sediment supply, in setting the pace of river meandering across different environmental settings. Here, we combine the analysis of satellite imagery and global-in-scale sediment and water discharge models to evaluate the controls on lateral migration rates of 139 meandering rivers that span a wide range in size, climate, and bank vegetation. We show that migration rates normalized by the channel width monotonically increase with the volumetric sediment flux normalized by the characteristic size of the river. This relation is consistent across rivers in vegetated and unvegetated catchments, indicating that enhanced lateral migration rates in unvegetated basins is likely not only facilitated by lower bank mechanical strength, but also by higher normalized sediment supply in ephemeral rivers. Using three case examples, we also demonstrate that width-normalized meander migration rates respond to spatial gradients in sediment supply caused by river impoundments, highlighting the prominent role of sediment supply in setting the pace of meander migration. Our results suggest that sediment-supply variations caused by climate, land-cover and land-use changes can lead to predictable changes in meandering river evolution and ultimately drive architectural changes in sedimentary stratigraphy. This dataset includes all underlying data used for the associated manuscript. We include all surface water mask files (.tif), derived channel centerline files (.csv, .pkl), bar-averaged migration files, and aggregated tabular data. To work with any of the derived data, we recommend using a Python-based workflow. The dataset includes three sections: 1) Dammed_Rivers This includes underlying data for the upstream-to-downstream comparisons of river mobility across river dams. For each included river dam, we provide files that show the study area, binary geotiffs of channel water, generated centerlines, all migration data, and samples WBMsed model sediment flux and water discharge information. There are data for 3 rivers (Flint, Iowa, and Red) included in this section. 2) Single_Rivers This includes all underlying data for the individual river analyses included in our data compilation. We include the study location, binary Geotiffs of channel water, centerlines, migration data, and WBMsed model data. There are data for 55 rivers included in this section. 3) Tabular_Data This includes the aggregated tabular data for the data compilations. We aggregate the 55 rivers (from the Single_Rivers section) into a tabular .csv database. We also include data from 84 additional rivers that have published migration rates. More detail on the file structure and data contents can be found in the README.md file. For more detail on the Python-based workflow to generate channel water masks, centerline vector products, and migration rate measurements, please see the associated software.

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    The River Sediment Database-Amazon (RivSed-Amazon) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the Amazon River Basin that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of SWOT River Database (SWORD, Altenau et al., 2021) centerlines (median reach length = 10 km) where high quality river water pixels were detected within each Landsat image from 1984-2018. The methods used to produce this database were initially developed in the following publications: Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. Environmental Research Letters. https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8 and Gardner et al. (2020). The color of rivers. Geophysical Research Letters. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL088946 The publication associated with RivSed-Amazon is in review. Files: 1) Metadata (rivSed_Amazon_metadata_v1.01.pdf): Description key data files associated with this repository. 2) RiverSed (RiverSed_Amazon_v1.1.txt). Table of SSC and associated data that is joinable to SWORD based on the ""reach_id". 3) Shapefile of river centerlines over South America to which the reflectance data can be attached (SWORD_SA.shp). 4) Shapefile of the reach polygons associated with SWORD_SA over the Amazon Basin. (reach_polygons_amazon.shp). 5) SSC-Landsat matchup database with extended metadata on locations and in-situ data (train_full_v1.1.csv). 6) The final training data used to build the xgboost machine learning model (train_v1.1.csv). 7) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (tssAmazon_model_v1.1.rds and .rda). The model can only be loaded and used in R at this time. 8) The correction coefficients applied to Landsat 5 and 8 to harmonized surface reflectance across Landsat 5,7,8 and over all bands to enable time series analysis.

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  • Authors: Aitor, Ibarguren; Javier, Gonzalez Huarte;

    Results obtained during the experiment of screw fastening on moving objects using a mobile manipulator.

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  • Authors: González-Hernández, Antonio; Nieto-Lugilde, Diego; Peñas de Giles, Julio; Alba-Sánchez, Francisca;

    The presence of Abies pinsapo in the Baetic Mountain Range was verified in sampling campaigns that took place in 2012 and 2013. A total of 141 stands of natural origin were sampled, in wich the presence of the different life stages was recorded. Three datasets were arranged for a SDM analysis, based on the presence of the life stages of interest. Two of those groups consisted of stands including individuals at either extreme of the age distribution (i.e., "sapling" and "mature"), while the third included all of the stands recorded (i.e., "whole"); these were defined as follows: Sapling: stands that included the presence of young individuals whose height did not exceed 40 cm; n = 41. Mature: stands that included the presence of individuals whose diameter at 130 cm above the ground (dbh) exceeded 20 cm; n = 134. Whole: included all of the stands of natural origin, regardless of the life stages they included; n = 141. The file LeanPatternOccurrences.csv contains the following fields: POINT: Code that designates the stand with verified presence of Abies pinsapo in the Baetic Mountain Range. LON: Longitude expressed in decimal degrees (Datum WGS84). LAT: Latitude expressed in decimal degrees (Datum WGS84). X: X coordinate in UTM (zone 30S). Y: Y coordinate in UTM (zone 30S). WHO: Presence (1) or absence (0) of Abies pinsapo in the record. MAT: Presence (1) or absence (0) of mature individuals of Abies pinsapo in the record. SAP: Presence (1) or absence (0) of Abies pinsapo saplings in the record. This data set is used to explore the altitudinal shift of Abies pinsapo Boiss. in the Baetic System. We analysed the potential distribution of the realised and reproductive niches of A. pinsapo populations in the Ronda Mountains (Southern Spain) by using species distribution models (SDMs) for two life stages within the current populations. The realised and reproductive niches of A. pinsapo are different to one another, which may indicate a displacement in its altitudinal distribution.

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  • Authors: Wagner-Riddle, Claudia; Congreves, Katelyn; Brown, Shannon E; Helgason, Warren; +1 Authors

    This dataset contains the data used in the publication "Overwinter and spring thaw nitrous oxide fluxes in a northern Prairie cropland are limited but a significant proportion of annual emissions" in Global Biogeochemical Cycles. This study presented micrometeorological N2O fluxes measured using the flux-gradient method over 4 years in Saskatchewan, Canada, to evaluate the magnitude of freeze-thaw N2O emissions and investigate its driving factors. The files contain the daily average N2O emissions and supporting environmental data.

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    Authors: Islam, Md. Masudul;

    This extensive dataset presents a meticulously curated collection of low-resolution images showcasing 20 well-established rice varieties native to diverse regions of Bangladesh. The rice samples were carefully gathered from both rural areas and local marketplaces, ensuring a comprehensive and varied representation. Serving as a visual compendium, the dataset provides a thorough exploration of the distinct characteristics of these rice varieties, facilitating precise classification. #Dataset Composition# The dataset encompasses 20 distinct classes, encompassing Subol Lota, Bashmoti (Deshi), Ganjiya, Shampakatari, Sugandhi Katarivog, BR-28, BR-29, Paijam, Bashful, Lal Aush, BR-Jirashail, Gutisharna, Birui, Najirshail, Pahari Birui, Polao (Katari), Polao (Chinigura), Amon, Shorna-5, and Lal Binni. In total, the dataset comprises 4,730 original JPG images and 23,650 augmented images. #Image Capture and Dataset Organization# These images were captured using an iPhone 11 camera with a 5x zoom feature. Each image capturing these rice varieties was diligently taken between October 18 and November 29, 2023. To facilitate efficient data management and organization, the dataset is structured into two variants: Original images and Augmented images. Each variant is systematically categorized into 20 distinct sub-directories, each corresponding to a specific rice variety. #Original Image Dataset# The primary image set comprises 4,730 JPG images, uniformly sized at 853 × 853 pixels. Due to the initial low resolution, the file size was notably 268 MB. Employing compression through a zip program significantly optimized the dataset, resulting in a final size of 254 MB. #Augmented Image Dataset# To address the substantial image volume requirements of deep learning models for machine vision, data augmentation techniques were implemented. Total 23,650 images was obtained from augmentation. These augmented images, also in JPG format and uniformly sized at 512 × 512 pixels, initially amounted to 781 MB. However, post-compression, the dataset was further streamlined to 699 MB. #Dataset Storage and Access# The raw and augmented datasets are stored in two distinct zip files, namely 'Original.zip' and 'Augmented.zip'. Both zip files contain 20 sub-folders representing a unique rice variety, namely 1_Subol_Lota, 2_Bashmoti, 3_Ganjiya, 4_Shampakatari, 5_Katarivog, 6_BR28, 7_BR29, 8_Paijam, 9_Bashful, 10_Lal_Aush, 11_Jirashail, 12_Gutisharna, 13_Red_Cargo,14_Najirshail, 15_Katari_Polao, 16_Lal_Biroi, 17_Chinigura_Polao, 18_Amon, 19_Shorna5, 20_Lal_Binni. # Train and Test Data Organization To ease the experimenting process for the researchers we have balanced the data and split it in an 80:20 train-test ratio. The ‘Train_n_Test.zip’ folder contains two sub-directories: ‘1_TEST’ which contains 1125 images per class and ‘2_VALID’ which contains 225 images per class.

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  • Authors: Schrenk, Dieter; Bignami, Margherita; Bodin, Laurent; Chipman, James Kevin; +22 Authors

    The file contains the raw occurrence dataset on PCNs in food and feed as extracted from EFSA DWH on 25th October 2022 and presented in the EFSA scientific opinion on the risks for animal and human health related to the presence of polychlorinated naphthalenes (PCNs) in feed and food. This dataset is compliant with EFSA SSD2 data model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the outcome of the action taken (column: outcome) to address the issue (e.g., delete record or update values in specific fields). The link to the catalogues of controlled terminologies for the updated textual description of fields values can be found under "Related works, supplemented by”. EU; eng; xlsx; FEEDCO@efsa.europa.eu

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  • Authors: Burton, Chad; Rifai, Sami; Renzullo, Luigi; Van Dijk, Albert;

    AusENDVI (Australian Emprical NDVI) is a monthly, 5-km gridded estimate of NDVI across Australia from 1982-2022. It is built by calibrating and harmonising NOAA's Climate Data Record AVHRR NDVI data to MODIS MCD43A4 NDVI using a gradient boosting ensemble decision tree method. Additionally, the datasets are gapfilled using a synthetic NDVI dataset. The methods are extensively described in an Earth System Science Data (pre)-publication here. AusENDVI consists of several datasets, each dataset has a description in the attributes of the NetCDF file that describes its provenance. The naming convention is "AusENDVI___.nc". AusENDVI-clim_1982_2013. Calibrated and harmonised NOAA's Climate Data Record AVHRR NDVI data from Jan. 1982 to Dec. 2013. This version of the dataset used climate data in the calibration and harmonisation process. The dataset has not been gap filled, and extra data has been filtered/removed beyond the typical QA filtering using methods described in the publication. AusENDVI-noclim_1982_2013. Calibrated and harmonised NOAA's Climate Data Record AVHRR NDVI data from Jan. 1982 to Dec. 2013. This version of the dataset did not use climate data in the calibration and harmonisation process. The dataset has not been gap filled, and extra data has been filtered/removed beyond the typical QA filtering using methods described in the publication. AusENDVI-synthetic_1982_2022. This dataset consists of synthetic NDVI data that was built by training a model on the joined _AusENDVI-clim_ and _MODIS-MCD43A4 NDVI_ timeseries using climate, woody-cover-fraction, and atmospheric CO2 as predictors. AusENDVI-clim_gapfilled_MCD43A4_1982_2022. This dataset consists of calibrated and harmonised NOAA's Climate Data Record AVHRR NDVI data from Jan. 1982 to Feb. 2000, joined with MODIS-MCD43A4 NDVI data from Mar. 2000 to Dec. 2022. This version of the dataset _used climate data_ in the calibration and harmonisation process. The dataset has been gap filled using _AusENDVI-synthetic_ AusENDVI-noclim_MCD43A4_1982_2022. This dataset consists of calibrated and harmonised NOAA's Climate Data Record AVHRR NDVI data from Jan. 1982 to Feb. 2000, and MODIS-MCD43A4 NDVI data from Mar. 2000 to Dec. 2022. This version of the dataset _did not use climate data_ in the calibration and harmonisation process. The dataset has not been gap filled. All datasets are in "EPSG:4326" projection, and have a spatial resolution of 0.05 degrees. Geographic coordinate information is contained in the "spatial_ref" variable. A Jupyter Notebook is also provided that shows how to load, plot, QC mask, reproject, and gap-fill AusENDVI datasets. The notebook is effectively a 'readme' file. The notebook is also available to view/download here An open-source github repository details the methods used to create these datasets https://github.com/cbur24/AusENDVI

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  • Authors: Hill, Dominic;

    Traits that rapidly respond to stress in important agricultural crops have the potential to provide growers with actionable feedback. E.g., traits that respond to water-restriction could inform irrigation systems by identifying crop water status and requirements in real-time. This would be particularly useful for potato, which is extremely susceptible to drought. We conducted two pot experiments and one field experiment to evaluate the utility of two traits, canopy temperature and leaf greenness, for informing irrigation management in potatoes. We also evaluated the efficacy of Phenospex PlantEye F500 sensors for the remote sensing of leaf greenness. We found that canopy temperatures of the cvs. Maris Piper (Spring Pot Experiment, +0.8°C; Autumn Pot Experiment, +5.3°C) and Désirée (Autumn Pot Experiment, +2.5°C) increased with water-restriction and that the canopy temperatures of Maris Piper return to baseline within three days after the resumption of well-watered conditions. We also found that these responses varied between cultivars, with predictable outcomes based on reported and corroborated drought tolerance ratings. We found inconclusive evidence of leaf greenness increasing due to water-restriction (Spring Pot Experiment, +0.8°C; Autumn Pot Experiment, +5.3°C) and found no evidence that post-drought recovery periods return this trait to baseline. However, leaf greenness measurements from the Phenospex PlantEye F500 were moderately to strongly correlated with SPAD values, suggesting this tool might be useful in the screening of drought-tolerant cultivars in the future.

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  • Authors: Qing, Ying; Poulter, Benjamin; Watts, Jennifer D.; Arndt, Kyle A.; +23 Authors

    This dataset (WetCH4) contains methane (CH4) emissions using three different wetland maps, their uncertainties, and underlying flux intensities from northern wetlands (>45° N). The dataset is a data-driven upscaling product using observations from northern eddy covariance CH4 flux sites and random forest machine learning. WetCH4 provides daily CH4 fluxes of northern wetlands at 10-km resolution from 2016 to 2022 and can be used to study regional CH4 budgets and wetland responses to climate change. The data products are provided in netCDF format files (.nc) with more details in the attributes of the files.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Greenberg, Evan; Ganti, Vamsi;

    # Data for "The Pace of Global River Meandering Set by Fluvial Sediment Supply" --- This dataset includes all underlying data used in the manuscript. This includes surface water mask files (.tif), derived channel centerline files (.csv, .pkl), bar-averaged migration, and aggregated tabular data. To work with any of the derived data, we recommend using a Python-based workflow. ## Description of the data and file structure The contents of the dataset is organized into 3 directories: ### 1) Dammed\_Rivers: This includes the underlying data for the upstream-to-downstream comparisons of river mobility across dams. It includes files for the Flint, Iowa, and Red Rivers. Files are organized by: ├── Dammed_Rivers/ ├── FlintRiver/ ├── compare.py ├── FlintRiver_WBMdata.csv ├── gpkg_shapes/ ├── masks/ ├── FlintDownstream/ ├── bar_migration/ ├── centerline/ ├── centerline_csv/ ├── mask/ ├── FlintUpstream/ ├── WBM_columns.txt ├── IowaRiver/ ├── compare.py ├── IowaRiver_WBMdata.csv ├── gpkg_shapes/ ├── masks/ ├── IowaDownstream/ ├── bar_migration/ ├── centerline/ ├── centerline_csv/ ├── mask/ ├── IowaUpstream/ ├── WBM_columns.txt ├── RedRiver/ ├── codes/ ├── data/ ├── 1995/ ├── mask/ ├── width/ ├── 2015/ ├── migration/ ├── bar/ ├── combine.py ├── DownstreamMigration.csv ├── points/ ├── UpstreamMigration.csv The structures of the Iowa and Flint River directories are roughly the same: * **compare.py**: A Python script that provides the analysis for the two rivers comparing the upstream and downstream (of the dam) portions of the reaches. * The derived **WBMsed data** along the river path. e.g. FlintRiver_WBMdata.csv. I've included a description of columns as an additional text file (WBM_columns.txt). * **gpkg_shapes/**: A directory that holds .gpkg files of Polygon shape files that cover the analyzed reaches. * **masks/**: Holds all of the geospatial and derived migration data. * **bar_migration/**: Holds the aggregated bar-scale migration data. The naming convention includes the year1 and year2 over which the migration is measured. e.g. 1990_2021 indicates the migration comparing the 1990 and 2021 year centerlines. The column descriptions are given in the directory (bar_migration_csv_column_desc.txt). * **centerline/**: Holds .pkl objects of the centerlines derived from the channel masks. The method to open and work with these pickle files is provided in the github repository: 10.5281/zenodo.8341894. * **centerline_csv/**: Holds .csv files for the channel masks. The file naming convention includes the channel mask year the centerline is derived from. e.g. FlintDownstream_1990_centerline.csv is the centerline from 1990. The column descriptions are given as a separate file in the directory (centerline_csv_column_desc.txt). * **mask/**: Contains the raster data for the channel masks used to generate the centerlines. These are provided as single band binary .tif files. The structure for the Red River directory is slightly different because this analysis was completed earlier than the other two rivers. Descriptions follow: * **codes/:** Holds a number of codes used to merge all derived centerline files, calculate the migration rates, and compare upstream and downstream portions of the reach. * **combine_widths.py**: Combines all the width csv files into a single dataframe. * **compare.py**: Statistically compares the upstream and downstream portions of the reach. * **get_migration.py**: Calculates the migration rates from the width dataframes. * **get_sinuosity.py**: Calculates the sinuosity from the width dataframes. * **Data/**: Holds all the used data for this analysis. * 1995 and 2015 are the two years compared to get the migration rate. * **mask/**: Holds all of the .tif raster files for channel water. The entire measured reach is broken down into 65 segments. * **width/**: Holds the centerline .csv files. The column descriptions are given in a separate file (red_river_width_column_desc.csv). * **migration/**: contains the migration data comparing the two timesteps. * **bar/**: Bar aggregated migration distances for each of the 65 segments. Column descriptions are given in separate file (bar_column_desc.txt). * **points/**: Point comparisons pinned to the 1995 centerline showing the migration distances. Column descriptions are given in separate file (point_column_desc.txt). * **combine.py**: Python script combining the 65 segment data into single data tables. * **DownstreamMigration.csv**: Bar-scale migration data downstream of Lake Texoma. Column descriptions are found in a separate file (migration_csv_column_desc.txt). * **UpstreamMigration.csv**: Bar-scale migration data upstream of Lake Texoma. Column descriptions are found in a separate file (migration_csv_column_desc.txt). * **RedRiver_WBMdata.csv**: Contains the WBMsed data for the Red River portion. Column descriptions are given as a separate file (red_river_wbm_column_desc.txt). ### 2) Single\_Rivers: This includes the underlying data for the individual rivers for which I estimated my own migration rates. The file structure is the same for each river. I give one example below, which follows: ├── Single_Rivers/ ├── Algeria/ ├── Algeria/ ├── bar_migration/ ├── centerline/ ├── centerline_csv/ ├── mask/ ├── Algeria.gpkg ├── ... ├── Column_Desc/ ├── bar_migration_column_desc.txt ├── centerline_csv_column_desc.txt ├── ... Descriptions of what each of the subfolders contains: * **bar_migration/**: .csv file containing bar-scale migration rates for the compared timesteps. The file naming convention contains the compared years. e.g. Algeria_1991_2021_bar_migration.csv is the migration data between 1991 and 2021. The column descriptions are provided in a separate file (Column_Desc/bar_migration_column_desc.txt). * **centerline/**: .pkl objects containing the centerline data. This data format is used by the software I use to generate the centerline data. You can find more information on this in the github repository: **TODO** * **centerline_csv/**: .csv files for the centerlines generated from the channel water masks. Column descriptions are given in a separate file (Column_Desc/centerline_csv_column_desc.txt). * **mask/**: Binary raster .tif files containing channel water. I used these to generate centerlines. ### 3) Tabular\_Data This contains all of the aggregated tabular data used in the analysis. I have here the collected primary data, collated published data, and averaged WBMsed data. Note that N/A values populate empty cells. These are missing values that are not available in the published sources or not present in the WBMsed model. ├── Single_Rivers/ ├── Column_Desc/ ├── combine_data.py ├── FullCombinedAvgData_050423.csv ├── FullCombinedData_050423.csv ├── FullWBM_data.csv ├── Primary_Data_050423.csv ├── Published_Data/ ├── bend_data/ ├── "river".csv ├── citations.txt ├── Column_Desc ├── Published_Data_050423.csv ├── PublishedBendData_050423.csv For .csv files, column descriptions are given as separate files in the Column_Desc/ directory following the pattern of "*_file_name_column_desc.txt*." There is overlap between column names. I've included enough to understand all columns in the files provided. * **combine_data.py**: A Python script used to aggregate the individual bend-scale river migrationi data. * **FullCombinedAvgData_050423.csv**: All reach-averaged data for the rivers within the dataset. * **FullCombinedData_050423.csv**: All bend-scale data for the rivers within the dataset. * **FullWBM_data.csv**: All WBMsed data for the measured rivers. * **Primary_Data_050423.csv**: Just the primary data. * **bend_data/**: Contains the published bend-scale data for each river it exists for. Note, the meander wavelength field was measured by me for this study. * **citations.txt**: Sources used for published migration rates. * **Published_Data_050423.csv**: Aggregated reach-average published data. * **PublishedBendData_050423.csv**: Aggregated bend-scale published data. ## Sharing/Access information We leverage Google Earth Engine (GEE) Landast data for the natural data. Links to the relevant datasets are: [Landsat catalog on GEE](https://developers.google.com/earth-engine/datasets/catalog/landsat) Meandering rivers move gradually across the floodplains, and this river movement presents socioeconomic risks along river corridors and regulates terrestrial biogeochemical cycles. Experimental and field studies suggest that fluvial sediment supply can exert a primary control on lateral migration rates of rivers. However, we lack an understanding of the relative importance of environmental boundary conditions, such as floodplain vegetation and sediment supply, in setting the pace of river meandering across different environmental settings. Here, we combine the analysis of satellite imagery and global-in-scale sediment and water discharge models to evaluate the controls on lateral migration rates of 139 meandering rivers that span a wide range in size, climate, and bank vegetation. We show that migration rates normalized by the channel width monotonically increase with the volumetric sediment flux normalized by the characteristic size of the river. This relation is consistent across rivers in vegetated and unvegetated catchments, indicating that enhanced lateral migration rates in unvegetated basins is likely not only facilitated by lower bank mechanical strength, but also by higher normalized sediment supply in ephemeral rivers. Using three case examples, we also demonstrate that width-normalized meander migration rates respond to spatial gradients in sediment supply caused by river impoundments, highlighting the prominent role of sediment supply in setting the pace of meander migration. Our results suggest that sediment-supply variations caused by climate, land-cover and land-use changes can lead to predictable changes in meandering river evolution and ultimately drive architectural changes in sedimentary stratigraphy. This dataset includes all underlying data used for the associated manuscript. We include all surface water mask files (.tif), derived channel centerline files (.csv, .pkl), bar-averaged migration files, and aggregated tabular data. To work with any of the derived data, we recommend using a Python-based workflow. The dataset includes three sections: 1) Dammed_Rivers This includes underlying data for the upstream-to-downstream comparisons of river mobility across river dams. For each included river dam, we provide files that show the study area, binary geotiffs of channel water, generated centerlines, all migration data, and samples WBMsed model sediment flux and water discharge information. There are data for 3 rivers (Flint, Iowa, and Red) included in this section. 2) Single_Rivers This includes all underlying data for the individual river analyses included in our data compilation. We include the study location, binary Geotiffs of channel water, centerlines, migration data, and WBMsed model data. There are data for 55 rivers included in this section. 3) Tabular_Data This includes the aggregated tabular data for the data compilations. We aggregate the 55 rivers (from the Single_Rivers section) into a tabular .csv database. We also include data from 84 additional rivers that have published migration rates. More detail on the file structure and data contents can be found in the README.md file. For more detail on the Python-based workflow to generate channel water masks, centerline vector products, and migration rate measurements, please see the associated software.

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