Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
154
datasets available to search
ShareScore release 0.9.0
Dataset results
154 results for “Cropland”
Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2010 North America product 30 m V001
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over North America for nominal year 2010 at 30 meter resolution (GFSAD30NACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30NACE data product uses a combination of the pixel-based supervised classifier, Random Forest (RF), and the object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30NACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area.Known Issues* Known issues, including constraints and limitations, are provided on page 39 of the ATBD.
A new global hybrid map of annual herbaceous cropland at a 500 m resolution for the year 2019: Accompanying file.
<p>The zip file contains the map accompanying the publication "A new global hybrid map of annual herbaceous cropland at a 500 m resolution for the year 2019" (in submission).<br><br>The hybrid map of global cropland extent has a 500 m resolution and was created by fusing two of the latest high resolution remotely sensed cropland products: the European Space Agency’s WorldCereal and the cropland layer from the University of Maryland.<br><br>The data set used for the validation of this map is <a href="../doi/10.5281/zenodo.11517295" target="_blank" rel="noopener">available here</a>.<br><br></p>
Large opportunities to expand winter wheat production on abandoned croplands for climate adaptation
<p>These are source datasets used to generate the figures in the paper "<strong>Large opportunities to expand winter wheat production on abandoned croplands for climate adaptation</strong>"</p> <p>(1) Fig.1.xlsx: Data for Fig. 1.<br>(2) Fig.2.xlsx: Data for Fig. 2.<br>(3) Fig.3.xlsx: Data for Fig. 3.<br>(4) Fig.4.xlsx: Data for Fig. 4.</p> <p> </p> <p>For any questions, please contact Liyin He (lhe@carnegiescience.edu) or Lorenzo Rosa (lrosa@carnegiescience.edu).</p>
Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"
<p># Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"<br> This data was prodused using the the MadRat framework and the mrsoil R-library by the R-script SOCBudget.R, which is stored together with the data. mrsoil is based on the R-libraries mrcommons, mrmagpie and mrvalidation.</p> <p>### REFERENCES<br> Dietrich J, Baumstark L, Wirth S, Giannousakis A, Rodrigues R, Bodirsky B, Kreidenweis U, Klein D (2020). _madrat: May All Data be<br> Reproducible and Transparent (MADRaT)_. doi: 10.5281/zenodo.1115490 (URL: https://doi.org/10.5281/zenodo.1115490), R package version<br> 1.86.0, <URL: https://github.com/pik-piam/madrat>.</p> <p>rstens K, Dietrich J (2020). _mrsoil: MadRat Soil Organic Carbon Budget Library_. doi: 10.5281/zenodo.4317933 (URL:<br> https://doi.org/10.5281/zenodo.4317933), R package version 1.1.0, <URL: https://github.com/pik-piam/mrsoil>.</p> <p>Bodirsky B, Karstens K, Baumstark L, Weindl I, Wang X, Mishra A, Wirth S, Stevanovic M, Steinmetz N, Kreidenweis U, Rodrigues R, Popov<br> R, Humpenoeder F, Giannousakis A, Levesque A, Klein D, Araujo E, Beier F, Oeser J, Pehl M, Leip D, Molina Bacca E, Martinelli E,<br> Schreyer F, Dietrich J (2020). _mrcommons: MadRat commons Input Data Library_. doi: 10.5281/zenodo.3822009 (URL:<br> https://doi.org/10.5281/zenodo.3822009), R package version 0.11.10, <URL: https://github.com/pik-piam/mrcommons>.</p> <p>Karstens K, Dietrich J, Chen D, Windisch M, Alves M, Beier F, v. Jeetze P, Mishra A, Humpenoeder F (2020). mrmagpie: madrat based MAgPIE Input Data Library. doi: 10.5281/zenodo.4319612 (URL: https://doi.org/10.5281/zenodo.4319612), R package version 0.31.0, <URL: https://github.com/pik-piam/mrmagpie>.</p> <p>Bodirsky B, Wirth S, Karstens K, Humpenoeder F, Stevanovic M, Mishra A, Biewald A, Weindl I, Chen D, Molina Bacca E, Kreidenweis U, W. Yalew A, Humpenoeder<br> F, Wang X, Dietrich J (2020). _mrvalidation: madrat data preparation for validation purposes_. doi: 10.5281/zenodo.4317826 (URL:<br> https://doi.org/10.5281/zenodo.4317826), R package version 2.5.0, <URL: https://github.com/pik-piam/mrvalidation>.</p> <p>## LICENSE<br> This data is open-source: you can redistribute it and/or modify it under the terms of the **CC Attribution 4.0 International** as published by the Creative Commons Corporation at https://creativecommons.org/licenses/by/4.0/legalcode.</p> <p>## CONTACT<br> karstens@pik-potsdam.de</p>
The supplement material of 'Could land abandonment with human intervention benefit cropland restoration? From the perspective of soil microbiota'
<p>Figure S1 The study area and experimental plots. Figure S2 The correlation analysis of soil properties in each year, based on Spearman’s Rank correlation analysis. Only significant Spearman’s coefficients are shown (p < 0.05). Figure S3 Mantel test between soil properties and microbial community structure. *, ** and *** are used to show statistical significance at the 0.05, 0.01, and 0.001 level, respectively. Figure S4 The results of linear discriminant analysis effect size (LEfSe) on bacterial and fungal communities. The cutoff of LDA score is 2. Figure S5 The relative importance ranks of edaphic and human factors on microbial communities, based on %IncMSE of random forests model.</p>
Global Agricultural Lands: Croplands, 2000
The Global Croplands data set represents the proportion of land areas used as cropland (land used for the cultivation of food) in the year 2000. Satellite data from Modetate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) Image Vegetation sensors were combined with agricultural inventory data to create a global data set. The visual presentation of these data demonstrates the extent to which human land use for agriculture has changed the Earth and in which areas this change is most intense. The data were compiled by Navin Ramankutty et al. (2008) and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Global Food Security-support Analysis Data (GFSAD) Cropland Extent-Product 2015 Validation 30 m V001
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data of the globe for nominal year 2015 at 30 meter resolution. The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30 Validation (GFSAD30VAL) data product provides a thorough and independent accuracy assessment and validation of the cropland extent products produced for each of the seven regions. The accuracy assessment and validation process utilizes a cluster of 3 by 3 pixels of 30 meter data to resample the product to 90 meter resolution. Each GFSAD30VAL shapefile contains information on sample locations, presence of cropland or no cropland, and the zones that were randomly selected for accuracy assessment across the globe.Known Issues* Known issues, including constraints and limitations, are provided on page 18 of the ATBD.
Input of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)
Open the record for dataset details and reuse information.
Management-induced changes in soil organic carbon and related crop yield dynamics in China's cropland
<p>Enhancing soil organic carbon (SOC) sequestration and food supply are vital for human survival when facing climate change. Site-specific best management practices (BMPs) are being promoted for adoption globally as solutions. However, how SOC and crop yield are related to each other in responding to BMPs remains unknown. Here, path analysis based on meta-analysis and machine learning was conducted to identify the effects and potential mechanisms of how the relationship between SOC and crop yield responds to site-specific BMPs in China. The results showed that BMPs could significantly enhance SOC and maintain or increase crop yield. The maximum benefits in SOC (30.6%) and crop yield (79.8%) occurred in mineral fertilizer combined with organic inputs (MOF). Specifically, the optimal SOC and crop yield would be achieved when the areas were arid, soil pH was ≥7.3, initial SOC content was ≤10 g kg<sup>-1</sup>, duration was >10 years, and the nitrogen (N) input level was 100-200 kg ha<sup>-1</sup>. Further analysis revealed that the original SOC level and crop yield change showed an inverted V-shaped structure. The association between the changes in SOC and crop yield might be linked to the positive role of the nutrient-mediated effect. The results generally suggested that improving the SOC can strongly support better crop performance. Limitations in increasing crop yield still exist due to low original SOC levels, and in regions where the excessive N inputs, inappropriate tillage or organic input is inadequate and could be diminished by optimizing BMPs in harmony with site-specific conditions.</p>
A large-scale high-resolution cropland non-agriculturalization (Hi-CNA) dataset
<p><span>The Hi-CNA is a high-resolution remote sensing dataset dedicated to the cropland non-agriculturalization (CNA) tasks, featuring high-quality semantic and change annotations for cropland. The study area covers parts of Hebei, Shanxi, Shandong, Hubei provinces in China, with a total area exceeding 1100 km<sup>2</sup>. These regions exhibit significant variations in crop planting, ensuring the diversity of cropland morphologies. The first temporal phase spans from 2015 to 2017, while the second phase ranges from 2020 to 2022, covering multiple phenological periods of crops. These characteristics provide a rich variety of samples for CNA tasks.</span></p> <p><span><span>The dataset is sourced from multispectral GF-2 fusion images with a spatial resolution of 0.8m, encompassing four bands including visible light and near-infrared. All images are cropped to 512*512, resulting in a total of 6797 pairs of dual-temporal images with corresponding annotations. Figure 1 illustrates different forms of cropland and some types of changes.</span></span></p>
The field surveys in smallhoder cropland in China
<p>There are photos from field survery in 2020, including sample photo of wheat, corn and vegetable. </p>
Winter field hydrological monitoring data for five artificially drained croplands in Eastern Canada.
Open the record for dataset details and reuse information.
U.S. annual cultivated extent and maps of cropland expansion and cropland abandonment
<p>This repository will eventually contain the data layers of several connected products related to U.S. cropland extent and dynamics, including:</p> <p><br> A set of 30-m resolution maps of <strong>annual cultivated extent </strong>for the conterminous US (CONUS) for the years 1986-2018</p> <p>A 30-m resolution nationwide map of <strong>abandoned croplands </strong>in the U.S., as described by Xie et al. (in review).</p> <p>A 30-m resolution nationwide map of <strong>stable croplands</strong> in the U.S., as described by Uludere-Aragon et al. (in review).</p> <p>A 30-m resolution nationwide map of <strong>cropland expansion</strong> in the U.S. for the period 1986-2018 (in prep). </p> <p> </p> <p> </p> <p>These data were also utilized in related publications, including:</p> <p>Xie and Lark (2021). Mapping annual irrigation from Landsat imagery and environmental variables across the conterminous United States. https://doi.org/10.1016/j.rse.2021.112445</p> <p>Xie et al. (2021). Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017. https://doi.org/10.5194/essd-13-5689-2021</p> <p>O'neil et al. (in prep) Effect of marginal land definitions on biofuel supply chain optimization outcomes.</p>
Cropland-scale interaction between maize evapotranspiration and groundwater in a well-irrigation district in Mu Us Sandy Land, Northwest China
<p>The data includes the observed meteorological factors, soil water content, evapotranspiration (ETc), irrigation, depth to the water table, maize growth, and so on.</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.