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506 results for “crop data”
Data for "Can Regenerative Agriculture increase national soil carbon stocks? Simulated country-scale adoption of reduced tillage, cover cropping, and ley-arable integration using RothC"
<p>R code and supplementary data for soil carbon simulations: "<em>Can Regenerative Agriculture increase national soil carbon stocks? Simulated country-scale adoption of reduced tillage, cover cropping, and ley-arable integration using RothC-26.3</em>"</p>
Data supporting "Crop yield after 5 decades of contrasting residue management"
<p>Data supporting the pubblication "Crop yield after 5 decades of contrasting residue management" by Piccoli et al. (2020). Nutr Cycl Agroecosyst (2020) 117:231–241. https://doi.org/10.1007/s10705-020-10067-9(0123456789().,-volV() 0123458697().,-volV)</p>
Data supporting "A multivariate approach to evaluate reduced tillage systems and cover crop sustainability"
<p>Data supporting "A multivariate approach to evaluate reduced tillage systems and cover crop sustainability" by Sartori et al. (2022) Land, 11, 55. https://doi.org/ 10.3390/land11010055</p>
Global data on fertilizer use by crop and by country
<p>Understanding how much inorganic fertilizer (referred to as fertilizer) is applied to different crops at national, regional and global levels is an essential component of fertilizer consumption analysis and demand projection. Good information on fertilizer use by crop (FUBC) is rarely available because it is difficult to collect and time-consuming to process and validate. To fill this gap, a first global FUBC report was published in 1992 for the 1990/1991 period, based on an expert survey conducted jointly by the Food and Agriculture Organization (FAO) of the UN, the International Fertilizer Development Center (IFDC) and the International Fertilizer Association (IFA). Since then, similar expert surveys have been carried out and published every two to four years in the main fertilizer-consuming countries. Since 2008 IFA has led these efforts and, to our knowledge, remains the only globally available data set on FUBC. This dataset includes data (in CSV format) from a survey carried out by IFA to represent the 2017–18 period as well as a collation of all historic FUBC data.</p>
Supporting data and code for: Host plant and insecticides shape the evolution of genetic and clonal diversity in a major aphid crop pest
<p>This is the first release of the final data and code for the article accepted for publication in <em>Evolutionary Applications</em> journal. It contains the necessary scripts to produce most of the analyses and figures of the manuscript. All the necessary data can be found in the 'data' folder.</p>
ACIA500: a 500 m annual cropping intensity dataset for monsoon Asia based on MODIS data
<p>This dataset provides 500m-grid crop intensity map of monsoon Asia (some countries) from 2001 to 2021.</p> <p>*** Updated crop intensity map for 2021</p> <p>*** The data file is in “.tif" format</p> <p>*** Temporal Resolution: Yearly</p> <p>*** Pixel size: 500 m</p> <p>*** Projection information: EPSG: 4326</p> <p>The map boundary employed in this database does not imply the expression of any opinion whatsoever on the part of us concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries.</p>
Data and metadata of crop yield and quality derived from H2020 Diverfarming project
<p>Data and metadata of crop yield and quality from different cases studies and long terms from WP3 "Crop production and quality", derived from H2020 Diverfarming project. This workpackage provides robust and sound data about how diversified cropping systems with low-input practices and efficient use of resources have positive effects on crop production and quality, and so, farm yields and economic revenues for farmers is increased. This can increase trading productivity with high quality outputs, so improving the competitiveness of European agriculture in the global market. http://www.diverfarming.eu.</p>
Estimating net carbon balances and greenhouse gas radiative balances of potato and pea crops on a conventional farm in western Canada (Flux and meteorological data)
<p>Data accompanying the paper titled as "Estimating net carbon and greenhouse gas balances of potato and pea crops on a conventional farm in western Canada". Data includes measurements from eddy covariance, chamber, and meteorological sensors. Measurements were mainly conducted in 2018 and 2019, please refer to the paper for the detailed information.</p>
Data for: Direct and indirect effects of management and landscape on biological pest control and crop pest infestation in apple orchards
<p>Biological pest control, relying on naturally occurring predator-prey dynamics, is considered a key element to achieve more sustainable farming systems. However, the combined effects of local management and landscape factors on communities of natural enemies as well as the cascading effects on pest infestations are rarely addressed, especially in perennial crops. Here, we used Piecewise Structural Equation Modelling (PSEM) to test direct and indirect effects of landscape composition, landscape configuration and local management practices on natural enemy communities, the pest control services they provide and ultimately on pest infestation and pest-related yield damage in apple crops. To this end, we surveyed 12 organic and 12 Integrated Pest Management (IPM) orchards during three consecutive years, and we also established a semi-natural benchmark to quantify the extent to which predator communities in the orchards were degraded. Natural enemies had a different community composition and were more abundant in organic orchards compared to IPM orchards. This had a small and positive effect on sentinel egg predation rates in organic orchards, but overall had very little impact on actual apple pest infestation. On the contrary, apple pest infestation levels were directly and positively affected by organic management practices and by increasing semi-natural habitat cover and landscape edge density. Compared to a semi-natural benchmark, both agricultural management systems showed degraded predator communities, which translated into an impaired delivery of biological control services. Synthesis and applications. Our results indicate that organic management and habitat conservation can enhance natural enemies and stimulate pest control, but also show that these factors can enhance pest infestations and can even lead to an overall increase in pest-related crop damage. Our study thus highlights the complex interplay of ecosystem services and disservices provided by biodiversity, which should be taken into account when advising farmers, policy makers and land managers on effective and sustainable strategies to control pest species and safeguard crop production.</p>
Ground Truthing Survey Data of Crop Type in Pakistan (Rabi 2022‒Kharif 2023)
<p>The dataset comprises ground truthing survey data collected during the winter (Rabi) season of 2022–23 and the summer (Kharif) season of 2023 in Pakistan. These surveys were conducted as part of the Asian Development Bank's (ADB) initiative to support Pakistan's Ministry of National Food Security and Research (MNFSR) and provincial Crop Reporting Service (CRS) departments in adopting technology-based data collection practices. There were 43,892 data points collected during the winter (Rabi) season and 92,951 during the summer (Kharif) season. The data collected is available in the below-mentioned format.</p> <div> <table> <tbody> <tr> <td> <p><strong>Variable Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data Type</strong></p> </td> <td> <p><strong>Example Values</strong></p> </td> </tr> <tr> <td> <p>ID</p> </td> <td> <p>Unique identifier for each data point</p> </td> <td> <p>Text</p> </td> <td> <p>3-324-20-2-19082023-1-1</p> </td> </tr> <tr> <td> <p>Season</p> </td> <td> <p>Season in which data was collected</p> </td> <td> <p>Text</p> </td> <td> <p>Rabi</p> </td> </tr> <tr> <td> <p>Province</p> </td> <td> <p>Name of the province where data was collected</p> </td> <td> <p>Categorical</p> </td> <td> <p>Khyber Pakhtunkhwa</p> </td> </tr> <tr> <td> <p>District</p> </td> <td> <p>Name of the district where data was collected</p> </td> <td> <p>Categorical</p> </td> <td> <p>Malakand</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td> <p>Date showing when the data was collected</p> </td> <td> <p>Date</p> </td> <td> <p>19/08/2023</p> </td> </tr> <tr> <td> <p>Latitude</p> </td> <td> <p>Latitude coordinate of the data point</p> </td> <td> <p>Float</p> </td> <td> <p>34.449521</p> </td> </tr> <tr> <td> <p>Longitude</p> </td> <td> <p>Longitude coordinate of the data point</p> </td> <td> <p>Float</p> </td> <td> <p>71.907877</p> </td> </tr> <tr> <td> <p>Code</p> </td> <td> <p>Numeric code representing specific crop (e.g. Wheat is given code 1)</p> </td> <td> <p>Integer</p> </td> <td> <p>14</p> </td> </tr> <tr> <td> <p>Land</p> </td> <td> <p>Type of land</p> </td> <td> <p>Categorical</p> </td> <td> <p>Rice, Intercropping</p> </td> </tr> <tr> <td> <p>Description</p> </td> <td> <p>Detail of land type</p> </td> <td> <p>Categorical</p> </td> <td> <p>Orchard (Apple)</p> </td> </tr> <tr> <td> <p>Stage</p> </td> <td> <p>Stage of crop at the time of data collection</p> </td> <td> <p>Categorical</p> </td> <td> <p>Reproductive</p> </td> </tr> </tbody> </table> </div> <p> </p> <p> </p>
Data from: Landscapes with higher crop diversity have lower aphid species richness but higher plant virus prevalence
<p>Diversifying agricultural systems by growing more than one crop species in an area can decrease pest and disease pressure and increase crop yields. However, there is a lack of information on how crop diversity at larger spatial scales influences pest and disease pressure. Here, we investigated how landscape-scale crop diversity affects aphid vector communities and prevalence of non-persistently transmitted potato virus Y (PVY). To test the influence of landscape-scale crop diversity on PVY prevalence and aphid communities, we conducted a field study during the 2020 and 2021 field seasons in the San Luis Valley, Colorado where we quantified aphid communities and PVY incidence at multiple sites. We then determined the association of aphid species richness and abundance and PVY incidence with landscape variables (crop diversity metrics and percentage cover of crop species) within 1, 2 and 3 km buffers from study sites. Higher crop diversity (measured as Shannon diversity index) led to decreased aphid species richness at a 3 km buffer in the 2021 field season. Percentage of alfalfa was positively associated with aphid species richness in 2020 and aphid abundance in 2021 within a 1 km buffer. Higher crop diversity led to increased PVY incidence at a 2 km buffer in 2021 and 3 km buffer in 2020 and 2021. At a 3 km buffer in 2021, we found a positive influence of crop species richness on PVY incidence and a negative influence of crop species evenness on PVY incidence. Also in 2021, we found a positive influence of percentage of potato (virus host) on PVY incidence and a negative influence of percentage of barley (virus non-host) on PVY incidence.</p> <p><strong>Synthesis and applications:</strong> In summary, we found that landscape-scale crop diversity impacts plant virus prevalence at spatial scales of >1 km. This suggests that potato growers could reduce PVY prevalence by geographically isolating potato fields from other potato or other PVY-hosts. Crop diversity had a negative influence on aphid vector communities so growers could reduce risk of virus spread by aphid vectors by using certified potato seed in a diversified landscape.</p>
Data from: The potential of undersown species identity vs. diversity to manage disease in crops
<p>In the absence of chemical control with its negative side effects, fungal pathogens can cause large yield losses, requiring us to develop agroecosystems that are inherently disease resistant. Grassland biodiversity experiments often find plant species diversity to reduce pathogen pressure, but whether incorporating high biodiversity levels in agricultural fields have similar effects remains largely unknown.</p> <p>We tested if undersown plant species diversity could reduce barley disease, and whether the effect was mediated through above- or belowground mechanisms, by combining an agricultural field trial with a soil transplant experiment.</p> <p>As predicted, barley disease decreased in the presence of undersown plants. Undersown species richness had no effect, but their abundance led to early season disease reduction. Aboveground mechanisms underpinned this disease reduction. Barley yield slightly decreased with increasing undersown species richness, and undersown species varied in their impact on yield.</p> <p>We identified two undersown species, <em>Trifolium repens</em> and <em>T. hybridum</em>, that contributed most to disease reduction and had the potential to increase barley yield. Furthermore, our results indicate that aboveground mechanisms caused this. We show that agroecosystem functioning can be improved without trade-offs on yield by targeted selection of undersown species.</p>
Crop performance, aerial, and satellite data from multistate maize yield trials
<p>Accurate genotype-specific early yield estimates at fields and plots offer potential benefits to farmers in optimizing their agronomic practices, breeders in screening hundreds and thousands of varieties, and policymakers in decisions contributing to the overall improvement of agriculture and food production systems. Effective, generalizable approaches to track plant growth and predict yield at the individual plot level require large matched datasets of remote sensing and ground truth data collected across multiple environments. Low-altitude drone flights are increasingly being used to collect data from field evaluations of new crop varieties, while satellite imagery is being explored to track yield and management practices at the regional and field scales. Despite their lower spatial resolution, satellite platforms exhibit multiple logistical and technical advantages in scalability and accessibility, and could facilitate plot-level predictions, especially with steadily improving spatial resolution. However, genotype-specific, plot-level, high-resolution satellite images from multiple environments integrated with the ground truth measurements are not yet publicly available. Here we generated, described, and evaluated a set of more than 20,000 plot-level images of over 80 hybrid maize (Zea mays) varieties grown in six locations across the US corn belt under various management practices collected from (near simultaneous) satellite and drone flights integrated with ground truth measurements of crop yield. Of the six baseline models examined, models employing data collected from satellite images often matched or exceeded the performance of models employing data collected from drones for both within-environment and cross-environment yield prediction. Large, multimodal, multi-environment, genetically diverse training datasets such as those generated in this study, along with more complex models could help unlock the power of satellite imagery as an important new addition to the tool of farmers, plant geneticists, crop breeders, and policymakers.</p>
Data from: 42 years of no-tillage and cover cropping improved soil oxygen availability and resilience
<p>Healthy soil air-water balance is critical for crop growth. Conservation agricultural practices improve soil physical properties to influence soil oxygen availability. We evaluated the impact of 42 years of hairy vetch (HV) cover cropping (CC) and no-tillage (NT) on soil oxygen dynamics during a cotton growing season experiencing multiple intensive rain events in silt loam soil. HV and NT treatments exhibited higher growing season soil oxygen availability (<em>p </em>< 0.05), and experienced 3 to 4 times fewer hours of oxygen limitation (i.e., oxygen concentration <10%) as compared to no cover (NC) and conventional tillage (CT) treatments. After heavy rainfall, NT-HV treatment exhibited the highest soil oxygen availability, followed by NT-NC, CT-HV, and CT-NC treatments (<em>p </em>< 0.05). While CC and/or NT treatments quickly regained soil oxygen status within 24 hours after saturating rain events, CT-NC suffered from sub-optimal soil aeration until the third day post-rainfall. The combination of CC with NT practices enhanced soil oxygen availability and resilience to extreme precipitation events.</p>
Data from: Increased intake of tree forage by moose is associated with intake of crops rich in non-structural carbohydrates
<p>Animals representing a wide range of taxonomic groups are known to select specific food combinations to achieve a nutritionally balanced diet. The nutrient balancing hypothesis suggests that, when given the opportunity, animals select foods to achieve a particular target nutrient balance, and that balancing occurs between meals and between days. For wild ruminants who inhabit landscapes dominated by human land use, nutritionally imbalanced diets can result from ingesting agricultural crops rich in starch and sugar (non-structural carbohydrates, NC), which can be provided to them by people as supplementary feeds. Here, we test the nutrient balancing hypothesis by assessing potential effects that the ingestion of such crops by Alces alces (moose) may have on forage intake. We predicted that moose compensate for an imbalanced intake of excess NC by selecting tree forage with macro-nutritional content better suited for their rumen microbiome during wintertime. We applied DNA metabarcoding to identify plants in faecal and rumen content from the same moose during winter in Sweden. We found that the concentration of NC-rich crops in faeces predicted the presence of Picea abies (Norway spruce) in rumen samples. The finding is consistent with the prediction that moose use tree forage as a nutritionally complementary resource to balance their intake of NC-rich foods, and that they ingested P. abies in particular (normally a forage rarely eaten by moose) because it was the most readily available tree. Our finding sheds new light on the foraging behaviour of a model species in herbivore ecology, and on how habitat alterations by humans may change the behaviour of wildlife.</p>
Data from: Earthworms promote crop growth by enhancing the connections among soil microbial communities
<p>Earthworms benefit plant growth and play a vital role in shaping soil microbial communities. However, how earthworms modify the soil microorganisms and thus affect plant growth is still unclear. Although fertilizers alter the assembly of microbial communities, further investigations are required to test the effect of fertilizer type on the relationship between earthworms, soil microbial communities, and plants. We evaluated the role of earthworms in soil microorganisms and maize plant growth characteristics under organic or chemical fertilizers in field and greenhouse experiments. We explored the relationships between earthworms, soil microbial community, and plant growth under different fertilizer types. We found that the presence of earthworms promoted plant growth, increased the amount of plant root exudates, and enhanced the connections between rhizosphere bacterial, fungal, and protist communities. Both earthworms and fertilizer application significantly changed the structure of soil bacterial, fungal, and protist communities. The complexity of the soil microbial community network increased under organic, compared to chemical fertilizer application. The greenhouse experiment showed that the effect of earthworms on plant growth was weakened when maize plants were grown in sterilized soil under organic or chemical fertilizers.</p> <p><em>Synthesis and applications:</em> Our study provides solid evidence that earthworms largely depend on soil microorganisms for their effects on plants under the application of different fertilizer conditions. This may provide new insights into reducing the amounts of fertilizer used by enhancing the role of earthworms and soil microorganisms.</p>
Data from: Comparative productivity of six bioenergy cropping systems on marginal lands in the Great Lakes Region, United States
<p>Growing lignocellulosic crops on marginal lands is a promising solution for sustainable biofuel production. We evaluated the productivity of bioenergy cropping systems (switchgrass [<em>Panicum</em> <em>virgatum</em> L., var. Cave‐In‐Rock], miscanthus [<em>Miscanthus</em> × <em>giganteus</em>, 'Illinois clone'], hybrid poplar [<em>Populus</em> <em>nigra</em> × <em>P. maximowiczii</em> A. Henry 'NM6'], native grasses [five species], early successional vegetation, and restored prairie vs. historical vegetation [as reference control]) with and without nitrogen fertilization on low‐fertility former cropland at five sites in the Great Lakes Region, United States. We reported biomass yields for the first 7 years after establishment. Switchgrass was most consistently productive across all sites, but miscanthus was more productive at three of the five sites. When averaged across sites, years, and nitrogen (N) treatments, biomass yields followed the order miscanthus > switchgrass > hybrid poplar ≈ native grasses > restored prairie > early successional vegetation ≈ historical vegetation, but varied substantially by crop and site, with a significant crop by site interaction. Yields of miscanthus and switchgrass peaked after four to five growing seasons and declined thereafter, while yields of both native grasses and restored prairie increased throughout 6 years with no sign of follow‐on decline, suggesting that polycultures may outperform monocultures over the long term. Yields of early successional vegetation—similar in composition to historical vegetation at each site—did not improve with time. Nitrogen fertilization increased the yields of all cropping systems at all sites. Our results demonstrate the viability of low‐productivity former cropland for long‐term bioenergy production and suggest there is no single crop best suited for all low-fertility soils.</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJ-GUESS potato
<p>This is model output from LPJ-GUESS for potato as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: CLM-Crop cotton
<p>This is model output from CLM-Crop for cotton as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: CLM-Crop soy
<p>This is model output from CLM-Crop for soy as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</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.