Browse Chicago SRW Wheat Futures (ZW) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

The CME Group Market Data Platform (MDP) 3.0 disseminates event-based bid, ask, trade, and statistical data for CME Group markets and also provides recovery and support services for market data processing. MDP 3.0 includes the introduction of Simple Binary Encoding (SBE) and Event Driven Messaging to the CME Group Market Data Platform. Simple Binary Encoding (SBE) is based on simple primitive encoding, and is optimized for low bandwidth, low latency, and direct data access. Since March 2017, MDP 3.0 has changed from providing aggregated depth at every price level (like CME's legacy FAST feed) to providing full granularity of every order event for every instrument's direct book. MDP 3.0 is the sole data feed for all instruments traded on CME Globex, including futures, options, spreads and combinations. Note: We classify exchange-traded spreads between futures outrights as futures, and option combinations as options.

Origin: Directly captured at Aurora DC3 with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

This dataset contains the raw-data (RGB-images) and reconstructed point clouds of 6 different wheat (Triticum aestivum) genotypes. The plants were imaged at two time instances, 14 days and 35 days after planting, and from each genotype 10 instances were imaged, for a total of 120 imaging sessions (6 genotypes x 2 imaging days x 10 pots) resulting in the same number of point clouds.

Data from the first imaging day (14 days after planting) shows 5 individual plants per pot, each pot containing plants from a single genotype. These multiple plants per pot had been thinned to a single plant per pot for the second imaging day (35 days after planting).

The raw data of each imaging session consists of 140 RGB-images, each with a resolution of 4056x3040 pixels. Further, for each imaging session a three-dimensional point cloud of the plant(s) was reconstructed using the method described in https://doi.org/10.48550/arXiv.2504.16840.

In addition to the above data the dataset contains three more csv-files. The first file is an assessment by a plant expert of the genotypes canopy architecture rating that ranges on a scale from 1 (extreme erectophile) to 10 (extreme planophile). The other two csv-files report measurements performed from the point-clouds for each plant, these measurements are defined in https://doi.org/10.48550/arXiv.2504.16840.

Provisional database: The data you have secured from the U.S. Geological Survey (USGS) database identified as Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022 have not received USGS approval and as such are provisional and subject to revision. The data are released on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.

Version 1 (January 2022) of the the Coastal Grain Size Portal (C-GRASP) database. This is a preliminary internal deliverable for the National Oceanography Partnership Program (NOPP) Task 1 / USGS Gesch team and project partners only.

The primary purpose of this Provisional data release is to provide National Oceanography Partnership Program (NOPP) project partners with programmatic access to this preliminary version of the Coastal Grain Size Portal (C-GRASP) database for internal project use. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.

This preliminary data release contains various files that list grain size information collated from secondary data already in the public domain, in the form of public datasets, or in published literature.

Where possible, we have indicated the source, location, and sampling methods used to obtain these data. Where not possible to establish these facts, those fields have been left empty.

More information on our methods, data sources, and data processing and analysis codes are found on our github page

The dataset consists of one zipped file, Source_Files.zip, and 4 comma separated value (csv) files

1. dataset_10kmcoast.csv- This is all data that is found to be within 10km of the Natural Earth coastline polyline
2. Data_EstimatedOnshore.csv- This is all the data from dataset_10kmcoast.csv that lies within the Natural Earth United States Polygon
3. Data_VerifiedOnshore.csv- This is all data that was able to be verified onshore from either sampling method, note, or location type data
4. Data_Post2012_VerifiedOnshore.csv- This is all the data from Data_VerifiedOnshore.csv that is after 2012

The files each have the following fields (no data is blank):

'ID': row ID integer

'Sample_ID': identifier to raw data source

'Sample_Type_Code': code of sample id

'Project': raw datasource project identifier

'dataset': raw dataset major identifier

'Date': date, where specified, and to whatever precision that is specified

'Location_Type': where specified, code indicating type of location information

'latitude': latitude in decimal degrees

'longitude': longitude in decimal degrees

'Contact': where specified, raw data originator

'num_orig_dists': number of unique grain size distributions

'Measured_Distributions': number iof measured grain size distributions

'Grainsize': grain size is sometimes reported without specification

'Mean', mean grain size in mm

'Median', median grain size in mm

'Wentworth', wentworth name (one of ['Clay', 'CoarseSand', 'CoarseSilt', 'Cobble', 'FineSand', 'FineSilt', 'Granule', 'MediumSand', 'MediumSilt', 'Pebble', 'VeryCoarseSand', 'VeryFineSand', 'VeryFineSilt'])

'Kurtosis', kurtosis value (non-dim)

'Kurtosis_Class', kurtosis category

'Skewness', skewness value (non-dim)

'Skewness_Class', skewness category

'Std', standard deviation of grain sizes

'Sorting', sorting category

'd5', grain size distribution 5th percentile

'd10', grain size distribution 10th percentile

'd16', grain size distribution 16th percentile

'd25', grain size distribution 25th percentile

'd30', grain size distribution 30th percentile

'd50', grain size distribution 50th percentile

'd65', grain size distribution 65th percentile

'd75', grain size distribution 75th percentile

'd84',grain size distribution 84th percentile

'd90', grain size distribution 90th percentile

'd95', grain size distribution 95th percentile

'Notes': notes - these can be informative and substantial, do not disregard

Source_Files.zip contains 11 comma separated value files, namely bicms.csv boem.csv clark.csv dbseabed.csv ecstdb.csv mass.csv mcfall.csv rossi.csv sandsnap.csv sbell.csv ussb.csv, which contain raw datasets that have been collated and extracted from their native formats into csv format

Description:

The datasets are constituted by two folders, namely, (A) data_features_and_metric.zip and (B) grain_area_prediction.zip.

(A) data_features_and_metric.zip:

The following are the contents of this folder

(i) grainTheta.csv file : The "grainTheta.csv" file consists the datasets generated from multiple phase field simulations. Name of the columns in the csv file are:

gnid = grain id number "n", ntheta = orientation angle of nth grain (o); nltheta = orientation angle of grain to the left of nth grain (o); nrtheta = orientation angle of grain to the right of nth grain (o); j = current density (A/m2); t = time (s); area = area of nth grain (m2); tl = horizontal length of the top edge of grain "n" (m) ; bl = horizontal length of the bottom edge of grain "n" (m)

The features gnid, ntheta, nltheta and nrtheta for a given observation are determined during the design of initial conditions of the corresponding phase field simulation. The value of "j" for the observation is determined via the boundary condition in the same numerical simulation. The result from the finite element method based phase field simulation has provided the numerical quantities for t, area, tl and bl attributes. The multiple observations in the data file have been obtained from multiple phase field simulations.

(ii) imc_theta.ipynb, imc_theta.py and imc_theta.html files: These files contain the code to build the Pearson's Correlation Coefficient (PCC) heatmap analysis of the data contained in grainTheta.csv file.

(iii) comparison_mse.csv: This data file includes the information about mean square error for training data (tmse) and mean square error for validation data (vmse) at Epoch = 199 resulting from 10 different artificial neural network (ANN) models distinguished by 10 different values of learning rates (lr) . Thus, the name of the columns in this csv file are modelno, lr, tmse and vmse.

(iv) mse_comparison.gnu: This file consists the codes required to output a png image from the data provided in comparison_mse.csv.

(v) train_loss.csv and val_loss.csv: These files consist of the data of tmse and vmse at all points of Epochs for the ANN model with lr = 2.5E-4 . Thus, the first column in train_loss.csv file corresponds to tmse whereas the second column is Epochs number. Similarly, vmse and Epochs represent the two columns in val_loss.csv file.

(vi) mse_lr2p5e-4.gnu : This file consists the codes required to output a png image from the data provided in train_loss.csv and val_loss.csv.

(B) grain_area_prediction.zip:

Inside this folder, there is a folder named "prediction_of_grain_area" consisting of the following files:

initial_area.csv file: This file consists the value of the initial grain area of grain 4. It is a constant at all orientation angle.

predicted_result_00_5e4.csv: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0o and 0o respectively, and for applied current density of 5.0E+4 J/m2 .

predicted_result_00_5e5.csv: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0o and 0o respectively, and for applied current density of 5.0E+5 J/m2 .

predicted_result_9090_5e4.csv: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90o and 90o respectively, and for applied current density of 5.0E+4 J/m2 .

predicted_result_9090_5e5.csv: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90o and 90o respectively, and for applied current density of 5.0E+5 J/m2 .

area_00_adj.gnu : This gnu file contains the code to produce the png image from the data contained in predicted_result_00_5e4.csv and predicted_result_00_5e5.csv . The information about the initial area of grain 4 is obtained from initial_area.csv file by the code.

area_9090_adj.gnu : This gnu file contains the code to produce the png image from the data contained in predicted_result_9090_5e4.csv and predicted_result_9090_5e5.csv . The information about the initial area of grain 4 is obtained from initial_area.csv file by the code.

1. SUMMARY

This database contains harmonized time series for the study of Climate induced migration in Africa. We collected information between 2016-01-04 and 2022–10–31 at a district level in spatial resolution and weekly level in temporal level. The variables and the corresponding sources are summarized in the following tables.

Climate and vegetation related variables

Variable name

Source

mean precipitation

CHIRPS (https://www.chc.ucsb.edu/data/chirps dataset)

maximum land surface temperature

MODIS

(https://modis.gsfc.nasa.gov/data/dataprod/mod11.php )

mean of normalized difference vegetation index

MODIS

(https://lpdaac.usgs.gov/products/mod09gav006/ )

Fatality related variables

Variable name

Source

interaction

The Armed Conflict Location & Event Data Project (ACLED) (https://acleddata.com/data-export-tool/ )

fatalities

The Armed Conflict Location & Event Data Project (ACLED) (https://acleddata.com/data-export-tool/ )

Social economic variables

Variable name

Source

white sorghum price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

wheat flour price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

cowpeas price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

sugar price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

tea leaves price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

salt price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

sesame oil price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

vegetable oil price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

goat price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

cattle price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

camel price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

camel milk price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

charcoal price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

diesel price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

red sorghum price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

water drum price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

labor rate

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

SomaliShillingToUSD

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

SomalilandShToUSD

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

water jerry price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

transport cost

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

labor rate agricultural

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

labor rate non-agricultural

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

number of people receiving remittance

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

number of people receiving credit

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

white maize price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

imported rice price

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

CPIAUSCL (The Consumer Price Index for All Urban Consumers)

Food Security and Nutrition Analysis Unit - Somalia (FSNAU) (https://fsnau.org/sectors/markets )

Internal displacement person (IDP)

Variable name

Source

IDP Drought

UNHCR-PRMN (https://prmn-somalia.unhcr.org/ ) (2017-2022)

Population Movement Tracking (PMT) (https://www.unhcr.org/4a9501239.pdf ) (2010-2016)

IDP conflict

UNHCR-PRMN (https://prmn-somalia.unhcr.org/ ) (2017-2022)

Population Movement Tracking (PMT) (https://www.unhcr.org/4a9501239.pdf ) (2010-2016)

1. CONTACT

For questions, please email Jose Maria Tárraga at Jose.Maria.Tarraga@uv.es

1. DATABASE

We provided a CSV file containing the time series of the variables described above. Furthermore, additional information for spatial and temporal identification such as a district identifier and a date are included.

Data and code used for the study : "From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat".

The script "Manuscript_Analyses.R" contains all code for the statistical analysis presented in the manuscript (main text & supplementary information). This script uses files produced in the folder "Locus-by-locus analysis" as inputs, and "manhattan_custom.R" as a source function ("manhattan_custom.R" is used to highlight SNPs in a given interval and to write specified SNPs name on manhattan plots). The file "Traits_monocultures.csv" contains the 20 functional traits measured on the 179 monoculture plots (see Supplementary Methods for more information on trait measurement). This file is used as an input in the script "Manuscript_Analyses.R".

The "Locus-by-locus analysis" folder contains all analyses conducted to test the effect of allelic richness on the four variables of interest: Grain Yield (GY, g/m²), Spike Number per m² (SNb, nb spikes/m²), Thousand Kernel Weight (TKW, g), and Septoria tritici blotch (STB) severity. The locus-by-locus analysis is performed with the script "Allelic_richness_locus_by_locus_analysis.R". This analysis generates a list of .csv files with one file per chromosome. Each file contains the pvalues and estimated effect sizes of the tested SNPs for the given chromosome. These output files are stored in folders named after the variables for which the effect of allelic richness was tested ("RAW_GY", "RAW_SNb", "RAW_TKW", and "RAW_severity"). The script "Allelic_richness_locus_by_locus_output_processing.R" combines all .csv files into a single dataframe and produces three diagnostic plots: Manahattan plots, histograms of p-value distributions, and p-value q-q plots. p-value thresholds were computed based on a Family-Wise Error Rate of 5% using the Galwey correction. This is done in the "pvalue_thresholds" folder with the "Meff_computation.R" script. "Meff_computation.R" uses the "Meff_function.R" as a source function and generates "GY_thresholds.csv" and "STB_thresholds.csv" as outputs (these files contains different thresholds computed according to different methods but we only retained the Galwey method (most recent) for the analyses. Since GY, SNb, and TKW were analyzed with the same number of SNPs (~19K), we used the same significance threshold for the three variables ("GY_thresholds.csv"), whereas we computed a different thresholds for STB ("STB_thresholds.csv") for which we could only include ~6K SNPs in the analysis. The "geno_pos.csv" file contains the physical positions of the SNPs.

Upstream the locus-by-locus analysis, phenotypic and genotypic files are prepared in the "Phenoytpic file preparation" and "Genotypic file preparation" folders, respecively.

The phenotypic file preparation includes the correction of yield-related variables (GY, SNb, and TKW) for spatial auto-correlation in the "Spatial_analyses_YLD_variables" folder, and the computation of plot-level variables from individual-level variables with the "Allelic_richness_phenotypic_file_prep.R" script. In this script, we compute both absolute plot values (termed "RAW_...) and relative plot values (termed "RYT_..., only for mixture plots). All phenotypic files have the same structure with the same first 6 columns: "focal" = identity of the focal genotype (the one for which the variable is measured, only relevant for variables measured at the individual-level), "neighbor" = identity of the neighbor genotype (the neighbor of the genotype for which the variable is measured, only relevant for variables measured at the individual-level), "pair" = identity of the genotypic pair (combines the identity of the focal and the neighbor genotypes), "assoc" = type of plot ("M" = monoculture or pure stand plot, "P" = mixture plot), "row" = position of the plot along the smallest dimension of the grid (see Figure 1), "column" = position of the plot along the largest dimension of the grid (see Figure 1).

The genotypic file preparation is done with the "Allelic_richness_genotypic_file_prep.R" script and includes SNP filtering, computation of matrices of allelic richness, and computation of matrices of genetic similarity between genotypic pairs. The analysis is done separatly for yield-related variables and for STB severity since the two types of variable were not measured on the same set of plots.

File List fine_data.csv (MD5: 9ec6458220867aba02650559267a47b2) coarse_data.csv (MD5: 66ba1aac22c1995ff2cf87afcfd67dce) grid_shapefiles.zip (MD5: 75f1050618a50cf2f1b4ffbdb8c9ca4d) R&JAGS_codes_and_descriptions.r (MD5: 1038789e9074783d00e3314b0414c201)

Description The fine.data.csv file is a comma-separated text file. It contains data for each fine-grain grid cell in the complete set of 6238 cells. Column definitions:

     id_coarse - String that is unique to the coarse-grain grid cell overlapping the focal fine-grain cell. All fine-grain cells that share the same id_coarse lay within the same coarse-grain cell.
     lon-lat025 - Unique string that identifies each fine-grain cell. It can also be used to link the data to the shapefile of the fine-grain grid.
     NPP - Net primary productivity [kg carbon m-2].
     LC - Shannon index of land-cover classes.
     PW - Precipitation in the wettest month [mm/month].
     HFP - Human footprint index [Units provided in Trombulak et al. 2010].
     PS - Precipitation seasonality [Coefficient of Variation].
     T - Mean annual temperature [C°].
     S.coarse - Number of species in the coarse-grain cell that encompasses the focal fine-grain cell.
     S.fine - Number of species in the cell.
     ref.data - Indicator variable that distinguishes the well-sampled 600 cells of the Reference data set (value of 1) from the rest (value of 0). 

    The coarse.data.csv file is a comma-separated text file. It contains data for each coarse-grain cell in the complete set of 107 coarse-grain cells. Column definitions:

     id_coarse - String that is unique to each coarse-grain grid cell. It can be used to link the data to the shapefile of the coarse-grain grid.
     S.coarse - Number of species in the coarse-grain cell.
     START - Index variable (see below). 
     STOP - Index variable. All fine-grain grid cells that lay within the coarse-grain cell have index j where START j STOP. The index j identifies rows in the fine.data.csv table.

    The file grid_shapefiles.zip is a .zip archive that contains two Esri shapefiles. The first one is the 0.25° fine-grain grid. The second one is the 2° coarse-grain grid. These shapefiles are not essential for the replication of the analyses, but they can be useful for visualization purposes or for further research. Both shapefiles can be linked to the fine_data.csv and coarse_data.csv by using the lon-lat025 and id_coarse fields respectively.

The file R&JAGS_codes_and_descriptions.r is a text file that contains R and JAGS codes (together with their descriptions) that can be used to run the three models presented in the study.