Money Supply M0 in Canada increased to 251190 CAD Million in January from 243244 CAD Million in December of 2024. This dataset provides - Canada Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Money Supply M2 in Canada increased to 2679786 CAD Million in January from 2654380 CAD Million in December of 2024. This dataset provides - Canada Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This is the full Global Wheat Head Dataset 2021. Labels are included in csv.

Tutorials available here: https://www.aicrowd.com/challenges/global-wheat-challenge-2021

🕵️ Introduction

Wheat is the basis of the diet of a large part of humanity. Therefore, this cereal is widely studied by scientists to ensure food security. A tedious, yet important part of this research is the measurement of different characteristics of the plants, also known as Plant Phenotyping. Monitoring plant architectural characteristics allow the breeders to grow better varieties and the farmers to make better decisions, but this critical step is still done manually. The emergence of UAV, camera and smartphone makes in-field RGB images more available and could be a solution to manual measurement. For instance, the counting of the wheat head can be done with Deep Learning. However, this task can be visually challenging. There is often an overlap of dense wheat plants, and the wind can blur the photographs, making identify single heads difficult. Additionally, appearances vary due to maturity, colour, genotype, and head orientation. Finally, because wheat is grown worldwide, different varieties, planting densities, patterns, and field conditions must be considered. To end manual counting, a robust algorithm must be created to address all these issues.

💾 Dataset

The dataset is composed of more than 6000 images of 1024x1024 pixels containing 300k+ unique wheat heads, with the corresponding bounding boxes. The images come from 11 countries and covers 44 unique measurement sessions. A measurement session is a set of images acquired at the same location, during a coherent timestamp (usually a few hours), with a specific sensor. In comparison to the 2020 competition on Kaggle, it represents 4 new countries, 22 new measurements sessions, 1200 new images and 120k new wheat heads. This amount of new situations will help to reinforce the quality of the test dataset. The 2020 dataset was labelled by researchers and students from 9 institutions across 7 countries. The additional data have been labelled by Human in the Loop, an ethical AI labelling company. We hope these changes will help in finding the most robust algorithms possible!

The task is to localize the wheat head contained in each image. The goal is to obtain a model which is robust to variation in shape, illumination, sensor and locations. A set of boxes coordinates is provided for each image.

The training dataset will be the images acquired in Europe and Canada, which cover approximately 4000 images and the test dataset will be composed of the images from North America (except Canada), Asia, Oceania and Africa and covers approximately 2000 images. It represents 7 new measurements sessions available for training but 17 new measurements sessions for the test!

📁 Files

Following files are available in the resources section:

• images: the folder contains all images

• competition_train.csv , competition_val.csv, competition_test.csv : contains the splits used for the 2021 Global Wheat Challenge

  • Val contains the "public test", which is the test set of Global Wheat Head 2020

  • Test contains the "private test".

• Metadata.csv : contains additional metadatas for each domain

💻 Labels

• All boxes are contained in a csv with three columns image_name, BoxesString and domain
• image_name is the name of the image, without the suffix. All images have a .png extension
• BoxesString is a string containing all predicted boxes with the format [x_min,y_min, x_max,y_max]. To concatenate a list of boxes into a PredString, please concatenate all list of coordinates with one space (" ") and all boxes with one semi-column ";". If there is no box, BoxesString is equal to "no_box".
• domain give the domain for each image

If you use the dataset for your research, please do not forget to quote:

@article{david2020global,
 title={Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods},
 author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul A and others},
 journal={Plant Phenomics},
 volume={2020},
 year={2020},
 publisher={Science Partner Journal}
}

@misc{david2021global,
title={Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods},
author={Etienne David and Mario Serouart and Daniel Smith and Simon Madec and Kaaviya Velumani and Shouyang Liu and Xu Wang and Francisco Pinto Espinosa and Shahameh Shafiee and Izzat S. A. Tahir and Hisashi Tsujimoto and Shuhei Nasuda and Bangyou Zheng and Norbert Kichgessner and Helge Aasen and Andreas Hund and Pouria Sadhegi-Tehran and Koichi Nagasawa and Goro Ishikawa and Sébastien Dandrifosse and Alexis Carlier and Benoit Mercatoris and Ken Kuroki and Haozhou Wang and Masanori Ishii and Minhajul A. Badhon and Curtis Pozniak and David Shaner LeBauer and Morten Lilimo and Jesse Poland and Scott Chapman and Benoit de Solan and Frédéric Baret and Ian Stavness and Wei Guo},
year={2021},
eprint={2105.07660},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

The available database Wood_db-chemistry is the result of extracting interesting data by analysing already published scientific literature. The data provide information on wood, knots and bark extracts from 4 forest species grown and processed in the East of France (Quercus robur, Quercus petraea, Fagus sylvatica, Pseudotsuga menziesii). Consulting the database provides information on (i) the extraction process used to obtain the extracts, the chemical content of the extracts and the determination of the extractives (ii) the active bioactive properties of the extracts (iii) the metadata of the extracts.The wood_db-chemistry database is opened by exporting 3 CSV files, each generated by an SQL query sent to the database. At any given time, the 3 CSV files proposed are a perfect representation of the contents of the wood_db-chemistry database. This dataset is presented and described in a paper published in Annals of Forest Science by Springer. This data paper is available at https//doi.org/10.1186/s13595-024-01223-0

The dataset contains large ensembles of bias adjusted daily climate model outputs of minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation, and incoming longwave radiation on a 0.5-degree grid over North America. Intended uses include hydrological/land surface impact modelling and related event attribution studies. The CanLEADv1 dataset is based on archived climate model simulations in the Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) https://open.canada.ca/data/en/dataset/83aa1b18-6616-405e-9bce-af7ef8c2031c and Canadian Earth System Model Large Ensembles (CanESM2 LE) https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c datasets. Specifically, CanLEADv1 provides bias adjusted daily climate variables over North America derived from 50 member initial condition ensembles of CanESM2 (ALL and NAT radiative forcings) and CanESM2-driven CanRCM4 (ALL radiative forcings) simulations (Scinocca et al., 2016; Fyfe et al., 2017). Raw CanESM2 LE and CanRCM4 LE outputs are bias adjusted (Cannon, 2018; Cannon et al., 2015) so that they are statistically consistent with two observationally-constrained historical meteorological forcing datasets (S14FD, Iizumi et al., 2017; EWEMBI, Lange, 2018). File names, formats, and metadata headers follow the recommended Data Reference Syntax for bias-adjusted Coordinated Regional Downscaling Experiment (CORDEX) simulations (Nikulin and Legutke, 2016). Multiple initial condition simulations can be used to investigate the externally forced response, internal variability, and the relative role of external forcing and internal variability on the climate system (e.g., Fyfe et al., 2017). Large ensembles of ALL and NAT simulations can be compared in event attribution studies (e.g., Kirchmeier-Young et al., 2017). Availability of bias adjusted outputs from the CanESM2-CanRCM4 modelling system can be used to investigate the added value of dynamical downscaling (Scinocca et al., 2016). Multiple observational datasets are used for bias adjustment to partly account for observational uncertainty (Iizumi et al., 2017). For CanESM2 LE, there are two sets of radiative forcing scenarios (ALL, which consists of historical and RCP8.5 forcings for the periods 1950-2005 and 2006-2100, respectively, and NAT, which consists of historicalNat forcings for the period 1950-2020), two observationally-constrained target datasets for bias adjustment (S14FD and EWEMBI), and 50 ensemble members, which gives a total of 2 × 2 × 50 = 200 sets of outputs. For CanRCM4 LE, historicalNat simulations were not run; hence, there are 2 × 50 = 100 sets of outputs. In both cases, CanLEADv1 provides variables on the CORDEX NAM-44i 0.5-degree grid. CanESM2 outputs (~2.8-degree grid) and CanRCM4 outputs (0.44-degree grid), are bilinearly interpolated onto the NAM-44i grid before bias adjustment. A multivariate version of quantile mapping (Cannon, 2018) is used to adjust the distribution of each simulated variable, as well as the statistical dependence between variables, so that these properties match those of the target observational dataset. Bias adjustment is performed on a grid cell by grid cell basis. Outside of the historical calibration period, the climate change signal simulated by the climate model is preserved (Cannon et al., 2015). References: Cannon, A. J. (2018). Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, 50(1-2), 31-49. Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015). Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate, 28(17), 6938-6959. Fyfe, J. C., Derksen, C., Mudryk, L., Flato, G. M., Santer, B. D., Swart, N. C., Molotch, N. P., Zhang, X., Wan, H., Arora, V. K., Scinocca, J., & Jiao, Y. (2017). Large near-term projected snowpack loss over the western United States. Nature Communications, 8, 14996. Iizumi, T., Takikawa, H., Hirabayashi, Y., Hanasaki, N., & Nishimori, M. (2017). Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes. Journal of Geophysical Research: Atmospheres, 122(15), 7800-7819. Kirchmeier-Young, M. C., Zwiers, F. W., Gillett, N. P., & Cannon, A. J. (2017). Attributing extreme fire risk in Western Canada to human emissions. Climatic Change, 144(2), 365-379. Lange, S. (2018). Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset. Earth System Dynamics, 9(2), 627-645. Nikulin, G., & Legutke, S. (2016). Data Reference Syntax (DRS) for bias-adjusted CORDEX simulations. https://is-enes-data.github.io/CORDEX_adjust_drs.pdf Scinocca, J. F., Kharin, V. V., Jiao, Y., Qian, M. W., Lazare, M., Solheim, L., Flato, G. M., Biner, S., Desgagne, & Dugas, B. (2016). Coordinated global and regional climate modeling. Journal of Climate, 29(1), 17-35.

The Blue Book shows who is doing business with government and selected payments from the General Revenue Fund. The following files provide the data in Excel format for supplies or services purchased by departments. Note: these files were moved to a new record effective May 8, 2020: https://open.alberta.ca/dataset/general-revenue-fund-details-of-expenditure-by-payee-data.

Catch, effort, location (latitude and longitude), and associated biological data from groundfish multi-species longline surveys in the southern portion of the inlets and protected waters east of Vancouver Island, British Columbia. Introduction The Inside South Hard Bottom Longline (HBLL) survey is one of a set of long-term and coordinated surveys that together cover most of the nearshore, hard-bottom habitat of coastal British Columbia. The other surveys are the Inside North HBLL survey, Outside South HBLL survey, and Outside North HBLL survey. The Inside South HBLL survey was first conducted in 2005. Starting in 2009, this survey has been repeated every second year, with the exception that no surveys were conducted in 2017, and the 2020 survey was postponed to 2021 due to the COVID-19 pandemic. The objective of these surveys is to provide fishery-independent abundance indices and associated biological data for the assessment of nearshore rockfishes and other groundfish species that live on untrawlable, hard bottom habitats. The surveys follow a random depth-stratified design and the sampling units are 2 km by 2 km blocks. The surveys use size 13/0 circle hooks, baited with frozen squid. The Inside South HBLL Survey is conducted by Fisheries and Oceans Canada (DFO) and takes place on the Canadian Coast Guard Research Vessel Neocaligus. This survey covers the southern portion of the inlets and protected waters east of Vancouver Island, including Desolation Sound, the Strait of Georgia and southern Gulf Islands in Pacific Fishery Management areas (PFMAs) 14 – 20, 28, and 29. Effort This table contains information about the survey trips and fishing events (sets) that are part of this survey series. Trip-level information includes the year the survey took place, a unique trip identifier, the vessel that conducted the survey and the trip start and end dates (the dates the vessel was away from the dock conducting the survey). Set-level information includes the date, time, location and depth that fishing took place, soak time, hook spacing, and numbers of hooks counted. All successful fishing events are included, regardless of what was caught. Catch This table contains the catch information from successful fishing events. Catches are identified to species or to the lowest taxonomic level possible. From 2005 to 2018, all catches are counted, and most catches are weighed; some catches are too small (“trace” amounts) or too large (e.g. very large Big Skate) to weigh. From 2020 onwards, catches are recorded as counts only. The unique trip identifier and set number are included so that catches can be related to the fishing event information (including capture location). Biology This table contains the available biological data for catches which were sampled. Data may include any or all of length, sex, weight, age. Different length types are measured depending on the species. Age structures are collected when possible for species where validated aging methods exist and are archived until required for an assessment; therefore, all existing structures have not been aged at this time. Tissue samples (usually a fin clip) may be collected for genetic (DNA) analysis for some individuals of particular species. Genetic samples may be archived until required for analysis; for more information please see the data contacts. The unique trip identifier and set number are included so that samples can be related to the fishing event and catch information.