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66 results for “commodities”
Annex B to the technical report on the raw primary commodity (RPC) model - Summary statistics of the output data
<p><strong>The raw primary commodity model</strong>:</p> <p>Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA’s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs.</p> <p><strong>Annex B to the technical report on the raw primary commodity model:</strong></p> <p>Annex B is an excel file which presents summary statistics of the output data generated by the RPC model. The following tables are included in Annex B:</p> <p>Table B.1 :Summary statistics of chronic RPC consumption expressed in g/kg bw per day (total population)</p> <p>Table B.2 :Summary statistics of chronic RPC consumption expressed in g/day (total population)</p> <p>Table B.3 :Summary statistics of acute RPC consumption expressed in g/kg bw (consumers only)</p> <p>Table B.4 :Summary statistics of acute RPC consumption expressed in g (consumers only)</p> <p>Table B.5 :Comparison of the RPC consumption data with RPC consumption data used in EFSA's Pesticides Residues Intake Model (PRIMo)</p> <p>Table B.6 :Contribution of processed products to the average chronic RPC consumption</p>
Annex A to the technical report on the raw primary commodity (RPC) model - Input data
<p><strong>The raw primary commodity model</strong>:</p> <p>Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA’s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs.</p> <p><strong>Annex A to the technical report on the raw primary commodity model:</strong></p> <p>Annex A is an excel file which presents input data tables used by the RPC model. The annex contains the following tables:</p> <p>Table A.1 (Survey table) - An overview of the food consumption surveys incorporated in the RPC model</p> <p>Table A.2 (FoodEx table) - An outline of the food classification system used in the RPC model (EFSA's FoodEx classification system with additional codes)</p> <p>Table A.3 (Probability table) - Manages foods coded at food group level (example, breakfast cereals)</p> <p>Table A.4 (Disaggregation table) - Disassembles composite foods into their single components (RPC derivatives and/or RPCs)</p> <p>Table A.5 (Conversion table) - Converts amounts of RPC derivatives into corresponding amounts of RPC</p> <p>Table A.6 (Component table) - Overview of the search strings used for the probability analysis of components</p>
D6.3 - Outputs of the assessment of the biodiversity loss induced by the soy commodities market between Brazil and the European Union
<p><span>This dataset compiles the results of an assessment of biodiversity loss caused by the soy commodities trade between Brazil and the European Union from 2004 to 2020. The file “SM” refers to supplementary material. The file “POA1” contains biodiversity loss characterisation factors for land occupation and land transformation, estimated based on CLEVER D2.4. The file “POA2” includes indicators of biodiversity loss due to soy cultivation in Brazil at the municipality level. Finally, the file “POA3” presents the biodiversity footprint of the Brazilian soy commodities traded in the European Union.</span></p>
A Policy and Infrastructure Evaluation Model of Commodity Flows through Inland Waterway Ports (Dataset)
<p>The purpose of this project is to guide strategic investment into port capacity through the development of a policy and infrastructure evaluation model of inland waterway commodity flows. A multi-stage stochastic optimization model will be developed to evaluate tradeoffs in strategic, long-term port infrastructure investment with mid-term capacity expansion decisions and provision of complementary highway infrastructure made by public and private stakeholders, and shorter-term operational practices made by shippers and carriers. This work builds on prior MarTREC projects which developed a Multi-Commodity Assignment Problem to estimate annual commodity flows through inland waterway ports from truck Global Positioning System (GPS), marine Automatic Identification System (AIS), and the Lock Performance Management System (LPMS). The proposed project will explore critical extensions of the assignment model: 1) disaggregation of the temporal scope to reflect monthly seasonality among commodities, 2) incorporation of uncertainty related to observed vehicle and vessel movement data, and 3) inclusion of transportation costs. With these extensions the team expects to increase the accuracy and resolution of the commodity-based port throughput estimates and to allow the model to be used to not only describe the current system but to prescribe policy and project investment strategies for public and private sector transportation decision makers. Calibration and validation of the multi-stage optimization model will be done through two case studies. The regional-based study will use historical truck GPS, marine AIS, and LPMS datasets. The national-based study will use data from the Billion Ton Study led by the US Department of Energy. This will ensure a feasible and realistic base-case on which to compare future policy scenarios. This project aligns with MarTREC’s research focus area in Maritime and Multimodal Logistics Management by modeling commodity flows through ports that serve as critical connections for the multimodal freight supply chain.</p>
Commodity canola and seed canola visitation and plant data
<p>Insect-mediated pollination of crops is an important service to agriculture, as increased insect visitation can increase fruit production by increasing pollen deposition. Unfortunately, pollination is often treated as a <span>"</span>black box<span>"</span>, and pollination management suffers from key knowledge gaps that hinder its greater utility, particularly the specific mechanisms underlying the processes of visitation, pollination, and fruit production. We present a causal model that links insect visitation to pollination to three separate components of yield, using field data from two types of canola (<span><em>Brassica</em> <em>napus</em></span>) production systems. Our results demonstrate that yield in commodity canola fields is primarily determined by plant size, and we found no relationship between honey bee (<span><em>Apis</em> <em>mellifera</em></span>) visitation and pollen deposition, or pollen deposition and seed yield. In contrast, yield in seed production canola fields was similarly controlled by plant size, but there was also a strong relationship between alfalfa leafcutting bee (<span><em>Megachile</em> <em>rotundata</em></span>) visitation and pollen deposition, as well as pollen deposition and seed yield. Leafcutting bee visitation in particular strongly increased pollen deposition in seed canola fields, whereas honey bee visitation did not. This model serves as a step towards a dynamic model of pollination services and highlights the relative importance of bee pollination in canola production.</p>
Commodity canola and seed canola visitation and plant data
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Alien insect dispersal mediated by the global movement of commodities
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Commodity canola insect visitation
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"Manille" commodity money - 1 piece
ID no.: MOK – M.O./A/658/1-24/ Museum: African Museum in Olkusz https://muzea.malopolska.pl/en/objects-list/1920 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
"Manille" commodity money
ID no.: MOK – M.O./A/658/1-24/ Museum: African Museum in Olkusz https://muzea.malopolska.pl/en/objects-list/1920 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Additional dataset to Trading deforestation—why the legality of forest-risk commodities is insufficient
<p>This dataset refers to the summary of unprotected native vegetation and carbon stocks per municipality in Brazil. Unprotected means native vegetation areas and spatially-linked carbon stocks present at farms with surplus of legal reserves according to the Brazilian Forest Code.</p>
Instances for Multi-Commodity Supply Vessel Planning Problem With Order Selection and Time Window Decisions
<p>Instances for Multi-Commodity Supply Vessel Planning Problem With Order Selection and Time Window Decisions</p>
Jupyter Notebooks for "Evaluating CephFS Performance vs. Cost on High-Density Commodity Disk Servers" 10.1007/s41781-021-00071-1
<p>Jupyter notebooks used to create plots in article DOI 10.1007/s41781-021-00071-1</p> <p>Title "Evaluating CephFS Performance vs. Cost on High-Density Commodity Disk Servers"</p> <p>Journal "Computing and Software for Big Science"</p>
Commode
This commode is of significance to Old Government House (National Trust) due to its direct provenance to a Parramatta craftsman. Made in 1880 by cabinetmakers Henry Tucker Jones & Sons the commode originally had a tin box that slid out through the bottom door. It is thought the cream ceramic bowl would have been a later addition. Henry Jones was a Councillor for the Municipal District of Prospect and Sherwood (later renamed Holroyd Council), and was also Mayor for five separate terms in the period between 1875 and 1903. We can be fairly certain that this commode was used for at least 30 years. Flushing toilets are only recently common in Australia. In fact, it wasn't until 1907 that tenders were called for a suitable sewerage scheme for Parramatta, and it wasn't until 1910 that it was installed at a cost of 100,000 pounds. Source: Objaverse 1.0 / Sketchfab
Supply chain sustainability standards in temperate farming commodities
<p>Supporting information for Supply chain sustainability standards in temperate farming commodities (Figure 1, Table 1).</p>
Studentwork_IndexMundi_Commodities_Dataset
<p>The dataset includes: grouped in folders by the day on which they were generated, the files corresponding to the commodities, <br> each one of which has its respective data of month, price and price change with respect to the previous month.</p> <p> </p> <p>We hereby declare that the data provided in the dataset is for academic purposes only, and we are not responsible for the accuracy of the data nor any potential damages or losses resulting from the use of the data.</p> <p> </p> <p>Esta obra está bajo una <a href="http://creativecommons.org/licenses/by-nc/4.0/">licencia de Creative Commons Reconocimiento-NoComercial 4.0 Internacional</a>.</p>
Commode / chest of drawers
To see this live go to https://www.wallacecollection.org/ Made in1735-1740 by A.R. Gaudreaus. The style is infuenced by the orient Source: Objaverse 1.0 / Sketchfab
Dodd Frank financial reform at the Commodity Futures Trading Commission (CFTC): Public comments, January 14th, 2010 – July 16th, 2014
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Data and code for zu Ermgassen et al. "Addressing indirect sourcing in zero deforestation commodity supply chains"
<p>Data and code required to reproduce figures and stats quoted in zu Ermgassen et al. "Addressing indirect sourcing in zero deforestation commodity supply chains"</p>
Statistical Analysis of Strategic Food Commodity Pricing Trends
<p>This material has presented on 2nd International Conference on Advance Research in Social and Economic Science in October 25, 2023.</p>
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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.