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66 results for “commodities”
Commodity-driven deforestation, associated carbon emissions and trade 2001-2022
<p><span>This dataset contains estimates of commodity-driven deforestation and associated carbon emissions for the period 2001-2022, estimated by the Deforestation Driver and Carbon Emission (DeDuCE) model (Singh & Persson 2024), which combines remote sensing data on forest loss and land-use with agricultural statistics to identify and attribute deforestation across the world to expansion of cropland, pastures and forest plantation, and the commodities produced on this land. This also contains data on deforestation embodied in the production, exports, imports, and consumption of agricultural and forestry commodities by country, year, and commodity for the time period 2005-2022 derived using physical and monetary trade models. The data is an update of the results presented in Pendrill et al. (2022) and the differences between the two datasets are detailed in the explainer available here.</span></p>
Metal content of commodities
<p>This is a dataset of the approximate contentation of the materials steel, plastics, paper, and cement and of the metals Fe, Cu, Al, Cr, Mn, Mo, and V of ca. 4000 Commodity groups in the Eurostat ProdCom database. (NACE v2)<br> The dataset was developed for the following work:<br> "Quantifying Impacts of Consumption Based Charge for Carbon Intensive Materials on Products " Stefan Pauliuk, Karsten Neuhoff, Anne Owen, Richard Wood DIW Discussion paper 1570, German Institute for Economic Research, Berlin.</p>
Supply chain sustainability standards in temperate farming commodities
<p>Could companies contribute to farmland biodiversity in temperate landscapes? Most of the business efforts to enhance sustainability in agricultural supply chains have been focused on tropics. Important temperate crops like cereals and oilseeds are left largely unchecked. This results in a missed opportunity to reverse biodiversity decline in about 47% of the world's cropland. But implementation of voluntary sustainability standards to restore biodiversity in temperate and Mediterranean biomes would require doing things differently than in tropical commodities. Temperate agriculture differs both in its operations (lack of divergence between staples and cash crops, crop rotation) and landscape context (lack of agricultural expansion frontiers, complex pressures on biodiversity). Consequently, agricultural certification and similar schemes need to be linked primarily to a unit of space rather than a specific commodity. However, necessary tools such as advanced criteria, metrics and data infrastructure are lacking in temperate agriculture, and their development depends on input from conservation scientists and practitioners.</p> <p>Supporting information for<em> Supply chain sustainability standards in temperate farming commodities </em>consists of:</p> <ol> <li>Dataset for Figure 1;</li> <li>Additional information on methodology (VSSs uptake in retail industry) and sources (LEAF Marque case study, references for evidence of impact of feasible objectives for VSSs in conventional temperate agriculture).</li> </ol> <p> </p>
Trade policy announcements can increase price volatility in global food commodity markets (Replication Data)
<p>Replication data for "Trade policy announcements can increase price volatility in global food commodity markets":</p> <ul> <li>Original dataset on trade policy announcements from 2005 to 2017 for wheat and maize (corn) (details in codebook)</li> <li>Daily price ranges based on the highest and lowest price recorded for wheat and corn futures (traded at the Chicago Board of Trade, CBOT)</li> <li>Stocks-to-use data for the United States, which is compiled by the United States Department for Agriculture (USDA) and available at monthly frequency from their World Supply and Demand Estimates report</li> </ul>
Risk Commodity Dataset (RCDD) from Alibaba's e-commerce platform
<p>This is a risk commodity detection dataset (RCDD) that is based on a real risk detection scenario from Alibaba's e-commerce platform. All the <em>.csv </em>files are the initial data which consists of edges, node features, supervised information as well as IDs of candidate items. More details can see in <em>README.md</em>, we list the type of each column in each file as follows:</p> <p>1. RCDD_edges.csv (edge file): <br> source_node_id int, target_node_id int, source_node_type string, target_node_type string, edge_type string<br> 2. RCDD_nodes.csv (node file):<br> node_id int, node_type string, node_atts string (notice that node_atts are 256-dimensional feature vector strings with delimiter ":")<br> 3. RCDD_train_labels.csv (training labels):<br> item_id int, label int<br> 4. RCDD_test_ids.csv (testing ids):<br> item_id int<br> 5. RCDD_test_labels.csv (testing labels):<br> item_id int, label int </p> <p>Besides that,<em> graph.bin</em> is the format in DGL which is constructed by all <em>*.csv</em> files, and a general method to load this graph as follows:</p> <pre><code class="language-python">from dgl import load_graphs #should install dgl ds,_ = load_graphs("./graph.bin") g = ds[0] print(g)</code></pre> <p>And then you can easily get a large-scale heterogeneous graph with 157,814,864 edges and 13,806,619 nodes, our graph task is node classification: detect risk product.</p>
FIG. 2. — Oil painting from a in The wonder whale: a commodity, a monster, a show and an icon
FIG. 2. — Oil painting from a private collection depicting the shore-based sperm-whaling off the Azores (19 th century). Photo by the authors.
FIG. 1 in The wonder whale: a commodity, a monster, a show and an icon
FIG. 1. — Beached fin whale (Balaenoptera physalus (Linnaeus, 1758)) in Parede (nearby Lisbon, Portugal), January 2016. Photo by Inês Carvalho.
FIG. 5 in The wonder whale: a commodity, a monster, a show and an icon
FIG. 5. — "There are two whales in this watercolour. One, its tail raised, is diving. The back of the second is indicated by a long, curved pencil line below the tail of the first. A harpoon appears to have struck one of them, for the sea is stained red with blood." A Harpooned Whale (1845) by Joseph Mallord William Turner (1775–1851), part of Ambleteuse and Wimereux Sketchbook. © Tate Collection, CC-BY-NC-ND 3.0 (Unported). http://www.tate.org.uk/art/artworks/turner-aharpooned-whale-d35391, last consultation: 16/01/2019.
Data, materials, methods and codes for publication Reis et al., "Understanding the stickiness of commodity supply chains is key to improving their sustainability", 2020, One Earth.
<p>Explanation of the data and code used for the article: "Understanding the stickiness of commodity supply chains is key to improving their sustainability", 2020, One Earth.</p> <p> The file: "StickinessAnalysisCompleteGeneric_CleanUpdate18-5-2020.R" is the R code/ script containing all the data preparation and the stickiness analysis.</p> <p>The file: "BRAZIL_SOY_V2.3_WITH_DOMESTIC_TRADERS.csv" contains the raw data of Brazil's soy exports and domestic consumption from trase.earth. This dataset can also be obtained from trase.earth in the latest version.</p> <p>The file: "NodesCiS.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: A. logistics hubs supplying traders, and D. traders supplying countries. They are put together in the same table because they are all "sending" relationships.</p> <p>The file: "NodesCiR.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: C. traders sourcing from logistics hubs, and E. countries sourcing from traders. They are put together in the same table because they are all "receiving" relationships.</p> <p>The file "NodesCiSMunCountry.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: B. logistics hubs supplying countries (directly not passing through traders). This is separate in another table because it is a direct sending relationship from LHs to countries.</p> <p>The file "NodesCiRMunCountry.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: F. countries sourcing from logistics hubs (directly not passing through traders). This is separate in another table because it is a direct receiving relationship from LHs to countries.</p> <p>The file: "NodesWPiS.csv" is a table with the WPi (stickiness on flows) measured for the types of supply chain relationships: A. logistics hubs supplying traders, and D. traders supplying countries. They are put together in the same table because they are all "sending" relationships.</p> <p>The file: "NodesWPiR.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: C. traders sourcing from logistics hubs, and E. countries sourcing from traders. They are put together in the same table because they are all "receiving" relationships.</p> <p>The file "NodesWPiSMunCountry.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: B. logistics hubs supplying countries (directly not passing through traders). This is separate in another table because it is a direct sending relationship from LHs to countries.</p> <p>The file "NodesWPiRMunCountry.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: F. countries sourcing from logistics hubs (directly not passing through traders). This is separate in another table because it is a direct receiving relationship from LHs to countries.</p>
Really a nontraded commodity? A look at the international potato trade network (dataset and R code)
<p>This archive contains code and data for a social network analysis of international potato trade that was published at https://perspectivesandforesight.wordpress.com/2012/11/08/really-a-nontraded-commodity-a-look-at-the-international-potato-trade-network/ on 08 November, 2012.</p> <p>It can serve as a reference for understanding the analysis, and as a basis for replication of the results as well as for carrying out more detailed analyses of international potato trade.</p> <p>The following information and data is included:</p> <p>- The R code used for the social network analysis of international potato trade.</p> <p>- Data files with bilateral matrices of global trade in fresh, frozen and seed potatoes.</p> <p>- Data files with supplementary data used for the analysis.</p> <p>- A copy of the original blog post that was written using the data and code provided herewith.</p>
Agricultural Production Data by Commodity for Congo (2014-2018)
<p><strong>Description</strong>: This dataset represents a detailed extraction of agricultural production volumes and cultivated area statistics for staple commodities in the Republic of Congo. The data is sourced from the 2018 national and departmental statistical directories published by the Congolese National Institute for Statistics (INS). These statistics have been compiled and made available on the INS website, with specific access links at <a href="https://ins-congo.cg/annuaire-statistique-du-congo-2018/" target="_new">National Statistical Directory 2018</a> and <a href="https://ins-congo.cg/annuaires-statistiques-du-congo/" target="_new">Departmental Statistical Directories</a>.</p> <p><strong>Scope of Data</strong>: The dataset encompasses data from 2014 to 2018, covering both departmental (administrative level 1, ADM1) and district (administrative level 2, ADM2) levels. The granularity of the data depends on the availability within the accessed directories. It focuses exclusively on staple agricultural commodities, omitting horticultural productions but includes certain arboricultural commodities.</p> <p><strong>Project Context</strong>: This data extraction is part of the <a href="https://www.cirad.fr/dans-le-monde/cirad-dans-le-monde/projets/projet-pudt-congo">PUDT Congo</a> project. </p> <p><strong>List of Commodities Covered</strong>:</p> <ul> <li>Avocado</li> <li>Banana</li> <li>Bean</li> <li>Cassava</li> <li>Cocoa</li> <li>Coffee</li> <li>Ginger</li> <li>Maize</li> <li>Mango</li> <li>Palm oil</li> <li>Peanut</li> <li>Pineapple</li> <li>Plantain</li> <li>Rice (paddy)</li> <li>Safoutier</li> <li>Sesame</li> <li>Sweet potato</li> <li>Taro</li> <li>Voandzou (Bambara groundnut)</li> <li>Watermelon</li> <li>Yam</li> </ul> <p><strong>Data Collection and Processing</strong>: Data were extracted from the aforementioned sources and processed to ensure accuracy and relevance for analytical purposes.</p>
Commodity Dataset | Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform
<p>(Commodity data in raster format) Supplementary materials for “Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform” that had been published on Land MDPI (2020). doi:<a href="https://doi.org/10.3390/land9100377">10.3390/land9100377</a> </p> <p>The data included:</p> <p>1) Raster data of commodity maps (TIFF Compressed in ZIP)</p> <p>2) READ ME for the dataset (DOCX)</p> <p>3) Legend for raster data in ArcGIS Format (LYR)</p> <p> </p>
Alien insect dispersal mediated by the global movement of commodities
<p>Globalization and economic growth are recognized as key drivers of biological invasions. Alien species have become a feature of almost every biological community worldwide, and rates of new introductions continue to rise as the movement of people and goods accelerates. Insects are among the most numerous and problematic alien organisms, and are mainly introduced unintentionally with imported cargo or arriving passengers. However, the processes occurring prior to insect introductions remain poorly understood. We used a unique dataset of 1,902,392 border interception records from inspections at air, land and maritime ports in Australia, New Zealand, Europe, Japan, the United States of America and Canada to identify key commodities associated with insect movement through trade and travel. A total of 8,939 species were intercepted, and commodity association data were available for 1,242 species recorded between 1960 and 2019. We used rarefaction and extrapolation methods to estimate the total species richness and diversity associated with different commodity types. Plant and wood products were the main commodities associated with insect movement across cargo, passenger baggage and international mail. Furthermore, certain species were mainly associated with specific commodities within these, and other broad categories. More closely related species tended to share similar commodity associations, but this occurred largely at the genus level rather than within orders or families. These similarities within genera can potentially inform pathway management of new alien species. Combining interception records across regions provides a unique window into the unintentional movement of insects, and provides valuable information on establishment risks associated with different commodity types and pathways.</p>
Commodity canola insect visitation
<p>These data were used in a simulation study to examine how honey bees distribute themselves across foraging landscapes.</p> <p>The ideal-free distribution and central-place foraging are important ecological models that can explain the distribution of foraging organisms in their environment. However, this model ignores distance-based foraging costs from a central place (hive, nest), while central-place foraging ignores competition. Different foraging currencies and cooperation between foragers also create different optimal distributions of foragers, but are limited to a simple two-patch model. We present a hybrid model of the ideal-free distribution that uses realistic competitive effects while accounting for distance-based foraging, and test it using honey bees (<em>Apis mellifera</em> L.) foraging in canola fields (<em>Brassica napus</em> L.). Our simulations show that foragers maximizing efficiency (energy profits/losses) prioritize distance to their aggregation more than those maximizing net-rate (energy profits/time), and that social foragers move to more distant patches to maximize group benefits, meaning that social foragers do not approach an ideal-free distribution. Simulated efficiency-maximizers had a hump-shaped relationship of trip times with distance, spending shorter amounts of time in both nearby and far-away patches. Canola fields were far more attractive to simulated foragers than semi-natural areas, suggesting limited foraging on semi-natural lands during the bloom period of canola. Finally, we found that the observed distribution of honey bees in canola fields most closely resembled the optimal distribution of solitary efficiency-maximizers. Our model has both theoretical and practical uses, as it allows us to model central-place forager distributions in complex landscapes as well as providing information on appropriate hive stocking rates for agricultural pollination.</p>
Dataset: First Trust Global Tactical Commodity Strategy Fund (FTGC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Invesco Electric Vehicle Metals Commodity Strategy No K-1 ETF (EVMT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Davis Commodities Limited (DTCK) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares GSCI Commodity Dynamic Roll Strategy ETF (COMT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Invesco Optimum Yield Diversified Commodity Strategy No K-1 ETF (PDBC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Invesco Agriculture Commodity Strategy No K-1 ETF (PDBA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.