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96 results for “supply chain”
Data set open access of Palm Oil Supply Chain
<p>This data set including interview recorded as the qualitative data, picture, draft article, and interview transcript. This is open access data.</p>
Supply Chain Hoarding and Contingent Sourcing Strategies in Anticipation of Price Hikes and Product Shortages
<p>The uploaded zip-file provides the Matlab code for producing Figs.4-5 in the paper of ''Supply Chain Hoarding and Contingent Sourcing Strategies in Anticipation of Price Hikes and Product Shortages".</p>
A Network-of-Networks Coordination for Cross-Industry Supply Chain Adaptation
<p>Data and code for reproducing the results in paper entitled </p> <p> </p> <p>"A Network-of-Networks Coordination for Cross-Industry Supply Chain Adaptation"</p>
European Commission - 2023 foresight report on raw materials and strategic supply chains
<p>This dataset contains the results of the material demand scenarios for strategic technologies developed in the following report by the European Commission's Joint Research Centre (JRC) in partnership with DG GROW:</p> <p>Carrara, S., Bobba, S., Blagoeva, D., Alves Dias, P., Cavalli, A., Georgitzikis, K., Grohol, M., Itul, A., Kuzov, T., Latunussa, C., Lyons, L., Malano, G., Maury, T., Prior Arce, Á., Somers, J., Telsnig, T., Veeh, C., Wittmer, D., Black, C., Pennington, D., Christou, M., <em>Supply chain analysis and material demand forecast in strategic technologies and sectors in the EU – A foresight study</em>, Publications Office of the European Union, Luxembourg, 2023, doi:10.2760/386650, JRC132889</p> <p>The material demand scenarios are complemented with information on the current global supply for the relevant materials.</p> <p>The report can be downloaded at the following link:</p> <p><a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC132889">https://publications.jrc.ec.europa.eu/repository/handle/JRC132889</a></p>
Mongodb Database dump for TOSEM submission "Characterizing Deep Learning Package Supply Chains in PyPI: Domains, Clusters, and Disengagement"
<p>The Mongodb Database dump for TOSEM submission "Characterizing Deep Learning Package Supply Chains in PyPI: Domains, Clusters, and Disengagement"</p>
Dashboard "Communication technologies used and parameters measured in Food Supply Chains in the scientific literature"
<p>This dashboard collects information about the communication technologies used and parameters measured in several scientific papers. Data can be filtered according to different criteria:</p> <ol> <li>Year of publication of the paper.</li> <li>Main topic in which the objetive of the paper is framed within.</li> <li>Stage of the food supply chain where the study is developed.</li> <li>Product group according to the Classification of Products by Activity (CPA) of the European Union.</li> </ol> <p>This dashboard was created by Manuel Amador Cervera for the University of Deusto in the context of the Horizon 2020 project "FOODRUS". The information contained in it refers to the scientific publications outlined in the table that can be found in the "References" sheet.<br> This project has received funding from the European Union’s Horizon 2020 Research & Innovation programme under Grant Agreement no. 101000617. This dashboard is the sole responsibility of the University of Deusto, and the European Union or any user is not liable for any use that may be made of the information contained therein.</p>
Benchmark for supply chain monitoring properties using BeepBeep
<p>This is an instance of the <a href="https://github.com/liflab/labpal">LabPal</a> experimental environment to benchmark the execution of processor chains for the <a href="https://liflab.github.io/beepbeep-3">BeepBeep</a> event stream engine.</p> <p>The considered use case is related to the concept of <em>hyperconnected logistics</em>. In this model, the entire world can be split at the smallest scale into <em>unit zones</em>, whose size depends on expected demand density. Adjacent unit zones are grouped into local <em>cells</em>, which in turn are gathered into <em>areas</em>, which form <em>regions</em>. Simultaneously, several hub networks are defined to link these different layers: <em>access hubs</em> link unit zones together; <em>local hubs</em> link local cells, and <em>gateway hubs</em> link areas. Different hub levels may exist inside the same physical entity (e.g., a local hub might also be an access hub), thus allowing interactions between the different layers.</p> <p>In a recent work (see citation below), the authors showed how to concretely adapt a hyperlogistics simulation in order to integrate an Ethereum blockchain backend, in such a way that every action made by carriers is publicly stored in transactions on the blockchain itself. The combination of such simulation and blockchain backend allowed us to generate the traces used to monitor a number of properties.</p> <p>The experiments in this benchmark measure the throughput of a variety of BeepBeep processor chains on simulated logs of blockchain events generated on the fly.</p>
Nutritional Status and Emotional Well-being of the Worker-consumer in the Food Supply Chain.
ClinicalTrials.gov study NCT06896877. IPD Sharing: NO. Countries: 1. Publications: 8.
Resolved-EXIOBASE (REX) – A highly resolved MRIO database for analyzing supply-chain impacts (A. Code and data from 2006–2015)
<p>This repository provides the code and the R-MRIO database for the years 2006–2015 of the study "A highly resolved MRIO database for analyzing environmental footprints and Green Economy Progress":</p> <p><a href="https://doi.org/10.1016/j.scitotenv.2020.142587">https://doi.org/10.1016/j.scitotenv.2020.142587</a></p> <p>The R-MRIO database for the years 1995–2005 is stored under the repository <a href="http://doi.org/10.5281/zenodo.3994795">http://doi.org/10.5281/zenodo.3994795</a></p> <p>The folder "R-MRIO_CODE" provides the code to resolve the spatial resolution of EXIOBASE3 from 44 countries and 5 Rest of the World (RoW) regions into 189 individual countries while keeping the high sectoral resolution (163 sectors) by the integration of data from Eora26, FAOSTAT and previous studies. It implements the environmental impact categories climate change impacts, particulate-matter related health impacts, water stress and land-use related biodiversity loss into EXIOBASE3, Eora26 and the resolved MRIO database.<br> The folder includes:<br> Exiobase_resolved.m: MATLAB code to resolve the EXIOBASE3 database according to the procedure described in Section 2.3–2.6 of the manuscript.<br> Folder ‘Files’: Includes all files required to run ‘Exiobase_resolved.m’, except for the MRIO tables from EXIOBASE3 and Eora26, which need to be downloaded from the EXIOBASE3 and Eora26 homepage and stored in the provided folder “Files/Exiobase/” and “Files/Eora/bp/”, respectively. These data can be downloaded from:<br> https://www.exiobase.eu/index.php/data-download/exiobase3mon<br> https://worldmrio.com/eora26/</p> <p>The folders "Year_RMRIO" provide the R-MRIO database for each year from 2006–2015. Each folder contains the following files (<em>*.mat-files</em>):<br> A_RMRIO: the coefficient matrix<br> Y_RMRIO: the final demand matrix<br> Ext_RMRIO and Ext_hh_RMRIO: the satellite matrix of the economy and the final demand<br> TotalOut_RMRIO: the total output vector<br> The labels of the matrices are provided by the separate folder "Labels_RMRIO "</p> <p>A script for importing and indexing the RMRIO database files in Python as Pandas DataFrames can be found here:</p> <p><a href="https://github.com/jbnsn/RMRIO-database-py-import">https://github.com/jbnsn/RMRIO-database-py-import</a></p>
Data for "The origin, supply chain, and deforestation risk of Brazil's beef exports"
<p>The processed data set supporting the publication "The origin, supply chain, and deforestation risk of Brazil's beef exports".</p> <p>These data can be visualised with the code at: https://github.com/ErasmuszuE/zuErmgassen_2020_PNAS</p>
Deep Learning-Enhanced GNSS Signal Integrity for Critical Supply Chain Logistics (Data availability statement)
Open the record for dataset details and reuse information.
GLOBAL SUPPLY CHAIN RESILIENCE: IMPLICATIONS FOR US TRADE POLICY AND NATIONAL SECURITY
Open the record for dataset details and reuse information.
Supplementary material 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
ChickenEgg-SCNM
Figure 3 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 3 Choropleth map for production of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model.
Figure 2 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 2 Choropleth map for total consumption of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model .
Figure 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 1 Simplified schematic representation of the food supply chain network in Germany illustrating actors (indicated by boxes) and transport processes (indicated by arrows) according to the dynamic freight flow model from Balster and Friedrich (2019). *Regarding Warehouse and Store: the respective 28 brands are implemented as aggregated and as individual actors in the "egg supply chain network model".
Capacities of a large supply chain with 4 layers and 10 nods
<p>We consider a case study of general supply chain with four layers and ten nodes at each layer , including suppliers, manufacturing plants, warehouses and customers. Indeed, such a model represents a complex network in which products are shipped from suppliers to plants where they are processed then redirected to warehouses, and finally directed to the customers. The shared data is regarding:</p> <p>- sites capacities,</p> <p>-customers yearly demands,</p> <p>-costs of purchasing and shipping products to manufacturing sites,</p> <p>-costs of shipping products from manufacturing sites to warehouses,</p> <p>-costs of shipping products from warehouse to markets</p>
Dataset for "Decoding Web3: In-depth Analysis of the Third-Party Package Supply Chain"
Open the record for dataset details and reuse information.
Development of public dynamic spatio-temporal monitoring and analysis tool of supply chain vulnerability, resilience, and sustainability
<p>Supply chains play a pivotal role in driving economic growth and societal well-being, facilitating the efficient movement of goods from producers to consumers. However, the increasing frequency of disruptions caused by geopolitical events, pandemics, natural disasters, and shifts in commerce poses significant challenges to supply chain resilience. This draft update report discusses the development of a dynamic spatio-temporal monitoring and analysis tool to assess supply chain vulnerability, resilience, and sustainability. Leveraging news data, macroeconomic metrics, inbound cargo data (for sectors in California), and operational conditions of California's highways, the tool employs Natural Language Processing (NLP) and empirical regression analyses to identify emerging trends and extract valuable information about disruptions to inform decision-making. Key features of the tool include sentiment analysis of news articles, topic classification, visualization of geographic locations, and tracking of macroeconomic indicators. By integrating diverse and dynamic data sources (e.g., news articles) and using empirical and analytical techniques, the tool offers a comprehensive framework to enhance our understanding of supply chain vulnerabilities and resilience, ultimately contributing to more effective strategies for decision-making in supply chain management. The dynamic nature of this tool enables continuous monitoring and adaptation to evolving conditions, thereby enhancing the analysis of resilience and sustainability in global supply chains.</p>
Dataset for Evaluating geopolitical gas supply chain security in the EU: A literature-based index and a clustering analysis
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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)
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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.