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32 results for “Value chains”
Value chains under the framework of life cycle assessment indicators
<p>Tables included in the article "Monitoring the bioeconomy: value chains under the framework of life cycle assessment indicators"</p>
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Production and trade data on cocoa value chain in Ghana
<p>This dataset covers upstream, midstream and downstream behavioural patterns that were used to identify precursors of vulnerabilities in Ghana’s cocoa value chain. Data were obtained via focus group discussions and individual interviews with cocoa farmers in Ghana and extracted from annual reports of the global cocoa industry published by the International Cocoa Organisation. Behavioural patterns were established from transcripts and reports using NVivo as the computer-assisted qualitative data analysis software.</p>
Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access
<p><strong>Description</strong><br> <br> The datasets demonstrate the malware economy and the value chain published in our paper, <a href="https://doi.org/10.1145/3600160.3605047"><em>Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access</em></a>, at the 12th International Workshop on Cyber Crime (IWCC 2023), part of the ARES Conference, published by the International Conference Proceedings Series of the ACM ICPS.</p> <p>Using the well-documented scripts, it is straightforward to reproduce our findings. It takes an estimated 1 hour of human time and 3 hours of computing time to duplicate our key findings from MalwareInfectionSet; around one hour with VictimAccessSet; and minutes to replicate the price calculations using AccountAccessSet. See the included README.md files and Python scripts.</p> <p>We choose to represent each victim by a single JavaScript Object Notation (JSON) data file. Data sources provide sets of victim JSON data files from which we've extracted the essential information and omitted Personally Identifiable Information (PII). We collected, curated, and modelled three datasets, which we publish under the <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>1. MalwareInfectionSet</strong><br> We discover (and, to the best of our knowledge, document scientifically for the first time) that malware networks appear to dump their data collections online. We collected these infostealer malware logs available for free. We utilise 245 malware log dumps from 2019 and 2020 originating from 14 malware networks. The dataset contains 1.8 million victim files, with a dataset size of 15 GB.</p> <p><strong>2. VictimAccessSet</strong><br> We demonstrate how Infostealer malware networks sell access to infected victims. Genesis Market focuses on user-friendliness and continuous supply of compromised data. Marketplace listings include everything necessary to gain access to the victim's online accounts, including passwords and usernames, but also detailed collection of information which provides a clone of the victim's browser session. Indeed, Genesis Market simplifies the import of compromised victim authentication data into a web browser session. We measure the prices on Genesis Market and how compromised device prices are determined. We crawled the website between April 2019 and May 2022, collecting the web pages offering the resources for sale. The dataset contains 0.5 million victim files, with a dataset size of 3.5 GB.</p> <p><strong>3. AccountAccessSet</strong><br> The Database marketplace operates inside the anonymous Tor network. Vendors offer their goods for sale, and customers can purchase them with Bitcoins. The marketplace sells online accounts, such as PayPal and Spotify, as well as private datasets, such as driver's licence photographs and tax forms. We then collect data from Database Market, where vendors sell online credentials, and investigate similarly. To build our dataset, we crawled the website between November 2021 and June 2022, collecting the web pages offering the credentials for sale. The dataset contains 33,896 victim files, with a dataset size of 400 MB.</p> <p><strong>Credits Authors</strong></p> <ul> <li>Billy Bob Brumley (Tampere University, Tampere, Finland)</li> <li>Juha Nurmi (Tampere University, Tampere, Finland)</li> <li>Mikko Niemelä (Cyber Intelligence House, Singapore)</li> </ul> <p><strong>Funding</strong></p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under project numbers 804476 (SCARE) and 952622 (SPIRS).<br> <br> <strong>Alternative links to download:</strong> <a href="https://mega.nz/folder/aJwVyIYJ#9SWh-Z3-TpPfjHZeFxbeew">AccountAccessSet</a>, <a href="https://mega.nz/folder/iUQ3RaKB#48ZkXnFYSR0qXLcbkrZLqw">MalwareInfectionSet</a>, and <a href="https://mega.nz/folder/aNYCFCrK#pbDkJL-PNWjn1ABXbtdR4w">VictimAccessSet</a>.</p>
Data on Companies of the Hungarian Battery Value Chain
<p>This dataset compiles data on companies of the Hungarian electric vehicle battery value chain. The data is accurate as of November 2024. </p> <p>The following data is presented:</p> <ul> <li>Position in the value chain;</li> <li>Company name;</li> <li>Data on the investment and investment subsidies provided by the Hungarian Government;</li> <li>Company financials (2023),</li> <li>Employee numbers, information on agency workers, average wages.</li> </ul>
Indicator values for the many-body localization of the Heisenberg spin chain at various energies, system sizes and disorder magnitude
<p>This data set accompanies the preprint <em>Scalable approach to many-body localization via quantum data </em>(arXiv: <a href="https://arxiv.org/abs/2202.08853">2202.08853</a>) and its code, available at <a href="https://github.com/GreschAl/MBLlearning">GitHub</a>.</p> <p>It consists of the numerically obtained indicator values for the many-body localization for the Heisenberg model (see preprint for details and background) for various values of the energy density <span class="math-tex">\(\epsilon = 0.05, 0.1, \dots, 0.9, 0.95\)</span> and for chain lengths <span class="math-tex">\(L = 10, 12, 14\)</span>. For each tuple <span class="math-tex">\((\epsilon,L)\)</span>, there exist two files, representing the training and the test set, respectively. Each such file contains various values of the disorder parameter <span class="math-tex">\(h = 0.5, 1, \dots, 14.5, 15\)</span> with <span class="math-tex">\(N = 1000\ (100)\)</span> sampled realizations of the disorder vector for each <span class="math-tex">\(h\)</span> for the training (test) set, followed by the three calculated values of the three indicators.</p> <p>The data is automatically processable by the code provided in the GitHub repository.</p>
Innovative agrifood value chains
<p>This data-set has been produced in the frame of H2020 project CO-FRESH, in Deliverable 1.2 "List of value chains and a data-set on the characteristics of the identified sustainable, innovative and competitive agrifood value chains"</p> <p>Grand societal challenges, such as climate change, environmental degradation, food security, immigration, and digital transformation, urge value chains actors from farm to fork to interact for improving the system. In this context, innovation, that has long been considered as a factor of growth and competitiveness, becomes an increasingly important factor to create value while addressing sustainability challenges (both environmental, social and economic).</p> <p><strong>The CO-FRESH project</strong> aims to enhance collaboration for sustainability-oriented innovation in the agri-food sector, by proposing interventions for re-designing fruit and vegetables (F&V) value chains across Europe. To reach these objectives, Work Package 1 takes the lead in <em>Identification, Analysis and Design of Innovative and Sustainable Agri-food Value Chains</em>. The current report is the outcome of <em>Task 1.2. Identify innovative agri-food value chains and collect empirical data. </em></p> <p><strong>Deliverable 1.2.</strong> presents a list of value chains and a data-set on the characteristics of the identified sustainable, innovative and competitive agrifood value chains. This list is the results of a participatory inventory led by WUR and involving all the CO-FRESH consortium partners.</p> <p>This report contains three core elements. <strong>First,</strong> it presents the methodology that was used for the inventory creation, drawing on the concepts and selection criteria developed in <em>D1.1.Review state of the art</em>. <strong>Second,</strong> the report provides the list of the selected 100+ value chains and their general characteristics. <strong>Third,</strong> the report shows the different types of sustainability-oriented innovations implemented in these value chains, as well as the various modes of collaboration used to design and implement the innovations.</p> <p>The inventory has been presented to the CO-FRESH consortium during a workshop held 7 October 2021 (CO-FRESH Milestone 2). The inventory will be used in the next step of the project for further data collection and analysis (Task 1.3). For the next milestone, a survey will be addressed to a representative of each of these 100+ value chains in order to gain better understanding of the drivers of collaboration and to assess the impact of the sustainability-oriented innovation implemented. Second, the inventory will be used to select and investigate a portfolio of business models in the Task 1.4. Based on this inventory and the following analysis in Task 1.3 and Task 1.4, recommendations will be developed for interventions to redesign the value chains of the CO-FRESH pilot cases.</p>
Mapping the existing food systems, value chains and markets for agroecological products
<table> <tbody> <tr> <td>The food systems and markets of our focal farming systems are mapped as well as communities across the value chain, including operations from production over to food disposal after consumption, all along with the contribution of these operations to socio-economic and environmental outcomes. The datasets are the results of survey/interviews on the above topic in the CANALLS project ALLs.<span> </span></td> </tr> </tbody> </table>
Value chain around Ecuadorian cocoa based on products and their derivatives.
<p><strong>Map all the actors in the supply chain of the Ecuadorian cocoa sector.</strong></p>
Classification and structural analysis of value chain contracts for biodiversity conservation in the European Union
<p><span>The data provided by this dataset are the raw data published in the paper "<strong>Classification and structural analysis of value chain contracts for biodiversity conservation in the European Union</strong>" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.sftr.2024.100372" target="_blank" rel="noopener"><span><span>https://doi.org/10.1016/j.sftr.2024.100372</span></span></a>). </span></p>
Dataset for small pelagics value chain in Mauritania
<p>Datasets for the study of the value chain of small pelagic in Mauritania. Includes data on reported landings and catches, trade flow, imports and exports.<br> </p>
Innovafrica project data on agricultural food value chains
<p>A dataset was generated using the baseline survey data of the project entitled "Innovations in Technology, Institutional and Extension Approaches towards Sustainable Agriculture and enhanced Food and Nutrition Security in Africa (Acronym - Innovafrica)", which involved 16 istitutions from Europe and Africa and was implemented in six countries namely Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania. It includes the following information: smallholders' socio-demographic and economic characteristics; improved agriculture practices and seed systems adopted; climate change related aspects; membership in agricultural associations, and access to subsidies, agricultural inputs and credit. </p>
Dataset on production, processing and trading activities in Ghana's cocoa value chain
<p>This covers data on production, processing and trading activities in Ghana's cocoa value chain that were used to populate a stock-and flow diagram representing the baseline model of the cocoa value chain. Data are retrieved from different secondary sources, collated in excel sheets</p>
Data - Sow what you sell: strategies for integrating organic breeding and seed production into value chain partnerships
<p>Online survey targeting organic farmers on organic seed use; 25 questions in the survey. The survey was conducted between November 2018 and June 2019 and distributed through the networks of partners involved in the Horizon2020 project LIVESEED, including 23 breeding & research institutes, seven breeding companies, eight seed companies, and 11 organic associations. 752 complete entries by farmers from 20 countries from Central, Northern, Southern and Eastern Europe could be used from the 1,475 total accesses to the survey.</p>
New1_Korespondensi Propagation of Economic Shocks from the United States, China, The European Union, and Japan to Selected Asian Economies: Does the Global Value Chain Matters?
<p><strong>ABSTRACT</strong></p> <p>A panel vector autoregression (VAR) model is employed to estimate whether growth shocks from the United States (US), China, Japan, and the European Union (EU) can be transferred to selected Asian countries. We examine 1) the effect of shocks through five channels: international trade, monetary policy, finance, global uncertainty, and oil prices; 2) whether a country’s deeper integration with the global value chain (GVC) enhances or decreases the effect of growth shocks from major economies more intensively than trade openness. We found evidence of the shock transfer from major economies to Asia through the five channels. The impact differs across countries depending on their participation in GVC; for example, the impact is high in Indonesia and low in South Korea. Moreover, Asian countries are more exposed to trade shocks through China’s trade channel than other major economies. Zooming in on the channels’ impacts, global uncertainty affects countries’ growth (e.g., Indonesia) more significantly than other channels (i.e., GVC); and Asian countries respond positively to oil prices in the short run but negatively in the long run.</p> <p><strong>Keywords</strong>: Foreign spillovers, global uncertainty, oil prices, global value chain, monetary policy, trade openness, globalization</p> <p><strong>JEL Classification: </strong>F44, F62, O53, E43</p>
BIO4AFRICA_Report on novel bio-based value chains and markets analysis_D5.1_31/05/2022_v1.0
<p>The BIO4Africa project will contribute to food and nutritional security and combat poverty in rural Africa through inclusive and sustainable development.</p> <p>This dataset includes information on the analysis of the prospective markets in which the outputs of the BIO4AFRICA project are reformed via the pilot cases of the project in Uganda, Ghana, Cote D’ Ivoire, and Senegal.</p>
D4.1 - Circular supply/value chains development report - Main variables, initial values, and their units of measure.
<p>This data set was used to develop simulation models of the demonstrators' supply chains and what-if analysis to explore the aspect of centralized and decentralized supply chains scenarios while keeping the aspect of economic and environmental performance in focus.</p>
Innovafrica project data on agricultural food value chains
Open the record for dataset details and reuse information.
Appendix from: Understanding challenges and strengths in the post-dairy farm surplus calf value chain: An interview study
Open the record for dataset details and reuse information.
Data for a scoping study on the plastic value chain in India
<p>This is a dataset with the bibliographic information on the literature used for a scoping study on the plastic value chain in India.</p>
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International Brain Laboratory public data
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