Blockchain data dashboard: On-Chain Analysis of Tokenized U.S. Treasury Products

On-Chain Metrics.xlsx contains a description of the on-chain metrics. Merged_df.xlsx is the main data source containing the BTC prices, the on-chain metrics and the sentiment scores. btc_twets_new.csv and training.1600000.processed.noemoticon.csv are the data sources for calculating the sentiment scores. Sentiment_Analysis.py contains the code to calculate the sentiment scores. The scores are in Merged_df.xlsx BTC_Prediction.py contains the implementation of the main approach described in the paper, especially in Fig. 11.

Blockchain data dashboard: RugCheck On-Chain Analysis

Blockchain data dashboard: Onchain Transaction Fees - A Crosschain analysis

This dataset contains web3-related on-chain and off-chain data, which can be used to build quantitative models.

Daily cryptocurrency data (transaction count, on-chain transaction volume, value of created coins, price, market cap, and exchange volume) in CSV format. The data sample stretches back to December 2013. Daily on-chain transaction volume is calculated as the sum of all transaction outputs belonging to the blocks mined on the given day. “Change” outputs are not included. Transaction count figure doesn’t include coinbase transactions. Zcash figures for on-chain volume and transaction count reflect data collected for transparent transactions only. In the last month, 10.5% (11/18/17) of ZEC transactions were shielded, and these are excluded from the analysis due to their private nature. Thus transaction volume figures in reality are higher than the estimate presented here, and NVT and exchange to transaction value lower. Data on shielded and transparent transactions can be found here and here. Decred data doesn’t include tickets and voting transactions. Monero transaction volume is impossible to calculate due to RingCT which hides transaction amounts.

Daily cryptocurrency data (transaction count, on-chain transaction volume, value of created coins, price, market cap, and exchange volume) in CSV format. The data sample stretches back to December 2013. Daily on-chain transaction volume is calculated as the sum of all transaction outputs belonging to the blocks mined on the given day. “Change” outputs are not included. Transaction count figure doesn’t include coinbase transactions. Zcash figures for on-chain volume and transaction count reflect data collected for transparent transactions only. In the last month, 10.5% (11/18/17) of ZEC transactions were shielded, and these are excluded from the analysis due to their private nature. Thus transaction volume figures in reality are higher than the estimate presented here, and NVT and exchange to transaction value lower. Data on shielded and transparent transactions can be found here and here. Decred data doesn’t include tickets and voting transactions. Monero transaction volume is impossible to calculate due to RingCT which hides transaction amounts.

Different from the Ethereum On-chain Data, the Ethereum Partial Transaction Datasets are three relatively small Ethereum datasets (namely EthereumG1, EthereumG2, EthereumG3) for easier analysis. The transaction datasets are modeled as complex networks, which can be used in graph analysis such as link prediction.

In the constructed network, a node represents an Ethereum account and a link (i.e. edge) represents an Ethereum transfer transaction.

In our work, we conduct temporal link prediction with these three datasets. We use the existing links in the past (with smaller timestamps) as the training data to predict the occurrences of links in the future (with larger timestamps). You can learn more details in the Related Research.

The data details of EthereumG1 are described below. The file structure of EthereumG2 and EthereumG3 are similar to EthereumG1. You can know more information in the README file.

For more details about blockchain dataset, please click here.

The Coffee Sales Data dataset provides valuable insights into the performance of a coffee chain across various locations.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13090611%2F0f25997ba9b63b33c901471cb874aa83%2Fcisco_blog_canada_coffee.png?generation=1696189849175111&alt=media" alt=""> Key Attributes:

1.Area Code: A unique identifier for different geographical areas or regions where the coffee chain operates.

2.COGS (Cost of Goods Sold): The total cost incurred by the coffee chain in producing or purchasing the products it sells.

3.Difference between Actual and Target Profit: This attribute indicates how well the company performed in terms of profit compared to its target. It reflects the financial performance against predefined goals.

4.Date: The date of sales transactions, which allows for time-based analysis of sales trends and patterns.

5.Inventory Margin: The difference between the cost of maintaining inventory and the revenue generated from selling those inventory items.

6.Margin: The profit margin, which is the percentage of profit earned from sales. It's a critical financial metric.

7.Market Size: Information about the size of the market in each area, helping to understand the potential customer base and market dynamics.

8.Profit: financial gain achieved by the company after deducting the cost of goods sold (COGS) and other expenses from the revenue generated through sales.

9.Sales: represent the revenue generated from the coffee chain's products, reflecting its financial performance and customer demand.

The global chain guide market is experiencing robust growth, driven by increasing automation across various industries and a rising demand for efficient material handling solutions. Our analysis projects a market size of $1.5 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033. This growth is fueled by several key factors: the expanding pipeline equipment and packaging machinery sectors, which heavily rely on chain guides for precise and reliable operation; the burgeoning automation industry, which necessitates robust and durable chain guides capable of withstanding high-speed and continuous operation; and the increasing adoption of advanced materials in chain guide manufacturing, leading to improved performance and longevity. Straight-line chain guides currently dominate the market due to their simplicity and wide applicability, although arc-shaped guides are gaining traction in specialized applications requiring more complex movement control. The market is geographically diverse, with North America and Europe currently holding significant market shares. However, the Asia-Pacific region, particularly China and India, is expected to witness the fastest growth in the forecast period due to rapid industrialization and increasing investments in infrastructure projects. While the market faces certain restraints such as material cost fluctuations and competition from alternative technologies, the overall positive outlook is underpinned by sustained demand from key industries and ongoing technological advancements leading to more efficient and reliable chain guide solutions. Key players in the market are continuously innovating to meet the diverse requirements of various applications, fostering a competitive landscape marked by strategic partnerships and product diversification. This includes developing customized solutions for niche applications and incorporating advanced materials and designs to enhance performance and durability.

In this study, we have undertaken a robust analysis of the global supply chain and manufacturing costs for components of Organic Rankine Cycle (ORC) Turboexpander and steam turbines used in geothermal power plants. We collected a range of market data influencing manufacturing from various data sources and determined the main international manufacturers in the industry. The data includes the manufacturing cost model to identify requirements for equipment, facilities, raw materials, and labor. We analyzed three different cases; 1) 1 MW geothermal ORC Turboexpander 2) 5 MW ORC Turboexpander 3) 20 MW geothermal Steam Turbine

Blockchain data dashboard: Uniswap Governance: Voting Behavior Analysis Across Phases (onchain voting & offchain check on Snapshot)

Daily cryptocurrency data (transaction count, on-chain transaction volume, value of created coins, price, market cap, and exchange volume) in CSV format. The data sample stretches back to December 2013. Daily on-chain transaction volume is calculated as the sum of all transaction outputs belonging to the blocks mined on the given day. “Change” outputs are not included. Transaction count figure doesn’t include coinbase transactions. Zcash figures for on-chain volume and transaction count reflect data collected for transparent transactions only. In the last month, 10.5% (11/18/17) of ZEC transactions were shielded, and these are excluded from the analysis due to their private nature. Thus transaction volume figures in reality are higher than the estimate presented here, and NVT and exchange to transaction value lower. Data on shielded and transparent transactions can be found here and here. Decred data doesn’t include tickets and voting transactions. Monero transaction volume is impossible to calculate due to RingCT which hides transaction amounts.

The Coffee Value Chain Analysis Market report provides a comprehensive overview of the industry's key segments and dynamics. Get five years of historical data alongside five-year market forecasts.

Qualitative Datasets of Gendered Aquaculture Value Chain Analysis in Northwestern Bangladesh. This data presents a value chain study with an integrated gender lens of the aquaculture sector in Rajshahi and Rangpur in northwestern Bangladesh. The study forms part of the contextual knowledge foundation for the IDEA project, which works in all 16 districts of the Rangpur and Rajshahi divisions. Its ultimate goal is to reach 1 million households for its aquaculture production outcomes and 2 million households for its nutrition outcomes. The aim of the value chain study was to generate a knowledge base for designing project interventions. These focus specifically on inclusive aquaculture value chains that are both more productive and contribute to poverty reduction, and in which women and youths can be equitably included and benefit in safe and dignified manners. A market study and an empowerment study (using WEAI) were also conducted but never analyzed for the report.

The global Wallet On Chain market, encompassing both online and offline retail channels, is experiencing robust growth, driven by the increasing demand for luxury accessories and the rising popularity of designer brands. The market's value, estimated at $1.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching an estimated $2.8 billion by 2033. This growth is fueled by several key factors. Firstly, the rising disposable incomes in developing economies, particularly in Asia-Pacific, are boosting demand for luxury goods, including designer wallets. Secondly, the prevalence of social media and influencer marketing significantly impacts consumer purchasing decisions, driving the desire for high-end, recognizable brands. Thirdly, the ongoing preference for practical yet stylish accessories contributes to the consistent appeal of wallets, with the “on-chain” feature adding a unique element of modern convenience and brand association for many consumers. The market segmentation reveals that online retailers are experiencing faster growth compared to traditional shopping malls, indicating a shift in consumer purchasing habits towards e-commerce platforms offering wider selections and convenient access to luxury goods. The "Normal Size" wallet segment currently dominates the market but is anticipated to see strong competition from the growing "Mini Size" segment, appealing to a younger, more fashion-conscious demographic. Key players like Chanel, LVMH, and Gucci, alongside emerging luxury brands, are continuously innovating in terms of design, materials, and functionalities to maintain a competitive edge. Geographic analysis suggests that North America and Europe are currently the largest markets, owing to established consumer preference for luxury goods and higher disposable incomes. However, the Asia-Pacific region, especially China and India, is poised for substantial growth in the coming years due to rapid economic expansion and a burgeoning middle class exhibiting increasing interest in luxury brands. This expansion presents significant opportunities for existing market players and emerging brands to tap into this region's immense potential. Competitive pressures remain high, with brands engaging in strategies of product diversification, targeted marketing campaigns, and strategic partnerships to gain market share and maintain brand prominence. Despite some potential restraints like economic downturns and changing consumer preferences, the long-term outlook for the Wallet On Chain market remains positive, reflecting continued growth and evolution within the luxury accessories segment.

Daily cryptocurrency data (transaction count, on-chain transaction volume, value of created coins, price, market cap, and exchange volume) in CSV format. The data sample stretches back to December 2013. Daily on-chain transaction volume is calculated as the sum of all transaction outputs belonging to the blocks mined on the given day. “Change” outputs are not included. Transaction count figure doesn’t include coinbase transactions. Zcash figures for on-chain volume and transaction count reflect data collected for transparent transactions only. In the last month, 10.5% (11/18/17) of ZEC transactions were shielded, and these are excluded from the analysis due to their private nature. Thus transaction volume figures in reality are higher than the estimate presented here, and NVT and exchange to transaction value lower. Data on shielded and transparent transactions can be found here and here. Decred data doesn’t include tickets and voting transactions. Monero transaction volume is impossible to calculate due to RingCT which hides transaction amounts.

AbstractContext: Static analyses are well-established to aid in understanding bugs or vulnerabilities during the development process or in large-scale studies. A low false positive rate is essential for the adaption in practice and for precise results of empirical studies. Unfortunately, static analyses tend to report where a vulnerability manifests rather than the fix location. This can cause presumed false positives or imprecise results. Method: To address this problem, we designed an adaption of an existing static analysis algorithm that can distinguish between a manifestation and fix location and reports error chains. Each error chain presents the dependency between the fix location with at least one manifestation location. We used our tool for a case study of 471 GitHub repositories and conducted an expert interview to investigate usability implications of the change. Further, we benchmarked both analysis versions to compare the runtime impact. Result: We found that 50% of the projects with a report had at least one error chain. During our expert interview, all participants required fewer executions of the static analysis if they used our adapted version. Our performance benchmark demonstrated that our improvement caused only a minimal runtime overhead of less than 4%. Conclusion: Our results indicate that error chains occur frequently in real-world projects and ignoring them can lead to imprecise evaluation results. The performance benchmark indicates that our tool is a feasible and efficient solution for detecting error-chains in real-world projects. Further, our results indicate that the usability of static analyses benefits from supporting error chains.DataThis artefact contains additional information for our evaluation.Folder code_study (RQ1)The folder JavaCryptographicAchitecture_BET contains the CrySL rules for the JCA that we used for the code study.The file SUBS.jar is the version of SUBS that we used for our code study.The file README.md describes how to use the Docker image for scanning the code with CogniCryptSUBS.The file CREDENTIALS.txt is a dummy file for the GitHub tokens required for the analysis.The file run_cc_subs.sh is a helper script to execute CogniCryptSUBS and used by the Docker container.The file Dockerfile is the Docker image used for the code study.Folder performance_analysis (RQ2)The folder 1_run_performance_analysis/JavaCryptographicArchitecture contains the CrySL rules for the JCA that we used for the benchmark that do not support Backward Error Tracking (BET).The folder 1_run_performance_analysis/JavaCryptographicArchitecture_BET contains the CrySL rules for the JCA that we used for the benchmark that support BET.The different 1_run_performance_analysis/*.jar files are the different evaluated versions of CogniCrypt and CogniCrypt_SUBS.The file 1_run_performance_analysis/Dockerfile is the Docker image used to execute the benchmark.The file 1_run_performance_analysis/run_performance_analysis.sh includes the commands to execute the different tools on our benchmark and the different target folders for the different configurations/groups of the benchmark.The folder 2_parse_results/data contains the results obtained for the five different configurations for the different tools.The file 2_parse_results/generate_graphics.py generates the graphics used in the paper.The folder results contains the graphics, such as Fig. 4, for the different configurations.Folder expert_interview (RQ3)The code examples for task 1 and 2 are in the folder expertinterview_examplecode1 and expert interview_examplecode2, respectively.The invitation and questions are in the file expert interview.md.An overview of the obtained results are in the file expert interview_results.csv.Further, we include the graphics for the runtime evaluation as pdf-files.ChangesVersion 2: Restructure the main folder to include one folder for each research question answered in the paper. Further, added data for the code study and more details for the performance benchmark.Version 1: Add details for the expert interview and pdf-files for the performance benchmark. All files were added to the main folder.

The Grapes Value Chain Analysis Market Report Offers An Overview, Price Markups, Stakeholders, and Issues and Challenges of the Value Chain and Supply Chain of Grapes.

The global value chain analysis software market size was valued at USD 2.3 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 12.5% from 2023 to 2030. The market growth is primarily attributed to the increasing adoption of digital technologies, globalization and outsourcing, and the need to optimize supply chain operations. Moreover, factors such as the rising complexity of global supply chains, growing focus on sustainability, and increasing demand for real-time visibility into supply chain performance are further driving the market growth. The on-premises segment held a significant market share in 2022 and is expected to maintain its dominance throughout the forecast period. However, the cloud-based segment is projected to grow at a faster CAGR during the forecast period due to the increasing adoption of cloud computing services and the benefits it offers, such as scalability, cost-effectiveness, and ease of use. The manufacturing and retail sectors are the major end-users of value chain analysis software, and they are expected to continue to drive the market growth in the coming years. Additionally, the Asia Pacific region is expected to witness the highest growth rate during the forecast period due to the growing manufacturing and retail sectors in the region.