In March 2024 Bitcoin BTC reached a new all-time high with prices exceeding 73000 USD marking a milestone for the cryptocurrency market This surge was due to the approval of Bitcoin exchange-traded funds ETFs in the United States allowing investors to access Bitcoin without directly holding it This development increased Bitcoin’s credibility and brought fresh demand from institutional investors echoing previous price surges in 2021 when Tesla announced its 15 billion investment in Bitcoin and Coinbase was listed on the Nasdaq By the end of 2022 Bitcoin prices dropped sharply to 15000 USD following the collapse of cryptocurrency exchange FTX and its bankruptcy which caused a loss of confidence in the market By August 2024 Bitcoin rebounded to approximately 64178 USD but remained volatile due to inflation and interest rate hikes Unlike fiat currency like the US dollar Bitcoin’s supply is finite with 21 million coins as its maximum supply By September 2024 over 92 percent of Bitcoin had been mined Bitcoin’s value is tied to its scarcity and its mining process is regulated through halving events which cut the reward for mining every four years making it harder and more energy-intensive to mine The next halving event in 2024 will reduce the reward to 3125 BTC from its current 625 BTC The final Bitcoin is expected to be mined around 2140 The energy required to mine Bitcoin has led to criticisms about its environmental impact with estimates in 2021 suggesting that one Bitcoin transaction used as much energy as Argentina Bitcoin’s future price is difficult to predict due to the influence of large holders known as whales who own about 92 percent of all Bitcoin These whales can cause dramatic market swings by making large trades and many retail investors still dominate the market While institutional interest has grown it remains a small fraction compared to retail Bitcoin is vulnerable to external factors like regulatory changes and economic crises leading some to believe it is in a speculative bubble However others argue that Bitcoin is still in its early stages of adoption and will grow further as more institutions and governments recognize its potential as a hedge against inflation and a store of value 2024 has also seen the rise of Bitcoin Layer 2 technologies like the Lightning Network which improve scalability by enabling faster and cheaper transactions These innovations are crucial for Bitcoin’s wider adoption especially for day-to-day use and cross-border remittances At the same time central bank digital currencies CBDCs are gaining traction as several governments including China and the European Union have accelerated the development of their own state-controlled digital currencies while Bitcoin remains decentralized offering financial sovereignty for those who prefer independence from government control The rise of CBDCs is expected to increase interest in Bitcoin as a hedge against these centralized currencies Bitcoin’s journey in 2024 highlights its growing institutional acceptance alongside its inherent market volatility While the approval of Bitcoin ETFs has significantly boosted interest the market remains sensitive to events like exchange collapses and regulatory decisions With the limited supply of Bitcoin and improvements in its transaction efficiency it is expected to remain a key player in the financial world for years to come Whether Bitcoin is currently in a speculative bubble or on a sustainable path to greater adoption will ultimately be revealed over time.

Bitcoin's blockchain size was close to reaching 5450 gigabytes in 2024, as the database saw exponential growth by nearly one gigabyte every few days. The Bitcoin blockchain contains a continuously growing and tamper-evident list of all Bitcoin transactions and records since its initial release in January 2009. Bitcoin has a set limit of 21 million coins, the last of which will be mined around 2140, according to a forecast made in 2017. Bitcoin mining: A somewhat uncharted world Despite interest in the topic, there are few accurate figures on how big Bitcoin mining is on a country-by-country basis. Bitcoin's design philosophy is at the heart of this. Created out of protest against governments and central banks, Bitcoin's blockchain effectively hides both the country of origin and the destination country within a (mining) transaction. Research involving IP addresses placed the United States as the world's most Bitcoin mining country in 2022 - but the source admits IP addresses can easily be manipulated using VPN. Note that mining figures are different from figures on Bitcoin trading: Africa and Latin America were more interested in buying and selling BTC than some of the world's developed economies. Bitcoin developments Bitcoin's trade volume slowed in the second quarter of 2023, after hitting a noticeable growth at the beginning of the year. The coin outperformed most of the market. Some attribute this to the announcement in June 203 that BlackRock filed for a Bitcoin ETF. This iShares Bitcoin Trust was to use Coinbase Custody as its custodian. Regulators in the United States had not yet approved any applications for spot ETFs on Bitcoin.

This is the market data of Bitcoin in terms of price and volume from August 2015 to August 2021. The time interval of sampling is selected as four-hour, that is to say, we choose every kind of price and volume every of four-hour as the original data. The original market data of Bitcoin are obtained from Poloniex, one of the most active crypto-asset exchanges. Download link on XBlock: http://xblock.pro/#/dataset/5

We present a high-frequency dataset of algorithmic trading. Given that, the dataset contains different time intervals depending on the timestamp when an arbitrage opportunity occurred. Our dataset has 9,799,130 tick-level records of the Bitcoin-to-Euro exchange rate starting from 2019-01-01 00:00:31 until 2020-03-30 23:59:48. Data covered information about different cryptocurrency pairs from 18 cryptocurrency exchanges. These pairs contained information about exchanges in which it was possible to buy and sell simultaneously. Each row presented the amount of arbitrage that it was possible to earn if a transaction would have been executed. The dataset contains information about the amount of arbitrage that could be earned after executing a transaction in given cryptocurrency exchanges, the quantity which had to be bought to earn arbitrage, the best sell, and the best buy prices, the balance of fiat currency in “Exchange 1” and the balance of cryptocurrency in “Exchange 2”. If there was enough fiat currency in “Exchange 1” and enough cryptocurrency in “Exchange 2” it means that the transaction was successfully executed and given arbitrage amount was earned. This information could be used by investors to discover potential earning capabilities, and create effective arbitrage trading strategies. Moreover, this dataset could serve academics for deeper analysis of efficiency and liquidity questions as well as it could be used to spot and evaluate risks in the market, identify patterns in the market. Short description of the dataset: ID - Unique ID arb_timestamp - timestamp of arbitrage opportunity arb_exch1 - presents exchanges where one was able to successfully buy Bitcoin arb_exch2 - presents exchanges where one was able to successfully sell Bitcoin arb_ticker - BTCEUR exchange rate arb_prc - percentage earned compared to the invested amount arb_amount - the amount of arbitrage that would be earned if a transaction had been executed arb_quantity - Bitcoin quantity that needed to be bought in order to execute a transaction and to earn arbitrage best_sell_price - best price at which it was possible to sell Bitcoin in "Exchange 2" best_buy_price - best price at which it was possible to buy Bitcoin in "Exchange 1" balance_fiat - the amount of Euros available in “Exchange 1” balance_crypto - the amount of Bitcoin available in “Exchange 2”

Context:

This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

Introduction

This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

The Data

For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

1. **timestamp** - A timestamp for the minute covered by the row.
2. **Asset_ID** - An ID code for the cryptoasset.
3. **Count** - The number of trades that took place this minute.
4. **Open** - The USD price at the beginning of the minute.
5. **High** - The highest USD price during the minute.
6. **Low** - The lowest USD price during the minute.
7. **Close** - The USD price at the end of the minute.
8. **Volume** - The number of cryptoasset u units traded during the minute.
9. **VWAP** - The volume-weighted average price for the minute.
10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
12. **Asset_Name** - Human readable Asset name.

Indexing

The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

Usage Example

The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

Baseline Example Notebooks:

• Neural Network Starter
• LightGBM Starter
• Catboost Starter
• XGBoost Starter
• TabNet Starter
• Reinforcement Learning (PPO) Starter

These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

Loose-ends:

This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

• VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
• Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
• Filtering: No filtration of 0 volume data is taken place.

Example Visualisations

Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

License

This data is being collected automatically from the crypto exchange Binance.

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.

Context

Bitcoin and other cryptocurrencies have captured the imagination of technologists, financiers, and economists. Digital currencies are only one application of the underlying blockchain technology. Like its predecessor, Bitcoin, the Ethereum blockchain can be described as an immutable distributed ledger. However, creator Vitalik Buterin also extended the set of capabilities by including a virtual machine that can execute arbitrary code stored on the blockchain as smart contracts.

Both Bitcoin and Ethereum are essentially OLTP databases, and provide little in the way of OLAP (analytics) functionality. However the Ethereum dataset is notably distinct from the Bitcoin dataset:

• The Ethereum blockchain has as its primary unit of value Ether, while the Bitcoin blockchain has Bitcoin. However, the majority of value transfer on the Ethereum blockchain is composed of so-called tokens. Tokens are created and managed by smart contracts.

• Ether value transfers are precise and direct, resembling accounting ledger debits and credits. This is in contrast to the Bitcoin value transfer mechanism, for which it can be difficult to determine the balance of a given wallet address.

• Addresses can be not only wallets that hold balances, but can also contain smart contract bytecode that allows the programmatic creation of agreements and automatic triggering of their execution. An aggregate of coordinated smart contracts could be used to build a decentralized autonomous organization.

Content

The Ethereum blockchain data are now available for exploration with BigQuery. All historical data are in the ethereum_blockchain dataset, which updates daily.

Our hope is that by making the data on public blockchain systems more readily available it promotes technological innovation and increases societal benefits.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.crypto_ethereum.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Cover photo by Thought Catalog on Unsplash

Inspiration

• What are the most popularly exchanged digital tokens, represented by ERC-721 and ERC-20 smart contracts?
• Compare transaction volume and transaction networks over time
• Compare transaction volume to historical prices by joining with other available data sources like Bitcoin Historical Data

This is a submission for Challenge #24 by Desights User

Click here for Challenge Details Note: This submission is in REVIEW state and is only accessible by Challenge Reviewers. So you might get errors when you try to download this asset directly from Ocean Market.

Submission Description

The cryptocurrency is not just a new form of value store and exchange, it is a revolution of its own. Beginning with its use to provide peer-to-peer payment network (or digital money) like Bitcoin, today’s cryptocurrency, or crypto for short, have evolved way beyond its humble start. Underlying the crypto world, there lies amazing technology called Blockchain. In simple term, blockchain is a decentralized and shared digital ledger that records transactions transparently and immutably across nodes in the network. Today’s Crypto community has slowly turned into industry of its own introducing a whole spectrum of enigmatic pattern, trends, and economic framework. In this report we will explore the trend, correlations, and dynamics related to 20 selected Crypto projects to derived insights and build models that predict the future of crypto. Key Findings: Our exploratory data analysis (EDA) underlines the span and general pattern of the Google Trend and Price related data. The data being analyzed span from the earliest entry on 2014-09-17 up to the latest on 2024-04-07. Time series decomposition was performed to extract trend, seasonal cycle, and residuals that made up the Google Interest Trend data. Analysis on the time-series decomposition help us distinguish cluster (a) with projects on the rise such as Solana, SingularityNet, Fetch.ai, and Ocean Protocol; and cluster (b) containing old project such as Dogecoin, Litecoin Filecoin, Tezos that are facing stagnant/downfall trend. Based on the Google Trends’s Correlation across projects we characterize Highly correlated projects cluster with correlation of about >0.8, and up to 0.92 with Bitcoin-Ethereum-Chainlink-Litecoin-Monero as the prominent group members. By introducing additional Google Trend data to understand Crypto Narrative, we worked toward building interpretable Event/Entity driving the market sentiment to explain our decomposed Time-series data. Based on Lag Characteristics in Correlation of Google Trend and Price/Trade Volume we highlight the tendency for the correlation to accumulate at longer lag time. Using NeuralProphet Framework we build forecasting models for Google Trend and Token Price for all 20 projects investigated here. We deployed these models to predict Trend and Price for all 20 projects for the following 52 weeks (up until April 2025). The developed models performed extraordinarily well with the R^2 value for most fall between the range of 0.75-0.88, while the highest goes up to 0.919. We highlight the correlation between Bitcoin, Ethereum, Ocean, with the rest of other projects. Ocean and Bitcoin, also Ethereum and Solana are the most correlated, both with correlation value of 0.89. The Kucoin’s KCS token is the least correlated with both Ocean and Bitcoin (0.31), while with Ethereum, Filecoin have the least correlation (0.41).

Conclusion This investigative study presents a thorough data analysis and exploration of correlations, time-lag characteristics, and time-series decomposition concerning Google Trends and token prices for 20 selected crypto/blockchain projects. By decomposing the time-series data, we have identified several clusters of crypto projects that is moving up in popularity such as Fetch.ai, SingularityNet, Solana, Ocean and some others that are stuck or in downfall trend, such as Dogecoin and Litecoin. Our analysis also includes a detailed exploration of various factors that contribute to understanding the data better, such as the incorporation of event-driven trends that explain outlier spikes in the residual data from our decomposed time-series.

In addition to our in-depth analysis, we build strong mini-library of forecasting models for predicting the Google Trend as well as price for the upcoming year with R^2 score that goes as high as 0.88 for most cases. Moreover, in order to demonstrate the utility of our exploratory data analysis tools and pipeline in full we also include all the results and analysis output produced in this work.

Looking ahead, we plan to expand our developed forecasting models and the presented data into a "CryptoForecast MiniApp." This application, based on the Streamlit package, will be hosted on a decentralized cloud (Akash) and connected to the Ocean marketplace and Predictoor, enhancing accessibility and utility for users interested in real-time data for Google Trends and Crypto Token Price forecasts.

Bitcoin's transaction volume was at its highest in December 2023, when the network processed over 724,000 coins on the same day. Bitcoin generally has a higher transaction activity than other cryptocurrencies, except Ethereum. This cryptocurrency is often processed more than one million times per day. Note that the transaction volume here refers to transactions registered within the Bitcoin blockchain. It should not be confused with Bitcoin's 24-hour trade volume, a metric associated with crypto exchanges. The more Bitcoin transactions, the more it is used in B2C payments? A Bitcoin transaction recorded in the blockchain can be any transaction, including B2C but also P2P. While it is possible to see in the blockchain which address sent Bitcoin to whom, details on who this person is and where they are from are often missing. Bitcoin was designed to go against monetary authorities and prides itself on being anonymous. An important argument against Bitcoin replacing cash or cards in payments is that the cryptocurrency was not allowed for such a task: Bitcoin ranks among the slowest cryptocurrencies in terms of transaction speed. Are cryptocurrencies taking over payments? Cryptocurrency payments are set to grow at a CAGR of nearly 17 percent between 2022 and 2029, although the market is relatively small. The forecast is according to a market estimate made in early 2023, based on various conditions and sources available at that time. Research across 40 countries during the same time suggested that the market share of cryptocurrency in e-commerce transactions was "less than one percent" in all surveyed countries, with predictions being this would not change in the future.

Interest in Bitcoin and cryptocurrencies in 2020 was seemingly higher in Africa and Latin America than some of the world's developed economies. This shows after analyzing Bitcoin trading volume against domestic currencies used for the transaction of the digital coin. In 2020, roughly 420 million U.S. dollars worth of Russian rubles were used to buy Bitcoin on an exchange, against 400 million U.S. dollars worth of Nigerian naira. The source assumes the currencies are mainly used by the domestic population - e.g. transactions made with British pounds are likely done by UK residents -, and makes the same assumption for the United States, despite the international appeal of the U.S. dollar on foreign exchange markets.

Africa and Latin America lead the way

Although the source does not mention all countries in Africa and Latin America, the few entries these regions do have in the list stand out. Bitcoin trading volume in Nigeria, for instance, was twice as high as that of the eurozone in 2020. Colombia's market size was twice that of Canada. Whether this interest is for actual payment use on a day-to-day basis or as a tool for investment is not really clear. Data from Statista's Global Consumer Survey on payment methods in Egypt reveals that eight percent of Egyptians either owned or used Bitcoin, but does not specify the exact use or purpose of the cryptocurrency.

Bitcoin: the "Renaissance"

Believed by some to fade into obscurity after hitting the news in 2017 and price declines that followed afterwards, the world's most well-known cryptocurrency witnessed a "rebirth" at the end of 2020: Within five days in January 2021, the price of Bitcoin soared from 30,000 U.S. dollars to 40,000 U.S. dollars. Bitcoin's market cap - calculated by multiplying the total number of Bitcoins in circulation against its price - grew as well, more than doubling in early January 2021 against November 2020

This dataset is about cryptos per hour and is filtered where the crypto is Bitcoin, featuring 3 columns: crypto, datetime, and highest price. The preview is ordered by datetime (descending).

Graph and download economic data for Coinbase Bitcoin (CBBTCUSD) from 2014-12-01 to 2025-03-25 about cryptocurrency and USA.

In this work, i will be analyzing the Bitcoin growth trends from January 2012 to March 2021 in comparison with year indicators where Bitcoin usages/patronage, volumes and prices were high and low across the world.

The estimated time with which Kraken would confirm a deposit of certain cryptocurrencies varied from between near-instantaneous to up to several hours. Bitcoin, for instance, could take around 40 minutes - depending on the fees involved - whereas tokens like Cardano or Solana could be handled almost immediately. The transaction speed matters as it indicates which cryptocurrency is more efficient. A higher efficiency means that the blockchain underneath the coin is more capable of transferring data from one party to the other and confirm transactions. Transaction speed can be influenced by several factors, including block time, block size, transaction fees, and network traffic.

Context:

This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

Introduction

This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

The Data

For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

1. **timestamp** - A timestamp for the minute covered by the row.
2. **Asset_ID** - An ID code for the cryptoasset.
3. **Count** - The number of trades that took place this minute.
4. **Open** - The USD price at the beginning of the minute.
5. **High** - The highest USD price during the minute.
6. **Low** - The lowest USD price during the minute.
7. **Close** - The USD price at the end of the minute.
8. **Volume** - The number of cryptoasset u units traded during the minute.
9. **VWAP** - The volume-weighted average price for the minute.
10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
12. **Asset_Name** - Human readable Asset name.

Indexing

The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

Usage Example

The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

Baseline Example Notebooks:

• Neural Network Starter
• LightGBM Starter
• Catboost Starter
• XGBoost Starter
• TabNet Starter
• Reinforcement Learning (PPO) Starter

These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

Loose-ends:

This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

• VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
• Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
• Filtering: No filtration of 0 volume data is taken place.

Example Visualisations

Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

License

This data is being collected automatically from the crypto exchange Binance.

The Billion Triple Challenge (BTC) 2019 Dataset is the result of a large-scale RDF crawl (accepting RDF/XML, Turtle and N-Triples) conducted from 2018/12/12 until 2019/01/11 using LDspider. The data are stored as quads where the fourth element encodes the location of the Web document from which the associated triple was parsed. The dataset contains 2,155,856,033 quads, collected from 2,641,253 RDF documents on 394 pay-level domains. Merging the data into one RDF graph results in 256,059,356 unique triples. These data (as quads or triples) contain 38,156 unique predicates and instances of 120,037 unique classes.

If you would like to use this dataset as part of a research work, we would ask you to please consider citing our paper:

José-Miguel Herrera, Aidan Hogan and Tobias Käfer. "BTC-2019: The 2019 Billion Triple Challenge Dataset ". In the Proceedings of the 18th International Semantic Web Conference (ISWC), Auckland, New Zealand, October 26–30, 2019 (Resources track).

The dataset is published in three main parts:

1. Quads: (*nq.gz): contains the quads retrieved during the crawl (N-Quads, GZipped). These data are divided into individual files for each of the top 100 pay-level-domains by number of quads contributed (btc2019-[domain]_000XX.nq.gz). While most domains have one file, larger domains are further split into parts (000XX) with approximately 150,000,000 quads each. Finally, quads from the 294 domains not in the top 100 are merged into one file: btc2019-other_00001.nq.gz.
2. Triples: (btc2019-triples.nt.gz): contains the unique triples resulting from taking all quads, dropping the fourth element (indicating the location of the source document) and computing the unique triples.
3. VoID (void.nt): contains a VoID file offering statistics about the dataset.

For parsing the files, we recommend a streaming parser, such as Raptor, RDF4j/Rio, or NxParser.

The data are sourced from 2,641,253 RDF documents. The top-10 pay-level-domains in terms of documents contributed are:

1. dbpedia.org 162,117 documents (6.14%)
2. loc.gov 150,091 documents (5.68%)
3. bnf.fr 146,186 documents (5.53%)
4. sudoc.fr 144,877 documents (5.49%)
5. theses.fr 141,228 documents (5.35%)
6. wikidata.org 141,207 documents (5.35%)
7. linkeddata.es 130,459 documents (4.94%)
8. getty.edu 130,398 documents (4.94%)
9. fao.org 92,838 documents (3.51%)
10. ontobee.org 92,812 documents (3.51%)

The data contain 2,155,856,033 quads. The top-10 pay-level-domains in terms of quads contributed are:

1. wikidata.org 2,006,338,975 quads (93.06%)
2. dbpedia.org 36,686,161 quads (1.70%)
3. idref.fr 22,013,225 quads (1.02%)
4. bnf.fr 12,618,155 quads (0.59%)
5. getty.edu 7,453,134 quads (0.35%)
6. sudoc.fr 7,176,301 quads (0.33%)
7. loc.gov 6,725,390 quads (0.31%)
8. linkeddata.es 6,485,114 quads (0.30%)
9. theses.fr 4,820,874 quads (0.22%)
10. ontologycentral.com 4,633,947 quads (0.21%)

The data contain 256,059,356 unique triples. The top-10 pay-level-domains in terms of unique triples contributed are:

1. wikidata.org 133,535,555 triples (52.15%)
2. dbpedia.org 32,981,420 triples (12.88%)
3. idref.fr 16,820,681 triples (6.57%)
4. bnf.fr 11,769,268 triples (4.60%)
5. getty.edu 6,571,525 triples (2.57%)
6. linkeddata.es 5,898,762 triples (2.30%)
7. loc.gov 5,362,064 triples (2.09%)
8. sudoc.fr 4,972,647 triples (1.94%)
9. ontologycentral.com 4,471,962 triples (1.75%)
10. theses.fr 4,095,897 triples (1.60%)

If you wish to download all N-Quads files, the following may be useful to copy and paste in Unix:

wget https://zenodo.org/record/2634588/files/btc2019-acropolis.org.uk_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-aksw.org_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-babelnet.org_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-bbc.co.uk_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-berkeleybop.org_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-bibliotheken.nl_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-bl.uk_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-bne.es_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-bnf.fr_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-camera.it_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-cervantesvirtual.com_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-chemspider.com_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-cnr.it_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-comicmeta.org_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-crossref.org_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-cvut.cz_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-d-nb.info_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-datacite.org_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-dbpedia.org_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-dbtune.org_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-drugbank.ca_00001.nq.gz
wget https://zenodo.org/record/2634588/files/btc2019-ebi.ac.uk_00001.nq.gz
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CoinAPI delivers ultra-low latency cryptocurrency market data built for professional traders who demand absolute precision. Our tick-by-tick updates capture every market movement in real-time, providing the critical insights needed for split-second decisions in volatile markets.

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The recent extreme volatility in cryptocurrency prices occurred in the setting of popular social media forums devoted to the discussion of cryptocurrencies. We develop a framework that discovers potential causes of phasic shifts in the price movement captured by social media discussions. This draws on principles developed in healthcare epidemiology where, similarly, only observational data are available. Such causes may have a major, one-off effect, or recurring effects on the trend in the price series. We find a one-off effect of regulatory bans on bitcoin, the repeated effects of rival innovations on ether and the influence of technical traders, captured through discussion of market price, on both cryptocurrencies. The results for Bitcoin differ from Ethereum, which is consistent with the observed differences in the timing of the highest price and the price phases. This framework could be applied to a wide range of cryptocurrency price series where there exists a relevant social media text source. Identified causes with a recurring effect may have value in predictive modelling, whilst one-off causes may provide insight into unpredictable black swan events that can have a major impact on a system.

The recent technological advent of cryptocurrencies and their respective benefits have been shrouded with a number of illegal activities operating over the network such as money laundering, bribery, phishing, fraud, among others. In this work we focus on the Ethereum network, which has seen over 400 million transactions since its inception. Using 2179 accounts flagged by the Ethereum community for their illegal activity coupled with 2502 normal accounts, we seek to detect illicit accounts based on their transaction history using the XGBoost classifier. Using 10 fold cross-validation, XGBoost achieved an average accuracy of 0.963 ( ± 0.006) with an average AUC of 0.994 ( ± 0.0007). The top three features with the largest impact on the final model output were established to be ‘Time diff between first and last (Mins)’, ‘Total Ether balance’ and ‘Min value received’. Based on the results we conclude that the proposed approach is highly effective in detecting illicit accounts over the Ethereum network. Our contribution is multi-faceted; firstly, we propose an effective method to detect illicit accounts over the Ethereum network; secondly, we provide insights about the most important features; and thirdly, we publish the compiled data set as a benchmark for future related works.

This dataset contains hardware trojans of RISC-V and Web3 (Crypto Wallet and Proof-of-Work Miner) in Verilog.

A total of 10 golden models were obtained from GitHub (https://github.com/Saazh/Trojan-D2/tree/main/TrojanD2/Trojan_D2/RISC-V/ALL_FILES_IN_ONE_FOLDER, https://github.com/progranism/Open-Source-FPGA-Bitcoin-Miner/tree/master/src, https://github.com/jmaldon1/Crypto_wallet/tree/master/firmware).

There are 10 generated hardware trojans for each golden model. The hardware trojans have a "T" in their name. For example, the golden model for the top module of the miner is "fpgaminer_top.v", and one of related hardware trojan files is "fpgaminer_top_T0.v".