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36 results for “cryptocurrency”
A Panel Data Set of Cryptocurrency Development Activity on GitHub
<p>Contents:</p> <ul> <li><strong>all-sorted-recovered-normalized-2018-01-21-to-2019-02-04.csv</strong>: CSV format of all data, sorted by date. This file contains some imputed values for missing data, and all fields across all repositories and normalized to "null". This is the most convenient form to use.</li> <li><strong>all-sorted-2018-01-21-to-2019-02-04.csv</strong>: CSV format of all, sorted by date. It is the raw data after processing the raw format.</li> <li><strong>raw-data-2018-01-21-to-2019-02-04.tar.gz</strong>: The raw format of data collected (S-expressions). Contains additional contributor data and CoinMarketCap data not currently in the CSV datasets.</li> <li><strong>recovered.patch</strong>: The modification on <strong>all-sorted-2018-01-21-to-2019-02-04.csv</strong> after recovering (imputing) data<strong>, </strong>showing what was recovered.</li> <li><strong>recovered-normalized.patch</strong>: The modification of <strong>all-sorted-2018-01-21-to-2019-02-04.csv </strong>after normalizing the recovered data set. Thus, patching <strong>all-sorted-2018-01-21-to-2019-02-04.csv </strong>with<strong> recovered.patch</strong>, then <strong>recovered-normalized.patch </strong>gives <strong>all-sorted-recovered-normalized-2018-01-21-to-2019-02-04.csv</strong></li> <li><strong>missing-dates.txt</strong>: Days for which we missed GitHub data collection (partial or completely).</li> </ul> <p>Related publications:</p> <pre><code>@inproceedings{van-tonder-crypto-oss-2019, title = {{A Panel Data Set of Cryptocurrency Development Activity on GitHub}}, booktitle = "International Conference on Mining Software Repositories", author = "{van~Tonder}, Rijnard and Trockman, Asher and {Le~Goues}, Claire", series = {MSR '19}, year = 2019 } @inproceedings{trockman-striking-gold-2019, title = {{Striking Gold in Software Repositories? An Econometric Study of Cryptocurrencies on GitHub}}, booktitle = "International Conference on Mining Software Repositories", author = "Trockman, Asher and {van~Tonder}, Rijnard and Vasilescu, Bogdan", series = {MSR '19}, year = 2019 }</code></pre> <p>Related code: <a href="https://github.com/rvantonder/CryptOSS">https://github.com/rvantonder/CryptOSS</a></p>
Cryptocurrency_History
<p>Dataset about different cryptocurrency price since 2013 </p>
Strategic selection of cryptocurrency investment portfolios based on discrete multi-criteria methods.
<p>In recent years, cryptocurrencies have gone from an unknown plane to a protagonist one, with strategic investment in these assets becoming increasingly popular for organizations seeking to diversify their portfolios. However, cryptocurrencies carry a high risk due to their high volatility. Based on the multi-criteria decision making theory, a decision support system was developed for the design of business investment portfolios in cryptocurrencies, starting from the definition of criteria based on historical data that characterize returns and risks in different ways, all this for short time windows of 7 and 15 days. Then, the importance of criteria was analyzed by various methods and their impact was evaluated assuming changes in stock prices. Finally, multi-criteria methods were applied for the design of cryptocurrency investment portfolios in the above-mentioned time windows. The developed system is applicable to companies of any size, since the studied methods provide bigger rationality and objectivity to the strategic decision-making process. The resulting method can be extended in the future to incorporate criteria that take advantage of information on the market capitalization of cryptocurrencies, as well as capturing investor sentiment or the expected shortfall of the investment.</p>
Dataset of cryptocurrency & NFT datasets
<p>This dataset contains information about various datasets regarding cryptocurrencies and NFTs across the internet</p>
Dataset of transactions of 10 Ethereum addresses controlled by a private key, each has at least 2000 output transactions, which include a transfer of cryptocurrency, and all transactions are performed within no longer than three months period.
<p>Starting from the block Nr. 8578851, we have evaluated every block to find enough unique addresses that satisfy the following requirements. The address that appeared in a block must be controlled by private key (i.e., CA), have at least 2000, and not more than 6000 outgoing transactions (i.e., digitally signed), which however include a transfer of any amount of cryptocurrency (i.e., Ether) and must be performed within a period no longer than three months. The whole dataset includes a total of 30505 transactions.</p> <p>Each transaction has data about:</p> <ul> <li>transaction hash</li> <li>block number</li> <li>timestamp (Unix),</li> <li>date and time (m/dd/yyyy)</li> <li>from (Ethereum address)</li> <li>to (Ethereum address)</li> <li>output value (ETH)</li> <li>transaction fee (ETH)</li> <li>transaction fee (USD)</li> <li>historical price (USD)</li> </ul>
CryptoSentiment: A large scale sentiment dataset for cryptocurrencies
<p>CryptoSentiment is a dataset, which contains sentiment information about cryptocurrency assets, gathered by various online sources, and analyzed by FinBERT sentiment extractor. More specifically, we provide a publicly available dataset containing fine-grained sentiment analysis data (minute-basis) about cryptocurrency market collected by different online sources. CryptoSentiment dataset includes 235,907 sentiment scores for 14 different cryptocurrencies gathered from various online sources such as news articles and social media.</p>
A Dataset of Coordinated Cryptocurrency-Related Social Media Campaigns
<p> </p> <p>Note: This version simply updates the dataset with a new <em>Readme - Supplementary Material</em> file.</p> <p> </p> <p>The paper describing this dataset can be cited as:</p> <p>Zilius, K., Spiliotopoulos, T., & van Moorsel, A. (2023). A Dataset of Coordinated Cryptocurrency-Related Social Media Campaigns. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 1112-1121. <a href="https://doi.org/10.1609/icwsm.v17i1.22219">https://doi.org/10.1609/icwsm.v17i1.22219</a></p> <p> </p> <p><strong>Abstract</strong></p> <p>The rise in adoption of cryptoassets has brought many new and inexperienced investors in the cryptocurrency space. These investors can be disproportionally influenced by information they receive online, and particularly from social media. This paper presents a dataset of crypto-related bounty events and the users that participate in them. These events coordinate social media campaigns to create artificial "hype" around a crypto project in order to influence the price of its token. The dataset consists of information about 15.8K cross-media bounty events, 185K participants, 10M forum comments and 82M social media URLs collected from the Bounties(Altcoins) subforum of the BitcoinTalk online forum from May 2014 to December 2022. We describe the data collection and the data processing methods employed and we present a basic characterization of the dataset. Furthermore, we discuss potential research opportunities afforded by the dataset across many disciplines and we highlight potential novel insights into how the cryptocurrency industry operates and how it interacts with its audience.</p> <p> </p>
Data From: Analysing social media forums to discover potential causes of phasic shifts in cryptocurrency price series
<p>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.</p>
Are cryptocurrencies currencies? Bitcoin as legal tender in El Salvador
<p>A currency's essential feature is to be a medium of exchange. We leverage a quasi-natural experiment––El Salvador as the first country to make Bitcoin legal tender––to study a cryptocurrency's potential to be used in daily transactions. The government also launched and provided incentives to download and use a digital wallet named Chivo, which shares features with Central Bank Digital Currencies (CBDCs) and allows users to trade bitcoins and dollars. Were Chivo Wallet and Bitcoin actually adopted after this "big push"? Conducting a representative face-to-face survey and relying on blockchain data to obtain all Chivo transactions, we document how usage of digital payments and Bitcoin is low, concentrated, and has been decreasing over time. We find that privacy concerns are key barriers to adoption, which speaks to a policy debate on crypto and CBDCs that has had anonymity at its core. We also estimate the technology's adoption cost and its network externalities.</p>
Evaluation of the importance of criteria for the selection of cryptocurrencies
<p>This database contains a copy of the historical time series of 9 cryptocurrencies from the <a href="https://www.kaggle.com/sudalairajkumar/cryptocurrencypricehistory">https://www.kaggle.com/sudalairajkumar/cryptocurrencypricehistory</a> dataset.</p> <p>It also calculates 6 criteria for each of these time series in two windows 15 and 7 days.<br> Then there is an evaluation of these criteria through 4 techniques for determining relative weights; and<br> evaluate their differences with a discrete multi-criteria method.</p>
Cryptocurrency Fraud and Code Sharing Data Set and Analysis Code
<p>This release covers the state of the data and associated analysis code for determining code sharing between cryptocurrency codebases funded through the end of the original NSF CRII award. This material is based on work supported by the National Science Foundation under Grant CNS-1849729.</p>
Dataset literature review on cyberlaw and cryptocurrency
<p>Merupakan data dari literature review tentang hukum cyber dan cryptocurrency</p>
Cryptocurrency Historical Data Snapshot
<p>En esta práctica se hace repaso de los procesos de web scraping. </p>
Cryptocurrency Historical Data Snapshot
<p>En esta práctica se hace repaso de los procesos de web scraping, extrayendo informacion de la pagina coinmarketcap. </p>
Data From: Analysing social media forums to discover potential causes of phasic shifts in cryptocurrency price series
Open the record for dataset details and reuse information.
Are cryptocurrencies currencies? Bitcoin as legal tender in El Salvador
Open the record for dataset details and reuse information.
Snapshot Cryptocurrencies
<p>Aquest dataframe mostra una instantena de les propietats de les monedes seleccionades.</p>
THE EMERGENCE OF CRYPTOCURRENCIES AND THEIR IMPLICATION FOR MONETARY POLICY: THE CASE OF UNITED STATES OF AMERICA.
<p>Dataset used for research paper titled "<span>THE EMERGENCE OF CRYPTOCURRENCIES AND THEIR IMPLICATION FOR MONETARY POLICY: THE CASE OF UNITED STATES OF AMERICA"</span></p>
Spams meet Cryptocurrencies: Sextortion in the Bitcoin Ecosystem
<p>In the past year, a new spamming scheme has emerged: sexual extortion messages requiring payments in the cryptocurrency Bitcoin, also known as sextortion. This scheme represents a first integration of the use of cryptocurrencies by members of the spamming industry. Using a dataset of 4,340,736 sextortion spams, this research aims at understanding such new amalgamation by uncovering spammers’ operations. To do so, a simple, yet effective method for projecting Bitcoin addresses mentioned in sextortion spams onto transaction graph abstractions is computed over the entire Bitcoin blockchain. This allows us to track and investigate monetary flows between involved actors and gain insights into the financial structure of sextortion campaigns. We find that sextortion spammers are somewhat sophisticated, following pricing strategies and benefiting from cost reductions as their operations cut the upper-tail of the spamming supply chain. We discover that one single entity is likely controlling the financial backbone of the majority of the sextortion campaigns and that the 11-month operation studied yielded a lower-bound revenue between $1,300,620 and $1,352,266. We conclude that sextortion spamming is a lucrative business and spammers will likely continue to send bulk emails that try to extort money through cryptocurrencies.</p>
Data for: Connectedness and spillover effect between cryptocurrency and financial assets
<p><span>Cryptocurrencies have quickly become one type of important financial asset. Accordingly, it is important to understand the interaction between cryptocurrency and other financial asset markets. However, previous literature paid less attention to the correlation between the price trend of cryptocurrencies and other financial assets. Using the vector autoregression model, we analyzed price correlation and spillover effect between cryptocurrencies and financial assets between November 2017 and February 2022. The study concludes that stock price has a spillover effect on cryptocurrencies, government bonds, and precious metals. The research results are useful while allocating portfolios or hedge strategies that include cryptocurrencies and financial assets such as stocks, government bonds, and precious metals.</span></p>
ScienceDex guides
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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)
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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.