Dataset

This dataset was created by VISHAL GAUTAM

Contents

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

Imbalance prices applied for balance responsible parties (BRPs) settlemnt. One minute imbalance prices are published as fast as possible and are never validated. The 1 min prices give an indication for the final imabalnce price of the ISP (imbalance settlement period which is 15 min). Contains the historical data and is refreshed daily.This dataset contains data from 22/05/2024 (MARI local go-live) on.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This layer summarises VBA fauna records against a standard grid of 1 degree longitude/latitude (GDA94).

Any VBA taxa record with its centre in a cell is counted as a record for that cell. The number of times a

taxon has been recorded in a cell is collated in the RECORDS column. The first date and last date that a

taxon has been recorded in a cell are also summarised. VBA data summarised against 10 and 5 minute

grids are also available in related datasets. VBA records with a spatial accuracy worse than +/- 1 km are

excluded from this layer.

Purpose

This layer can be used to indicate general localities where wildlife have been recorded. It can also be used

as an index to assist in the analysis of the VBA_FAUNA25 and VBA_FAUNA100 layers. This layer

includes restricted records and can act as an alternative to VBA_FAUNA_RESTRICTED.

For the purposes of biodiversity management and conservation.

Dataset History

Data Set Source: The Victorian Biodiversity Atlas database

Completeness: Records are available from throughout Victoria, although some area have extremely good coverage, e.g. Central Highlands, East Gippsland.

Dataset Citation

Victorian Department of Environment and Primary Industries (2014) Victorian Biodiversity Atlas fauna - 1 minute grid summary. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/516f9eb1-ea59-46f7-84b1-90a113d6633d.

Stratis [New] price history tracking allows crypto investors to easily monitor the performance of their investment. You can conveniently track the opening value, high, and close for Stratis [New] over time, as well as the trade volume. Additionally, you can instantly view the daily change as a percentage, making it effortless to identify days with significant fluctuations. According to our Stratis [New] price history data, its value soared to an unprecedented peak in 2024-03-28, surpassing $0.1613 USD. On the other hand, the lowest point in Stratis [New]'s price trajectory, commonly referred to as the "Stratis [New] all-time low", occurred on 2024-08-05. If one had purchased Stratis [New] during that time, they would currently enjoy a remarkable profit of 33%. By design, 2.05B Stratis [New] will be created. As of now, the circulating supply of Stratis [New] is approximately 2,013,008,300. All the prices listed on this page are obtained from Bitget, a reliable source. It is crucial to rely on a single source to check your investments, as values may vary among different sellers. Our historical Stratis [New] price dataset includes data at intervals of 1 minute, 1 day, 1 week, and 1 month (open/high/low/close/volume). These datasets have undergone rigorous testing to ensure consistency, completeness, and accuracy. They are specifically designed for trade simulation and backtesting purposes, readily available for free download, and updated in real-time.

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The 1 min imbalance prices are published as fast as possible and give an indication for the final imbalance price of the ISP (imbalance settlement period which is 15min). This report contains data for the current hour and is refreshed every minute. Notice that in this report we only provide non-validated data. This dataset contains data from 22/05/2024 (MARI local go-live) on.

Pioneer 11 Vector Helium Magnetometer (HVM) data from the Jupiter encounter period between 1974-12-03T01:06:31.679 and 1974-12-03T10:58:30.720. The data set provides 1.0 minute magnetic field averages and spacecraft trajectory data in JG coordinates.

Dataset

This dataset was created by testingkagglesdfs

Released under Apache 2.0

Contents

Christmas Pump price history tracking allows crypto investors to easily monitor the performance of their investment. You can conveniently track the opening value, high, and close for Christmas Pump over time, as well as the trade volume. Additionally, you can instantly view the daily change as a percentage, making it effortless to identify days with significant fluctuations. According to our Christmas Pump price history data, its value soared to an unprecedented peak in 2023-12-22, surpassing $0.5586 USD. On the other hand, the lowest point in Christmas Pump's price trajectory, commonly referred to as the "Christmas Pump all-time low", occurred on 2024-04-20. If one had purchased Christmas Pump during that time, they would currently enjoy a remarkable profit of -100%. By design, 2,512.25T Christmas Pump will be created. As of now, the circulating supply of Christmas Pump is approximately 0. All the prices listed on this page are obtained from Bitget, a reliable source. It is crucial to rely on a single source to check your investments, as values may vary among different sellers. Our historical Christmas Pump price dataset includes data at intervals of 1 minute, 1 day, 1 week, and 1 month (open/high/low/close/volume). These datasets have undergone rigorous testing to ensure consistency, completeness, and accuracy. They are specifically designed for trade simulation and backtesting purposes, readily available for free download, and updated in real-time.

1--minute resolution surface meteorological observations from the Automated Surface Observing System (ASOS) network of ~890 stations in the United States and select locations elsewhere. These data were collected by the National Centers for Environmental Information.

Description This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. The goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. We also include sample codes of baseline models for benchmarking of more elaborated models.

Data usage The usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper: Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494

Although every effort was made to ensure the quality of the data, no guarantees or liabilities are implied by the authors or publishers of the data.

Sample code As part of the data release, we are also including the sample code written in Python 3. The preprocessed data used in the scripts are also provided. The code can be used to reproduce the results presented in this work and as a starting point for future studies. Besides the standard scientific Python packages (numpy, scipy, and matplotlib), the code depends on pandas for time-series operations, pvlib for common solar-related tasks, and scikit-learn for Machine Learning models. All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip.

Units All time stamps are in UTC (YYYY-MM-DD HH:MM:SS). All irradiance and weather data are in SI units. Sky image features are derived from 8-bit RGB (256 color levels) data. Satellite images are derived from 8-bit gray-scale (256 color levels) data.

Missing data The string "NAN" indicates missing data

File formats All time series data files as in CSV (comma separated values) Images are given in tar.bz2 files

Files

Folsom_irradiance.csv Primary One-minute GHI, DNI, and DHI data.

Folsom_weather.csv Primary One-minute weather data.

Folsom_sky_images_{YEAR}.tar.bz2 Primary Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2.

Folsom_NAM_lat{LAT}_lon{LON}.csv Primary NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node’s coordinates listed in Table I in the paper.

Folsom_sky_image_features.csv Secondary Features derived from the sky images.

Folsom_satellite.csv Secondary 10 pixel by 10 pixel GOES-15 images centered in the target location.

Irradiance_features_{horizon}.csv Secondary Irradiance features for the different forecasting horizons ({horizon} 1⁄4 {intra-hour, intra-day, day-ahead}).

Sky_image_features_intra-hour.csv Secondary Sky image features for the intra-hour forecasting issuing times.

Sat_image_features_intra-day.csv Secondary Satellite image features for the intra-day forecasting issuing times.

NAM_nearest_node_day-ahead.csv Secondary NAM forecasts (GHI, DNI computed with the DISC algorithm, and total cloud cover) for the nearest node to the target location prepared for day-ahead forecasting.

Target_{horizon}.csv Secondary Target data for the different forecasting horizons.

Forecast_{horizon}.py Code Python script used to create the forecasts for the different horizons.

Postprocess.py Code Python script used to compute the error metric for all the forecasts.

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This data set contains the 1-minute resolution surface meteorological observations from the Automated Surface Observing System (ASOS) network of ~860 stations in the United States for the March-April 2018 period. These data were collected by the National Centers for Environmental Information (NCEI; formerly the National Climatic Data Center).

Dataset contains direct, diffuse, global and downward longwave irradiances at 60 seconds time resolution. Dataset also contains air temperature, relative humidity and air pressure at instrument height. Supplemental information

Archive of volume data of all polarimetric radar variables, including those related to quality for the radar in Herwijnen. Time interval is 5 minutes. Data have been archived in one .tar file per day.

Climatological radar rainfall dataset of 5 minute precipitation depths at a 1-km grid, which have been adjusted employing validated and complete rain gauge data from both KNMI rain gauge networks. Same dataset as "RAD_NL25_RAC_MFBS_5min", except that now an Extended Mask (EM) has been applied to this dataset. As a result, data are also available up to tens of kilometers away from the land surface of the Netherlands, i.e. above Belgium, Germany, and above open water. This dataset is updated once a month providing data up to a few months ago.

Dataset

This dataset was created by testingkagglesdfs

Released under Apache 2.0

Contents

BTC Price Dataset with Technical Indicators

Welcome to the BTC / USDT Price Dataset with Technical Indicators, hosted by the WinkingFace Team. This dataset is designed to provide comprehensive historical data on Bitcoin prices along with a variety of technical indicators to aid in cryptocurrency trading analysis and research. The dataset is updated every 3 minutes (delayed 1 minute).

  Dataset Description

This dataset includes the following columns:

timestamp: The date and… See the full description on the dataset page: https://huggingface.co/datasets/WinkingFace/CryptoLM-Bitcoin-BTC-USDT.

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