NOTE 2023-01-23: Not compatible with Python 3.7. A user reported that the dataframes are pickled with protocol 5 only available from python 3.8 (thanks Beth!).

EDIT 2022-12-19: Added an extended jupyter notebook with the tutorial presented at the MIAPbP workshop

EvE contains a number of tables summarising the BPASSv2.2.1 stellar library to facilitate identifying progenitor systems.

If you are a user you only need EvE.hdf5. The rest is the code used to make this version of eve. The code is versioned on github (closed repo as of the publication date) but this acts as a "frozen" version of the code made to create this particular file.

This database was created with BPASSv2.2.1 and hoki

References

BPASSv2.2.1: Eldridge et al. 2017 and Stanway et al. 2018 | hoki: Stevance et al. 2022

Contact: hfstevance@gmail.com

EVE level 2 data files were created at the Laboratory for Atmospheric and Space Physics in Boulder, Colorado for the NASA Solar Dynamics Observatory (SDO) Extreme Ultraviolet Variability Experiment (EVE). The Science Processing and Operations Center (SPOC) is responsible for creating and maintaining access to all EVE products. For a high-level introduction to the EVE instrumentation please visit:https://lasp.colorado.edu/eve/science/instrument/Level 2 products are defined to contain 10-second intervals and span the time range from 2010 day 120 through 2018 day 108. The integration rate was changed to 60-seconds so the level 2B products were created to provide future spectra. This release of EVE Level 2 data products replaces all previous versions. We have made significant effort at verification and validation, but if you have any questions or encounter any problems with the data, please let us know about them.Two types of EVE level 2 products are routinely created: Spectra (EVS) and Lines (EVL). Level 2 spectra are the merged spectral measurements from the two spectrographs, MEGS-A and MEGS-B. The A detector is designed to measure from 6 –17 nm, and 17–37 nm using two filters, while the B detector is designed to measure 37–106. After the MEGS-A anomaly, MEGS-B was extended down to 33.33 nm. Level 2 processing stitches these pieces to form one spectrum. This version includes all measured wavelengths. The file spans 3.01-106.99 nm, but this extends beyond where there is no signal on the detectors, so there will be fill values at the extreme ends of the range. All level 2 irradiances are adjusted to 1 AU. Level 2 lines files contain selected lines derived from the level 2 spectra, ESP diode values and bands that correspond to other SDO instruments and some derived proxies.

This dataset contains the usage data of a single electric car collected in as part of the EVE study (Enquête des Vehicles Electrique) run by the Observatoire du Transition Energétique Grenoble (OTE-UGA). This dataset includes the following variables for a single Renault ZOE 2014 Q90: - Speed, distance covered, and other drivetrain data variables; - State of charge, State of health and other battery characteristics; as well as - external temperature variables. The Renault ZOE 2014 Q90 has a battery capacity of 22 KWh and a maximum speed of 135 KM/h. More information about on the specifications can be found here If you find this dataset useful or have any questions, please feel free to comment on the discussion dedicated to this dataset on the OTE forum . The electric car is used for personal use exclusively including occasional transit to work but mostly for personal errands and trips. The dataset was collected using a CanZE app and a generic car lighter dongle. The dataset spans three years from October 2020 to October 2023. A simple Python notebook that visualises the datasets can be found here. More complex use-cases for the datasets can be found in the following links: - Comparison of the carbon footprint of driving across countries: link - Feedback indicators of electric car charging behaviours: link There is also more information on the collection process and other potential uses in the data paper here. Please don't hesitate to contact the authors if you have any further questions about the dataset.

EVE (End-to-end Video-based Eye-tracking) is a dataset for eye-tracking. It is collected from 54 participants and consists of 4 camera views, over 12 million frames and 1327 unique visual stimuli (images, video, text), adding up to approximately 105 hours of video data in total.

Official competition on Codalab: https://competitions.codalab.org/competitions/28954

Forming part of my doctoral research on representations of Eve in French Romanesque sculpture, this file includes a CSV database, field research notebooks (PDF), shape files for an interactive map, and related documents. The database includes a series of 176 images of mostly sculptures, with a few examples of manuscript illuminations, stained glass windows, metal works, wall paintings, and mosaics. It is accompanied by field research notebooks in the form of handwritten floor plans and sketches created for each church visited and studied in the course of my field research in France, the United Kingdom, and Switzerland. The floor plans locate my collection of 24,800 catalogued images of artworks photographed during my field research. The sketched plans are complemented with a series of descriptions for each catalogued image. The interactive map situates the 176 artworks in their geographical context (including Roman and medieval roads, Crusades routes, and ca. 1000 French dioceses). The map is found in the form of shape files and raw data. It is also be accessed on ArcGIS: https://arcg.is/10bL4u. This digital humanities approach permits the study and contextualization of in situ sculptures and other artworks, within their stylistic, iconographical, geographical and socio-historical characteristics, while also highlighting their location. It can be modified and applied to other research projects in the Humanities to provide original contributions assisted by digital and non-Humanities scientific methods.

This dataset contains a diverse collection of pre-processed flow cytometry data assembled to support the training and evaluation of machine learning (ML) models for the gating of cell populations. The data was curated through a citizen science initiative embedded in the EVE Online video game, known as Project Discovery. Participants contributed to scientific research by gating bivariate plots generated from flow cytometry data, creating a crowdsourced reference set. The original flow cytometry datasets were sourced from publicly available COVID-19 and immunology-related studies on FlowRepository.org and PubMed. Data were compensated, transformed, and split into bivariate plots for analysis. This datset includes: 1) CSV files containing two-channel marker combinations per plot, 2) A SQL database capturing player-generated gating polygons in normalized coordinates, 3) Scripts and containerized environments (Singularity and Docker) for reproducible evaluation of gating accuracy and consensus scoring using the flowMagic pipeline, 4) Code for filtering bot inputs, evaluating user submissions, calculating F1 scores, and generating consensus gating regions. This data is especially valuable for training and benchmarking models that aim to automate the labor-intensive gating process in immunological and clinical cytometry applications.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Eve Expanding Horizons? (EVEX)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

📈 Daily Historical Stock Price Data for EVE ENERGY (2009–2025)

A clean, ready-to-use dataset containing daily stock prices for EVE ENERGY from 2009-10-30 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: EVE ENERGY Ticker Symbol: 300014.SZ Date Range: 2009-10-30 to 2025-05-28 Frequency: Daily Total Records: 3782 rows (one per trading day)

  🔢 Columns… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-eve-energy-20092025.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset provides information about the number of properties, residents, and average property values for Eve Court cross streets in Orlando, FL.

The extracted bands from the EVE spectrum that correspond to the 7 AIA spectral bands, two GOES-14 bands, 4 extracted MEGS spectral bands corresponding to the ESP diodes, two very broadbands used for creating the QEUV proxy, two MEGS-A broadbands representing each slit, and 3 MEGS-B bands.

The SDO-EVE Extreme ultraViolet Experiment Level 2b data product contains 60-second integrations of extracted solar irradiance in selected lines spanning a wide range of solar temperature measured by the MEGS-B detector. The A detector lines are not included but placeholders remain for compatibility with other products. The B detector measures from about 33 to 106 nm. The irradiances are adjusted to 1-AU.

📈 Daily Historical Stock Price Data for Eve Holding, Inc. (2022–2025)

A clean, ready-to-use dataset containing daily stock prices for Eve Holding, Inc. from 2022-05-10 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Eve Holding, Inc. Ticker Symbol: EVEX Date Range: 2022-05-10 to 2025-05-28 Frequency: Daily Total Records: 765 rows (one per trading day)… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-eve-holding-inc-20222025.

As of May 2022, mobile period apps Eve, Clover, and My Calendar were collecting the largest number of data on user identifiers among all the commercial female health apps examined. Almost all the examined apps collected at least two data points on users' sensitive information, while none of the examined apps appeared to collect data on users' contacts. euros.

Eve Holding Inc. Common Stock (EVE) predictions indicate a potential for moderate to high returns in the long term. However, these predictions come with certain risks, including market volatility, competition from established players, and the company's reliance on technological advancements.

Historical price data and trends for EVE SmartScooter Utility including current price, price changes, and market analysis.

Historical price data and trends for Eve E-Scooter including current price, price changes, and market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Protein-Protein, Genetic, and Chemical Interactions for EVE (Drosophila melanogaster) curated by BioGRID (https://thebiogrid.org); DEFINITION: even skipped

This dataset provides information about the number of properties, residents, and average property values for Eve Avenue cross streets in Lynwood, CA.