This lists datasets published by CTA in the City of Chicago Data Portal.

This dataset shows systemwide boardings for both bus and rail services provided by CTA, dating back to 2001. Daytypes are as follows: W = Weekday, A = Saturday, U = Sunday/Holiday. See attached readme file for information on how these numbers are calculated.

General Transit Feed Specification (formerly "Google Transit Feed Specification") data package for CTA system, including stops, route patterns and full service schedule.

This list of 'L' stops provides location and basic service availability information for each place on the CTA system where a train stops, along with formal station names and stop descriptions.

CTA Bus Stops - Point data representing over 11,000 CTA bus stops. The Stop ID is used to get Bus Tracker information.

Projected Coordinate System: NAD_1983_StatePlane_Illinois_East_FIPS_1201_Feet

This dataset gives annual ridership totals dating to the mid-1980s. Numbers are presented in boardings (see attached readme file for information on how these numbers are calculated).

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the City of Chicago. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore the City of Chicago using Kaggle and all of the data sources available through the City of Chicago organization page!

• Update Frequency: This dataset is updated monthly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under the following licenses: Public Domain

Download Historical Cotton (Combined) Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Point data representing locations of CTA bus stops. See attachment below for information on STATUS and POS fields To view or use these files, compression software and special GIS software, such as ESRI ArcGIS is required. Projected Coordinate System: NAD_1983_StatePlane_Illinois_East_FIPS_1201_Feet

The intersection between 279 CTAs in the CD database and the up and down-regulated genes of both other databases found 11 and, 8 CTAs, respectively.

This dataset lists monthly station entry averages, by day type (Weekday, Saturday or Sunday/Holiday), as well as monthly totals, beginning in 2001. Note that some stations (such as on the Cermak Branch--now Pink Line) and Skokie did not have Saturday and/or Sunday/holiday service until more recent years, although, in cases where weekday service ran past midnight, late evening fares may appear as part of Saturday tallies.

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.

Machine Learning stock prediction: LON:CTA Stock Prediction

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

What is this data?CTA bus stops as of January 2025. Data includes a system stop ID, street/cross street, direction, and route information.How can it be used? What projects use this data?CTA bus stops data can be used for base mapping, existing condition reports, transit-oriented development (TOD) planning, accessibility planning, and more.Who created this data? How and when?This data layer was created by CMAP staff, which downloaded the data from the City of Chicago Data Portal on January 16, 2025. Where can I find the latest data? How frequently is it updated?The latest data is available at the City of Chicago Data Portal, however, updates to this dataset are rare (City metadata indicates it was last updated in 2023).

What is this data?Current CTA rail lines. Data includes route identifiers, names, and links to RTAMS and CTA sites.How can it be used? What projects use this data?CTA rail lines data can be used for base mapping, existing condition reports, transit-oriented development (TOD) planning, ridership analysis, and more.Who created this data? How and when?This data layer is referenced from the RTA's Mapping and GIS Portal, specifically the Transit Services Feature Server layers that power RTA's shapefile downloads.Where can I find the latest data? How frequently is it updated?This data layer is referenced directly from the Regional Transportation Authority's (RTA) Mapping and GIS Portal, which means this layer should update automatically. RTA staff update the source data quarterly.

Survey DHT18 contains superior data, so this survey of CTA 1 is not included in this archive.

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.

CTA^A Stock: A Bubble Waiting to Burst

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

Author: Cherenkov Telescope Array Observatory; Cherenkov Telescope Array Consortium
Contact: open-data@cta-observatory.org

The Cherenkov Telescope Array Observatory (CTAO) will be the next-generation gamma-ray observatory and is currently under construction on the island of La Palma (Spain) and near Paranal (Chile).
This repository provides access to reconstructed event information (DL2- and DL1-level, simulation parameters) from Monte Carlo simulations of the CTAO Northern Array (production 5).

The Monte Carlo simulations for prod5 are described in arXiv:2108.04512, the simulation telescopes models used in the telescope simulation program sim_telarray and the configuration used in the air-shower code CORSIKA are available from the Zenodo archive for CTA Prod5 Telescope Models.

MC events are calibrated and reconstructed using the ctapipe package and stored for the following data levels:

• R1-MC: simulated raw data (output from sim_telarray)
• DL1 (this repository): telescope level data including images and image parameters
• DL2 (this repository): reconstructed event parameters such as energy, direction, gamma/hadron discrimination parameters. Note that this repository only contains the direction information.
• DL3: selected events with associated instrument response functions (IRFs). Preliminary DL3-IRFs are available from [here](https://doi.org/10.5281/zenodo.5499839).

For a description of the file format and data model, see ctapipe Data Model.

Data set description:

• using ctapipe version 0.17 (GitHub release page, Zenodo page)
• CTAO Northern Array on La Palma for the Alpha configuration (4 large-sized telescopes, 9 mid-sized telescopes)
• Zenith angles of 20 deg, telescope pointing direction north and south
• Primary particles: photons, protons
• Contained information: DL1 image and parameter data, DL2 geometry, simulation truths
• For convenience, the same data is provided in a single, large file per particle type for the whole dataset which do not contain the low-level DL1 image information and a larger number of files including this information.

We explicitly note that the products provided are preliminary and do not reflect the final performance of the CTA Observatory, neither are data structure or formats finalized. We also note that these data products are different to those used for the CTAO Instrument Response Functions.
In cases in which the data provided in this repository are used in a research project, we ask that the following acknowledgment is added in any resulting publication:

"This research has made use of the CTA DL1 and DL2 Event lists provided by the CTA Observatory and Consortium (version prod5-DL2-release1-DL2)" and cite this repository in the reference section of your publication.

We would like to thank the computing centers that provided resources for the generation of the Prod5 simulation set, click here for a list of service providers.

Clinical Trials Database (CTA), is providing to the public a listing of specific information relating to phase I, II and III clinical trials in patients. The database is managed by Health Canada and provides a source of information about Canadian clinical trials involving human pharmaceutical and biological drugs.

Lines representing approximately where the CTA rail lines are.

This dataset is in a format for spatial datasets that is inherently tabular but allows for a map as a derived view. Please click the indicated link below for such a map.

To export the data in either tabular or geographic format, please use the Export button on this dataset.

Data feed that provides a pathway for developers to retrieve estimated arrival times from the CTA Train Tracker (SM) service and incorporate it into third-party apps and projects.