Intrexon stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Intrexon reported $351M in Market Capitalization this April of 2024, considering the latest stock price and the number of outstanding shares.Data for Intrexon | XON - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Historical ownership data of XON by First Washington CORP

Intrexon reported $717K in Sales Revenues for its fiscal quarter ending in June of 2024. Data for Intrexon | XON - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for XON held by First Washington CORP from Q1 2014 to Q1 2020

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company XON-Martin-Vindheim.

Credit report of Xon Systems Pty Ltd contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Intrexon reported $4.85M in Trade Creditors for its fiscal quarter ending in June of 2024. Data for Intrexon | XON - Trade Creditors including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Historical ownership data of XON by Fore Capital LLC

Historical ownership data of XON by STATE STREET CORPORATION

Historical ownership data of XON by ORACLE INVESTMENT MANAGEMENT INC

Historical ownership data of XON by Michael & Susan Dell Foundation

Data Con

## Overview

Data Con is a dataset for object detection tasks - it contains Conc annotations for 1,023 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Credit report of Nec Xon contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Intrexon reported $73.16M in Assets for its fiscal quarter ending in June of 2024. Data for Intrexon | XON - Assets including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Con Espressione Game Dataset

A piece of music can be expressively performed, or interpreted, in a variety of ways. With the help of an online questionnaire, the Con Espressione Game, we collected some 1,500 descriptions of expressive character relating to 45 performances of 9 excerpts from classical piano pieces, played by different famous pianists. More specifically, listeners were asked to describe, using freely chosen words (preferably: adjectives), how they perceive the expressive character of the different performances. The aim of this research is to find the dimensions of musical expression (in Western classical piano music) that can be attributed to a performance, as perceived and described in natural language by listeners.

The Con Espressione Game was launched on the 3rd of April 2018.

Dataset structure

Listeners’ Descriptions of Expressive performance

piece_performer_data.csv: A comma separated file (CSV) containing information about the pieces in the dataset. Strings are delimited with ". The columns in this file are:

music_id: An integer ID for each performance in the dataset.

performer_name: (Last) name of the performer.

piece_name: (Short) name of the piece.

performance_name: Name of the the performance. All files in different modalities (alignments, MIDI, loudness features, etc) corresponding to a single performance will have the same name (but possibly different extensions).

composer: Name of the composer of the piece.

piece: Full name of the piece.

album: Name of the album.

performer_name_full: Full name of the performer.

year_of_CD_issue: Year of the issue of the CD.

track_number: Number of the track in the CD.

length_of_excerpt_seconds: Length of the excerpt in seconds.

start_of_excerpt_seconds: Start of the excerpt in its corresponding track (in seconds).

end_of_excerpt_seconds: End of the excerpt in its corresponding track (in seconds).

con_espressione_game_answers.csv: This is the main file of the dataset which contains listener’s descriptions of expressive character. This CSV file contains the following columns:

answer_id: An integer representing the ID of the answer. Each answer gets a unique ID.

participant_id: An integer representing the ID of a participant. Answers with the same ID come from the same participant.

music_id: An integer representing the ID of the performance. This is the same as the music_id in piece_performer_data.csv described above.

answer: (cleaned/formatted) participant description. All answers have been written as lower-case, typos were corrected, spaces replaced by underscores (_) and individual terms are separated by commas. See cleanup_rules.txt for a more detailed description of how the answers were formatted.

original_answer: Raw answers provided by the participants.

timestamp: Timestamp of the answer.

favorite: A boolean (0 or 1) indicating if this performance of the piece is the participant’s favorite.

translated_to_english. Raw translation (from German, Russian, Spanish and Italian).

performer. (Last) name of the performer. See piece_performer_data.csv described above.

piece_name. (Short) name of the piece. See piece_performer_data.csv described above.

performance_name. Name of the performance. See piece_performer_data.csv described above.

participant_profiles.csv. A CSV file containing musical background information of the participants. Empty cells mean that the participant did not provide an answer. This file contains the following columns:

participant_id: An integer representing the ID of a participant.

music_education_years: (Self reported) number of years of musical education of the participants

listening_to_classical_music: Answers to the question “How often do you listen to classical music?”. The possible answers are:

1: Never

2: Very rarely

3: Rarely

4: Occasionally

5: Frequently

6: Very frequently

registration_date: Date and time of registration of the participant.

playing_piano: Answer to the question “Do you play the piano?”. The possible answers are

1: No

2: A little bit

3: Quite well

4: Very well

cleanup_rules.txt: Rules for cleaning/formatting the terms in the participant’s answers.

translations_GERMAN.txt: How the translations from German to English were made.

Metadata

Related meta data is stored in the MetaData folder.

Alignments. This folders contains the manually-corrected score-to-performance alignments for each of the pieces in the dataset. Each of these alignments is a text file.

ApproximateMIDI. This folder contains reconstructed MIDI performances created from the alignments and the loudness curves. The onset time and offset times of the notes were determined from the alignment times and the MIDI velocity was computed from the loudness curves.

Match. This folder contains score-to-performance alignments in Matchfile format.

Scores_MuseScore. Manually encoded sheet music in MuseScore format (.mscz)

Scores_MusicXML. Sheet music in MusicXML format.

Scores_pdf. Images of the sheet music in pdf format.

Audio Features

Audio features computed from the audio files. These features are located in the AudioFeatures folder.

Loudness: Text files containing loudness curves in dB of the audio files. These curves were computed using code provided by Olivier Lartillot. Each of these files contains the following columns:

performance_time_(seconds): Performance time in seconds.

loudness_(db): Loudness curve in dB.

smooth_loudness_(db): Smoothed loudness curve.

Spectrograms. Numpy files (.npy) containing magnitude spectrograms (as Numpy arrays). The shape of each array is (149 frequency bands, number of frames of the performance). The spectrograms were computed from the audio files with the following parameters:

Sample rate (sr): 22050 samples per second

Window length: 2048

Frames per Second (fps): 31.3 fps

Hop size: sample_rate // fps = 704

Filterbank: log scaled filterbank with 24 bands per octave and min frequency 20 Hz

MIDI Performances

Since the dataset consists of commercial recordings, we cannot include the audio files in the dataset. We can, however, share the 2 synthesized MIDI performances used in the Con Espressione game (for Bach’s Prelude in C and the second movement of Mozart’s Sonata in C K 545) in mp3 format. These performances can be found in the MIDIPerformances folder.

Dataste CON CAJA

## Overview

Dataste CON CAJA is a dataset for object detection tasks - it contains Medidores TUwN annotations for 5,443 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Historical ownership data of XON by HAP Trading LLC

timaeus/prefix-con dataset hosted on Hugging Face and contributed by the HF Datasets community

The latest closing stock price for Exxon as of June 27, 2025 is 109.38. An investor who bought $1,000 worth of Exxon stock at the IPO in 1984 would have $41,833 today, roughly 42 times their original investment - a 9.60% compound annual growth rate over 41 years. The all-time high Exxon stock closing price was 122.12 on October 07, 2024. The Exxon 52-week high stock price is 126.34, which is 15.5% above the current share price. The Exxon 52-week low stock price is 97.80, which is 10.6% below the current share price. The average Exxon stock price for the last 52 weeks is 112.58. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.