Free recovers data from software for mac

Recover eyeTV2 Code is a Python script that will recover the registration code from your eyeTV 2 installation.

Data Crow 1.9 brings a powerful, perfect and popular media cataloguer. For those who like to collect software, books, movies, and/or music (audio CD's, MP3, OGG, FLAC, and APE files support). The program needs only a little input from you to register your collection. For example, registering a piece of software is as easy as inputting the software's name or publisher and Data Crow retrieves the rest of the information from Amazon. For audio files just browse to the directory that contains the audio files and Data Crow reads and imports the embedded tag information (artist, album name, and track name).

iList Data 3.8.0 is created to be an effective and helpful database software which can be used for the Macintosh. Create databases for daily use at home, educational use at school, and business use at work.

Major Features:

1. Ten database templates: Create your first database in seconds
2. User designed windows for creating, editing, and finding data
3. Ten find modes including find on field, find by value, and find parent
4. Support for text, number, date, and time fields plus integrated support for boolean, path, URL, color, JPEG, and calculation values
5. Built-in relational support for searching and displaying data across tables
6. Mac OS X compliant interface controls including date and time fields, color swatches, and image canvases
7. On-the-fly calculations and column summaries
8. Dynamic reports: Retrieve both the data and shell of a report from the database itself
9. Nine window sets: Save and recall custom interface setups with one click
10. Search history and bookmarks
11. Four batch editing modes including multi edit and auto number
12. Inline editing and pop-up editing of fields via keyword lists
13. Inline and summary graphing. View dot, line, bar, and fill graphs inline with your data
14. Advanced SQL editing for experienced users and built-in help by example support for novices
15. A built in resizable image viewer and full screen slideshow mode
16. A dedicated thumbnail palette for viewing JPEG and other web-based images
17. Speech synthesis
18. Apple iLife integration: Send images to iPhoto 5 with one click
19. Microsoft Office X and 2004 integration: Send record lists to Excel and Word with one click
    

Requirements:
Mac OS X 10.1 or later, Driver 10 v1.1.

Enhancements:
Version 1.1.5 adds the following:

• Global updates to the application.
• Minor update for Japanese localization.
• Updates for full Mac OS 10.3.x compatibility.
• Updates to the Volume Structure validation and repair routines.
• Updates to the Optimization and Rebuild Volume Structure routines

MDM uses a neural network to learn correlations between notes and control changes in a MIDI file. After training MDM can augment a live MIDI stream, adding control changes based on notes received.

Briefly: use MDM by connecting a MIDI device, open preferences and set MIDI input and output ports. Open a MIDI file, create a new neural net and train. Now play notes on your MIDI device, MDM will automatically add control changes.

This is an early alpha release, buggy and incomplete. Please send feedback! Note that this alpha version only reads from the first track in a MIDI file, and only uses the first controller. Look at the included MIDI file in the working/midi folder for details.

MIDI devices not showing on OS X? Install the Mandolane library into MDMs lib folder (http://www.mandolane.co.uk/index.html)

Fast Data Transfer (FDT) is an Application for Efficient Data Transfers which is capable of reading and writing at disk speed over wide area networks (with standard TCP). It is written in Java, runs an all major platforms and it is easy to use.
FDT is based on an asynchronous, flexible multithreaded system and is using the capabilities of the Java NIO libraries.
FDT may be used to stream a large set of files across the network, so that a large dataset composed of thousands of files can be sent or received at full speed, without the network transfer restarting between files.
Main features:
- Streams a dataset (list of files) continuously, using a managed pool of buffers through one or more TCP sockets.
- Uses independent threads to read and write on each physical device
- Transfers data in parallel on multiple TCP streams, when necessary
- Uses appropriate-sized buffers for disk I/O and for the network
- Restores the files from buffers asynchronously
- Resumes a file transfer session without loss, when needed