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Mnemosyne (named for the Greek goddess of memory, Mnemosyne) is a line of spaced repetition software developed since 2003. Spaced repetition is an evidence-based learning technique that has been shown to increase the rate of memorization.
Research, Features & Software
Explore the main themes, entities and connections around Mnemosyne (software). Start with the topic map, then use the sections below for research and deeper semantic analysis.
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the full topic structure. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
See the strongest relationship patterns around the current topic before diving into the raw triples.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
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| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Mnemosyne (software) | Developer | Peter Bienstman | 1.00 | infobox |
| Mnemosyne (software) | License | AGPL v3 (except sync client), LGPL v3 (sync client) | 1.00 | infobox |
| Mnemosyne (software) | Operating system | Windows, Linux, macOS, Android | 1.00 | infobox |
| Mnemosyne (software) | Release | February 8, 2006; 20 years ago (2006-02-08) | 1.00 | infobox |
| Mnemosyne (software) | Repository | github.com/mnemosyne-proj/mnemosyne | 1.00 | infobox |
| Mnemosyne (software) | Size | 119.3 MB | 1.00 | infobox |
| Mnemosyne (software) | Stable release | 2.11 / November 12, 2023; 2 years ago (2023-11-12) | 1.00 | infobox |
| Mnemosyne (software) | Type | Accelerated Learning & Memory Software | 1.00 | infobox |
| Mnemosyne (software) | Website | mnemosyne-proj.org | 1.00 | infobox |
| Mnemosyne (software) | Written in | Python | 1.00 | infobox |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.