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MDPI (Multidisciplinary Digital Publishing Institute) is a publisher of open-access scientific journals. It publishes over 390 peer-reviewed, open-access journals. MDPI is among the largest publishers in the world in terms of journal article output, and was the largest publisher of open access articles in 2020.
The analysis highlights History, Science and Art as prominent areas in the source structure around MDPI.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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The extracted context around MDPI shows recurring relationship patterns in the source. For example, MDPI → Basel, Benoit, Business Media, Entropy, Environmental Research, International Journal, Marine Drugs, MDPI AG, MDPI Verein, Molecular Diversity Preservation International, Molecular Sciences, Molecules, Public Health, Sensors, Several, Shu-Kun Lin, Springer Science, Springer-Verlag, Turin, Verein Another extracted example is MDPI → Alzheimer's, Andrulis, Anthony Samsel, Ars Technica, Discover, Entropy, Erik, Evolution, In December, Jeffrey Beall, Life, Life's, Nature, Origin, Parkinson's, Popular Science, Stephanie Seneff, Theory, Will MDPI. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
journals published journal article special issues scientific paper research publishing papers list 2021 editorial publisher international open access publishers also
TTTA extracted 193 structured relationships around MDPI. Examples in this analysis include MDPI → Country of origin → Switzerland and MDPI → Distribution → Worldwide. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| MDPI | Country of origin | Switzerland | 1.00 | infobox |
| MDPI | Distribution | Worldwide | 1.00 | infobox |
| MDPI | Founded | 1996 | 1.00 | infobox |
| MDPI | Founder | Shu-Kun Lin | 1.00 | infobox |
| MDPI | Headquarters location | Basel, Switzerland | 1.00 | infobox |
| MDPI | No. of employees | 6,750 (in 2022) | 1.00 | infobox |
| MDPI | Official website | mdpi.com | 1.00 | infobox |
| MDPI | Publication types | Open access scientific journals | 1.00 | infobox |
| MDPI | Status | Active | 1.00 | infobox |
| Web of Science | instance of | while noting limitations compared with commercial bibliographic databases | 0.80 | text |
| Scopus | instance of | while noting limitations compared with commercial bibliographic databases | 0.80 | text |
| MDPI | related to "Who's Afraid of Peer Review?" sting | Cancers | 0.60 | section |
The concept neighborhoods around MDPI bring nearby vocabulary together. In this analysis, examples include Journals, Published and Journal. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For MDPI, one of the stronger structural bridges in this analysis connects MDPI with Challenges. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around MDPI to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Science & Art, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — MDPI · EN edition · Analysis: TopicsToTalkAbout
