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Disk Detective: Applications & Science
Disk Detective is the first NASA-led and funded-collaboration project with Zooniverse. It is NASA's largest crowdsourcing citizen science project aiming at engaging the general public in search of stars, which are surrounded by dust-rich circumstellar disks, where planets usually dwell and are formed. Initially launched by NASA Citizen Science Officer…
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Disk Detective topic overview
The analysis highlights Applications and Science as prominent areas in the source structure around Disk Detective.
Related topics
68
Source areas
7
Connected nodes
75
Extracted relationships
76
Concept neighborhoods
30
Bridge connections
75
What this topic covers Research coverage
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.
Details · 26 topics
Discoveries · 21 topics
Overview · 8 topics
False positive rate and applications · 6 topics
Classification · 4 topics
Advanced user group · 2 topics
Seeking objects · 1 topics
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.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. 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.
How Disk Detective connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Disk Detective shows recurring relationship patterns in the source. For example, Disk Detective → Australia's SkyMapper, Digitized Sky Survey, DSS, Explorer, In April, January, May, NASA's Wide-field Infrared Survey, Pan-STARRS, Panoramic Survey Telescope, Rapid Response System, SDSS, Sloan Digital Sky Survey, Survey, The, Two Micron All Sky, Version, WISE, Zooniverse, Zooniverse's Another extracted example is Disk Detective → All, American Astronomical Society, At, Be, Carina, Centaurus, Columba, HD, Peter Pan, Scorpius, The, The Disk Detective, Three, Together, WISE J080822, WISEA J164540. Use these groups to spot repeated connection types before inspecting the individual relationships.
Disk Detective
Top relations
related to Details· 20
Disk Detective → Australia's SkyMapper, Digitized Sky Survey, DSS, Explorer, In April, January, May, NASA's Wide-field Infrared Survey, Pan-STARRS, Panoramic Survey Telescope, Rapid Response System, SDSS, Sloan Digital Sky Survey, Survey, The, Two Micron All Sky, Version, WISE, Zooniverse, Zooniverse's
related to Discoveries· 16
Disk Detective → All, American Astronomical Society, At, Be, Carina, Centaurus, Columba, HD, Peter Pan, Scorpius, The, The Disk Detective, Three, Together, WISE J080822, WISEA J164540
has application· 11
Disk Detective → AllWISE, Dupont/Retrocam, From, It, MAST, Mikulski Archive, Out, Robo-AO, SED, Space Telescopes, The
Disk Detective relationships Subject–Predicate–Object triples
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 76 structured relationships around Disk Detective. Examples in this analysis include Disk Detective → Available in → English, Spanish, German, Polish, Chinese, Indonesian, Hungarian, Romanian, Russian, Portuguese and Disk Detective → Commercial → No. The table shows each extracted connection, where it came from and its confidence.
first NASA-led and funded-collaboration project with Zooniverse
0.90
text
Disk Detective
has application
The
0.60
section
Disk Detective
has application
AllWISE
0.60
section
Disk Detective
has application
Out
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Disk Detective bring nearby vocabulary together. In this analysis, examples include Disk, Project and Candidates. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Disk Detective
Disk
Project
Candidates
First
Found
Group
Images
Zooniverse
New
Wise
Dwarf
Team
disk detective
Disk
Project
Zooniverse
Candidates
First
Team
Images
Found
Group
Website
Wise
New
circumstellar disks
Stars
New
Dwarf
Pan
Peter
Young
Debris
Disks
Science
Found
Project
Candidates
circumstellar disk
Project
Dwarf
Stars
Young
Candidates
Disks
Science
First
Found
Group
Images
Zooniverse
debris disks
Stars
New
Pan
Peter
Young
Debris
Disks
Found
Group
Project
Candidates
Nasa's
advanced user group
Citizen
Group
Good
Science
Candidates
Debris
Classification
Launched
Project
Follow-up
Found
Literature
citizen science
Science
Advanced
Team
Launched
Classification
Group
Project
Circumstellar
Dwarf
Good
Nasa's
Pan
peter pan disks
Peter
Stars
New
Pan
Debris
Found
Project
Candidates
Dwarf
Nasa's
Team
Young
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Disk Detective, one of the stronger structural bridges in this analysis connects Disk Detective with Details. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Disk Detective
Nodes76
Edges75
Triples76
Avg. degree1.97
Density0.026316
Components1
Source & methodology
TTTA analyzes the structure around Disk Detective to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications & Science, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
