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Data sharing: Science, Ideals in data sharing & Data sharing problems in academia

Data sharing denotes the dissemination of research datasets to enable access and use by other investigators. Policies governing this practice are increasingly instituted by funding agencies, academic institutions, and scholarly journals, reflecting the consensus that transparency and openness constitute foundational principles of the scientific method.

Language: English [EN]
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Data sharing topic overview

The analysis highlights Science, Ideals in data sharing and Data sharing problems in academia as prominent areas in the source structure around Data sharing.

Related topics
29
Source areas
6
Connected nodes
38
Extracted relationships
49
Concept neighborhoods
19
Bridge connections
38

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.

Overview · 11 topics
Ideals in data sharing · 7 topics
Data sharing problems in academia · 4 topics
Differing approaches in different fields · 3 topics
U.S. government policies · 3 topics
International policies · 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.

Explore all related topics Closing gaps

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.

Overview

U.S. government policies

Ideals in data sharing

International policies

Data sharing problems in academia

Differing approaches in different fields

Literature

  • Doi Doi (identifier)
  • ISBN ISBN (identifier)

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Data sharing connects Entity context

The extracted context around Data sharing shows recurring relationship patterns in the source. For example, Data sharing → Aging, Data Conservancy, DataONE, Dr, Earth, Hodes, In, National Institute, National Science Foundation, Reproducible Research, Richard, Some, Stanford University's WaveLab, The, The Data Observation Network, These, WaveLab Another extracted example is Data sharing → Academic Genetics, American Psychological AssociationThe Public, Data, David Blumenthal, Digital Research Data, Domain, Eric Campbell, May, The Selfish Gene Archived, Wayback Machine, Withholding. Use these groups to spot repeated connection types before inspecting the individual relationships.

Data sharing

Top relations

related to Ideals in data sharing · 17
Data sharing → Aging, Data Conservancy, DataONE, Dr, Earth, Hodes, In, National Institute, National Science Foundation, Reproducible Research, Richard, Some, Stanford University's WaveLab, The, The Data Observation Network, These, WaveLab
related to External links · 11
Data sharing → Academic Genetics, American Psychological AssociationThe Public, Data, David Blumenthal, Digital Research Data, Domain, Eric Campbell, May, The Selfish Gene Archived, Wayback Machine, Withholding
related to International policies · 11
Data sharing → Accessibility Policy, AustraliaAustriaEurope, Catalogue, Commission, Data PoliciesIndia, European CommunitiesGermanyUnited Kingdom'Omic Data, FAIRsharing, Government, India, National Data Sharing, Sharing
related to Differing approaches in different fields · 8
Data sharing → Funding, Health, However, National Institutes, National Science Foundation, Private, Requirements, These

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

data sharing research may policies science access authors information national also scientists published study scientific withholding agencies health use funding

Data sharing relationships Subject–Predicate–Object triples

TTTA extracted 49 structured relationships around Data sharing. Examples in this analysis include the National Institutes of Health → instance of → and the responsibility for covering the costs associated with sharing.Funding bodies and Data sharing → related to Differing approaches in different fields → Requirements. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
the National Institutes of Healthinstance ofand the responsibility for covering the costs associated with sharing.Funding bodies0.80text
the National Science Foundation generally impose stronger expectations for data sharinginstance ofand the responsibility for covering the costs associated with sharing.Funding bodies0.80text
Data sharingrelated to Differing approaches in different fieldsRequirements0.60section
Data sharingrelated to Differing approaches in different fieldsThese0.60section
Data sharingrelated to Differing approaches in different fieldsFunding0.60section
Data sharingrelated to Differing approaches in different fieldsNational Institutes0.60section
Data sharingrelated to Differing approaches in different fieldsHealth0.60section
Data sharingrelated to Differing approaches in different fieldsNational Science Foundation0.60section
Data sharingrelated to Differing approaches in different fieldsHowever0.60section
Data sharingrelated to Differing approaches in different fieldsPrivate0.60section
Data sharingrelated to External linksThe Selfish Gene Archived0.60section
Data sharingrelated to External linksWayback Machine0.60section

Related concept clusters Concept neighborhoods

The concept neighborhoods around Data sharing bring nearby vocabulary together. In this analysis, examples include Sharing, Research and Science. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Data sharing
    • Sharing
    • Research
    • Science
    • May
    • Published
    • National
    • Public
    • Withholding
    • Study
    • Authors
    • Policies
    • Access
  • data sharing
    • Sharing
    • Research
    • National
    • Nih
    • Science
    • May
    • Policies
    • Published
    • Use
    • Public
    • Scientific
    • Scientists
  • raw data
    • Sharing
    • Research
    • Science
    • May
    • Published
    • National
    • Withholding
    • Study
    • Authors
    • Policies
    • Access
    • Nih
  • data archiving
    • Sharing
    • Research
    • Policies
    • Number
    • Funding
    • Journals
    • Science
    • May
    • Published
    • National
    • Public
    • Withholding
  • data processing
    • Sharing
    • Research
    • Science
    • May
    • Published
    • National
    • Withholding
    • Study
    • Authors
    • Policies
    • Access
    • Nih
  • national data sharing and accessibility policy – government of india
    • Sharing
    • Research
    • Science
    • National
    • Nih
    • May
    • Policies
    • Health
    • Published
    • Use
    • Public
    • Scientific
  • ideals in data sharing
    • Sharing
    • Research
    • National
    • Nih
    • Science
    • May
    • Policies
    • Published
    • Use
    • Public
    • Scientific
    • Scientists
  • data sharing problems in academia
    • Sharing
    • Research
    • National
    • Nih
    • Science
    • May
    • Policies
    • Published
    • Use
    • Public
    • Scientific
    • Scientists

Connections between topic areas Semantic bridges

For Data sharing, one of the stronger structural bridges in this analysis connects Data sharing with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Data sharing — Overview · splits 27 ⟂ 12
Data sharing — Ideals in data sharing · splits 31 ⟂ 8
Data sharing — Data sharing problems in academia · splits 34 ⟂ 5
Data sharing — U.S. government policies · splits 35 ⟂ 4
Data sharing — Differing approaches in different fields · splits 35 ⟂ 4
Data sharing — Literature · splits 36 ⟂ 3

Map overview Semantic statistics

Data sharing

Nodes39
Edges38
Triples49
Avg. degree1.95
Density0.051282
Components1

Source & methodology

TTTA analyzes the structure around Data sharing to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Science, Ideals in data sharing & Data sharing problems in academia, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Data sharing · EN edition · Analysis: TopicsToTalkAbout

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