Reuse data from prior literature review papers and curated repositories (Consume)

Reuse well-documented datasets, search strategies, and screening/extraction decisions from prior reviews when they fit the current review’s scope.

Why this matters

• Cumulative research: Reuse makes review work additive over time—new evidence extends existing curated datasets instead of repeatedly rebuilding them.
• Efficiency without sacrificing rigor: Prior review data reduces duplicated effort while keeping the workflow systematic.
• Stronger comparability: Shared extraction forms, coding frames, and decision logs enable longitudinal and cross-review comparisons.
• Better coverage and fewer blind spots: Prior queries and seed sets surface synonyms and venues that are easy to miss.

Practical implementation

• Assess fit before reuse (scope, timeframe, databases, inclusion criteria) and document what is reused as-is vs. adapted.
• Reuse and adapt search strategies and publish them to searchRXiv (search queries), so others can discover, cite, and build on them.
• Reuse screening decision datasets (included/excluded decisions) from SYNERGY datasets to calibrate screening and support transparent decision lineage.
• Import prior seed sets (key papers) to validate recall and align interpretation of inclusion criteria.
• Contact prior authors/review teams to request reusable samples (e.g., screening decisions, extraction sheets, codebooks) when they are not publicly available.
• Preserve provenance: store reused artifacts with citations/links and record reuse decisions in the protocol (what, from where, and why).

Resources

• SYNERGY: open dataset on study selection in systematic reviews.
• searchRXiv: shared search strategies for systematic reviews.
• Literature Reviews in Information Systems: collection of literature reviews in the Information Systems discipline.