Check Claims Before You Publish

Evidence & provenance

1. Primary author metadata (paper list, counts, h-index=5, total citations=104) was included in the request and used to compute the metric visuals above.
   User-supplied author metadata
2. OpenAlex name-disambiguation matches show multiple individuals named "Yuyan Liang"; the dataset includes higher-impact profiles (e.g., Yao-Yun Liang with h-index 17) and several lower-impact profiles. This creates ambiguity when interpreting metrics for the single supplied identity — disambiguation risk is non-trivial and must be resolved by ORCID or institutional affiliation before strong claims about career influence are made.
   OpenAlex name matches

Critical analysis — strengths, weaknesses, blind spots

• Strengths: Diverse topical reach (clinical reproductive health, environmental impacts, assay development, structural biology) suggests methodological flexibility and interdisciplinary capability (useful for translational or applied projects).
• Weaknesses: Low mean citations per paper (104 citations / 20 papers = ~5.2 citations/paper) and h-index 5 indicate limited lasting impact within primary research communities; many titles are clinical or applied which sometimes attract fewer citations but also suggests lower basic-science visibility.
• Traceability & disambiguation: No institutional affiliation or ORCID in the supplied metadata increases the chance of conflating multiple authors with similar names; OpenAlex results confirm multiple distinct profiles under this name string.
• Topical focus: The publication list shows heterogeneous topics which can be a strength for applied work but usually slows deep accumulation of field-specific citations that build high h-indexes and central influence.
• Potential biases to watch: Without access to full texts or author order/roles, we cannot assess relative contribution (first/last/middle author), funding sources, or conflicts of interest — all affect interpretation of scientific leadership.

What evidence would shift this evaluation?

• Definitive author disambiguation (ORCID or consistent institutional affiliation) linking these 20 papers to a single researcher would either confirm or revise metrics substantially.
• Contribution-level data (which papers are first/last, grant PI status) would increase confidence about the author's independent scientific leadership.
• Access to citation contexts (are the citations supportive, methodological references, or perfunctory?) would help judge scientific influence quality rather than quantity.

Actionable recommendations

1. Obtain and use an ORCID and ensure it is present on all publications to remove disambiguation risk.
2. Concentrate publications in 1–2 core domains to build deeper, cumulative citation impact, and pursue review articles where appropriate (higher immediate visibility).
3. Where possible, take first/last authorship on key works and deposit data/analysis code in public repositories to increase reuse and citations.
4. Clarify affiliations and list consistent institutional addresses on manuscripts for better tracking and networking.

User-supplied metrics and titles used directly for counts and topical binning; OpenAlex name-match summary used to evaluate disambiguation risk.

User-supplied author metadata

OpenAlex name matches