Evidence map›Paper›PMID 36124300›Full record

ArticlePatterns (New York, N.Y.)2022

Systematic analysis of 50 years of Stanford University technology transfer and commercialization.

Weixin Liang, Scott Elrod, Daniel A McFarland, James Zou

Abstract read
In one paragraph

Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Multivariate Pharma Technology Transfer Analysis: Civilization Diseases and COVID-19 Perspective.International journal of environmental research and public health · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Weixin LiangDepartment of Computer Science, Stanford University, 350 Jane Stanford Way, Stanford, CA 94305, USA.
Scott ElrodOffice of Technology Licensing, Stanford University, Stanford, CA 94305, USA.
Daniel A McFarlandGraduate School of Education, Stanford University, Stanford, CA 94305, USA.
James ZouDepartment of Computer Science, Stanford University, 350 Jane Stanford Way, Stanford, CA 94305, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article systematically investigates the technology licensing by Stanford University. We analyzed all the inventions marketed by Stanford's Office of Technology Licensing (OTL) between 1970 to 2020, with 4,512 inventions from 6,557 inventors. We quantified how the innovation landscape at Stanford changed over time and examined factors that correlate with commercial success. We found that the most profitable inventions are predominantly licensed by inventors' own startups, inventions have involved larger teams over time, and the proportion of female inventors has tripled over the past 25 years. We also identified linguistic features in how the inventors and OTL describe the inventions that significantly correlate with the invention's future revenue. Interestingly, inventions with more adjectives in their abstracts have worse net income. Our study opens up a new perspective for analyzing the translation of research into practice and commercialization using large-scale computational and linguistics analysis.

Indexed as

data scienceinnovationtechnology transfer

Identifiers

PMID36124300
PMCPMC9481953

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.