Difference between revisions of "University Patents"

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{{McNair Projects
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{{Project
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|Project Title=University Patents,
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|Has project output=Data,Content,Guide
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|Topic Area=Patents and Innovation,
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|Has sponsor=McNair Center
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|Owner=Julia Wang,
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|Has title=University Patents
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|Keywords=Patent,
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|Has owner=Julia Wang, Meghana Pannala, Anne Dayton
 +
|Has keywords=Patent
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|Has project status=Active
 
}}
 
}}
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 +
==Research Paper==
 +
Since university system file for patents out of a central office, its mot possible to determine where the research was conducted. This presents problems when ranking universities by patent production.
 +
 +
In order to publish this paper as a Baker Institute Research Report, we will need to do more to resolve this issue beyond weighting for the University of California.
 +
 +
==Carnegie Classifications==
 +
We propose using public data from the  Carnegie Classifications of Institutes of Higher Education to resolve the university systems issue.
 +
 +
TASKS:
 +
1) Separate schools affiliated with universities/university systems in our rankings from all others included in Carnegie data.
 +
 +
2)Group these by system.
 +
  Determine the number of schools in each system
 +
3) Determine their classification.
 +
  1 if Doctoral 0 if not (In Carnegie 2015 Basic Classification [BASIC2015] 15, 16, 17)
 +
  1 if 4-year medical school 0 if not (BASIC2015 25) Check (BASIC2015 26)
 +
  1 if 4-year engineering 0 if not (BASIC2015 27) Check (BASIC2015 28)
 +
4) Determine the total count of the categories in number 3 for each university system.
 +
 +
5) Use total count to weight regressions
 +
 +
6) Create ranking tables that rank university systems and universities by dividing number of patents by number of doctoral, med-school, 4-year engineering.
  
 
==Project Overview==
 
==Project Overview==
−
Goal: list of all universities and # of patents each university and patent licensing
+
Goal: list of all universities and # of patents associated with each university and patent licensing activity
  
 
patent reassignment to startups associated with these universities
 
patent reassignment to startups associated with these universities
Line 13: Line 38:
 
clinical trials (from Catherine) data to rank universities R&D engagement
 
clinical trials (from Catherine) data to rank universities R&D engagement
  
−
identify list of universities: board of regents, universities in patent data (find patterns associated with university assignees)
+
identify list of universities: board of regents, universities in patent data (find patterns associated with university assignees) -- @Julia: Where did we get this list? I'd love to be able filter by country - Meghana
  
 
AUTM?
 
AUTM?
 +
 +
==Timeline to Deliverable & Documentation==
 +
 +
Meghana: focus on grants
 +
 +
Julia: focus on patent counts
 +
 +
Working in E:McNair/University Patents
 +
 +
'''3/20-3/24'''
 +
 +
Learn SQL, clean data
 +
 +
'''3/27-3/31'''
 +
 +
Counts of data, name matching
 +
 +
Patent Counts
 +
*Ran the matcher in the server, only matched ~40,000 entries
 +
*Working with Jeemin to develop matcher
 +
 +
'''4/3-4/7'''
 +
 +
Counts of data, name matching
 +
 +
Patent Counts
 +
*Jeemin is a savior, matched and counted 128,000 entries (Jeemin_matcher_matched.txt), manually went through the 6,000 unmatched (Jeemin_matcher_unmatched.txt), Check [[University Patent Matching]]
 +
*Ranking for all time is in Patent Counting ( >Total (All Time) )
 +
*Need to talk with Meghana about grant data
 +
*Need to find variables for regression, develop ranking for last decade and last year
 +
*Also need to account for school size (faculty? students? research funding?)
 +
 +
'''4/10-4/14'''
 +
 +
Develop ranking
 +
 +
Patent Counts
 +
*Finish all of above
 +
*Start drafting
 +
*Load data onto database -> E:McNair/University Patents/unidatajoin.sql in univ (database)
 +
 +
'''4/17-4/21'''
 +
 +
*Combined Patent Counts, R&D Expenditure, NSF Grant Data
 +
*STATA regressions in E:McNair/University Patents/STATA
 +
**for historical data
 +
 +
'''4/24'''
 +
 +
From Ed:
 +
*Copied inventors table out of PatentDB database on RDP
 +
*Script in E:McNair/PatentData, MoveInventors.sql
 +
*Copy of data in Z:AllPatents and ran import into patent (database)
 +
For Marcela:
 +
*Need table of all patents associated with matches, join patent numbers with inventors
 +
*Group by last name and first initial (=1 inventor)
 +
 +
'''5/1'''
 +
 +
*Joined patent numbers with inventors -> unipatentinventorcount.sql
 +
**Z:univ/InventorCounts.txt
 +
*Decade patent counts -> Z:univ/DecadePatentCounts
 +
*Calculated Hirschman-Herfindahl index -> E:McNair/University Patents/Inventor Counts and Superstars
 +
 +
'''5/5'''
 +
 +
Artifacts:
 +
#10-Year Ranking All
 +
#10-Year Ranking Private
 +
#10-Year Ranking Public
 +
#Top 10 Movers and Shakers
 +
#Graph of Concentration of Innovation
 +
#Number of University Patents Over Time
 +
#Inventor Concentration
 +
#Regression Table
 +
 +
*Created Graph of Concentration of Innovation -> E:McNair/University Patents/Inventor Counts and Superstars -> GraphUPS
 +
*Created Top Inventors Chart -> University Patents/Report Articles/Inventor Rankings
 +
 +
'''5/9'''
 +
 +
*Figuring out what's up with the university puller
 +
*Developing new 10-Year Rankings
 +
*Need to update above two articles
 +
*Need to load into STATA and re-run regressions -> University Patents/STATA/univdata.do
 +
 +
'''5/10'''
 +
 +
*Figured out what was wrong with the data pull and now everything's good!
 +
*Updated information on the draft below
 +
*Fantastic Artifacts and Where to Find Them
 +
**10-Year Ranking All
 +
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
 +
**10-Year Ranking Public
 +
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
 +
**10-Year Ranking Private
 +
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
 +
**Top 10 Movers & Shakers (% change between first 5 years (2006-2010) vs next 5 years (2011-2015) within top 100
 +
**Graph of Concentration of Innovation (numbers of patents)
 +
***Pie graph (University Patents > Report Artifacts > Concentration of Innovation)
 +
***(Excel sheet where this came from is University Patents > Inventor Counts and Concentrations)
 +
**Trends Over Time
 +
***Total vs. top 10
 +
***Graph (University Patents > MoversandTop10)
 +
**Inventor Concentration
 +
***Chart (University Patents > Report Artifacts > Inventor Rankings)
 +
**Reg Table
 +
***+Explanation of variables
 +
***(STATA do-file with the most statistically significant regressions is in University Patents > STATA > univdata.do)
 +
 +
Issue brief draft (VERY rough): https://docs.google.com/a/rice.edu/document/d/1MayXQEQ_pM0LMeiV39iaNFjtMZYSB-Ah9gCx1rcgW9M/edit?usp=sharing
 +
 +
==Ranking Development Notes==
 +
Sources of University Funding R&D [https://www.aaas.org/page/rd-colleges-and-universities]
 +
*Roughly 60% comes from the Federal Government
 +
*Less than 10% comes from state and local governments
 +
*Roughly 20% is from the University itself (endowment?? tuition? not 100% sure)
 +
*Roughly 5% is from private industry
 +
*Less than 10% Other
 +
 +
Federal Government Funding Breakdown [https://www.aaas.org/page/rd-colleges-and-universities]
 +
*Roughly 60% of federal funding is from the NIH
 +
*Roughly 15% is from the NSF
 +
*Roughly 11% from Department of Defense
 +
*Roughly 4% from NASA
 +
*Roughly 4% from the Department of Energy
 +
*Roughly 3% from the USDA
 +
*Roughly 1% from the EPA (probably will go to zero with new admin)
 +
*Roughly 1% from the Department of Education
 +
*Marginal amounts from everywhere else (<1%)
 +
 +
 +
NOTES
 +
*overall ranking (total number of patents)
 +
*weight by faculty (people)
 +
*weight by research funding
 +
*weight by endowment (maybe)
 +
*public vs private
 +
*individuals: surname and first initial
 +
**ranking by inventors - who are the superstar inventors and where are they (define superstar inventor - idk 20 patents and rank universities by how many superstars are present
 +
**Herfindahl-Hirschmann Index
 +
 +
==Key Words==
 +
 +
===Universities (Patent Assignees)===
 +
BOARD OF REGENTS - pretty much exclusively describes universities
 +
 +
UNIVERSITY - also exclusively describes universities
 +
*Can we do a close match with University? (its the only word on this list that's frequently misspelled)
 +
 +
SCHOOL - often used in combination with medicine or medical to describe medical schools
 +
*also are used to describe actual schools (i.e. HISD) and other businesses
 +
*sometimes present in addresses - can we cut off the adresses?
 +
*also used in combination with Business (i.e. Harvard Business School)
 +
*use school only in conjuction with medical, medicine, and business
 +
 +
COLLEGE
 +
*need to cut off addresses for this to work
 +
*need to ensure that we include the space after
 +
*excl. College Boulevard, college blvd,  etc.
 +
*double check everything with College Park
 +
 +
INSTITUTE OF TECHNOLOGY - almost exclusively describes universities but sometimes describes external research institutes
 +
 +
POLYTECHNIC - exclusively university (i.e. RPI, Virginia Tech)
 +
 +
RESEARCH FOUNDATION: VAST majority are university
 +
*Exclusions: it's really difficult to distinguish between university affiliated and not, besides just looking them up on the google
 +
**Novartis
 +
**Progeria
 +
**Washington Research Foundation
 +
**Blood Center of Wisconsins
 +
**Mental Hygiene
 +
**Celiac Sprue
 +
**Fidia
 +
**Samuel Waxman Cancer Center
 +
***founded by a faculty member at Mt. Sinai Medical School,not affil w/a single university but researchers are also usually faculty at various universities
 +
**lifenet
 +
**HealthPartners
 +
**Dr. Susan Love
 +
**La Jolla Cancer Research Foundation
 +
**Children's Hospital (? grants degrees but is a hospital system)
 +
**Medforte
 +
**International Mask
 +
**Palo Alto Medical Foundation
 +
 +
====Exclusions====
 +
*LLC
 +
*LLP
 +
 +
===Technology Transfer Offices===
 +
 +
====Job Titles====
 +
*Patent Portfolio Manager
 +
*Intellectual Property Manager
 +
*Licensing Associate/Licensing Liaison
 +
*Biological Materials Specialist
 +
*Industrial Contracts Officer
 +
*Technology Licensing Officer
 +
*Associate Officer
 +
*Technology Licensing Associates
 +
*Patent Coordinator/Patent Administrator
 +
*IP Portfolio Specialist
 +
*MTA Coordinator
 +
*Sponsored Research Administrator
 +
*Technology Transfer Specialist/Technology Transfer Associate
 +
*Contracts Associate
 +
*Portfolio Director
 +
*Contracts Specialist
 +
*Industry Contracts Analyst
 +
*Patent Prosecution Analyst
 +
*(Associate) Director of Intellectual Property
 +
*(Associate) Director of Technology Transfer Policy
 +
*(Associate) Director of Technology Transactions
 +
*(Associate) Director of Agreement Administration
 +
*Senior Associate - watch
 +
*Patent Attorney/Patent Agent/Patent Counsel/Paralegal - watch
 +
 +
====TTO Names====
 +
*Office of Technology Transfer/Technology Transfer Office
 +
*Office of Technology Licensing/Technology Licensing Office
 +
*Office of Technology Development
 +
*Technology Ventures
 +
*Innovation Services
 +
*Intellectual Property & Industry Research Alliances
 +
*InnovationAccess
 +
*Invention Transfer Group
 +
*Technology Development Group
 +
*Office of Business Development
 +
*Office of Technology Commercialization
 +
*Office of Innovation and Commercialization
 +
*Innovation, Technology & Alliances
 +
*Technology & Industry Alliances
 +
*Office for Management of Intellectual Property
 +
*Innovation & Partnerships Office
 +
 +
===University-Affiliated Startups (Patent Assignees)===
  
 
==What to get from other people==
 
==What to get from other people==
Line 21: Line 283:
 
Avesh has clinical trial data on wiki and bulk drive (FDA Trials Data), need to build it into normal form, use clinical trials data to rank R&D engagement of universities, will be building up portfolios of different types of companies
 
Avesh has clinical trial data on wiki and bulk drive (FDA Trials Data), need to build it into normal form, use clinical trials data to rank R&D engagement of universities, will be building up portfolios of different types of companies
  
−
Marcela is cleaning up patent data
+
Marcela is cleaning up patent data, has given patent assignee names -- got it! Could we filter by country and get strictly American names?
  
 
Catherine has zip codes of medical centers, use to look through patent data
 
Catherine has zip codes of medical centers, use to look through patent data
Line 33: Line 295:
 
==To Do==
 
==To Do==
  
−
1. lit review (look at for 1-2 days), look for holistic picture, what has/hasn’t been done? what are the questions people are asking?
+
*Create ranking
 +
**Based on patent portfolio
 +
**Licensing income
 +
**Quality of TTO
 +
**Publications (specifically based on research)?
 +
**Amount received in grants/Amount of grants - we have numbers on amount of NIH grants and NSF grants
 +
***NIH/NSF/STTR
 +
**Control for school size/endowment
 +
**Startups affiliated with university
 +
***Value generated from these startups? (ask Avesh)
 +
**Existence of entrepreneurship program
 +
**Geography
 +
 
 +
*Describe average patent portfolio of university
 +
**Number of patents
 +
**Maybe categorize by research area?
 +
*Describe how universities license
 +
**USPTO earned revenue
  
−
2. look at patent data, determine how to find universities in patent database: board of regents, universities in patent data (find patterns associated with university assignees)
+
Deliverable
 +
*Ranking
 +
*Explanation of what makes a good ranking/what factors a school needs to be more successful
 +
 
 +
Artifacts
 +
*Grant Data
 +
*Research Funding
 +
*Star Scientists
 +
*Regression with variables from Carnegie Classification (10-15 variables)
 +
*Licensing (?)
 +
 
 +
===What We Have===
 +
 
 +
*Lit Review
 +
*List of all post-secondary institutions (University Accreditation 3.2016_trim) - U.S. Department of Education
 +
*Classification of universities (Carnegie Classification 2015) - Carnegie Classification
 +
** includes all accredited degree-granting colleges and universities that are included in the National Center for Education Statistics Integrated Postsecondary Education Data System (IPEDS).
 +
** Coverage: 4666 institutions of higher learning
 +
** Variables: includes 94 variables
 +
*** Enrollment data - subdivided into graduate and undergraduate
 +
*** degrees conferred - divided into level and division
 +
*** information on faculty (number, divided into levels i.e. assistant, assoc. and full time)
 +
**** coverage for this variable is REALLY LOW
 +
*** non-faculty research staff (includes post docs)
 +
*** Science and Engineering Research and Development expenditures
 +
*** Non-STEM research and Development expenditures
 +
*Amount spent on R&D (Higher Ed R&D Rankings) - National Science Foundation
 +
** includes just total R&D expenditures
 +
*STTR full data 1995-2015 (STTRData) - Small Business Administration
 +
*University Patent Numbers 1969-2012 - USPTO
 +
*Patents that resulted from NIH grants
 +
*Information about NSF grants
 +
 
 +
===What We Need===
 +
 
 +
*List of university-affiliated startups
 +
**looks like AUTM STATT data has this because it was used in a study by Yael Hochberg [https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2418000]
 +
**[https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-research-universities-2015/#3cb9829b3823| Rankings of Research Universities (2015)]
 +
**FORBES ranked the country’s most entrepreneurial schools based on the numbers of alumni and students who have identified themselves as founders and business owners on LinkedIn (adjusted to total student body size). This year we rank both research universities and [https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-colleges-2015/#2b2909e04604| smaller colleges] separately.
 +
*data about TTOs (# and quality of employees)
 +
**LinkedIn crawler: working with Jeemin and Peter
 +
*licensing data from AUTM [http://www.autm.net/statt] or already within the patent data?
 +
 
 +
==Name Matching==
 +
*Jeemin has found all the correct and incorrect spellings of assignees with "University"
 +
*Julia working on "Institute"
 +
*We have a list of Universities that have patents from NIH grants, which could be a starting point for our ranking constituents
  
 
==Lit Reviews==
 
==Lit Reviews==
Line 182: Line 507:
  
 
*Current emphasis on licensing patents, but most university TTOs do not generate enough to cover operating expenses
 
*Current emphasis on licensing patents, but most university TTOs do not generate enough to cover operating expenses
 +
*Asymmetry in distribution of resources across the university system, only a few universities benefit from high licensing revenues
 +
**top 8 universities took 50% of licensing income, top 16 universities took nearly 75% of income
 +
**only 37 universities have been in the top 20 during the last decade (listed in University Patents > LicensingGrossIncome2003-2012.txt)
 +
*Universities face much more pressure to demonstrate the economic impact of their R&D contracts
 +
**97.6% of total public contracts obtained by universities are for basic/applied research
 +
**2% of university research delivers ready-to-use technologies
 +
**academic research is much more dependent on government funding than industry research is
 +
*Government pressure for universities to be more responsive to market forces, more entrepreneurial, more attuned to needs of industry
 +
*TTOs are costly to universities
 +
*1979: 30 universities with TTO -> 1999: 174 universities (AUTM)
 +
*2010: 206 US universities have very high or high research activity, all with TTOs (but not all report to AUTM) (Carnegie Classification of Higher Education)
 +
*Over last 20 years, 87% of universities did not break even
 +
*Clash between aims of university (non-profit) and TTO (essentially a business unit)
 +
*By nurturing start-ups, TTOs can add most economic value to an invention disclosure
 +
*2003: universities initiated 330 startups, 2012: 647 startups
 +
*2012: 3715 operating university startups, almost double the number in 2000 (AUTM 2013)
 +
*Startups mitigate financial risk by reducing reliance on blockbuster patents, increase diversification of portfolio
 +
*Policy proposal: the government should increase funding for Small Business Technology Transfer Program (directed to university start-ups)
 +
**H.R.2981: reapportions STTR funds at 0.05% in the next 2 years and 0.1% henceforth for university enterprises at proof of concept stage
 +
*Should increase portion that agencies set aside for STTR
 +
**STTR should have Phase III like SBIR to fund commercialization efforts
 +
*Need equitable distribution across university system
  
 
=== The Bayh-Dole Act and High-Technology Entrepreneurship in U.S. Universities: Chicken, Egg, or Something Else? (2004) ===
 
=== The Bayh-Dole Act and High-Technology Entrepreneurship in U.S. Universities: Chicken, Egg, or Something Else? (2004) ===
Line 228: Line 575:
 
*University-owned patents (assigned to universities or their TTOs) and university-invented patents (assigned to university-affiliated authors)
 
*University-owned patents (assigned to universities or their TTOs) and university-invented patents (assigned to university-affiliated authors)
  
−
*matching lists: matching lists of university staff/professors with inventor data
+
Matching lists: traditional matching of lists of university staff/professors with inventor data
−
**time-consuming, costly, possibly not updated
+
*time-consuming, costly, possibly not updated
−
**typically limited to tenured professors
+
*typically limited to tenured professors
−
**does not include Ph.D. students, assistants, lecturers
+
*does not include Ph.D. students, assistants, lecturers
−
*matching authors of scientific publications and inventors on patents
+
 
−
**beware of homonyms
+
Matched authors of scientific publications and inventors on patents
 +
*beware of homonyms
  
 +
Patent data from EPO Worldwide Patent Statistical Database (PATSTAT)
 +
 +
Publication data from Elsevier (Scopus)
 +
 +
Country of origin: Inventor Country/Assignee Country/Applicant Country = Germany, Location of Organization to which author is affiliated =Germany
 +
*Restrict dataset to authors from German organizations and inventors with residence in Germany
 +
 +
The organization
 +
 +
Names: To keep precision high, leave out names with initials only
 +
 +
Location: Postal/zip codes (PATSTAT provides address of inventor's residence, SCOPUS provides info for organization, 96.5% of first digit of inventor and organization postal codes are the same, 85.9% first two digits)
 +
 +
Time window: 2-year window between application and publication date
 +
 +
=== Singh A. and Wong P.K: University patenting activities and their link to the quantity and quality of scientific publications (2009) ===
  
−
===Singh A. and Wong P.K: University patenting activities and their link to the quantity and quality of scientific
 
−
publications (2009) ===
 
 
[https://pdfs.semanticscholar.org/bb3a/df09ca693cdcff6c1f6aaf18113cabec2655.pdf]
 
[https://pdfs.semanticscholar.org/bb3a/df09ca693cdcff6c1f6aaf18113cabec2655.pdf]
 
==== Findings ====
 
==== Findings ====
Line 248: Line 610:
 
==== Data Sources ====
 
==== Data Sources ====
 
* USPTO Patenting Data
 
* USPTO Patenting Data
−
* Shanghai Jia Tong University's Academic Ranking of World Universities (ARWU)
+
* Shanghai Jiao Tong University's Academic Ranking of World Universities (ARWU)
 
* Times Higher Education Supplement's World University Ranking (WUR)
 
* Times Higher Education Supplement's World University Ranking (WUR)
 
* Quantity was measured by counts of publications
 
* Quantity was measured by counts of publications
Line 255: Line 617:
 
** At the institutional level: patents assigned to universities
 
** At the institutional level: patents assigned to universities
 
** At the individual level: patents with university researchers as the inventors
 
** At the individual level: patents with university researchers as the inventors
−
 
−
 
  
 
==== Critiques ====
 
==== Critiques ====

Latest revision as of 12:42, 21 September 2020


Project
University Patents
Project logo 02.png
Project Information
Has title University Patents
Has owner Julia Wang, Meghana Pannala, Anne Dayton
Has start date
Has deadline date
Has keywords Patent
Has project status Active
Has sponsor McNair Center
Has project output Data, Content, Guide
Copyright © 2019 edegan.com. All Rights Reserved.


Research Paper

Since university system file for patents out of a central office, its mot possible to determine where the research was conducted. This presents problems when ranking universities by patent production.

In order to publish this paper as a Baker Institute Research Report, we will need to do more to resolve this issue beyond weighting for the University of California.

Carnegie Classifications

We propose using public data from the Carnegie Classifications of Institutes of Higher Education to resolve the university systems issue.

TASKS: 1) Separate schools affiliated with universities/university systems in our rankings from all others included in Carnegie data.

2)Group these by system. 

  Determine the number of schools in each system

3) Determine their classification. 

 1 if Doctoral 0 if not (In Carnegie 2015 Basic Classification [BASIC2015] 15, 16, 17)
 1 if 4-year medical school 0 if not (BASIC2015 25) Check (BASIC2015 26)
 1 if 4-year engineering 0 if not (BASIC2015 27) Check (BASIC2015 28)

4) Determine the total count of the categories in number 3 for each university system.

5) Use total count to weight regressions

6) Create ranking tables that rank university systems and universities by dividing number of patents by number of doctoral, med-school, 4-year engineering.

Project Overview

Goal: list of all universities and # of patents associated with each university and patent licensing activity

patent reassignment to startups associated with these universities

clinical trials (from Catherine) data to rank universities R&D engagement

identify list of universities: board of regents, universities in patent data (find patterns associated with university assignees) -- @Julia: Where did we get this list? I'd love to be able filter by country - Meghana

AUTM?

Timeline to Deliverable & Documentation

Meghana: focus on grants

Julia: focus on patent counts

Working in E:McNair/University Patents

3/20-3/24

Learn SQL, clean data

3/27-3/31

Counts of data, name matching

Patent Counts

  • Ran the matcher in the server, only matched ~40,000 entries
  • Working with Jeemin to develop matcher

4/3-4/7

Counts of data, name matching

Patent Counts

  • Jeemin is a savior, matched and counted 128,000 entries (Jeemin_matcher_matched.txt), manually went through the 6,000 unmatched (Jeemin_matcher_unmatched.txt), Check University Patent Matching
  • Ranking for all time is in Patent Counting ( >Total (All Time) )
  • Need to talk with Meghana about grant data
  • Need to find variables for regression, develop ranking for last decade and last year
  • Also need to account for school size (faculty? students? research funding?)

4/10-4/14

Develop ranking

Patent Counts

  • Finish all of above
  • Start drafting
  • Load data onto database -> E:McNair/University Patents/unidatajoin.sql in univ (database)

4/17-4/21

  • Combined Patent Counts, R&D Expenditure, NSF Grant Data
  • STATA regressions in E:McNair/University Patents/STATA
    • for historical data

4/24

From Ed:

  • Copied inventors table out of PatentDB database on RDP
  • Script in E:McNair/PatentData, MoveInventors.sql
  • Copy of data in Z:AllPatents and ran import into patent (database)

For Marcela:

  • Need table of all patents associated with matches, join patent numbers with inventors
  • Group by last name and first initial (=1 inventor)

5/1

  • Joined patent numbers with inventors -> unipatentinventorcount.sql
    • Z:univ/InventorCounts.txt
  • Decade patent counts -> Z:univ/DecadePatentCounts
  • Calculated Hirschman-Herfindahl index -> E:McNair/University Patents/Inventor Counts and Superstars

5/5

Artifacts:

  1. 10-Year Ranking All
  2. 10-Year Ranking Private
  3. 10-Year Ranking Public
  4. Top 10 Movers and Shakers
  5. Graph of Concentration of Innovation
  6. Number of University Patents Over Time
  7. Inventor Concentration
  8. Regression Table
  • Created Graph of Concentration of Innovation -> E:McNair/University Patents/Inventor Counts and Superstars -> GraphUPS
  • Created Top Inventors Chart -> University Patents/Report Articles/Inventor Rankings

5/9

  • Figuring out what's up with the university puller
  • Developing new 10-Year Rankings
  • Need to update above two articles
  • Need to load into STATA and re-run regressions -> University Patents/STATA/univdata.do

5/10

  • Figured out what was wrong with the data pull and now everything's good!
  • Updated information on the draft below
  • Fantastic Artifacts and Where to Find Them
    • 10-Year Ranking All
      • Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
    • 10-Year Ranking Public
      • Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
    • 10-Year Ranking Private
      • Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
    • Top 10 Movers & Shakers (% change between first 5 years (2006-2010) vs next 5 years (2011-2015) within top 100
    • Graph of Concentration of Innovation (numbers of patents)
      • Pie graph (University Patents > Report Artifacts > Concentration of Innovation)
      • (Excel sheet where this came from is University Patents > Inventor Counts and Concentrations)
    • Trends Over Time
      • Total vs. top 10
      • Graph (University Patents > MoversandTop10)
    • Inventor Concentration
      • Chart (University Patents > Report Artifacts > Inventor Rankings)
    • Reg Table
      • +Explanation of variables
      • (STATA do-file with the most statistically significant regressions is in University Patents > STATA > univdata.do)

Issue brief draft (VERY rough): https://docs.google.com/a/rice.edu/document/d/1MayXQEQ_pM0LMeiV39iaNFjtMZYSB-Ah9gCx1rcgW9M/edit?usp=sharing

Ranking Development Notes

Sources of University Funding R&D [1]

  • Roughly 60% comes from the Federal Government
  • Less than 10% comes from state and local governments
  • Roughly 20% is from the University itself (endowment?? tuition? not 100% sure)
  • Roughly 5% is from private industry
  • Less than 10% Other

Federal Government Funding Breakdown [2]

  • Roughly 60% of federal funding is from the NIH
  • Roughly 15% is from the NSF
  • Roughly 11% from Department of Defense
  • Roughly 4% from NASA
  • Roughly 4% from the Department of Energy
  • Roughly 3% from the USDA
  • Roughly 1% from the EPA (probably will go to zero with new admin)
  • Roughly 1% from the Department of Education
  • Marginal amounts from everywhere else (<1%)


NOTES

  • overall ranking (total number of patents)
  • weight by faculty (people)
  • weight by research funding
  • weight by endowment (maybe)
  • public vs private
  • individuals: surname and first initial
    • ranking by inventors - who are the superstar inventors and where are they (define superstar inventor - idk 20 patents and rank universities by how many superstars are present
    • Herfindahl-Hirschmann Index

Key Words

Universities (Patent Assignees)

BOARD OF REGENTS - pretty much exclusively describes universities

UNIVERSITY - also exclusively describes universities

  • Can we do a close match with University? (its the only word on this list that's frequently misspelled)

SCHOOL - often used in combination with medicine or medical to describe medical schools

  • also are used to describe actual schools (i.e. HISD) and other businesses
  • sometimes present in addresses - can we cut off the adresses?
  • also used in combination with Business (i.e. Harvard Business School)
  • use school only in conjuction with medical, medicine, and business

COLLEGE

  • need to cut off addresses for this to work
  • need to ensure that we include the space after
  • excl. College Boulevard, college blvd, etc.
  • double check everything with College Park

INSTITUTE OF TECHNOLOGY - almost exclusively describes universities but sometimes describes external research institutes

POLYTECHNIC - exclusively university (i.e. RPI, Virginia Tech)

RESEARCH FOUNDATION: VAST majority are university

  • Exclusions: it's really difficult to distinguish between university affiliated and not, besides just looking them up on the google
    • Novartis
    • Progeria
    • Washington Research Foundation
    • Blood Center of Wisconsins
    • Mental Hygiene
    • Celiac Sprue
    • Fidia
    • Samuel Waxman Cancer Center
      • founded by a faculty member at Mt. Sinai Medical School,not affil w/a single university but researchers are also usually faculty at various universities
    • lifenet
    • HealthPartners
    • Dr. Susan Love
    • La Jolla Cancer Research Foundation
    • Children's Hospital (? grants degrees but is a hospital system)
    • Medforte
    • International Mask
    • Palo Alto Medical Foundation

Exclusions

  • LLC
  • LLP

Technology Transfer Offices

Job Titles

  • Patent Portfolio Manager
  • Intellectual Property Manager
  • Licensing Associate/Licensing Liaison
  • Biological Materials Specialist
  • Industrial Contracts Officer
  • Technology Licensing Officer
  • Associate Officer
  • Technology Licensing Associates
  • Patent Coordinator/Patent Administrator
  • IP Portfolio Specialist
  • MTA Coordinator
  • Sponsored Research Administrator
  • Technology Transfer Specialist/Technology Transfer Associate
  • Contracts Associate
  • Portfolio Director
  • Contracts Specialist
  • Industry Contracts Analyst
  • Patent Prosecution Analyst
  • (Associate) Director of Intellectual Property
  • (Associate) Director of Technology Transfer Policy
  • (Associate) Director of Technology Transactions
  • (Associate) Director of Agreement Administration
  • Senior Associate - watch
  • Patent Attorney/Patent Agent/Patent Counsel/Paralegal - watch

TTO Names

  • Office of Technology Transfer/Technology Transfer Office
  • Office of Technology Licensing/Technology Licensing Office
  • Office of Technology Development
  • Technology Ventures
  • Innovation Services
  • Intellectual Property & Industry Research Alliances
  • InnovationAccess
  • Invention Transfer Group
  • Technology Development Group
  • Office of Business Development
  • Office of Technology Commercialization
  • Office of Innovation and Commercialization
  • Innovation, Technology & Alliances
  • Technology & Industry Alliances
  • Office for Management of Intellectual Property
  • Innovation & Partnerships Office

University-Affiliated Startups (Patent Assignees)

What to get from other people

Avesh has clinical trial data on wiki and bulk drive (FDA Trials Data), need to build it into normal form, use clinical trials data to rank R&D engagement of universities, will be building up portfolios of different types of companies

Marcela is cleaning up patent data, has given patent assignee names -- got it! Could we filter by country and get strictly American names?

Catherine has zip codes of medical centers, use to look through patent data

Questions

  • How innovative are universities compared to publicly-traded firms, etc.? (firms: 100 active patents at any given time)
  • How do universities license?
  • What does the average portfolio look like for universities? (compared to publicly traded, VC-backed, etc)
  • What can explain the differences in rankings? (size, quality of universities, TTOs and quality/experience of workers - searched LinkedIns, geography, entrepreneurship programs, NIH/NSF grants)

To Do

  • Create ranking
    • Based on patent portfolio
    • Licensing income
    • Quality of TTO
    • Publications (specifically based on research)?
    • Amount received in grants/Amount of grants - we have numbers on amount of NIH grants and NSF grants
      • NIH/NSF/STTR
    • Control for school size/endowment
    • Startups affiliated with university
      • Value generated from these startups? (ask Avesh)
    • Existence of entrepreneurship program
    • Geography
  • Describe average patent portfolio of university
    • Number of patents
    • Maybe categorize by research area?
  • Describe how universities license
    • USPTO earned revenue

Deliverable

  • Ranking
  • Explanation of what makes a good ranking/what factors a school needs to be more successful

Artifacts

  • Grant Data
  • Research Funding
  • Star Scientists
  • Regression with variables from Carnegie Classification (10-15 variables)
  • Licensing (?)

What We Have

  • Lit Review
  • List of all post-secondary institutions (University Accreditation 3.2016_trim) - U.S. Department of Education
  • Classification of universities (Carnegie Classification 2015) - Carnegie Classification
    • includes all accredited degree-granting colleges and universities that are included in the National Center for Education Statistics Integrated Postsecondary Education Data System (IPEDS).
    • Coverage: 4666 institutions of higher learning
    • Variables: includes 94 variables
      • Enrollment data - subdivided into graduate and undergraduate
      • degrees conferred - divided into level and division
      • information on faculty (number, divided into levels i.e. assistant, assoc. and full time)
        • coverage for this variable is REALLY LOW
      • non-faculty research staff (includes post docs)
      • Science and Engineering Research and Development expenditures
      • Non-STEM research and Development expenditures
  • Amount spent on R&D (Higher Ed R&D Rankings) - National Science Foundation
    • includes just total R&D expenditures
  • STTR full data 1995-2015 (STTRData) - Small Business Administration
  • University Patent Numbers 1969-2012 - USPTO
  • Patents that resulted from NIH grants
  • Information about NSF grants

What We Need

  • List of university-affiliated startups
    • looks like AUTM STATT data has this because it was used in a study by Yael Hochberg [3]
    • Rankings of Research Universities (2015)
    • FORBES ranked the country’s most entrepreneurial schools based on the numbers of alumni and students who have identified themselves as founders and business owners on LinkedIn (adjusted to total student body size). This year we rank both research universities and smaller colleges separately.
  • data about TTOs (# and quality of employees)
    • LinkedIn crawler: working with Jeemin and Peter
  • licensing data from AUTM [4] or already within the patent data?

Name Matching

  • Jeemin has found all the correct and incorrect spellings of assignees with "University"
  • Julia working on "Institute"
  • We have a list of Universities that have patents from NIH grants, which could be a starting point for our ranking constituents

Lit Reviews

Thursby, J. & Thursby, M.: Who Is Selling the Ivory Tower? Sources of Growth in University Licensing (2002)

[5] 

 @article{thursby2002who,
   title={Who Is Selling the Ivory Tower? Sources of Growth in University Licensing},
   author={Thursby, Jerry G. and Thursby, Marie C.},
   journal={Management Science},
   volume={48},
   number={1},
   pages={90--104},
   year={2002},
   publisher={INFORMS},
   filename={Thursby Thursby (2002) - Who Is Selling the Ivory Tower}
 }
  • Reliance of industry on university inventions has increased
    • AUTM surveys show 7.1% growth in yearly inventions disclosure from 1994-1998 for 64 universities that responded every year
  • Primary reason for more disclosures may be increased propensity for faculty to disclose, rather than change in research focus
  • Universities becoming more receptive to industry contracts
  • Negative total TFP growth of licenses executed (-1.7% annual growth) - growth in disclosures and patent applications greater than the corresponding growth in licenses executed.
    • Marginal university innovation offered to the market has declined in commercial appeal
    • Universities are delving more deeply into the available pool of innovations to increase commercial activities
  • No evidence on the importance of learning by doing on the part of TTOs except to note negative association between TTO growth and TFP growth in licensing
    • Suggests at least the possibility of learning by doing effects

Other literature

  • On the role of patents and publications in the transfer process: Adams 1990, Henderson et al. 1998, and Jaffe et al. 1993
  • On consulting, sponsored research or institutional ties: Cohen et al. 1998; Mansfield 1995; Zucker et al. 1994, 1998
  • On the nature of university licensing: Jensen and Thursby 2001, Mowery et al. 2001a,b, Mowery et al. 2001, Siegel et al. 1999, Thursby et al. 2001, Thursby and Kemp 2001

Thursby, J., Jensen, Thursby, M.: Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities (2001)

[6] 

 @article{thursby2001objectives,
   title={Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities},
   author={Thursby, Jerry G., Jensen, Richard, and Thursby, Marie C.},
   journal={The Journal of Technology Transfer},
   volume={26},
   number={1},
   pages={59--72},
   year={2001},
   publisher={Springer},
   abstract={This paper describes results of our survey of licensing at 62 research universities. We consider ownership, income splits, stage of development, marketing, license policies and characteristics, goals of licensing and the role of the inventor in licensing. Based on these results we analyze the relationship between licensing outcomes and both the objectives of the TTO and the characteristics of the technologies. Patent applications grow one-to-one with disclosures, while sponsored research grows similarly with licenses executed. Royalties are typically larger the higher the quality of the faculty and the higher the fraction of licenses that are executed at latter stages of development. Sponsored research is more likely to be included in a license if the new technology is at an early stage of development or if the TTO evaluates it as important. We find that additional disclosures generate smaller percentage increases in licenses, and those increases in licenses generate smaller percentage increases in royalties.},
   filename={Thursby et al (2001) - Objectives, Characteristics and Outcomes of University Licensing}
 }
  • University licensing has increased dramatically post-Bayh-Dole (1980)
    • According to AUTM 1996, licenses executed increased 75% from 1991-1996, (total: 13,087)
  • Survey of TTOs of 62 major US universities
    • Majority of universities retain titles to inventions
    • All universities split income with inventors
    • Royalties generate most of the revenue of licensing
  • Open question: Is the increased propensity of faculty to disclose a response to financial incentives or an increase in the effectiveness of TTOs in inducing disclosure?

Survey

  • 62/135 universities responded
  • 63% public, and 62% of public universities that responded were land-grant
  • 37% private
  • average industry sponsored research $16.9 mil, federally sponsored $149.6 mil (1996)
  • average TTO: 26.3 licenses executed, 92.3 invention disclosures, 30.1 new patent apps, $4.2 mil income (1996)
  • 35% of respondents had reorganized TTO since 1990
  • 90% of universities allow faculty to establish and operate businesses based on technology owned by university but developed in faculty's research
  • Inventions disclosed: 33% med schools, 29% engineering, 22% science, 6% agriculture, 10% other
  • Majority of invention disclosures in nascent stage (proof of concept - 45% or prototype - 37%)
  • Patents often applied for after knowing commercial viability, licensed technologies often not protected by patents
  • 60% of universities said small companies more likely to take early stage technologies and large companies more likely to take late stage - small firms may have advantage in "innovative" research (Holmstrom 1989)
  • TTOs obtain smaller upfront fees the more uncertain the technology being licensed is
  • Universities usually do not take equity in the license

Regression of licensing outcomes

LicensingOutcomeReg.png

  • Dependent variables: royalties, sponsored research, patents (new applications), licenses executed
  • Independent variables: importance of outcome to TTO, types of inventions, measure of size of university's licensing operation/potential
  • Logs of all variables except indicator variables
  • Probit for frequency of sponsored research
  • INVDIS: number of disclosures
  • TTOSIZE: number of licensing individuals
  • TTOEVAL_1 = 1: if TTOs that said licenses/patents are "not very important"
  • TTOEVAL_2 = 1: if TTOs that said licenses/patents are "moderately important"
  • PROOF: % of licensed disclosures that were "proof of concept but no prototype"
  • PTYPE: "Prototype available but only lab scale"
  • MEDSCHL: 1 if med school exists
  • QUAL: academic quality of faculty (1993 NRC's survey results of academic quality of Ph.D. granting departments)
  • LICENSES: number of licenses executed
  • SPONRES: amount of sponsored research
  • SPONFREQ: frequency that sponsored research is tied to license (according to TTO)
  • patent apps grow 1-to-1 with disclosures
  • sponsored research grows with licenses executed
  • more licenses executed at universities with large TTOs and med schools
  • higher royalties with higher quality of faculty and higher fraction of licenses executed at later stages of development
  • additional disclosures generate smaller % increases in licenses, which generate smaller % increases in royalties (TTOs generally effective at tapping pool of available technologies in their universities)

Thursby, J., Fuller, Thursby, M.: US Faculty Patenting: Inside and Outside the University (2009)

[7] 

 @article{thursby2009us,
  title = "US Faculty Patenting: Inside and Outside the University",
  author = "Jerry G. Thursby, Anne W. Fuller, and Marie C. Thursby",
  journal={Research Policy},
  volume={38},
  number={1},
  pages={14--25},
  year={2009},
  publisher={Elsevier},
  abstract = {This paper examines the empirical anomaly that in a sample of 5811 patents on which US faculty are listed as inventors, 26% of the patents are assigned solely to firms rather than to the faculty member's university as is dictated by US university employment policies or the Bayh Dole Act. In this paper we estimate a series of probability models of assignment as a function of patent characteristics, university policy, and inventor fields in order to examine the extent to which outside assignment is nefarious or comes from legitimate activities, such as consulting. Patents assigned to firms (whether established or start-ups with inventor as principal) are less basic than those assigned to universities suggesting these patents result from faculty consulting. A higher inventor share increases the likelihood of university assignment as compared with assignment to a firm in which the inventor is a principal but it has no effect on consulting with established firms versus assignment to the university. Faculty in the physical sciences and engineering are more likely to assign their patents to established firms than those in biological sciences.},
  filename={Thursby et al (2009) - US Faculty Patenting},
 }
  • only 62.4% of patents by university faculty members of 87 universities were assigned solely to universities
  • identifying US university patents by institutional assignment misses significant percentage of faculty innovation in US universities
  • higher inventor share increases likelihood of university assignment compared with assignment to a firm where inventor is principal
  • possibilities: faculty in low share universities may be more willing to seek outside remuneration via assignment to start-up where they are principal; revenue shares may not affect startup activity but simply reduce number of inventions disclosed to university

Sources

  • Faculty names from NRC
  • Compared with inventor names in NBER Patent Database
  • Excluded faculty who do not patent

Patent/Inventor Pairs

  • MIT: 315
  • Wisconsin: 232
  • Stanford: 223
  • UC San Diego: 216
  • UC Berkeley: 207

Out of 5811 patents:

  • 1513 assigned solely to firms
  • 241 assigned to both firms and universities
  • 327 unassigned
  • faculty are principals in assignee firms for 32.3% of patents assigned solely to firms and 24% of patents assigned to both (lower bound)

Valdivia: University Start-ups: Critical for Improving Technology Transfer (2013)

[8]

  • Current emphasis on licensing patents, but most university TTOs do not generate enough to cover operating expenses
  • Asymmetry in distribution of resources across the university system, only a few universities benefit from high licensing revenues
    • top 8 universities took 50% of licensing income, top 16 universities took nearly 75% of income
    • only 37 universities have been in the top 20 during the last decade (listed in University Patents > LicensingGrossIncome2003-2012.txt)
  • Universities face much more pressure to demonstrate the economic impact of their R&D contracts
    • 97.6% of total public contracts obtained by universities are for basic/applied research
    • 2% of university research delivers ready-to-use technologies
    • academic research is much more dependent on government funding than industry research is
  • Government pressure for universities to be more responsive to market forces, more entrepreneurial, more attuned to needs of industry
  • TTOs are costly to universities
  • 1979: 30 universities with TTO -> 1999: 174 universities (AUTM)
  • 2010: 206 US universities have very high or high research activity, all with TTOs (but not all report to AUTM) (Carnegie Classification of Higher Education)
  • Over last 20 years, 87% of universities did not break even
  • Clash between aims of university (non-profit) and TTO (essentially a business unit)
  • By nurturing start-ups, TTOs can add most economic value to an invention disclosure
  • 2003: universities initiated 330 startups, 2012: 647 startups
  • 2012: 3715 operating university startups, almost double the number in 2000 (AUTM 2013)
  • Startups mitigate financial risk by reducing reliance on blockbuster patents, increase diversification of portfolio
  • Policy proposal: the government should increase funding for Small Business Technology Transfer Program (directed to university start-ups)
    • H.R.2981: reapportions STTR funds at 0.05% in the next 2 years and 0.1% henceforth for university enterprises at proof of concept stage
  • Should increase portion that agencies set aside for STTR
    • STTR should have Phase III like SBIR to fund commercialization efforts
  • Need equitable distribution across university system

The Bayh-Dole Act and High-Technology Entrepreneurship in U.S. Universities: Chicken, Egg, or Something Else? (2004)

[9]

Findings

  • University research has an unusually significant impact on industrial innovation in the biomedical sector
  • " This work also suggests that academic research rarely produces “prototypes” of inventions for development

and commercialization by industry—instead, academic research informs the methods and disciplines employed by firms in their R&D facilities."

  • The U.S. higher education system is much larger and more heterogenous than other developed countries - this encourages competition
  • The passage of the Bayh-Dole Act was one part of a broader shift in U.S. policy toward stronger

intellectual property rights

  • "Universities increased their share of patenting from less than 0.3% in 1963 to nearly 4% by 1999, but the rate of growth in this share begins to accelerate before rather than after 1980."
  • "the Act's provisions expressed Congressional support for the negotiation of exclusive licenses between universities and industrial firms for the results of federally funded research"
  • licensing revenues account for only a miniscule portion of universities' overall academic budgets
  • the acceleration in growth of patenting and licensing began before the passage of the Bayh-Dole Act so this acceleration cannot be wholly attributed to the Act
  • "the flow of knowledge and technology between university and industrial research is a two-way flow," despite previous characterization as wholly from academia to industry
  • patents seem to be "especially important channels for technology transfer" in the biomedical sector

Data Sources

  • 5 different case studies

1. Cotransformation: a process to transfer genes into mammalian cells (Columbia University).

2. Gallium Nitride: a semiconductor with both military and commercial applications (University of California).

3. Xalatan: a glaucoma treatment (Columbia University).

4. Ames II Tests: a bacteria assay for testing potential carcinogenic properties of pharmaceuticals and cosmetics (University of California).

5. Soluble CD4: a prototype for a drug to fight AIDS (Columbia University).

Critiques

  • focuses on case studies as source of data

Dornbusch, Schmoch, Schulze, Bethke: Identification of University-Based Patents: A New Large-Scale Approach (2012)

[10]

Specific for German case, but certain points can help us

European convergence to US model (Bayh-Dole seen as main driver behind growing patent portfolios of US universities)

  • University-owned patents (assigned to universities or their TTOs) and university-invented patents (assigned to university-affiliated authors)

Matching lists: traditional matching of lists of university staff/professors with inventor data

  • time-consuming, costly, possibly not updated
  • typically limited to tenured professors
  • does not include Ph.D. students, assistants, lecturers

Matched authors of scientific publications and inventors on patents

  • beware of homonyms

Patent data from EPO Worldwide Patent Statistical Database (PATSTAT)

Publication data from Elsevier (Scopus)

Country of origin: Inventor Country/Assignee Country/Applicant Country = Germany, Location of Organization to which author is affiliated =Germany

  • Restrict dataset to authors from German organizations and inventors with residence in Germany

The organization

Names: To keep precision high, leave out names with initials only

Location: Postal/zip codes (PATSTAT provides address of inventor's residence, SCOPUS provides info for organization, 96.5% of first digit of inventor and organization postal codes are the same, 85.9% first two digits)

Time window: 2-year window between application and publication date

Singh A. and Wong P.K: University patenting activities and their link to the quantity and quality of scientific publications (2009)

[11]

Findings

  • patenting by 281 leading world universities has consistently grown faster than general American patenting from 1977 - 2000
  • North American university patenting growth has slowed relative to universities outside North America since the mid-1990s
  • Between 2003-2005, they found that university patenting output has significant correlation with the both the quality and quantity of scientific publishing in North America
  • In European and Australian universities, patenting correlated only with the quantity of scientific publishing, not with the quality
  • In universities Europe, Australia, and North America, patenting correlated only with the quality of scientific publishing

Data Sources

  • USPTO Patenting Data
  • Shanghai Jiao Tong University's Academic Ranking of World Universities (ARWU)
  • Times Higher Education Supplement's World University Ranking (WUR)
  • Quantity was measured by counts of publications
  • Quality was measured by citations to said publications
  • the relationship between research and patenting was evaluated in two ways
    • At the institutional level: patents assigned to universities
    • At the individual level: patents with university researchers as the inventors

Critiques

  • citations are not really a perfect measure of research quality and citations have little to do with practical use of the study (i.e. how much technological innovation is generated as a result of academic research publications)

Other Discoveries

  • Study by Landry R., Amara N., and Saihi, M. (2006)
  • (Owen Smith and Powell 2003) found that "organizations involved in technological commercialization tend to have higher publication rates than those who are not"
  • (Lach and Schankerman 2003) found that "licensing revenues at the university level are positively influenced by publication citations per faculty"
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