120 likes | 267 Views
The TTN Questionnaire: a first glance at the data. Massimo Caccia INFN & Universita’ dell’Insubria. TTN mid-term workshop, CERN – June 23-24, 2009. The data sample (1/2). benchmark institutions:.
E N D
The TTN Questionnaire: a first glance at the data Massimo Caccia INFN & Universita’ dell’Insubria TTN mid-term workshop, CERN – June 23-24, 2009
The data sample (1/2) • benchmark institutions: * Possible misunderstanding: data for the full lab, not only for the HEP division (221/982 FTE’s)
The data sample (2/2): our statistical population ( xxx % addressed institutions) • By the end of the day: • 7 institutions split into 2 categories • 1 extra benchmark • NOT WORTH ANYTHING TERRIBLY SOPHISTICATED!
Labs by size [FTE] CERN FTE GSI DESY PSI FTE in HEP • an averaging procedure weighted by FTE in HEP will be dominated by CERN • DESY and PSI do represent a good example of labs where HEP and no-HEP live together
(poor) analysis method • constrained by the limited statistical population and the large spread (standard deviation) of the data • assume as basic figures the Executive Summary indicators of the 2006 ASTP survey for fiscal year 2006 [excluding financial data on the income & start-up’s], namely: • Invention disclosures • Patent applications • Patent grants • License agreements • Research agreements Normalized to 1 year and per 1000 FTE’s • assume as a reference the ASTP mean data + BNL and EPFL • compare to the mean and weighted mean values for labs & institutions (weights defined by FTE in HEP)
A closer to look to the indicators for the labs (normalized to 1000 FTE’s, per annum) (1/3) GSI DESY disclosures applications CERN PSI granted licensed
A closer to look to the indicators for the labs (normalized to 1000 FTE’s, integrated) (2/3) families DESY GSI PSI CERN licensed RATIO
A closer to look to the indicators for the labs (normalized to 1000 FTE’s) (3/3) IP transfer/ annum DESY PSI CERN GSI Agreement/annum
The performance indicator summary table (per 1000 FTE’s) ASTP mean weighted by the data size in the 2 samples
a picture is worth a thousand words: Conclusions (1/3)
Conclusions (2/3) • labs do it better • German labs do it a lot better! • the spread among the different institutions is terrifying (a lot higher than among benchmarks, irrespective of their intrinsic differences…) • there’s a solid rock motivation for the TTN • KE towards other disciplines and Research agreements with other scientific community has definitely to be pursued (DESY is, to me, a fairly good example!)