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Massimo Caccia INFN & Universita’ dell’Insubria TTN mid-term workshop, CERN – June 23-24, 2009 The TTN Questionnaire: a first glance at the data.

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Presentation on theme: "Massimo Caccia INFN & Universita’ dell’Insubria TTN mid-term workshop, CERN – June 23-24, 2009 The TTN Questionnaire: a first glance at the data."— Presentation transcript:

1 Massimo Caccia INFN & Universita’ dell’Insubria TTN mid-term workshop, CERN – June 23-24, 2009 The TTN Questionnaire: a first glance at the data

2 The data sample (1/2)  benchmark institutions: LBLNo reply TRIUMFI fear they did not get the point FNALRather incomplete feedback BNLRather complete feedback!* KEKNo reply * Possible misunderstanding: data for the full lab, not only for the HEP division (221/982 FTE ’ s)

3 The data sample (2/2): our statistical population ( xxx % addressed institutions) Universities EPFL Good quality feedback small HEP community (50/3280 FTE’s)  Use it as a second benchmark! Bern Incomplete (e.g. no FTE’s etc.) Zurich As above ETH As above NCSR As above labs CERN ok DESY ok GSI ok PSI ok Natl. Institutes FTE FTE in HEP NIKHEF200 IN2P33000 CEA3500500 By the end of the day: 7 institutions split into 2 categories 1 extra benchmark  NOT WORTH ANYTHING TERRIBLY SOPHISTICATED!

4 Labs by size [FTE] FTE FTE in HEP CERN DESY GSI PSI  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

5 (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)

6 A closer to look to the indicators for the labs (normalized to 1000 FTE ’ s, per annum) (1/3) disclosures licensed applications granted CERN DESY GSI PSI

7 A closer to look to the indicators for the labs (normalized to 1000 FTE ’ s, integrated) (2/3) CERN DESY GSI PSI families licensed RATIO

8 A closer to look to the indicators for the labs (normalized to 1000 FTE ’ s) (3/3) IP transfer/ annum Agreement/ annum CERN DESY GSI PSI  

9 The performance indicator summary table (per 1000 FTE ’ s) labsNatl. Inst.BNLEPFL ASTP UNI. ASTP PRO ASTP mean W xx W xx disclosures799.82.31.3 332415.620.416.6 Patent applications 4.44.15.51.70.91.013125.59.26.3 Patent grants1.01.3 0.10.30.5962.67.83.2 License agreements 0.31.11.40.40.30.4384.811.66.3 IP agreements1.03.25.10.61.01.439NA Research Agreements 48.512218516.26.71.59211911195.8108.8 Patent families27.532.025.112.28.64.512078NA Overall Licensed patents 10.110.912.30.10.30.58838NA   ASTP mean weighted by the data size in the 2 samples

10 Conclusions (1/3)  a picture is worth a thousand words:

11  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!) Conclusions (2/3)

12 Conclusions (3/3)


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