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We Can Do It!
Bibliometrics as a Library Service
in Special Libraries
Bibliometrics and Research Assessment:
A Workshop fo...
Ease of access to citation data and a
(natural?) desire to reduce complex
evaluations to numbers has led to
“unreflected u...
Agenda
Why and How Librarians
Examples from APL
(about APL)
Questions Answered
Tools Used
Development Areas and What...
Why and How Librarians
Domain knowledge
To be successful librarians we know a bit
about:
Scholarly communication
Information retrieval in our a...
Data
We license the databases
We know how to do good searches
We know how to manage citations
coming from searches
But probably need to add…
The specifics of which calculations to
use for what
And the experts do not agree!
Tool knowle...
Ethics
What to measure and how
How to represent results so that they
are clear about what they tell you
See also
Leide...
Examples from APL
APL in Brief
Laboratory Statistics:
• Employees: ~5,400 staff
• Revenues: ~$1.3B
 Technically skilled
and operationally
o...
Critical Contributions to Critical Challenges
We are committed to public
service and strive for
excellence in all we do
Ou...
The Johns Hopkins University
Provost
President
of the University
JHU/APL LLC
Board of Managers
University
Board of Trustee...
Staff Demographics
Technical Professionals
Degree Field
46% Engineering
25% Math, computer science
23% Physics, chemistry,...
Sample Questions
In <research area>, do University
Affiliated Research Centers collaborate
more internationally than gover...
(no)
Spain
Australia
Switzerland
Germany
Singapore
Czech Republic
South Africa
speaker recognition
speech processing
feature ex...
Given these records (unicode csv file of
unknown origin), what can we say about
the country’s published research in <this
...
Collaboration at APL:
What is the level of collaboration among
departments?
Has <intervention> changed collaboration?
Tools Used
Sci2
VantagePoint*
UCInet/NetDraw*
iGraph in R
Pajek
Inspire**
Note: NodeXL is also very good, but I am u...
Takeaways
Librarians can and should leverage their
information science, subject, and
organization knowledge to support
bi...
Christina K. Pikas, BS, MLS
Christina.Pikas@jhuapl.edu
http://christinaslisrant.scientopia.org/
http://www.slideshare.net/...
Pikas bibliometricsfor21may2015
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Pikas bibliometricsfor21may2015

Presentation given to Fedlink/SLA Md workshop on bibliometrics and research assessment.

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Pikas bibliometricsfor21may2015

  1. 1. We Can Do It! Bibliometrics as a Library Service in Special Libraries Bibliometrics and Research Assessment: A Workshop for Librarians and Information Professionals May 21, 2015 Christina K. Pikas Librarian Christina.Pikas@jhuapl.edu
  2. 2. Ease of access to citation data and a (natural?) desire to reduce complex evaluations to numbers has led to “unreflected use of ready-made indicators offered by the owners of bibliometric databases” Librarians are ideally situated in our institutions to provide bibliometric support in a sensible and reliable way
  3. 3. Agenda Why and How Librarians Examples from APL (about APL) Questions Answered Tools Used Development Areas and What’s Next
  4. 4. Why and How Librarians
  5. 5. Domain knowledge To be successful librarians we know a bit about: Scholarly communication Information retrieval in our areas Organization of information Our organization’s research output (what types, where it goes, etc)
  6. 6. Data We license the databases We know how to do good searches We know how to manage citations coming from searches
  7. 7. But probably need to add… The specifics of which calculations to use for what And the experts do not agree! Tool knowledge How to go from a database to a network or count How to clean up database results Visualizing the results
  8. 8. Ethics What to measure and how How to represent results so that they are clear about what they tell you See also Leiden Manifesto (http://www.leidenmanifesto.org/ ) DORA (http://am.ascb.org/dora/)
  9. 9. Examples from APL
  10. 10. APL in Brief Laboratory Statistics: • Employees: ~5,400 staff • Revenues: ~$1.3B  Technically skilled and operationally oriented  Objective and independent  DoD  NASA  Critical contributions to critical challenges  DHS  IC  Division of Johns Hopkins University  University Affiliated Research Center
  11. 11. Critical Contributions to Critical Challenges We are committed to public service and strive for excellence in all we do Our goal is to strengthen the nation through transformative innovation and trusted technical leadership in national security and space We collaborate across the University in areas of national importance
  12. 12. The Johns Hopkins University Provost President of the University JHU/APL LLC Board of Managers University Board of Trustees Dean of Nitze School of Advanced Int’l Studies Dean of the Medical Faculty Dean of Krieger School of Arts and Sciences Dean of Peabody Institute Dean of Carey School of Business Dean of School of Nursing Director of Applied Physics Laboratory Dean of Bloomberg School of Public Health Dean of Whiting School of Engineering Dean of School of Education Dean of Libraries & Museums
  13. 13. Staff Demographics Technical Professionals Degree Field 46% Engineering 25% Math, computer science 23% Physics, chemistry, other 6% None Technical Professionals Degree Level 19% Doctorate 53% Master 22% Bachelor 6% None Supporting Staff 19% Technical Professionals 72% Administrative Professionals 9%
  14. 14. Sample Questions In <research area>, do University Affiliated Research Centers collaborate more internationally than government labs? Define the research area (reference interview! Discussion with local domain expert) Search domain databases (Inspec & Compendex) Co-authorship networks Keyword – Country and Institution graphs Check with domain experts
  15. 15. (no)
  16. 16. Spain Australia Switzerland Germany Singapore Czech Republic South Africa speaker recognition speech processing feature extraction Gaussian processes speech recognition hidden Markov models cepstral analysis support vector machines natural languages maximum likelihood estimation biometrics (access control neural nets face recognition speech codingspeech synthesis error statistics learning (artificial intelligence pattern classification statistical analysis probability acoustic signal processing signal classification pattern clustering Gaussian distribution Bayes methods regression analysis sensor fusion audio signal processing audio databases emotion recognition spectral analysis microphone arrays genetic algorithms multilayer perceptrons speech enhancement filtering theory audio-visual systems optimisation hearing speech-based user interfaces parameter estimation data compression correlation methods fuzzy set theory human computer interaction speech intelligibility authorisation linear predictive coding backpropagation array signal processing natural language processing belief networks gesture recognition statistical distributions interactive systems microphones time-frequency analysis linguistics Markov processes fingerprint identification Internet telephony computational complexity pattern matching transforms interpolation language translation discrete cosine transforms Internet music decision making security of data signal processing decoding information retrieval user interfaces iterative methods visual databases minimisation feedforward neural nets mobile robots radial basis function networks speech recognition equipment text analysis client-server systems training dynamic programming multimedia databases natural language interfaces blind source separation reliability sequences telecommunication security tracking virtual reality Viterbi decoding error analysis estimation theory field programmable gate arrays multimedia communication neurophysiology fuzzy neural nets database management systems handwriting recognition audio recording decision theory signal sampling telephony IP networks watermarking AWGN message authentication Monte Carlo methods OFDM modulation frequency-domain analysis fuzzy logic polynomials audio acoustics data mining self-organising feature maps delay estimation image matching adaptive systems image recognition very large databases image coding amplitude modulation entropy evolutionary computation expert systems gender issueshearing aids source separation sparse matrices image fusion time-of-arrival estimation information theory cognition least mean squares methods query formulation medical computing acoustic correlation approximation theory fast Fourier transforms architectural acoustics fractals nonlinear distortion particle filtering (numerical methods interleaved codes adaptive estimation data visualisation calibration heuristic programming deconvolution classification target tracking hyperbolic equations reliability theory convergence game theory function approximation benchmark testing frequency division multiple access quadrature amplitude modulation Java full-text databases Jacobian matrices government graph theory waveform generators distance learning frame based representation noise measurement police linear programming home computing delays online front-ends resource allocation logic design waveform analysis stress effects knowledge based systems dictation
  17. 17. Given these records (unicode csv file of unknown origin), what can we say about the country’s published research in <this large area>? Who are the primary researchers? What are the primary institutions? How much collaboration is there among institutions and internationally? What methods are associated with <terms>? What are the trends over time?
  18. 18. Collaboration at APL: What is the level of collaboration among departments? Has <intervention> changed collaboration?
  19. 19. Tools Used Sci2 VantagePoint* UCInet/NetDraw* iGraph in R Pajek Inspire** Note: NodeXL is also very good, but I am unable to install it at work. YMMV. I have also used Sitkis, BibExcel, CiteSpace… and maybe others! * Licensed ($) ** Only available to government or for government contract work, I think
  20. 20. Takeaways Librarians can and should leverage their information science, subject, and organization knowledge to support bibliometric activities There are many interesting and important questions to answer Additional training, self study, and experimentation might be required (start now!)
  21. 21. Christina K. Pikas, BS, MLS Christina.Pikas@jhuapl.edu http://christinaslisrant.scientopia.org/ http://www.slideshare.net/cpikas @cpikas

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