pikas bibliometricsfor21may2015

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We Can Do It! Bibliometrics as a Library S ervice in Special Libraries Bibliometrics and Research Assessment: A Workshop for Librarians and Information Professionals May 21, 2015 Christina K. Pikas Librarian [email protected]

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Page 1: Pikas bibliometricsfor21may2015

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

[email protected]

Page 2: Pikas bibliometricsfor21may2015

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

Page 3: Pikas bibliometricsfor21may2015

Agenda

Why and How Librarians

Examples from APL

(about APL)

Questions Answered

Tools Used

Development Areas and What’s Next

Page 4: Pikas bibliometricsfor21may2015

Why and How Librarians

Page 5: Pikas bibliometricsfor21may2015

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)

Page 6: Pikas bibliometricsfor21may2015

Data

We license the databases

We know how to do good searches

We know how to manage citations

coming from searches

Page 7: Pikas bibliometricsfor21may2015

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

Page 8: Pikas bibliometricsfor21may2015

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/)

Page 9: Pikas bibliometricsfor21may2015

Examples from APL

Page 10: Pikas bibliometricsfor21may2015

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

Page 11: Pikas bibliometricsfor21may2015

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

Page 12: Pikas bibliometricsfor21may2015

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 MedicalFaculty

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

Page 13: Pikas bibliometricsfor21may2015

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 Staff19%

TechnicalProfessionals

72%

AdministrativeProfessionals

9%

Page 14: Pikas bibliometricsfor21may2015

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

Page 15: Pikas bibliometricsfor21may2015

(no)

Page 16: Pikas bibliometricsfor21may2015

Spain

Australia

Switzerland

Germany

Singapore

Czech Republic

South Africa

speaker recognitionspeech 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 retrievaluser interfaces

iterative methods

visual databases

minimisation

feedforward neural nets

mobile robotsradial 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 modulationfrequency-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

delaysonline front-ends

resource allocation

logic design

waveform analysis

stress effects

knowledge based systems

dictation

Page 17: Pikas bibliometricsfor21may2015

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?

Page 18: Pikas bibliometricsfor21may2015

Collaboration at APL:

What is the level of collaboration among

departments?

Has <intervention> changed collaboration?

Page 19: Pikas bibliometricsfor21may2015

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

Page 20: Pikas bibliometricsfor21may2015

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!)

Page 21: Pikas bibliometricsfor21may2015

Christina K. Pikas, BS, MLS

[email protected]

http://christinaslisrant.scientopia.org/

http://www.slideshare.net/cpikas

@cpikas

Page 22: Pikas bibliometricsfor21may2015