This presentation was provided by Paul Needham of Cranfield University and Johan Bollen of Indiana University, during the NISO webinar "Measuring Use, Assessing Success, Part Two: Count Me In: Measuring Individual Item Usage," which was held on September 15, 2010.

1. • Introduction
– Todd Carpenter, Managing Director, NISO
• Update on PIRUS 2:
Developing Practical Standards for Recording and Reporting
Online Usage at the Individual Article Level!!
– Paul Needham, PIRUS 2 Project Manager and Research and
Innovation Specialist at Cranfield University
• Applying Usage Metrics to Assess Scholarly Content Quality
– Johan Bollen, Associate Professor in the School of Informatics
and Computing, Indiana University!!
Count Me In:
Measuring Individual Item Usage
Thanks to our sponsor!
www.niso.org/news/events/2010/itemusage
NISO Webinar • September 15, 2010
NISO 2010 Events
http://www.niso.org/news/events/2010/
•September 13 (Teleconference):!NISO Open Teleconference
•October 7 (NISO Forum - Chicago, IL): E-Resource Management:
From Start to Finish (and Back Again)
•October 13 (Webinar): It’s Only as Good as the Metadata:
Improving OpenURL and Knowledgebase Quality
•October 18 (Teleconference): I2 (Institutional Identifiers)!Working Group
Update
•November 8 (Teleconference):!DAISY Revision Working Group Update
•November 10 (Webinar): The Case of the Disappearing Journal: Solving the
Title Transfer and Online Display Mystery
•December 8 (Webinar): Unprecedented Interaction:
Providing Accessibility for the Disabled
•December 13 (Teleconference): IOTA (Improving OpenURLs Through
Analytics) Working Group Update
NISO Webinar • September 8, 2010
PIRUS 2
Developing Practical Standards for Recording and
Reporting Online Usage at the Individual Article Level
Paul Needham, Cranfield University
- Project Manager, PIRUS2
NISO Webinar
15 September 2010
PIRUS Publisher and Institutional Repository Usage Statistics
! Sponsored by JISC
! UK Joint Information Systems Committee
! PIRUS 1 completed in January 2009
! Lead by COUNTER
! Report available at: http://www.jisc.ac.uk/media/documents/
programmes/pals3/pirus_finalreport.pdf
! PIRUS 2, October 2009-December 2010
! Lead by Mimas and Cranfield University
! Primary project team members: Mimas, Cranfield, COUNTER,
CrossRef, Oxford University Press

2. Usage statistics and journal metrics
! COUNTER
! Sets the standard for vendor-generated online usage statistics
! Covers over 15,000 full-text online journals
http://www.projectCounter.org
! MESUR
! Enriches the toolkit used for the assessment of the impact of scholarly
communication items with usage data
! Has created a map of science based on usage data
http://www.mesur.org/
! Journal Usage Factor
! Assess the feasibility of Journal Usage Factor as an alternative metric to
Journal Impact Factor
http://www.uksg.org/usagefactors
! PIRUS
! Aims to provide, publishers, repositories and other organizations with a
common standard for measuring usage at the individual article (item) level
PIRUS: why now?
Increasing interest in article-level usage
! More journal articles hosted by Institutional and other
Repositories
! Authors and funding agencies are increasingly
interested in a reliable, global overview of usage of
individual articles
! Online usage becoming an alternative, accepted
measure of article and journal value
! Knowledge Exchange report recommends developing
standards for usage reporting at the individual article level
! Usage-based metrics being considered as a tool for use in the
UK Research Excellence Framework and elsewhere.
PIRUS: why now?
Article-level usage metrics now more practical
! Implementation by COUNTER of XML-based usage
reports makes more granular reporting of usage a
practical proposition
! Implementation by COUNTER of the SUSHI protocol
facilitates the automated consolidation of usage
data from different sources.
The challenge
! An article may be available from:
! The main journal web site
! Ovid
! ProQuest
! PubMed Central
! Authors’ local Institutional Repositories
! If we want to assess article impact by
counting usage, how can we maximise the
actual usage that we capture?

3. PIRUS Project Mission
! To develop a global standard to
enable the recording, reporting and
consolidation of online usage statistics
for individual journal articles hosted by
Institutional Repositories, Publishers
and other entities
PIRUS Project Aims
! Develop COUNTER-compliant usage reports
at the individual article level
! Create guidelines which, if implemented,
would enable any entity that hosts online
journal articles to produce these reports
! Propose ways in which these reports might
be consolidated at a global level in a
standard way.
PIRUS: benefits
! Reliable usage data will be available for journal
articles, wherever they are held
! Repositories will have access to new functionality from
open source software that will allow them to produce
standardised usage reports from their data
! Digital repository systems will be more integral to
research and closely aligned to research workflows
and environments
! The authoritative status of PIRUS2 usage statistics will
enhance the status of repository data and content
! The standard can be extended to cover other
categories of content stored by repositories
PIRUS1: publisher response
! Majority enthusiasm for concept.
! All surveyed publishers use DOIs to identify all
versions of a single published work.
! Minority concern that article level reporting to
institutional customers is our goal.
! It isn’t.
! Concern about size of any reports providing
usage data at article level.
! Not the intention of the project to recommend publishers
produce reports relating to more than one article at a
time.

4. PIRUS1: repository response
GOOD NEWS
! The overwhelming majority of respondents add
DOIs to their records - where they are available.
BUT…….
! No standard process for allocating DOIs in IRs
! Great variation in the metadata element used to
store them:
! dc.description
! dc.identifier
! dc.identifier type DOI
! dc.identifier.citation
! dc.relation.isreferencedby
! dc.rights
! DOI
! relation
PIRUS1: outputs
1. A proof-of-concept COUNTER-compliant XML prototype
for an individual article usage report
2. A tracker code, to be implemented by repositories, that
sends usage data as OpenURL Context Objects to either:
! An external party
! The local repository server
3. A set of scenarios for collecting usage data in different
repository environments
4. A set of criteria for a central Clearing House that will
create (where required), or collect and consolidate the
usage statistics
PIRUS2: objectives
! Develop a suite of free, open access programmes to
support the generation and sharing of COUNTER-
compliant usage data and statistics that can be
extended to cover any and all individual items in
repositories
! Develop a prototype article-level publisher/repository
usage statistics service
! Define a core set of standard useful statistical reports
that repositories should produce for internal and
external consumption
PIRUS2: progress so far
WP 4: software, standards and protocols
! Technical aspects of project
! Gathering … usage data and statistics
! For full-text article downloads (not record/abstract views)
! From repositories and publishers
! Consolidating …
! In an article-level usage statistics demonstrator portal
! Experiment and illustrate possibilities
! Re-exposing …
! To authorized third parties

5. PIRUS2: progress so far
WP 4: software, standards and protocols
! Three scenarios for gathering …
! (A) ‘tracker’ code – a server-side ‘Google Analytics’ for
full-text article downloads
! (B) OAI-PMH harvesting – protocol familiar to repositories
! (C) SUSHI - Standardized Usage Statistics Harvesting
Initiative Protocol – familiar to publishers
PIRUS2: progress so far
WP 4: software, standards and protocols
! Usage data from Repositories
! Scenarios (A) Tracker & (B) OAI-PMH
! Usage data are exposed as:
! (A) OpenURL Key-Value Pair Strings
! (B) OpenURL Context Objects.
! OpenURL approach first suggested by MESUR. Taken forward in Europe
under ‘Knowledge Exchange’ – an initiative involving DEFF, DFG, JISC and
SURFfoundation, see:
http://wiki.surffoundation.nl/display/standards/OpenURL+Context+Objects
! Usage data must be:
! filtered according to COUNTER rules to eliminate Robots and Double clicks
! Processed into monthly statistics
PIRUS2: progress so far
WP 4: software, standards and protocols
! Usage statistics from Publishers
! Scenario (C) SUSHI
! SUSHI - a SOAP-based web service used by publishers to expose COUNTER
Release 3 compliant usage statistics to institutions and consortia
! Currently operates at journal level, e.g., JR1 report: Number of Successful
Full-Text Article Requests by Month and Journal
! PIRUS2 has devised a proposed COUNTER Article Report 1
(AR1) Report: Number of Successful Full-Text Article Requests
by Month and DOI
! Usage statistics are pre-filtered according to COUNTER rules
PIRUS2: progress so far
WP 4: software, standards and protocols
! PIRUS2 Repository software plug-ins/extensions
! Dspace – developed by @mire
! Eprints – developed by Tim Brody, Southampton University
! Fedora – developed by Ben O’Steen, Oxford University
! Links and downloads on PIRUS2 project web site
! PIRUS2 AR1 Report
! SUSHI ultimately
! Currently working with AR1 reports in MS Excel/CSV format from
participating publishers
! Draft AR1 report in MS-Excel and XML available on PIRUS2 project web site

6. PIRUS2: progress so far
WP 4: software, standards and protocols
! Current situation
! Loaded data from 6 publishers
! Over 555,000 articles
! From 5,500 journals
! Gathering data via tracker from 3 repositories
! Working on scripts to process and load data
! Creating user interface to demonstrate possibilities
! Next
! Load data from another 2 publishers
! Extend participation by repositories
! Ongoing development and testing of user interface
! Develop SUSHI server to re-expose statistics
PIRUS2: progress so far
WP5: prototype service
! Tests of publisher usage data
! Usage data from 8 publishers flowing in
! Define functions to be fulfilled by a Central Clearing
House
! Collect, collate and store usage data
! Define capabilities required of a Central Clearing House
! Conversion of logfiles, storage, access control, etc
! Define organizational options for a Central Clearing
House
! Global vs. local; identify candidate organizations
PIRUS2: progress so far
WP5: Demonstrator
! To demonstrate basic functionality of service
! Examples of core reports
! Test usage data from major publishers
! Feedback sought
For access to the Demonstrator contact Paul Needham
at: paul.needham11@btinternet.com
PIRUS 2: primary project team
! Ross MacIntyre (Mimas, Manchester University)
! Paul Needham (Cranfield University)
! Richard Gedye (Oxford University Press)
! Ed Pentz (CrossRef)
! Peter Shepherd (COUNTER)

7. For more information……….
http://www.cranfieldlibrary.cranfield.ac.uk/pirus2/
Thank you!
Indiana University
School of Informatics and Computing
The MESUR project: an overview and update
NISO Item Usage Webinar • September 14, 2010
Johan Bollen
Indiana University
School of Informatics and Computing
Center for Complex Networks and System Research
jbollen@indiana.edu
Acknowledgements:
Herbert Van de Sompel (LANL), Marko A. Rodriguez (LANL), Ryan Chute (LANL),
Lyudmila L. Balakireva (LANL), Aric Hagberg (LANL), Luis Bettencourt (LANL)
Research supported by the NSF and Andrew W. Mellon Foundation.
Indiana University
School of Informatics and Computing
When the obvious is staring you in the face
Indiana University
School of Informatics and Computing
When the obvious is staring you in the face

8. Indiana University
School of Informatics and Computing
When the obvious is staring you in the face
Indiana University
School of Informatics and Computing
When the obvious is staring you in the face
Indiana University
School of Informatics and Computing
When the obvious is staring you in the face
Indiana University
School of Informatics and Computing
When the obvious is staring you in the face

9. Indiana University
School of Informatics and Computing
The scientific process: the importance of early indicators
(Egghe & Rousseau, 2000; Wouters, 1997)
(Brody, Harnad, & Carr 2006),
Citation: final products
• Publication delays
• Focus on publications
• Focus on authors
Usage data
• Scale, cf. Elsevier downloads (+1B)
vs. Wos citations (650M)
• Immediate, early stages
• Variety of resources and actors
Indiana University
School of Informatics and Computing
Timeline and development
• 2006-2008:
o Andrew W. Mellon Foundation
o Digital Library Research and Prototyping team, Los Alamos National
Laboratory
o Collection of large-scale usage data from some of world’s most significant
publishers, aggregators and institutional consortia
o Feasibility: Usage data, usage-based network models of science, usage-
based impact metrics
• 2009 – infinity and beyond:
o NSF funding (SciSIP, 2009-2012)
o Indiana University, School of Informatics and Computing
• 2010: Andrew W. Mellon foundation
o Continuation of MESUR data collection and scientific work
o Investigate evolving to sustainable, open, community-supported
infrastructure

10. Indiana University
School of Informatics and Computing
Presentation structure
1. MESUR’s Usage reference data set
2. Mapping scientific activity
3. Metrics survey
4. Future research
5. Discussion
Indiana University
School of Informatics and Computing
Creating the MESUR usage reference data set
2006-2008: Collaborating publishers, aggregators and institutional consortia:
• BMC, Blackwell, UC, CSU (23), EBSCO, ELSEVIER, EMERALD, INGENTA, JSTOR, LANL,
MIMAS/ZETOC, THOMSON, UPENN (9), UTEXAS
• Scale:
o > 1,000,000,000 usage events, and growing…
o +50M articles, +-100,000 serials
• Period: 2002-2007, but mostly 2006
1B
Indiana University
School of Informatics and Computing
Data normalization and ingestion
Minimal requirements for all usage data
• Unique usage events (article level)
• Fields: unique session ID, date/time, unique document ID and/or metadata, request
type
• Note difference with usage statistics
2007 9 1 0 0 1 CFA cffoe A172080.N1.Vanderbilt.Edu unknown AST A 1996SPIE.2828..64S http://foe.edu/abs/1996SPIE.2828..64S http://www.google.com
2007 9 1 0 0 1 CFA cffoe 210.94.41.89 unknown PHY A 2007ApPhL.90a2120C http://foe.edu/abs/2007ApPhL.90a2120C http://www.google.co.kr
2007 9 1 0 0 1 CFA cffoe 24-196-228-125.dhcp.gwnt.ga.charter.com unknown AST A 2000ASPC.213.333S http://foe.edu/abs/2000bioa.conf.333S http://scholar.google.com
2007 9 1 0 0 4 CFA cffoe 163.152.35.114 4700387eae PHY A 1993WRR..29.133S http://foe.edu/abs/1993WRR..29.133S http://scholar.google.com
2007 9 1 0 0 6 CFA cffoe pd9e980fc.dip0.t-ipconnect.de 45f0c69881 AST X 2007AN..328.841H http://arXiv.org/abs/0708.1863 http://foe.edu
2007 9 1 0 0 1 CFA cffoe A172080.N1.Vanderbilt.Edu unknown AST A 1996SPIE.2828..64S http://foeabs.edu/abs/1996SPIE.2828..64S http://www.google.com
2007 9 1 0 0 1 CFA cffoe 210.94.41.89 unknown PHY A 2007ApPhL.90a2120C http://foeabs.edu/abs/2007ApPhL.90a2120C http://www.google.co.kr
2007 9 1 0 0 1 CFA cffoe 24-196-228-125.dhcp.gwnt.ga.charter.com unknown AST A 2000ASPC.213.333S http://foeabs.edu/abs/2000bioa.conf.333S http://scholar.google.com
2007 9 1 0 0 4 CFA cffoe 163.152.35.114 4700387eae PHY A 1993WRR..29.133S http://foeabs.edu/abs/1993WRR..29.133S http://scholar.google.com
2007 9 1 0 0 6 CFA cffoe pd9e980fc.dip0.t-ipconnect.de 45f0c69881 AST X 2007AN..328.841H http://arXiv.org/abs/0708.1863 http://foeabs.edu
2007 9 1 0 0 6 CFA cffoe foel25144.4u.com.gh 47002f8eda PHY A 2002AGUFM.S21A0965M http://foeabs.edu/abs/2002AGUFM.S21A0965M http://www.google.com
2007 9 1 0 0 6 CFA cffoe 66-215-171-214.dhcp.ccmn.ca.charter.com 4681d22a6f AST A 2001P&SS..49.657R http://foeabs.edu/cgi-bin/bib_query?bibcode=2001P%26SS..49.657R http://cfa-www.edu
2007 9 1 0 0 7 CFA cffoe nat-ptouser3.uspto.gov unknown PHY A 2005ApPhL.86g2106M http://foeabs.edu/abs/2005ApPhL.86g2106M http://www.google.com
2007 9 1 0 0 7 CFA cffoe cpe-71-65-25-115.ma.res.rr.com unknown PHY A 1980SPIE.205.153S http://foeabs.edu/abs/1980SPIE.205.153S http://www.google.com
2007 9 1 0 0 7 CFA cffoe customer3491.pool1.unallocated-106-0.orangehomedsl.co.uk unknown PHY A 1983ElL..19.883V http://foeabs.edu/abs/1983ElL..19.883V http://www.google.co.uk
2007 9 1 0 0 8 CFA cffoe Uranus.seas.ucla.edu 46672d96b2 PHY A 1966Phy..32.385K http://foeabs.edu/abs/1966Phy..32.385K http://www.google.com
2007 9 1 0 0 9 CFA cffoe 75-121-173-37.dyn.centurytel.net 46cf1fd8a6 AST D 1984ApJS..56.257J http://vizier.cfa.edu/viz-bin/VizieR?-source=III/92/ http://foeabs.edu
2007 9 1 0 0 13 CFA cffoe foel17-18.kln.forthnet.gr unknown AST A 1987cosm.book...C http://foeabs.edu/abs/1987cosm.book...C http://www.google.gr
2007 9 1 0 0 15 CFA cffoe hades.astro.uiuc.edu 46f707564d PRE A 2007arXiv0707.3146N http://foeabs.edu/abs/2007arXiv0707.3146N http://foeabs.edu
2007 9 1 0 0 17 CFA cffoe ool-43554752.dyn.optonline.net unknown PHY A 2000PhTea.38.132K http://foeabs.edu/abs/2000PhTea.38.132K http://www.google.com
2007 9 1 0 0 17 CFA cffoe c-68-33-176-222.hsd1.md.comcast.net unknown GEN A 1994RSPSB.256.177M http://foeabs.edu/abs/1994RSPSB.256.177M http://www.google.com
2007 9 1 0 0 19 CFA cffoe 74-36-139-46.dr02.brvl.mn.frontiernet.net unknown AST T 2002SPIE.4767.114W http://foeabs.edu/cgi-bin/nph-abs_connect?bibcode=2002SPIE.4767&db_key=ALL&sort=BIBCODE&nr_to_return=500&data_and=YES&toc_link=YES http://foeabs.edu
2007 9 1 0 0 19 CFA cffoe c-76-16-53-120.hsd1.il.comcast.net 46f667b71b AST F 1916PA...24.613L http://articles.foeabs.edu/cgi-bin/nph-iarticle_query?1916PA...24.613L&data_type=PDF_HIGH&whole_paper=YES&type=PRINTER&filetype=.pdf http://foeabs.edu
2007 9 1 0 0 20 CFA cffoe 74-39-37-62.nas03.roch.ny.frontiernet.net unknown PHY E 2007JSTEd.tmp..29B http://dx.doi.org/10.1007/s10972-007-9067-2 http://foeabs.edu
2007 9 1 0 0 22 ANU bio-mirror uatu-virtual1.anu.edu.au 46f9e8f87f AST A 2006ApJ..647.128E http://foe.grangenet.net/abs/2006ApJ..647.128E http://foe.grangenet.net
2007 9 1 0 0 22 CFA cffoe fw.hia.nrc.ca 46f1531d59 AST A 2002P&SS..50.745H http://foeabs.edu/abs/2002P%26SS..50.745H http://foeabs.edu
2007 9 1 0 0 22 CFA cffoe 24-117-0-220.cpe.cableone.net unknown AST A 1984BITA..15.268S http://foeabs.edu/abs/1984BITA..15.268S http://www.google.com
2
Indiana University
School of Informatics and Computing
Presentation structure
1. MESUR’s Usage reference data set
2. Mapping scientific activity
3. Metrics survey
4. Future research
5. Discussion

11. Indiana University
School of Informatics and Computing
Data set: subset of MESUR
• Common time period:
o March 1st 2006 - February 1st 2007
o Thomson Scientific (Web of
Science), Elsevier (Scopus),
JSTOR, Ingenta, University of
Texas (9 campuses, 6 health
institutions), and California State
University (23 campuses)
• 346,312,045 usage events
• 97,532 serials (many of which not
journals)
Indiana University
School of Informatics and Computing
How to generate a usage network.
Same session ~ documents relatedness
• Same session, same user: common
interest
• Frequency of co-occurrence = degree of
relationship
• Normalized: conditional probability
Usage data is on article level:
• Works for journals and articles
• Anything for which usage was recorded
Note: not something we invented:
association rule learning in data mining.
Beer and diapers!
Indiana University
School of Informatics and Computing
Johan Bollen, Herbert Van de Sompel, Aric Hagberg,Luis
Bettencourt, Ryan Chute, Marko A. Rodriguez, Lyudmila
Balakireva. Clickstream data yields high-resolution maps of
science. PLoS One, February 2009.
Indiana University
School of Informatics and Computing
Network science for impact metrics.
: Number of geodesics between vi and vj
Betweenness centrality
PR(vi): PageRank of node vi
O(vj): out-degree of journal vj
N: number of nodes in network
L: dampening factor
PageRank

12. Indiana University
School of Informatics and Computing
Presentation structure
1. MESUR’s Usage reference data set
2. Mapping scientific activity
3. Metrics survey
4. Future research
5. Discussion
Indiana University
School of Informatics and Computing
A variety of impact metrics
Note:
• Metrics can be calculated
both on citation and usage
data
• “Frequentist”
o Citation and usage rates
• “Structural”
o Citation graph, e.g. 2005
JCR
o Usage graph, e.g.
created by MESUR
• H-index, G-index, SJR,
etc
What do they MEAN?
What facets of impact do they represent?
Which are best suited?
Indiana University
School of Informatics and Computing
Set of metrics calculated on MESUR data set

13. Indiana University
School of Informatics and Computing
Presentation structure
1. MESUR’s Usage reference data set
2. Mapping scientific activity
3. Metrics survey
4. Future research
5. Discussion
Indiana University
School of Informatics and Computing
Samples of future work (can be skipped)
• Longitudinal studies:
o Network changes over time: collaboration with Carl Bergstrom (UW)
o Prediction of innovation using random walk models
• Logistics:
o Expand existing data set: focus on standardization, repeatability
o Establish continued funding, good home for project
o “Center” model: rather than data->scientists, scientists->data
Indiana University
School of Informatics and Computing
Animated maps: tracing bursts of scientific activity
Indiana University
School of Informatics and Computing
Coordinated bursts
1
2
3

14. Indiana University
School of Informatics and Computing
MESUR Mapping and ranking services
Indiana University
School of Informatics and Computing
MESUR Mapping and ranking services
Indiana University
School of Informatics and Computing
MESUR Mapping and ranking services
Indiana University
School of Informatics and Computing
MESUR: the good ...
After 3 years of MESUR:
• Scientific exploration of metrics for scholarly evaluation
• Creation of large-scale reference data set
• Mapping science from the viewpoint of users: there is structure!
• Variety of metrics that cover various aspects of scholarly impact and prestige
• MESUR dataset contains many more pearls for future research
• Foundation for future continued research program:
• Longitudinal studies
• Models of collective behavior of scientists

15. Indiana University
School of Informatics and Computing
Scalability of the approach:
• Lengthy negotiations to obtain log data
• No infrastructure standards (yet): Recording, aggregating, normalization,
ingestion, de-duplication,…
• No generally accepted policies: privacy, property, …
• No census data: when is a sample large and representative enough?
Quality control:
• Bots, Crawlers (detectable but never perfect)
• Cheating, manipulation (easier with usage statistics than network metrics)
Acceptance:
• Network-based usage metrics require session information. This is overlooked!
As a result, will we end up with usage-based statistics only?
• “As simple as possible, but not more simple!”
MESUR: the bad and the ugly …
Indiana University
School of Informatics and Computing
Planning process underway to establish sustainable, open, community supported infrastructure.
New support from Andrew W. Mellon foundation to figure it all out.
Moving towards community involvement
Logistics:
Data aggregation
Normalization
Data-related services
Data management
Science:
Metrics
Analysis
Prediction
Services
Ranking
Assessment
Mapping
=More than sum of parts:
• Each component supports the other
• Various business and funding
models
• Generate added value on all levels
Can fundamentally change scholarly
communication
Indiana University
School of Informatics and Computing
Some relevant publications (1).
Johan Bollen, Herbert Van de Sompel, Aric Hagberg, Luis
Bettencourt, Ryan Chute, Marko A. Rodriguez, Lyudmila
Balakireva. Clickstream data yields high-resolution maps
of science. PLoS One, March 2009 (In Press)
Johan Bollen, Herbert Van de Sompel, Aric HagBerg, Ryan
Chute. A principal component analysis of 39 scientific
impact measures. arXiv.org/abs/0902.2183
Johan Bollen, Marko A. Rodriguez, and Herbert Van de Sompel.
Journal status. Scientometrics, 69(3), December 2006
(arxiv.org:cs.DL/0601030)
Johan Bollen, Herbert Van de Sompel, and Marko A. Rodriguez.
Towards usage-based impact metrics: first results from
the MESUR project. In Proceedings of the Joint Conference
on Digital Libraries, Pittsburgh, June 2008.
Indiana University
School of Informatics and Computing
Some relevant publications (2).
Marko A. Rodriguez, Johan Bollen and Herbert Van de Sompel. A
Practical Ontology for the Large-Scale Modeling of Scholarly
Artifacts and their Usage, In Proceedings of the Joint
Conference on Digital Libraries, Vancouver, June 2007
Johan Bollen and Herbert Van de Sompel. Usage Impact Factor:
the effects of sample characteristics on usage-based impact
metrics. (cs.DL/0610154)
Johan Bollen and Herbert Van de Sompel. An architecture for the
aggregation and analysis of scholarly usage data. In Joint
Conference on Digital Libraries (JCDL2006), pages 298-307,
June 2006.
Johan Bollen and Herbert Van de Sompel. Mapping the structure of
science through usage. Scientometrics, 69(2), 2006.
Johan Bollen, Herbert Van de Sompel, Joan Smith, and Rick Luce.
Toward alternative metrics of journal impact: a comparison
of download and citation data. Information Processing and
Management, 41(6):1419-1440, 2005.

16. Indiana University
School of Informatics and Computing
Presentation structure
1. MESUR’s Usage reference data set
2. Mapping scientific activity
3. Metrics survey
4. Future research
5. Discussion Questions?
All questions will be posted with presenter
answers on the NISO website following the
webinar:
www.niso.org/news/events/2010/itemusage
Count Me In:
Measuring Individual Item Usage
Thanks to our sponsor!
NISO Webinar • September 15, 2010