Advanced Topics Snippets Lib Meter Zbw Hh Workshop
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Transcript of Advanced Topics Snippets Lib Meter Zbw Hh Workshop
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Advanced Topics Snippets
LibMeter - Library eMetrics
Workshop ZBW-HH Part 5Nutzungsanalyse elektronischer Informationsdienstleistungen für das
praktische Bibliotheks-Management
Veranstalter: Berufsverband Information
Bibliothek e. V. (BIB)Landesgruppe Hamburg
6. November 2009
Referent:Dr. Peter Ahrens
Freier Referent
Assistenz:Tanja Haberkorn
Beta 0.8 2009-11-01
© Peter Ahrens, Cologne, 2009
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LibMeter Seminar
x
Optional
© Peter Ahrens, Cologne, 2009
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A: Long Tail Approach
Produkt-Lebenszyklen Hype Cycle
© Peter Ahrens, Cologne, 2009
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Virtual Libraries & Long Tail Services
© Peter Ahrens, Cologne, 2009
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The Long Tail – Example Rhapsody
© Peter Ahrens, Cologne, 2009
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Service Introduction Phases
AHRENS Idealized SchemaPhase I – Embryo, Baby Start from zero base, very low numbers Development, Early adoption Often Below radar screen Huge growth rates ( >> 100 %
„Breakout“) Timeline: 0 - 33 %, 2-3 Years Early adoptersPhase II – Joungster Timeline: 34 – 66 %, 3-4 years Highest Growth rates (around 15-30 %) Absolute increase almost linear Mainstream AdoptersPhase III – Mature Adult Timeline: 67 – 100 %, 3-5 years Slowing Growth (< %), Flat, Plateau Beginning decline Late adopters, laggards
I II III
© Peter Ahrens, Cologne, 2009
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Product Life Cycle(Wikipedia.de)
© Peter Ahrens, Cologne, 2009
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Gartner Hype CycleFENN & RAKINO 2008
© Peter Ahrens, Cologne, 2009
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Gartner Hype CycleFENN & RAKINO 2008
© Peter Ahrens, Cologne, 2009
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TIPPINGPOINT‘s Life Cycleover Gartner Hype Cycle
© Peter Ahrens, Cologne, 2009
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2. Generation e-Businessand the Hype-Cycle
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B: Live-Demo Dynamic Usage Stats
Science Rating / Ranking
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Information Society of 2010 Usage is All about the Web HTML-Link Web-Surfing Web-Traffic Web-UsageWeb 2.0Web-ServicesWeb AnalyticsWebometrics
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How do you get these „Magical“ Web-Statistics?WebBrow- ser
Internet Provider
Web Server
Web Page
Web Logs
http / URLIP-AddressDate/TimeBrowserSettings
Etc.
Usage Data Repo-sitories
Log-Reporting Count Filter, GroupClassifyStoreReport …
Meta AnalysisAggregateCorrelateNormalizeRateRank …
Institutions Statistics
DB
User-Service Provider
StatsTablesGraphs
Stat Analysis &Presentation
© Peter Ahrens, Cologne, 2009
LibMeterLibMeterLinks to Dynamic Stat Tools Google Analytics (Web site statistics) GIS (Insights in Google Search actvity) Scimago Science Ranking
CHE ARWU
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Example for Webometrics:Ranking of World Universities
http://www.webometrics.info/top100_continent.asp?cont=europe [2009-04-25]
© Peter Ahrens, Cologne, 2009
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What „Google Insight“ can tell about German Libraries and Archives ? (I)
http://www.google.com/insights/search/#q=Anna%20amalia&cmpt=q [2009-05-18]
© Peter Ahrens, Cologne, 2009
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What „Google Insight“ tells about German Libraries and Archives II
http://www.google.com/insights/search/#q=anna%20amalia%2Cstadtarchiv%20k%C3%B6ln&cmpt=q (2009-04-24)
© Peter Ahrens, Cologne, 2009
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C: Live DemosOpenURL
Reporting-Server & Sites
© Peter Ahrens, Cologne, 2009
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OpenURL Reporting-Server & Public Reporting Sites Uni Düsseldorf OpenURLReport Gene
rator Online Reports - Publizierte Statistiken
California Digital Library CDL eLink http://www.cdlib.org/inside/projects/uc-elinks/
index.html#ucelinkstats Max-Planck public SFX Statistics Pages
http://sfx.mpg.de/statistics/sfx_statistics.html#source_dia
© Peter Ahrens, Cologne, 2009
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OpenURL Reporting-Server & Public Reporting Sites Report Generator
http://sfx.hbz-nrw.de/sfx_due/sfxadmin/user_admin.cgi Online Reports - Publizierte Statistiken
CDL http://www.cdlib.org/inside/projects/uc-elinks/
index.html#ucelinkstats Max-Planck
http://sfx.mpg.de/statistics/sfx_statistics.html#source_dia
© Peter Ahrens, Cologne, 2009
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D: Kurvendiskussion - Log, Annualität &
Periodizität
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Libraries can learn a lot from Economy and Econometrics
Library Service = ProductUsage (Value) = „Sales“Services have Life CyclesPatterns for product adoption
and substitution over time
© Peter Ahrens, Cologne, 2009
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Increased speed of Innovationrequires organizational learning Changes not in centuries or decades
any more 6-8 years growth phase for Web Service
„normal“ Web2.0 Services may even „pop up“
much quicker 2-4 years Gradual Service choice and substitution
will become normal (i.e. Zeitschriftenkatalog > EZB > ERM/OpenURL)
© Peter Ahrens, Cologne, 2009
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Service Introduction Phases
AHRENS Idealized SchemaPhase I – Embryo, Baby Start from zero base, very low numbers Development, Early adoption Often Below radar screen Huge growth rates ( >> 100 %
„Breakout“) Timeline: 0 - 33 %, 2-3 Years Early adoptersPhase II – Joungster Timeline: 34 – 66 %, 3-4 years Highest Growth rates (around 15-30 %) Absolute increase almost linear Mainstream AdoptersPhase III – Mature Adult Timeline: 67 – 100 %, 3-5 years Slowing Growth (< %), Flat, Plateau Beginning decline Late adopters, laggards
I II III
© Peter Ahrens, Cologne, 2009
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Databases show different usage cycles around year (student-staff)
Data Courtesy of Menno ERasch, University of Utrecht, Analysis & Chart by Author
© Peter Ahrens, Cologne, 2009
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Chat Bot „Stella“ SUB HAMBURG
Ggf. noch ausführen
© Peter Ahrens, Cologne, 2009
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Chatterbot ASKADEMICUS UB Do Usage monthly and rolling 12 mo.
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Statement of the UB-DO Chatbot Administrator regarding the Stats
Feb/Mar 2005 und April 2006: dort wurde der Chatterbot (um den Jahrestag der Einfüh-rung herum) beworben, mit kleinen Updates versehen und auf ihn gesondert hingewiesen
August 2005 wurde er ... im Radio bzw. WDR online vorgestellt.
Da ein solcher Dienst noch sehr exotisch in Bibliotheken ist sind häufige Werbeaktionen, gerade bei den neuen Studenten, unerläßlich. Hier könnte man diese Auswertung zur Planung und Erfolgskontrolle nutzen.
© Peter Ahrens, Cologne, 2009
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© Peter Ahrens, Cologne, 2009
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Max Planck SocietySFX Usage 2002 - 2009
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E: E: Tools (von Excel
zum Datawarehouse)
© Peter Ahrens, Cologne, 2009
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Useful Spreadsheet Functions Auto-Filter Statistik-Funktionen
Min, Max, Mittelwerrt, Median RANG, Prozentrang
PIVOT Tabellen Zeilen-/Spaltenverweis
© Peter Ahrens, Cologne, 2009
LibMeterLibMeterEntity relationship Model Speichern in Tabellen Denormalisieren Primär- und Sekundär-Schlüssel Data Warehouse Professionelle Tools
Consortial- / NationalTools (BiBS) ERM, Stats-DBs, Reporting Centers (ehemals) DBS-navigate Businesss Intelligence Solutions, OLAP
© Peter Ahrens, Cologne, 2009
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F: Management & Organisations-entwicklung
© Peter Ahrens, Cologne, 2009
LibMeterLibMeterParadigm Shift with Library 2.0Traditional: Libraries helping Science Librarians have high Information LiteracyNew Era: Science helps forming Library 2.0 Cybrarians need new techincal skills including
quantitative reasoning and information skills Quantitative literacy (Anglo-American =
Numeracy) Web-principles & design Marketing, Economics, Econometrics Quality Management/Controlling Usage data processing & Interpretation Statistics
© Peter Ahrens, Cologne, 2009
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Overcoming the Problem of „Organizational Lag“
1984
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Increased speed of Innovationrequires organizational learning Changes not in centuries or decades
any more 6-8 years growth phase for Web Service
„normal“ Web2.0 Services may even „pop up“
much quicker 2-4 years Gradual Service choice and substitution
will become normal (i.e. Zeitschriftenkatalog > EZB > ERM/OpenURL)
© Peter Ahrens, Cologne, 2009
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Indicators for Todays Services need to reflect Shift in Focus from Physical to Virtual From Visible to (primarily) Invisible From Owning to Providing From Text to Meta-Data From Content to Services
From Local to Global (Content) From Global to Local (Services)
Issue: Change of Library-Methods is
ongoing !
© Peter Ahrens, Cologne, 2009
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Libraries can learn a lot from Economy and Econometrics
Library Service = ProductUsage (Value) = „Sales“Services have Life CyclesPatterns for product adoption
and substitution over time
© Peter Ahrens, Cologne, 2009
LibMeterLibMeterThree Predictions Enhanced Electronic Usage Monitoring &
Reporting will rapidly become crucial for libraries (More Para-meters, more frequently, more easily accessible)
Libraries will become more quickly & easily adaptive to developments (earlier SWOT detection; Transi-tion Electronic library -> virtual library -> Library 2.0)
Libraries 2.0 will be much stronger and survive as important social web players . (e.g. fully exploring „Long-Tail“ concepts and potential).
© Peter Ahrens, Cologne, 2009
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G: Checklist, Toolbox
© Peter Ahrens, Cologne, 2009
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Essential steps in eService evaluation Curiousity (Create & ) Collect Stats Consolidate (suitable storage/ system ) Calculate (-> ratios, Indicators) Chart – reduce & visualize Compare (peers, best practices …) Consider/consult – review / discuss Conclusions / consequences / change
© Peter Ahrens, Cologne, 2009
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Beyond scope
© Peter Ahrens, Cologne, 2009
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New Kind of Science &
Library Stats on Web 2.0
© Peter Ahrens, Cologne, 2009
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Libraries can learn a lot from Economy and Econometrics
Library Service = ProductUsage (Value) = „Sales“Services have Life CyclesPatterns for product adoption
and substitution over time
© Peter Ahrens, Cologne, 2009
LibMeterLibMeterWidening Scope & Reach
of Usage Statistics
© Peter Ahrens, Cologne, 2009
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Digesting Usage Figures - IDetermining Relevant DerivativesHelp! Too many too confusing too large
numbers ...Various Aspects of „Usage“
Baseline Figures Normalized and Standardized Figures Change-Rates Previous Periods
(+/- Comparison with other Services) (+/- External Comparison)
Ranking, Rating
© Peter Ahrens, Cologne, 2009
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Library Service Profing based on OpenURL Data (SFX stats)
© Peter Ahrens, Cologne, 2009
LibMeterLibMeterAction List for Part New Slides
Management Awareness
Competences Methods
Logarithmic / Longtail separation Indexing (to total, reference time) Quantiles Running 12 mo
Tools Excel, ERM, Business Intelligence, OLAP Collecting, analysing, comparing
© Peter Ahrens, Cologne, 2009
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Looking at Distribution among University Libraries & Polytechs ?
A: Number of EZB calls correlated with conventional Loans* ? NO, there is no correlation
B: Number of EZB calls correlated with the Staff/Student ratio* ? YES, there seems to be a
weak positive correlation i.e. higher staff proportion
goes along with higher the EZB usage
In other words (Hypothesis): Staff uses EZB eJournal-Catalogue more than Students
This is not astonishing, but for the first time it becomes quantifiable
A
B