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

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LibMeter Seminar

Optional

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A: Long Tail Approach

Produkt-Lebenszyklen Hype Cycle

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Virtual Libraries & Long Tail Services

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The Long Tail – Example Rhapsody

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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

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Product Life Cycle(Wikipedia.de)

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Gartner Hype CycleFENN & RAKINO 2008

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Gartner Hype CycleFENN & RAKINO 2008

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TIPPINGPOINT‘s Life Cycleover Gartner Hype Cycle

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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

Usage Data Repo-sitories

Log-Reporting Count Filter, GroupClassifyStoreReport …

Meta AnalysisAggregateCorrelateNormalizeRateRank …

Institutions Statistics

User-Service Provider

StatsTablesGraphs

Stat Analysis &Presentation

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]

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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]

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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)

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C: Live DemosOpenURL

Reporting-Server & Sites

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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

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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

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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

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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)

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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

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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

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Chat Bot „Stella“ SUB HAMBURG

Ggf. noch ausführen

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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.

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Max Planck SocietySFX Usage 2002 - 2009

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E: E: Tools (von Excel

zum Datawarehouse)

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Useful Spreadsheet Functions Auto-Filter Statistik-Funktionen

Min, Max, Mittelwerrt, Median RANG, Prozentrang

PIVOT Tabellen Zeilen-/Spaltenverweis

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

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F: Management & Organisations-entwicklung

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

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Overcoming the Problem of „Organizational Lag“

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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)

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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 !

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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).

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G: Checklist, Toolbox

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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

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Beyond scope

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New Kind of Science &

Library Stats on Web 2.0

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LibMeterLibMeterWidening Scope & Reach

of Usage Statistics

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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

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Library Service Profing based on OpenURL Data (SFX stats)

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

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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

Advanced Topics Snippets Lib Meter Zbw Hh Workshop

Technology

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