CAS open glam_teil2_20150910

Berner Fachhochschule | Haute école spécialisée bernoise | Bern University of Applied Sciences

Praktische Einführung in OpenGLAM

Beat Estermann, 10. September 2015, CAS IS Information Topics, HTW-Chur

Chur 1820, David Alois Schmid, Schweizerische Nationalbibliothek (GS-GUGE-SCHMID-DA-C-2)

Wo nicht anders vermerkt, unterstehen die Inhalte dieser Präsentation der CC-by-sa 4.0 Lizenz. Bitte Markenrechte auf Logos beachten.

https://commons.wikimedia.org/wiki/File:Musee_Ariana_1_Geneve.JPG

http://creativecommons.org/licenses/by-sa/3.0/


▶ Auftakt▶ Einführung in OpenGLAM▶ Vorstellungsrunde: Teilnehmende + Daten▶ Praktische Aufgabenstellung

▶ Grundlagen ▶ Open-Data-Prinzipien, Rechtliches▶ OpenGLAM-Vorgehensmodell / Datenmonitoring▶ Hochladen von Digitalisaten auf Wikimedia Commons▶ Veröffentlichung von Daten als Open Data▶ Open Cultural Data Hackathons

▶ Praxis▶ Erstellen eines Benutzerkontos auf Wikipedia/Wikimedia Commons

(P)▶ Hochladen eines eigenen Bildes auf Wikimedia Commons (P)▶ Hochladen kleinerer Mengen von Digitalisaten (Demo)▶ Wahl des richtigen Templates auf Wikimedia Commons (Demo)

Kursunterlagen: http://tinyurl.com/openglamchur

Programm Teil 1 (Rückblende)

http://tinyurl.com/openglamchur


▶ Ausgewählte Ergebnisse der OpenGLAM-Umfrage 2015

▶ Erfahrungsaustausch bezüglich der Hausarbeit

▶ Wie lässt sich die Nutzung der hochgeladenen Inhalte fördern?▶ Einführung ins Kategorien-System auf Wikimedia Commons▶ Templates auf Wikimedia Commons

▶ Wie lässt sich die Nutzung der hochgeladenen Inhalte nachverfolgen?

▶ Offenes Atelier: Praktisches Üben

Programm Teil 2


OpenGLAM Benchmark Survey• Selected Results

Source: Estermann, Beat (2015) Diffusion of Open Data and Crowdsourcing among Heritage Institutions. Based on data from Finland, Poland, Switzerland, and The Netherlands. Paper Presented at the EGPA 2015 Conference, held on 26-28 August 2015 in Toulouse, France.


Early Adopters13.5%

Shar

e of

inst

itutio

ns (

%)

Innovators2.5%

Early Majority

34%

Late Majority34%

Laggards16%

Research Questions

Where do heritage institutions stand with regard to……Open Data?…Linked Data / Semantic Web?…Digitization…Open Content?…Engaging Audiences on the Internet…Collaborative Content Creation

What are the perceived risks and opportunities? (drivers vs. hindering factors)What are the expected benefits?What are the differences between different types of heritage institutions?

A further goal of the “OpenGLAM Benchmark Survey” are international comparisons: In what ways does the situation in the different countries vary?

Awareness Evaluation

AdoptionTrialInterest

Innovation Diffusion Model, Everett Rogers, 1962

Berner Fachhochschule | Haute école spécialisée bernoise | Bern University of Applied SciencesFinland, Poland, Switzerland, The Netherlands, all institution types combined, N = 584.Cases with «stagnation» / «discontinuance» have been ignored.

Shar

e of

inst

itutio

ns (

%)

Innovators2.5%

Early Majority

34%

Late Majority34%

Early Adopters13.5%

Laggards16%

Collaborative content creation

Social media

Open content

Digitization

Linked data

Open data

Advanced implementationAdoptionTrialEvaluationInterestNo interest

Diffusion of Innovative Practices among Heritage Institutions


Open data Linked data / semantic web Digitization Open content Social media Collaborative content creation0%

10%

20%

30%

40%

50%

60%

70%

37%

2%

44%

12%

45%

16%18%

5%

59%

20%

57%

12%

39%

14%

23%

9%

66%

10%

ArchivesMuseumsLibraries

Adoption Rates According to Institution Type

N = 584


Adoption Rates – Country Comparison

Open data Linked data / semantic web

Digitization Open content Social media Collaborative content creation

0%

10%

20%

30%

40%

50%

60%

70%

80%

30%

6%

58%

15%

74%

26%28%

6%

38%

12%

75%

13%

18%

4%

44%

12%

34%

8%

38%

10%

71%

31%

70%

17%

FinlandPolandSwitzerlandThe Netherlands

N = 584


Independent Variable(s) Open

Data Linked Data Digitization Open Content

Social Media Use

Collab. Cont.

CreationInstitution Type +++Country ++ ++ ++ +++ ++Typical Objects + ++ (+)Main Users +++Geographical Reach ++Size (paid FTE) +++Size (revenues)

Pct of Volunters in Workforce +++Revenue Sources ++ ++Form of OrganizationNumber of Metadata Types +++ ++Skills / Skills Acquisition ++ ++ ++Attitudes reg. Open Content ++ ++ ++

Ordinal Logistic Regression – Synthesis of Results


Attitudes: Conditions for Releasing Content

N = 457


▶ Three Estimators:▶ Perceived importance and desirability (opportunities vs. risks)▶ Presence (absence) of important prerequisites (or « show-stoppers »)▶ Indications regarding institutions’ future practice

▶ Projection:▶ 70% of institutions will have adopted open data as a practice in ca. 10

years from now. (limit to diffusion: ca. 30% don’t have metadata for their objects)

▶ 70% of institutions will have adopted open content as a practice in ca. 15 years from now.(limits to diffusion: advancement of digitization; copyright)

▶ Ca. 20% of institutions are using crowdsourcing today; the data suggest that the adoption rate will be lower than for open data and open content.

Expected Dynamics of Adoption of Open Data, Open Content and Crowdsourcing


Exchan

ging d

ata

Open d

ata

Linked

data

/ semant

ic web

Digitiza

tion

Open c

onten

t

Engagi

ng au

dience

s on t

he In

ternet

Colla

borat

ive co

ntent

creati

on0%

10%20%30%40%50%60%70%80%90%

100%

25 2311

50

21 2110

2823

16

21

20 23

16

1 - Not important at all2345 - Very important

Importance of Various Practices

N = 584

Data Content Participation


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

42

24 28

62

2435

23

32

2728

23

26

29

30

1 - Risks clearly prevail

2

3

4

5 - Opportunities clearly prevail

Desirability of Various Practices (Opportunities vs. Risks)

N = 584

Data Content Participation


1 - Not important at all

2 3 4 5 - Very important0%

5%

10%

15%

20%

25%

30%

3%6% 7%

15%

20%

4%

3%

7%

7%

3%

11% 6%

4%

3%2%

Open data: importance vs. desirability (in % of institutions; N = 475)

Risks prevailEqual risks and opportunitiesOpportunities prevail

35%

21%

Open Data: Importance vs. Desirability

Motors


Open Content: Importance vs. Desirability


2 3 4 5 - Very important

-5%

0%

5%

10%

15%

20%

25%

30%

3% 5%8%

16% 18%

5%4%

7%

5% 2%

11%7%

6%

2% 2%

Open content: importance vs. desirability (in % of institutions; N = 466)


Motors

34%

24%


Collaborative Content Creation: Importance vs. Desirability


2 3 4 5 - Very important0%

5%

10%

15%

20%

25%

30%

9% 10% 9%

15%

9%

9% 8% 8%

2%

1%

10%

5%2%

1%

0%

Collaborative content creation: importance vs. desirability (in % of institutions; N = 438)


24%

17%

Motors


Digitization as a Prerequisite for Open Content

0% 1-19% 20-49% 50-79% 80% or more0%

5%

10%

15%

20%

25%

30%

35%

40%

5%2% 2% 1% 2%

4%

19% 21%

9% 4%

1%

7%

11%

6%

0%1%

2%

1%

0%

1%

2%

0%

Expected digitization activities over the next 5 years (in % of institutions; N = 457)

80% or more50-79%20-49%1-19%0%

Average percentage of objects already digitized


Digitization: Different types of objects(today / in 5 years)


Digitization Today – Country Comparison

Text b

ased re

source

s (365)

Two-dim

ensio

nal visu

al res

ources (

357)

Archiva

l reso

urces (2

81)

Three

-dimen

sional

man-m

ade m

ovable

objects (

314)

Natural

resource

s (47)

Geogra

phy base

d resource

s (119)

Time b

ased re

sources (

220)0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1%

17%

0%

30%

50%

10%

2%6%

1%7%

0% 0%2%

10%

1%

10%5%

10%13%

50%

10%

50%

25%

50%

Percentage of resources already digitized by the average institution (median)(N is indicated for each object type; the values for natural resources are not shown due to the small

sample size)



Digitization in 5 Years – Country Comparison

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

3%

45%

10%

50% 50%

30%

12%

30%

15%

35%

28%

20%

10%

50%

10%

50%

45%

50%50%

80%

25%

80%

40%

50%

Percentage of resources expected to be digitized in 5 years by the average institution (median)(N is indicated for each object type; the values for natural resources are not shown due to the small

sample size)



Open Content: Different types of objects(today / in 5 years; in % of digitized objects)


Open Content Today – Country Comparison

Text b

ased re

source

s (344)

Two-dim

ensio

nal visu

al res

ources (

340)

Archiva

l resource

s (278)

Three

-dimen

sional

man-m

ade m

ovable

objects (

297)

Natural

resource

s (46)

Geogra

phy base

d resource

s (112)

Time b

ased re

sources (

204)

Digital i

nteracti

ve re

source

s (123)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

3% 5% 3% 1%

Percentage of resources already made available as open content by the average institution (median)(N is indicated for each object type; the values for natural resources are not shown due to the small sample

size; “0%” value tags have been suppressed)



Open Content in 5 Years – Country Comparison

Text b

ased re

source

s (289)

Two-dim

ensio

nal vis

ual res

ources (

290)

Archiva

l resource

s (239)

Three

-dimen

sional m

an-m

ade m

ovable

objects (

252)

Natural

resource

s (38)

Geogra

phy base

d resource

s (93)

Time b

ased re

sources (

179)

Digital i

nteracti

ve re

source

s (108)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0%5% 2% 5%

10%

1% 3%5% 8%2%

10%

30%

0% 0%0%5%

0%5%

10%

0%5%

15%

50%

20%25%

18%10%

19%

Percentage of resources expected to be made available as open content in 5 years by the average institution (median)

(N is indicated for each object type; the values for natural resources are not shown due to the small sample size)



Copyright Clearance as a Prerequisite for Open Content

Note: The data may be somewhat biased as it does - by definition - not include the data from institutions who weren’t able to provide numbers regarding the copyright status of their objects.

Text b

ased re

source

s (192)

Two-dim

ensio

nal vis

ual res

ources (

217)

Archiva

l resource

s (172)

Three

-dimen

sional m

an-m

ade m

ovable

objects (

169)

Natural

resource

s (25)

Geogra

phy base

d resource

s (62)

Time b

ased re

sources (

122)

Digital i

nteracti

ve re

source

s (58)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

31% 29%

47% 42%59%

44%

18%28%

27% 35%

35% 40%

29%

37%

37%

50%

33% 23%

10% 12%8%

13%

34%

15%

8% 13% 8% 7% 4% 6% 11% 6%

What is the copyright situation of the objects in your collection? (average percentages for each object type; N is indicated for each object type)

copyright status unknowncopyright controlled by third partiescopyright controlled by the institutionpublic domain


Driving and Hindering Factors of Open Content and Crowdsourcing Driving Factors1 Hindering Factors1

Open Content Improving the visibility of the institution and its holdings

Making content more easily available for existing users and attract new users

Facilitating networking among institutions

Improving interactions with users

Doing a better job at fulfilling the institution’s core mission

Extra time effort and expenses (digitization, documentation, rights clearance)

Feeling of loss of control

Wish to prevent commercial use of content by third parties without due compensation

Technical issues and insufficient staff skills

Crowdsourcing

Intention to get access to external expertise and to have certain tasks carried out by volunteers

Quest for an improved relationship with users/visitors (trust, loyalty, public ownership and responsibility)

Extensive preparation and follow-up

Difficulties to estimate the time scope; low planning security; continuity of data maintenance is not guaranteed

1 Factors which are of relevance for more than 50% of responding institutions


Hochladen von Bildern aus den Beständen einer Institution auf Wikimedia Commons


▶ Hochladen von mind. 20 Digitalisaten aus Ihrer Institution oder einer Drittinstitution auf Wikimedia Commons

▶ Neu-Publikation von Daten aus Ihrer Institution gemäss Open Data-Kriterien (auf der Website der eigenen Institution oder auf datahub.io) zur Verwendung anlässlich des nächsten Open Cultural Data Hackathons

Praktische Aufgabenstellung (Hausarbeit)


Wir haben Inhalte auf Wikimedia Commons hochgeladen. – Was nun?• Wie lässt sich die Nutzung der Inhalte fördern?• Wie lässt sich die Nutzung der Inhalte nachverfolgen?


▶ Möglichst ausführliche Metadaten (evtl. auch auf Englisch)▶ Information der Wikipedia-Community (speziell bei umfangreichen Uploads)

▶ Wikipedia-Kurier / The Signpost / Regards sur l'actualité de la Wikimedia▶ Mailinglisten

▶ Einordnung der Inhalte: Kategorisierung / Verwendung von Templates

▶ Anleitung für die Kategorisierung▶ Beispiel für Template:Institution und Template:Creator

▶ Bereitstellung von Tools (speziell bei umfangreichen Uploads)▶ Template {{Institution XY}} / Category:Media contributed by Institution XY (

Beispiel)▶ GLAM Dashboard (Sign-up Page, Beispiel)

▶ Veranstaltungen für (neue) Benutzer: Edit-a-thons, Hackathons, etc. ▶ Einbinden von Bildern in Wikipedia in Eigenregie (bei kleineren Uploads)

Wie lässt sich die Nutzung der Inhalte fördern?

https://de.wikipedia.org/wiki/Wikipedia:Kurier

https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_Signpost

https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_Signpost

https://fr.wikipedia.org/wiki/Wikip%C3%A9dia:Regards_sur_l'actualit%C3%A9_de_la_Wikimedia

https://fr.wikipedia.org/wiki/Wikip%C3%A9dia:Regards_sur_l'actualit%C3%A9_de_la_Wikimedia

https://commons.wikimedia.org/wiki/Commons:Kategorien

https://commons.wikimedia.org/wiki/File:E-codices_sbs-0008_005r_Beschneidung_von_Johannes_dem_T%C3%A4ufer.TIF

https://commons.wikimedia.org/wiki/Institution:Stadtbibliothek_Schaffhausen

https://commons.wikimedia.org/wiki/Creator:Unknown

https://commons.wikimedia.org/wiki/File:E-codices_sbs-0008_005r_Beschneidung_von_Johannes_dem_T%C3%A4ufer.TIF

https://commons.wikimedia.org/wiki/User:Faebot/GLAM_dashboard

https://commons.wikimedia.org/wiki/User:Faebot/GLAM_dashboard

https://commons.wikimedia.org/wiki/Commons:Swiss_National_Library/reports/improvement


Statistik-Tools:▶ BaGLAMa 2: Page-View-Statistik der Artikel, in denen Medien-

Inhalte aus einer bestimmten Kategorie zugeordnet wurden (z.B. Category:Media contributed by Institution XY)

▶ Im Zeitverlauf▶ Aufgeschlüsselt nach einzelnen Sprachversionen und Artikel

▶ GLAMorous: Anzahl der Medien-Dateien, die in den verschiedenen Wikipedia-Versionen aktuell genutzt werden

▶ Aufgeschlüsselt nach einzelnen Sprachversionen und Artikel▶ Ranking der Medien-Dateien nach Anzahl Nutzungen▶ Prozentsatz der genutzten Dateien (im Vergleich zu allen Dateien in

einer bestimmten Kategorie)▶ GLAM-Dashboard:

▶ Am häufigsten vergebene Kategorien▶ Die am häufigsten genutzten Dateien▶ Die Commons-Benutzer mit den meisten Beiträgen auf den

Beschriebsseiten▶ Die am häufigsten editierten Dateien▶ Die grössten Dateien

Wie lässt sich die Nutzung der Inhalte nachverfolgen?

https://tools.wmflabs.org/glamtools/baglama2/

https://tools.wmflabs.org/glamtools/baglama2/

https://tools.wmflabs.org/glamtools/glamorous.php?doit=1&category=Media+contributed+by+the+Swiss+National+Library&use_globalusage=1&ns0=1&depth=2&show_details=1&projects%5Bwikipedia%5D=1&projects%5Bwikimedia%5D=1&projects%5Bwikisource%5D=1&projects%5Bwikibooks%5D=1&projects%5Bwikiquote%5D=1&projects%5Bwiktionary%5D=1&projects%5Bwikinews%5D=1&projects%5Bwikivoyage%5D=1&projects%5Bwikispecies%5D=1&projects%5Bmediawiki%5D=1&projects%5Bwikidata%5D=1&projects%5Bwikiversity%5D=1

https://commons.wikimedia.org/wiki/Commons:Swiss_National_Library/reports/improvement


▶ In March and April 2013 Zentralbibliothek Zürich uploaded ca. 100 images from their prints and photography collection to Wikimedia Commons

▶ By the end of April 2014, 56 out of 96 uploaded files (58%) have been used in 159 Wikipedia articles belonging to 42 different language versions of Wikipedia

▶ In April 2014 these 159 Wikipedia articles were viewed over 500’000 times

Source: http://tools.wmflabs.org/glamtools/baglama2/

Measure the impact and talk about it…

Zürich, Sechseläuten fire around 1900. Anonymous artist, Public Domain (Wikimedia Commons).

Chamonix. Crossing the Sea of Ice, between 1902 and 1904. Anonymous artist, Public Domain (Wikimedia Commons).

The Iron Bridge across the Thur, view from east from Regensdorf, 1857. Jakob Eggli, Public Domain (Wikimedia Commons).

WP-de: Schweizerische Nordostbahn WP-fr: Chamonix-Mont-Blanc; Recul des glaciers depuis 1850

WP-ru: Шестизвонье


▶ Statistik zu Mobile-Zugriffen (werden in der aktuellen Statistik nicht berücksichtigt, treten aber mehr und mehr an die Stelle von klassischen Zugriffen)

▶ Zusammenfassende Länder-Statistiken:

▶ Beispiel Niederlande

▶ Forschungsergebnisse:▶ Beispiel: Borowiecki / Navarrete (forthcoming). Change in

access after digitization: ethnographic collections in Wikipedia▶ Comparative analysis of the access to heritage collections at the National

Museum of World Cultures in the Netherlands before and after publication on Wikimedia

▶ Access to collections increased substantially online (156’000 onsite visitors vs. over 1.5 mio. online views per year)

▶ There is a «long tail» in both mediums, but this broadens online: 8% of objects were exhibited onsite; 11% of available objects were used in Wikipedia articles

Ausblick

http://openglam.org/2015/06/23/dutch-cultural-heritage-reaches-millions-every-month/


▶ Einführung ins Kategorien-System von Wikimedia Commons:https://commons.wikimedia.org/wiki/Commons:Categories

▶ Einführung hinsichtlich Vorlagen («Templates») auf Wikimedia Commons:https://commons.wikimedia.org/wiki/Commons:Templates

▶ Anleitung für das Einfügen von Bildern in Wikipedia:https://de.wikipedia.org/wiki/Hilfe:Bilder

▶ Wikipedia-Starthilfe:https://de.wikipedia.org/wiki/Wikipedia:Starthilfe

Weiterführende Informationen

https://commons.wikimedia.org/wiki/Commons:Categories

https://commons.wikimedia.org/wiki/Commons:Categories

https://commons.wikimedia.org/wiki/Commons:Templates

https://commons.wikimedia.org/wiki/Commons:Templates

https://de.wikipedia.org/wiki/Hilfe:Bilder

https://de.wikipedia.org/wiki/Hilfe:Bilder

https://de.wikipedia.org/wiki/Wikipedia:Starthilfe

https://de.wikipedia.org/wiki/Wikipedia:Starthilfe


Kontakt

Berner FachhochschuleE-Government-Institut

Beat [email protected] 848 34 38

CAS open glam_teil2_20150910

Government & Nonprofit

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