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.

▶ 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)

▶ 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 Adopters
13.5%
Shareofinstitutions(%)
Innovators
2.5%
Early Majority
34%
Late Majority
34%
Laggards
16%
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.
Shareofinstitutions(%)
Innovators
2.5%
Early Majority
34%
Late Majority
34%
Early Adopters
13.5%
Laggards
16%
Collaborative content creation
Social media
Open content
Digitization
Linked data
Open data
Advanced implementation
Adoption
Trial
Evaluation
Interest
No interest
Diffusion of Innovative Practices among Heritage Institutions

37%
2%
44%
12%
45%
16%
18%
5%
59%
20%
57%
12%
39%
14%
23%
9%
66%
10%
0%
10%
20%
30%
40%
50%
60%
70%
Open data Linked data /
semantic web
Digitization Open content Social media Collaborative content
creation
Archives
Museums
Libraries
Adoption Rates According to Institution Type
N = 584

Adoption Rates – Country Comparison
30%
6%
58%
15%
74%
26%
28%
6%
38%
12%
75%
13%
18%
4%
44%
12%
34%
8%
38%
10%
71%
31%
70%
17%
0%
10%
20%
30%
40%
50%
60%
70%
80%
semantic web
Digitization Open content Social media Collaborative content
creation
Finland
Poland
Switzerland
The Netherlands
N = 584

Independent
Variable(s)
Open Data Linked Data Digitization Open Content
Social Media
Use
Collab. Cont.
Creation
Institution Type
+++
Country
++ ++ ++ +++ ++
Typical Objects
+ ++ (+)
Main Users
+++
Geographical
Reach ++
Size (paid FTE)
+++
Size (revenues)
Pct of Volunters
in Workforce +++
Revenue Sources
++ ++
Form of
Organization
Number 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

25 23
11
50
21 21
10
28
23
16
21
20
23
16
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Exchanging
data
semantic web
Digitization Open content Engaging
audiences on
the Internet
Collaborative
content
creation
1 - Not important at all
2
3
4
5 - Very important
Importance of Various Practices
N = 584
Data Content Participation

42
24
28
62
24
35
23
32
27
28
23
26
29
30
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Exchanging
data
semantic web
Digitization Open content Engaging
audiences on
the Internet
Collaborative
content
creation
1 - Risks clearly prevail
2
3
4
5 - Opportunities clearly
prevail
Desirability of Various Practices
(Opportunities vs. Risks)
N = 584
Data Content Participation

3%
6% 7%
15%
20%
4%
3%
7%
7%
3%
11% 6%
4%
3%
2%
0%
5%
10%
15%
20%
25%
30%
1 - Not important at
all
2 3 4 5 - Very important
Open data: importance vs. desirability (in % of institutions; N = 475)
Risks prevail
Equal risks and opportunities
Opportunities prevail
35%
21%
Open Data: Importance vs. Desirability
Motors

Open Content: Importance vs. Desirability
3%
5%
8%
16%
18%
5%
4%
7%
5%
2%
11%
7%
6%
2% 2%
0%
5%
10%
15%
20%
25%
30%
all
Open content: importance vs. desirability (in % of institutions; N = 466)
Risks prevail
Motors
34%
24%

Collaborative Content Creation:
Importance vs. Desirability
9% 10% 9%
15%
9%
9% 8% 8%
2%
1%
10%
5%
2%
1%
0%
0%
5%
10%
15%
20%
25%
30%
all
Collaborative content creation: importance vs. desirability
(in % of institutions; N = 438)
Risks prevail
24%
17%
Motors

Digitization as a Prerequisite for Open Content
5%
2% 2% 1% 2%
4%
19%
21%
9% 4%
1%
7%
11%
6%
0%
1%
2%
1%
0%
1%
2%
0%
0%
5%
10%
15%
20%
25%
30%
35%
40%
0% 1-19% 20-49% 50-79% 80% or more
Average percentage of objects already digitized
Expected digitization activities over the next 5 years
(in % of institutions; N = 457)
80% or more
50-79%
20-49%
1-19%
0%

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

Digitization Today – Country Comparison
1%
17%
0%
30%
50%
10%
2%
6%
1%
7%
0% 0%
2%
10%
1%
10%
5%
10%
13%
50%
10%
50%
25%
50%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Text based
resources (365)
Two-dimensional
visual resources
(357)
Archival resources
(281)
Three-dimensional
man-made
movable objects
(314)
Natural resources
(47)
Geography based
resources (119)
Time based
resources (220)
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)
Finland
Poland
Switzerland
The Netherlands

Digitization in 5 Years – Country Comparison
3%
45%
10%
50% 50%
30%
12%
30%
15%
35%
28%
20%
10%
50%
10%
50%
45%
50%50%
80%
25%
80%
40%
50%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Text based
resources (318)
Two-dimensional
visual resources
(317)
Archival resources
(251)
Three-dimensional
man-made
movable objects
(279)
Natural resources
(39)
Geography based
resources (99)
Time based
resources (196)
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)
Finland
Poland
Switzerland
The Netherlands

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

Open Content Today – Country Comparison
3%
5% 3%
1%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
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)
Finland
Poland
Switzerland
The Netherlands

Open Content in 5 Years – Country Comparison
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%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
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)
Finland
Poland
Switzerland
The Netherlands

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.
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%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
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 unknown
copyright controlled by third parties
copyright controlled by the institution
public 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?

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?

▶ 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

▶ 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

Kontakt
Berner Fachhochschule
E-Government-Institut
Beat Estermann
beat.estermann@bfh.ch
031 848 34 38

CAS open glam_teil2_20150910

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