The Venice Time Machine aims to digitally preserve and provide access to historical documents from Venice through various projects. These include digitizing documents using tomography, developing tools to extract text from images, modeling the information and relationships within documents, building an information system to allow searching across documents, linking documents to related scholarship to enrich the content, and creating digital experiences to promote research and teaching. The goal is to make the historical record of Venice available while ensuring long-term preservation, and to demonstrate the value of digital humanities approaches and tools.

1. A brief introduction to the
Venice Time Machine
Giovanni Colavizza EPFL

2. Who am I
Giovanni Colavizza
PhD student in Management of Technology
chair of Digital Humanities, EPFL
previously:
Computer Science, History, Archival and Library Sciences,
2 start-ups and some positions in IT and research.

3. Today
Venice Time Machine
1- Vision (where to go)
2- Pipeline (how) and Projects (what)
3- Methods and DH in context (or why, and how again)

4. VTM Vision

5. VTM Vision
Preservation (from analog to digital)
Access (from browsing to searching)
Valorisation by use

6. Preservation
Digitisation and replication as a preservation strategy..
Quite complicated:
1- metadata (digital provenance)
2- replication protocols: IT infrastructure (centralised vs
distributed)
3- rights and partners’ needs (far away goal of open
access for public heritage)

7. Access
An Information System down to contents:

8. Valorisation
1- research
2- teaching
3- digital reconstruction and outreach
4- technology transfer
5- methodology transfer

9. Pipeline illustrated by projects
1. Digitisation (Tomography)
2. Image processing (Pre-processing Suite)
3. Content extraction (Automatic transcription, READ Project)
4. Information modelling (Garzoni Project)
5. Building an information system (Document Viewer)
6. Content enrichment and network effects (Linked Books Project)
7. Valorisation and use (GIS, digital experiences, …)

10. Tomography
Fauzia Albertin EPFL

11. Tomography
Fauzia Albertin EPFL

12. Pipeline illustrated by projects
1. Digitisation (Tomography)
2. Image processing (Pre-processing Suite)
3. Content extraction (Automatic transcription, READ Project)
4. Information modelling (Garzoni Project)
5. Building an information system (Document Viewer)
6. Content enrichment and network effects (Linked Books Project)
7. Valorisation and use (GIS, digital experiences, …)

13. Image pre-processing suite
Andrea Mazzei ODOMA

14. Image pre-processing suite
Video pt. 1
Andrea Mazzei ODOMA

15. Pipeline illustrated by projects
1. Digitisation (Tomography)
2. Image processing (Pre-processing Suite)
3. Content extraction (Automatic transcription, READ Project)
4. Information modelling (Garzoni Project)
5. Building an information system (Document Viewer)
6. Content enrichment and network effects (Linked Books Project)
7. Valorisation and use (GIS, digital experiences, …)

16. Semi-automatic transcription
or the Big Data quest for script family resemblances
READ Horizon 2020 project: 8.2 million €, 7
partners, maximum peer reviewers’ score.

17. Opt. 1: Alignment
ello$ stara$ en$ carcere$ domentre$ chel$ fara$
queste$ chose$ opagera.$ Et$ e5am$ deo$
stagando$collui$encarcere$se$sauera$la$
che$ sia$ dellauer$ de$ collui$ lodoxe$
comandera$ chello$ sia$ entromesso$
edara$ sse$ allo$ so$ credetor.$ Et$ e5am$
deo$ selo$ creditor$ uora$ enues5r$
lapprietade$ del$ debitor$ enquella$ fia$
da$ alcreditor$ sera$ data$ en$ ues5xon.$
Mosella$ femena$ che$ none$ maritata$
sera$9depnata$segon$do$che$desoura$
edito$ tuto$ se$ fara$ segondo$ che$ nui$
auemo$ soura$ dito$ delomo$ remetuda$
questa$ cho$ sa$ chello$ stara$ enlo$
teratorio$de$san$ҫacharia$e$
$
Fouad Slimane EPFL

18. Opt. 1: Alignment
Fouad Slimane EPFL
! chose! opagera.! Et! e.am! deo!
stagando!collui!encarcere!se!sauera!la!
che! sia! dellauer! de! collui! lodoxe!
comandera! chello! sia! entromesso!
edara! sse! allo! so! credetor.! Et! e.am!
deo! selo! creditor! uora! enues.r!
lapprietade! del! debitor! enquella! fia!
da! alcreditor! sera! data! en! ues.xon.!
Mosella! femena! che! none! maritata!
sera!9depnata!segon!do!che!desoura!
edito! tuto! se! fara! segondo! che! nui!
auemo! soura! dito! delomo! remetuda!
questa! cho! sa! chello! stara! enlo!
teratorio!de!san!ҫacharia!e!
!

19. Opt. 1: Alignment
Fouad Slimane EPFL
!
!encarcere!se!sauera!la!
che! sia! dellauer! de! collui! lodoxe!
comandera! chello! sia! entromesso!
edara! sse! allo! so! credetor.! Et! e3am!
deo! selo! creditor! uora! enues3r!
lapprietade! del! debitor! enquella! fia!
da! alcreditor! sera! data! en! ues3xon.!
Mosella! femena! che! none! maritata!
sera!9depnata!segon!do!che!desoura!
edito! tuto! se! fara! segondo! che! nui!
auemo! soura! dito! delomo! remetuda!
questa! cho! sa! chello! stara! enlo!
teratorio!de!san!ҫacharia!e!
!

20. Opt. 1: Alignment
Fouad Slimane EPFL
!
!
lodoxe!
comandera! chello! sia! entromesso!
edara! sse! allo! so! credetor.! Et! e2am!
deo! selo! creditor! uora! enues2r!
lapprietade! del! debitor! enquella! fia!
da! alcreditor! sera! data! en! ues2xon.!
Mosella! femena! che! none! maritata!
sera!9depnata!segon!do!che!desoura!
edito! tuto! se! fara! segondo! che! nui!
auemo! soura! dito! delomo! remetuda!
questa! cho! sa! chello! stara! enlo!
teratorio!de!san!ҫacharia!e!
!

21. Opt. 1: Alignment
Fouad Slimane EPFL
!
!
sse! allo! so! credetor.! Et! e-am!
deo! selo! creditor! uora! enues-r!
lapprietade! del! debitor! enquella! fia!
da! alcreditor! sera! data! en! ues-xon.!
Mosella! femena! che! none! maritata!
sera!9depnata!segon!do!che!desoura!
edito! tuto! se! fara! segondo! che! nui!
auemo! soura! dito! delomo! remetuda!
questa! cho! sa! chello! stara! enlo!
teratorio!de!san!ҫacharia!e!
!

22. Opt. 2: Word spotting and Neural Networks
Andrea Mazzei ODOMA
Video pt. 2

23. Pipeline illustrated by projects
1. Digitisation (Tomography)
2. Image processing (Pre-processing Suite)
3. Content extraction (Automatic transcription, READ Project)
4. Information modelling (Garzoni Project)
5. Building an information system (Document Viewer)
6. Content enrichment and network effects (Linked Books Project)
7. Valorisation and use (GIS, digital experiences, …)

24. Information modelling
Garzoni Project
Lille University and EPFL
ANR+FNS funded
Valentina Sapienza Lille
Maud Ehrmann EPFL

25. Information modelling
Valentina Sapienza Lille
Maud Ehrmann EPFL

26. Information modelling
Valentina Sapienza Lille
Maud Ehrmann EPFL

27. Pipeline illustrated by projects
1. Digitisation (Tomography)
2. Image processing (Pre-processing Suite)
3. Content extraction (Automatic transcription, READ Project)
4. Information modelling (Garzoni Project)
5. Building an information system (Document Viewer)
6. Content enrichment and network effects (Linked Books Project)
7. Valorisation and use (GIS, digital experiences, …)

28. Information system
Fabio Bortoluzzi EPFL

29. Information system
Fabio Bortoluzzi EPFL
Not all documents are the same
in connecting to each other.
Fiscal declarations (for
taxation)
Personal acts (contracts,
testaments, etc.)
State machinery (office
holding)

30. Information system
Fabio Bortoluzzi EPFL
How Venetians indexed this information?

31. Information system
Fabio Bortoluzzi EPFL
Real estate surveys
Fiscal declarations
Testaments

32. Information system
Fabio Bortoluzzi EPFL
Entities
Indexes
Documents

33. Information system
Orlin Topalov EPFL

34. Pipeline illustrated by projects
1. Digitisation (Tomography)
2. Image processing (Pre-processing Suite)
3. Content extraction (Automatic transcription, READ Project)
4. Information modelling (Garzoni Project)
5. Building an information system (Document Viewer)
6. Content enrichment and network effects (Linked Books Project)
7. Valorisation and use (GIS, digital experiences, …)

35. Content enrichment
Linked Books Project
EPFL, Ca’ Foscari, Marciana
FNS funded
Approx. half of the citations in humanities are to primary
sources [Wiberley (2009)].
Their use has hardly ever been studied with citation analytic
methods.
Network effects: directly link scholarship with primary sources.

36. Content enrichment
• Primary and secondary sources
• Citation history (e.g. Google Scholar)
• Citation semantics
• Algorithmic History of the History of Venice

37. Content enrichment

38. Content enrichment

39. Content enrichment

40. Content enrichment
Network-based models. Remember primary and
secondary sources, how many graphs can we
build?
Bibliographic coupling and co-citation

41. Content enrichment: multiple perspectives

42. Pipeline illustrated by projects
1. Digitisation (Tomography)
2. Image processing (Pre-processing Suite)
3. Content extraction (Automatic transcription, READ Project)
4. Information modelling (Garzoni Project)
5. Building an information system (Document Viewer)
6. Content enrichment and network effects (Linked Books Project)
7. Valorisation and use (GIS, digital experiences, …)

43. Valorisation: some examples
Immersive reality

44. Valorisation: some examples
GIS and 3d virtual reconstructions

45. Valorisation: some examples
Teaching and interdisciplinary collaborations

46. Valorisation: some examples
Replication and transfer

47. VTM in the context of DH
1- The Big vs Small Data debate, or a proposal for
reframing
2- The quest for evidence of value, or overcoming the
DH drudgery conundrum
3- Humanities in the digital era, or why we need
historians more than ever ;)

48. VTM in the context of DH
The Big vs Small Data debate, or a proposal for
reframing
Big Data (for Humanities):
1- a matter of dimensions (in Tb or Pb)
2- networked, relational vs well-bounded (Kaplan 2015)
3- Telescope vs Microscope
“Data” are not big or small per se, but are so according to the
observer. Do I want to aggregate or disaggregate? Do I have
“larger” or “smaller” questions?

49. VTM in the context of DH
The Big vs Small Data debate, or a proposal for
reframing
Macro MicroMeso

50. VTM in the context of DH
The quest for evidence of value, or overcoming the DH
drudgery conundrum
Tool-building not an end in itself.
Developing tools to answer old questions should lead to
new questions and perspectives. The great quest in DH
now is for new arguments.

51. VTM in the context of DH
Humanities in the digital era, or why we need historians
more than ever ;)
“historians are fundamentally in the business of taking
complex, incomplete sources that are full of biases
and errors, and interpreting them critically to
develop an argument that answers a research
question. Digital sources do not change this.”
Ian Gregory

52. VTM in the context of DH
Humanities in the digital era, or why we need historians
more than ever ;)
“Data of different kinds must be
understood in their historical
relationship.”
Historians as critical arbiters of
information trained to work with time
(“comparative modelling of multiple
variables over time” in jargon).

53. A brief introduction to the
Venice Time Machine
Thank you
Giovanni Colavizza EPFL
“Computers are incredibly fast, accurate and stupid;
humans are incredibly slow, inaccurate and brilliant;
together they are powerful beyond imagination.”
Albert Einstein (or was it someone else??)