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data
driven
journalism
melinaforte
fulviapalacino
cristinasalonico

where journalism meets data
data
driven
journalism
31.10.2012
THE SECOND EDITION
OF THE ADVANCED COURSE
FOR DATA & KNOWLEDGE MODELLER

where journalism meets data
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DATA

data driven journalism is an
emergent journalism field, which
tells stories and occurrences
starting from data.
data journalist looks for news and
stories extracting them from rough
data.
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MIRKO
LORENZ
data driven journalism is a workflow
that consists of the following
elements: digging deep into data by
scraping, cleansing and structuring it,
filtering by mining for specific
information, visualizing it and making
a story

infomation architect
and multimedia
journalist

PAUL
BRADSHAW
data journalism
trainer and writer

data journalism is […] the
convergence of a number of fields […]
from investigative research and
statistics to design and programming
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Paul
Bradshow
writer and data
journalism trainer,
describes the process of
DDJ in this way
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Paul Bradshow
has introduced
the pyramidal
model
in 2011
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1.FINDING DATA0817237589746885376757345645678122345
2.SIFTING DATA12093698837234567892345612345676781
3.WRITE STORIES37123791238192012938019238011289
4.DISPLAYING DATA08341234567890125678902890
5.PUBLISHING DATA127891268123467895789090
6.BROADCASTING DATA123456123456788749
7.CONTROLLING78452092365253652980980980799
DATA STORIES IMPACT370980980967927

PROCESS
STEPS
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1.FINDING DATA081723758974688537675734564567812238RICERCA81389
• the preliminary work consists of a
deepened research aiming to gather and
sift data to reconstruct the stories to
tell.
• to realize an accurate data selection
it is crucial to know the topic the
ontology aims to describe.
• a data journalist has to analyze many
documents, even hard to find ones, often
incomplete. Document studying brings to
the T-box and A-box building.
• the challange is to create a good
data set.
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2.SIFTING DATA08172375897468853767573456456739309data set
creation
• a good data set creating is crucial:
it has to be well structured in order
to ease other users consultation.
• it is basic to sift and clean the
obtained data removing the what is not
essential.
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3.WRITE STORIES

•………………………cosa scrivere?
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claryfies
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4.DISPLAYING DATA081723758974688537675734564567
INFORMATION DESING

a valid data design
to users all data emploied to
tell stories.
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5.PUBLISHING DATA
completing visualisations adding
stories to data
there are many options to
publish data and their related
visualisation exsisting:
• basic models link data to the
single story.
• more advanced models lets you
create single dossier, articles
and links to data in one page.
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6.BROADCASTING DATA

• giving users the access to existing
data, especially to ones that the
journalist himself obtained through
Open Data and used to write the
article.
• supply others the chance to
investigate again on that data, making
possible a potential beginning of a new
cicle of interrogations that can bring
to new knowledge.
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7.CONTROLLING DATA STORIES IMPACT

The last phase of the process consists of
measuring how often a data set or one of its
graphic interfaces is consulted.
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TECHNICAL
MATTERS
the source to which the reader can
access to consult data is the
ONTOLOGY
which is a combination of:
•a vocabulary (T-boX)
• the collection of assertions
expressed using terms of that
vocabulary (A-box) showed to the
reader through an interactive
visualisation tool.
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fhidchriovybploi
uezxcvsdfovpoiuy www.informationsbeautiful.net
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at
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from http://blog.zeit.de/open-data/

example of
interactiveactive
visualisation
interface
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A CONCRETE
EXAMPLE
Our project consists of
realizing an ontology
describing how, through the
European Union invested
money in Somalian resources
to finance projects in the
cooperation and development
sector between 2008 and
2013.
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• how money is supplied
• credit lines
• beneficiaries
• sub-contractors
• Somalian area in which money is
invested
• kind of intervention
• amount
• co-financing percentual
• intervention duration
• population impact
• if the projects are concluded or
underway, or if the call
assignment procedure is in progress.
• if the valutation of the competition
is centralized or not.

questions
our work
is able
to answer
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BUILDING
THE
VOCABULARY
the questions which our
project answers to guided us
in the selection of terms
suitable for the T-box
realiziation.
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T-BOX
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còaijfomauufhufhi
sufhchriovybploi
uezxcvsdfovpoiuy
treasdfghjkmzxct
rnhdllrerreasghm
mzoikjamcipoiuyt
reasdfuzeijdhxkj
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akjshdxkayhrfaeh
hjtytgbfcamudlsj
kloklytreasdbksd
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hjqwrruiouapoijk
loklytreasdbksdd
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jqwrruiouapoiuty
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ncisduhfwoeridat
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cvsjahsdhadkstpp
bdfvgybskdjchxqz
eunkdjfhvbbhbcij
ncisduhfwoeriuty
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DATA
INSERTION
creation and population of A-BOX
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ncisduhfwoeriuty
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GRAPHIC VISUALISATION
IN ONTOGRAF

population of
our ontology
with A-box data

and
our knowledge
semantic
knowledge base:
T-box + A-box
sridropyhjolhxkm
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ncisduhfwoeriuty
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IN THE END18726127617269179
DON’T FORGET7623782379381
THAT…198279822798272238769736878
DAITHI Ó CRUALAOICH

Understanding data journalism
is the same thing as
understanding journalism. The
stress is in the wrong place
when we focus on the data. You
can't give a machine data and
get journalism out the other
end

software
developer
at The
Guardian
newspeare
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eunkdjfhvbbhbcij
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thanks to
epistematica

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Data driven journalism