Data journalism: Data rules, while data rule

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Introduction
Data: a new era of journalism (?)
Lying with data
Summary and conclusion
References
Data rules, while data rule
George Giannakopoulos 1,2
(ggianna@iit.demokritos.gr)
1SKEL Lab, NCSR “Demokritos”, Greece
2SciFY PNPC, Greece
2016
George Giannakopoulos (ggianna@iit.demokritos.gr) Data rules, while data rule

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Introduction
Lying with data
References
Motivation
What is data?
From data to big, linked, open data
A potential future: Nice projects and future tools
Why are we here?
Learn the basics of data

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Introduction
Lying with data
References
Motivation
What is data?
Why are we here?
Understand value and risks of using data

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Introduction
Lying with data
References
Motivation
What is data?
Why are we here?
Avoid pitfalls when reporting data-based
ﬁndings

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Introduction
Lying with data
References
Motivation
What is data?
Why are we here?
Avoid pitfalls when reporting data-based
ﬁndings
Identify the role of the journalist with respect
to data

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Introduction
Lying with data
References
Motivation
What is data?
Why can I help?
Not a journalist

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Introduction
Lying with data
References
Motivation
What is data?
Why can I help?
Not a journalist
Not a visualization expert

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Introduction
Lying with data
References
Motivation
What is data?
Why can I help?
Not a journalist
Data mining over 10 years

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Introduction
Lying with data
References
Motivation
What is data?
Why can I help?
Not a journalist
Communicating ﬁndings over 10 years

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Introduction
Lying with data
References
Motivation
What is data?
Why can I help?
Not a journalist
Communicating ﬁndings over 10 years
Reviewing over 10 years

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Introduction
Lying with data
References
Motivation
What is data?
Enough about me
A little more about
you?

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Introduction
Lying with data
References
Motivation
What is data?
Data and friends
Data measured or calculated value of
property
See also: Βλαχάβας et al. 2002, Chapter 3, Boisot and Canals 2004, Tuomi 1999

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Introduction
Lying with data
References
Motivation
What is data?
Data and friends
property
Information data extended with interpretation

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Introduction
Lying with data
References
Motivation
What is data?
Data and friends
property
Information data extended with interpretation
Knowledge contextualized, interpreted, validated
data, supporting action (e.g. prediction)

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Introduction
Lying with data
References
Motivation
What is data?
Kinds of data (based on structure)
Unstructured free text, blog entries

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Introduction
Lying with data
References
Motivation
What is data?
Fully structured databases, data tables, spreadsheets

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Introduction
Lying with data
References
Motivation
What is data?
Fully structured databases, data tables, spreadsheets
Semi-structured eXtensible Markup Language (XML), Resource
Description Framework (RDF)

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Introduction
Lying with data
References
Motivation
What is data?
Where can we ﬁnd data?
Finance index values, yearly reports

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Introduction
Lying with data
References
Motivation
What is data?
Health health record, lab measurements

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Introduction
Lying with data
References
Motivation
What is data?
Health health record, lab measurements
More? What do you think?

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Introduction
Lying with data
References
Motivation
What is data?
Do we generate data?
Social media posts, comments

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Introduction
Lying with data
References
Motivation
What is data?
Mobile device use position, usage of applications

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Introduction
Lying with data
References
Motivation
What is data?
Mobile device use position, usage of applications
More?

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Introduction
Lying with data
References
Motivation
What is data?
Who uses data?
State policy making, international relations,
public image

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Introduction
Lying with data
References
Motivation
What is data?
Who uses data?
public image
Businesses marketing, due diligence, risk
assessment, business policy

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Introduction
Lying with data
References
Motivation
What is data?
Who uses data?
public image
Researchers build hypotheses, conﬁrm ﬁndings,
discover relations

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Introduction
Lying with data
References
Motivation
What is data?
Who uses data?
public image
Researchers build hypotheses, conﬁrm ﬁndings,
discover relations
Citizens understand everyday changes,
understand choices, plan ahead

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Introduction
Lying with data
References
Motivation
What is data?
Why are data useful?
Problem indicators (e.g. deviations)

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Introduction
Lying with data
References
Motivation
What is data?
Common basis for discussion

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Introduction
Lying with data
References
Motivation
What is data?
Measurable

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Introduction
Lying with data
References
Motivation
What is data?
Measurable
Considered unbiased

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Introduction
Lying with data
References
Motivation
What is data?
Measurable
Considered unbiased
Considered veriﬁable

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Introduction
Lying with data
References
Motivation
What is data?
Measurable
Considered unbiased
Considered veriﬁable
Oftentimes interlinkable

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Introduction
Lying with data
References
Motivation
What is data?
Let us summarize
Data can be the basis for knowledge
Data have various levels of structure
All generate and consume data
Data can have value

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Introduction
Lying with data
References
Motivation
What is data?
Data ﬂavours
Big data Large scale, volatile, challenging to follow

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Introduction
Lying with data
References
Motivation
What is data?
Data ﬂavours
Linked data Inherent interconnectivity

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Introduction
Lying with data
References
Motivation
What is data?
Data ﬂavours
Linked data Inherent interconnectivity
Open data Available in a meaningful manner

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Introduction
Lying with data
References
Motivation
What is data?
What is Big Data?
Data in a new scale...and more...

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Introduction
Lying with data
References
Motivation
What is data?
Examples of Big Data
Social media posts per day: Gbytes per day on Twitter (50 million
tweets / day)

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Introduction
Lying with data
References
Motivation
What is data?
tweets / day)
Wind turbine sensors: Tbytes per day per turbine (KHz sampling
of values)

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Introduction
Lying with data
References
Motivation
What is data?
tweets / day)
of values)
Bioinformatics data: Tbytes per day per machine (up to 3 billion
reads per run)

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Introduction
Lying with data
References
Motivation
What is data?
tweets / day)
of values)
Bioinformatics data: Tbytes per day per machine (up to 3 billion
reads per run)
Overall: 2.5 exabytes of data generated per day (and going up)

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Introduction
Lying with data
References
Motivation
What is data?
Features of Big Data
Volume size

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Introduction
Lying with data
References
Motivation
What is data?
Volume size
Velocity speed of change

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Introduction
Lying with data
References
Motivation
What is data?
Volume size
Variety variety and diﬃculty to read and combine

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Introduction
Lying with data
References
Motivation
What is data?
Volume size
Veracity uncertainty and diﬃculty of validation

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Introduction
Lying with data
References
Motivation
What is data?
Volume size
Veracity uncertainty and diﬃculty of validation
Value diﬃculty to turn into actionable knowledge

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Introduction
Lying with data
References
Motivation
What is data?
What is Linked Data?
Small statements
<George, loves, Georgia>

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Introduction
Lying with data
References
Motivation
What is data?
Small statements
Fragments of knowledge
<Georgia,listensTo,folkRock>
<Dylan,plays,folkRock>

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Introduction
Lying with data
References
Motivation
What is data?
Small statements
Fragments of knowledge
Powerful when combined
Suggest to George a Dylan CD as
a gift for Georgia

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Introduction
Lying with data
References
Motivation
What is data?
Examples of Linked Data
Web annotations hidden in web pages, blogs, social media (e.g.
Facebook Graph)

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Introduction
Lying with data
References
Motivation
What is data?
Facebook Graph)
DBPedia linked-data view of Wikipedia

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Introduction
Lying with data
References
Motivation
What is data?
Facebook Graph)
DBPedia linked-data view of Wikipedia
Nano-publications scientiﬁc results and data as statements

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Introduction
Lying with data
References
Motivation
What is data?
Features of Linked Data
Usually automatically generated
Computer-friendly
No meaning

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Introduction
Lying with data
References
Motivation
What is data?
What is Open Data?
Openly available data

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Introduction
Lying with data
References
Motivation
What is data?
What is Open Data?
...usable

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Introduction
Lying with data
References
Motivation
What is data?
What is Open Data?
...usable
...reusable under well-speciﬁed licences

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Introduction
Lying with data
References
Motivation
What is data?
What is Open Data?
...usable
...reusable under well-speciﬁed licences
Usually public data

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Introduction
Lying with data
References
Motivation
What is data?
Examples of Open Data
Country-wide statistics and demographics
Geo-locations of monuments
Public consultation results

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Introduction
Lying with data
References
Motivation
What is data?
Let us summarize
Big data: many, volatile, diﬃcult to handle
Linked data: fragments of knowledge, computer
friendly
Open data: publicly available, reusable

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Introduction
Lying with data
References
Motivation
What is data?
Your Data Stories
Easily consume data
Combine and integrate data
Empower with social media
feedback
yourdatastories.eu

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Introduction
Lying with data
References
Motivation
What is data?
DemocracIT
Build on consultations
Quantify problems and biases
Crowdsourcing
democracit.org

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Introduction
Lying with data
References
Motivation
What is data?
Other projects

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Introduction
Lying with data
References
Motivation
What is data?
More to come?
Support editing in real-time
Find related data
Suggest visualization
Stay tuned.

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Introduction
Lying with data
References
Motivation
What is data?
An informal break
Teaser: Privacy, Security, Anonymity and Data

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Introduction
Lying with data
References
The value of data
Opportunities and risks in using data
Data analysis and visualization: mistakes and pitfalls
What data can do
Data contain stories
Data support stories
Data verify stories

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Introduction
Lying with data
References
The value of data
Case 1: Money trails
Find Mr Smith payments (public money) from Diavgeia

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Introduction
Lying with data
References
The value of data
Case 1: Money trails
Find Mr Smith payments (public money) from Diavgeia
Claim that researchers get too much money!

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Introduction
Lying with data
References
The value of data
Case 1: Risks
Source idiosyncracies: Diavgeia problems

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Introduction
Lying with data
References
The value of data
Case 1: Risks
Limited (?) documentation

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Introduction
Lying with data
References
The value of data
Case 1: Risks
Limited (?) documentation
Lack of cross-check method

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Introduction
Lying with data
References
The value of data
Case 2: Leaked documents
Thousands of leaked documents from a government

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Introduction
Lying with data
References
The value of data
Apply sentiment analysis to understand sentiment

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Introduction
Lying with data
References
The value of data
Apply sentiment analysis to understand sentiment
Show that all is gloomy!

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Introduction
Lying with data
References
The value of data
Case 2: Risks
Tool idiosyncracies: sentiment analysis approx. 75% accurate

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Introduction
Lying with data
References
The value of data
Case 2: Risks
Tool idiosyncracies: sentiment analysis approx. 75% accurate
Lack of cross-check method

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Introduction
Lying with data
References
The value of data
Data analysis
know what you look for
understand what you read
use your intuition (but not too much!)
double check (through a diﬀerent path!)

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Introduction
Lying with data
References
The value of data
Visualization: keep it simple
keep it simple
keep it focused
verify
document and support (through references)
tell a story
think of your audience

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Introduction
Lying with data
References
Why you shouldn’t trust scientists (too much)
Hiding Bias
Reporting research
Lines and hypotheses

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Self-fulﬁlled prophecy
A leader claims that the market will go down in a day

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Self-fulﬁlled prophecy
A leader claims that the market will go down in a day
The market will go down

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Correlation vs causality

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Beware of the index
An index of poverty
...based on the percentage of people that take less than 5% of the
maximum income
Is it a good idea?

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Axes: Normalization

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Axes: Units and scales

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Ratios not kept

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Other cases:
Ordering: Greek elections
Lying with percentages: increase from 10% to 12%; 20%
increase?
Question bias: important answers omitted
Question bias: generic question

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Pragmatics

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Introduction
Lying with data
References
Hiding Bias
Reporting research
What we should look for
Experimental setting

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Conﬁdence vs statistical conﬁdence

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Exact conclusion

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Exact conclusion
Funding sources

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Exact conclusion
Funding sources
Conﬁrmation by experts

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Introduction
Lying with data
References
Hiding Bias
Reporting research
Another informal break
Teaser: Changing our lives through reporting

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Introduction
Lying with data
References
How can I apply data analysis (if I am not an expert)?
Summary: things to remember
Locate sources
https://www.diavgeia.gov.gr/
http://data.gov.gr/
http://geodata.gov.gr/
http://open-data.okfn.gr/
https://delicious.com/
http://www.linkedopendata.gr/
https://data.europa.eu/euodp/
https://www.constituteproject.org/
http://www.ﬁndthedata.com/

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Introduction
Lying with data
References
Next steps
Study more

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Introduction
Lying with data
References
Next steps
Study more
Form a network

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Introduction
Lying with data
References
Next steps
Study more
Form a network
Collaborate

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Introduction
Lying with data
References
Next steps
Study more
Form a network
Collaborate
Focus

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Introduction
Lying with data
References
Who should I work with?
Visualization experts

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Introduction
Lying with data
References
Data mining experts

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Introduction
Lying with data
References
Data mining experts
Domain experts

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Introduction
Lying with data
References
Data mining experts
Domain experts
Developers... with a twist

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Introduction
Lying with data
References
Data is useful, but can be misinterpreted

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Introduction
Lying with data
References
Data contain, support, conﬁrm stories

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Introduction
Lying with data
References
...but they cannot tell the story

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Introduction
Lying with data
References
Analysis of data needs experts

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Introduction
Lying with data
References
Visualization of data needs experts

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Introduction
Lying with data
References
Journalism needs experts

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Introduction
Lying with data
References
Journalism needs experts
Working with data needs a team

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Introduction
Lying with data
References
Thank you
George Giannakopoulos 1,2
(ggianna@iit.demokritos.gr)
1SKEL Lab, NCSR “Demokritos”, Greece
2SciFY PNPC, Greece
2016

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Introduction
Lying with data
References
References I
Boisot, Max and Agustí Canals (2004). “Data, information and
knowledge: have we got it right?” In: Journal of Evolutionary
Economics 14.1, pp. 43–67. : 0936-9937, 1432-1386. :
10.1007/s00191-003-0181-9 (cit. on pp. 12–14).
Tuomi, I. (1999). “Data is more than knowledge: implications of the
reversed knowledge hierarchy for knowledge management and
organizational memory”. In: IEEE Comput. Soc, p. 12. :
978-0-7695-0001-0. : 10.1109/HICSS.1999.772795. :
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.
htm?arnumber=772795 (cit. on pp. 12–14).
Βλαχάβας, Ι. et al. (2002). Τεχνητή Νοημοσύνη. Εκδόσεις
Γαρταγάνη, Θεσσαλονίκη (cit. on pp. 12–14).

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