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www.know-center.at

The Student's and Researcher's Guide to Discovery:

Exploring Scientific Fields with Open Data and Tools

Mozfest 2015

London, November 7

Peter Kraker

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First things first

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Introduction:

Please say your name and add three hashtags that

describe you:

#1: Your occupation (student, researcher, activist…)

#2: Your current field of interest (biology, open peer

review…)

#3: Of your own choosing

If you can, please also add this to:

http://is.gd/mozfest

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The Hitchhiker‘s Guide to the Galaxy

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How to get an overview of the universe

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How to get an overview of a research field

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Discussion:

How do you get an overview of an unknown field?

Discuss with your neighbour(s)

Report back to the plenum

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How to get an overview of a research field

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How to get an overview of a research field

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Exemplary visualization of „educational technology“

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http://openknowledgemaps.org

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Overview of the publications in a conference

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Development of a knowledge domain

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Try it yourself!

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Group exploration:

Get together in groups of three (similar field of interest

preferred)

Go to http://openknowledgemaps.org/mozfest

and visualize a PLOS search!

Discuss the results that you got

Report back to the plenum

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Static visualization of all of science

[Bollen et al. 2009]

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What‘s needed for a knowledge domain

visualization?

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Bibliographic data of the works in a domain

Bibliometric data/full text of items to find interesting/important

works

Relational data to compute the

similarity between items

Domain experts and classification specialists to evaluate and

adapt the visualizations

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Challenge 1: Availability of open data – Bibliographic

data

Danowski et al. (2013)

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Challenge 2: Availability of open data –

Bibliometric data & full text

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Challenge 3: Usability and usefulness

Most static visualizations are useful to understand the

structure of science, but not in researchers‘ daily work

A lot of the existing tools are made for experts and they

are based on closed data (Web of Science, PubMed)

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Challenge 4: Systematic bias and algorithmic errors

Characteristics of the underlying dataset influence the

visualizations (Bollen et al. 2008, Kraker et al. 2014)

Algorithmic errors cannot be avoided in automated

systems

Dedicated community of domain experts,

classification specialists, programmers … (think

Wikipedia)

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Vision: Collaborative visualizations of all of science

based on open data

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If you‘d like to know more

Blog:

http://blogs.lse.ac.uk/impactofsocialsciences/2015/02/16/crowd-

sourced-overview-visualizations-of-knowledge-domains/

Publikation: Kraker, P., Schlögl, C., Jack, K., & Lindstaedt, S.

(2015). Visualization of Co-Readership Patterns from an Online

Reference Management System. Journal of Informetrics, 9(1),

169–182. http://arxiv.org/abs/1409.0348

Source Code: https://github.com/pkraker/Headstart

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Thank you for your attention!

Peter Kraker

pkraker@know-center.at

Twitter: @PeterKraker