Team:Paris Bettencourt/HumanPractice/collaborationMap

From 2011.igem.org

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<p>Nanotubes create random relationships between cells, possibly from different types/species. Those relationships could have a long or a short duration... Just the way it happens with people! And more specifically between teams. Indeed, teams can exchange imformation and knowledge by collaborating, sometimes at an international level. Collaboration between teams can be a long-term relationship (like the one between Paris Bettencourt and PKU), or shorter flings.</p>
<p>Nanotubes create random relationships between cells, possibly from different types/species. Those relationships could have a long or a short duration... Just the way it happens with people! And more specifically between teams. Indeed, teams can exchange imformation and knowledge by collaborating, sometimes at an international level. Collaboration between teams can be a long-term relationship (like the one between Paris Bettencourt and PKU), or shorter flings.</p>
<h2> Objectives </h2>
<h2> Objectives </h2>
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<p>We decided to scan the <em>wikis for collaborations between teams</em> from 2007 to today. Most of them were easy to find but some relationships between teams were almost kept secret.</p>
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<p>Extract from wikis (collaborations, medals, prizes, geographic location)</p>
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We also listed the number of prizes and medals for each team, grouped them by Region (Europe, America, Asia)and by country.</p>
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<p>Represent the collaboration between teams as a network </p>
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<p>With all this data, we <em>created a collaboration graph</em> with <a href="http://www.touchgraph.com/navigator">Touchgraph</a>. You can find a tutorial and our files to download below.</p>
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<p> Relate success in the competition to collaborations and other parameters</p>
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<h2> Methodology </h2>
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<p>With all the data obtained by scanning all the wikis from 2007 to 2011, we <em>created a collaboration graph</em> with <a href="http://www.touchgraph.com/navigator">Touchgraph</a>. You can find a tutorial and our files to download below.</p>
<p>We developped a graphe navigator for static représentation of those graphes in flash which can be tested below.</p>
<p>We developped a graphe navigator for static représentation of those graphes in flash which can be tested below.</p>
<p>The navigators bellow allow to zoom in and out.
<p>The navigators bellow allow to zoom in and out.

Revision as of 02:15, 29 October 2011

Team IGEM Paris 2011

Collaboration Map

Nanotubes create random relationships between cells, possibly from different types/species. Those relationships could have a long or a short duration... Just the way it happens with people! And more specifically between teams. Indeed, teams can exchange imformation and knowledge by collaborating, sometimes at an international level. Collaboration between teams can be a long-term relationship (like the one between Paris Bettencourt and PKU), or shorter flings.

Objectives

Extract from wikis (collaborations, medals, prizes, geographic location)

Represent the collaboration between teams as a network

Relate success in the competition to collaborations and other parameters

Methodology

With all the data obtained by scanning all the wikis from 2007 to 2011, we created a collaboration graph with Touchgraph. You can find a tutorial and our files to download below.

We developped a graphe navigator for static représentation of those graphes in flash which can be tested below.

The navigators bellow allow to zoom in and out. It also propose to choose between different graphe (one per year for example).

Collaboration map per year

this map represent the collaboration between teams for each year from 2007. There is no data about prizes and medal on IGEM site for 2008.

this map show us that teams witch are the most collaborating are ?majoritarely? new teams

Legend

Level of collaboration

this map propose Three differents graphs :
  • The first, show teams collapsed by level of collaboration for each region : the first lvl is no colaboration, the second is collaboration only with teams of the same region, and the third is collaboration between regions.
  • The second graph show us the repartition of collaborating teams by country : each teams of the same country have the same color
  • The third one show the ratio of collaboration per region : all the teams of each region have the same color

For example on this map we can see that countries with a lot of teams have just a little parts of them witch are collaborating (look at, China and US)

Japan collaboration over time

we took the example fo the Japan collaboration over the years because all the teams usually collaborate mostly with other Japan team.

TouchGraph simple manual for collaboration graphes

You can found here the database used : Data file and project file

all the graphes used on this page are extracted from TouchGraph representation

to use those data, download TouchGraph and instal it.

Next, load the project file and the Data file.

In the TouchGraph menu, choose settings-> filter to change the year, or for adding more filter.

All the parameters of the graph can be edited with the Settings-> show Dialog menu

More information about TouchGraph can be find there : TouchGraph Manual



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