Make your first map in 10 min (Part 1/2)

version 3.0.5.7

Note : click on the hot spots in the images to get more explanation.

Context

Gargantext is designed to produce living maps that evolve as you work with them. They can be used for dressing a state-of-the-art, mapping a bunch of documents, setting up a collective representation of a problem, etc. The map is not the ultimate goal, rather, it is the back and forth between the different levels of your corpora (document, terms, maps, etc.) that is the main resource and help you to build an adaptive representation of a question or a problem.

This tutorial explain you how to build the first map that will bootstrap your work with Gargantext. This first map should take few minutes.

Gargantext is a free and open source software developed by the CNRS Complex Systems Institute of Paris Ile-de-France (ISC-PIF). Its source code is available on Gitlab.

Several instances of Gargantext are running on the ISC-PIF Cloud at http://gargantext.org

To access one of these Gargantext instances, you will first have to register the ISC-PIF services.

Step 1 : create your project

At registration, you have been attributed to one of the GarganText instances (ex. CNRS, academia, ComplexSystems, etc.), in the following, we will work with the instance name formation, which URL is : https://formation.gargantext.org

Choose your instance on http://gargantext.org, log-in, then create your first project (here named ‘My Project’). A project is a set of analyses on different corpora that share a same thematic.

Step 2 : create an analysis by defining your corpus

An analysis starts with the definition of a set of documents to analyze. Gargantext accepts many input formats (RIS, ISI, zotero, csv, etc.) and new formats are added when there is a sufficient demand for it.

Gargantext is also connected to large open databases that makes it possible to query them directly through Gargantext. For now, the following archives are available (some access restriction might apply depending on the provider) :

Istex, the CNRS retrospective digital archiving of Science
PubMed, the main biomedical archive,
Scoap3, the Open Access Publishing in Particle Physics

To create an analysis, click on the flower beside your corpus in the tree (left panel) to open the modal box

Step 3 : Generate your first map

Your corpora takes few minutes to process (time depends on the size of the corpus).

At the end of the import, three new items appear below your corpora section :

Docs displays your documents : you can read, curate, annotate, favorite them
Terms displays several entries to curate the expressions relevant for your analysis, let it be ngrams, authors, intitutions, etc. The terms tab propose you by default about 350 terms that have been considered as statistically relevant for the topic covered by your corpora.
Graph give access to the living map. Clic on this to access the map, and on the flower to access the update modal box if you have added new terms to your graph.

Step 4 : Visualize the map

In the map view, you can access to a high level and synthetic view of your corpora in the form of a graph. Nodes are the terms that are considered as relevant for your topic. Link are proximities between these terms as inferred from the analysis of the whole corpora. Two different measures of proximity are currently implemented :

Order 1. This graph uses a metric call confidence to set the strength between node. This is simply the probability of having term B in a document knowing that it already contain term A. This distance will give you the landscape of interactions between terms in your corpora. It is best suited for large enough corpora (>500).
Order 2. This graph uses a distributional proximity measure. This proximity measure compares, for two terms A and B the similarity of their co-occurrence profiles with all the other terms of the maps. This is not an indication of interaction (two terms can be linked without occurring even once together in the corpora) but it assesses a kind of structural equivalence. For exemple synonyms will have a high probability to be linked.

Bad spatialization

Good spatialization

The visualization engine has an algorithm that maximizes the information conveyed by a map by positioning the nodes that are strongly related close on the map. The spatialization button makes it possible you to run this algorithm when you load the map or when you filter some edges/nodes. Click to launch the algorithm and click again to stop it. Links and labels are not displayed during repositioning of nodes.

At the end of the repositioning, nodes with the same color should be close on the map.