updated readme

Author: vlabatut

README.md

@@ -8,7 +8,7 @@ NaNet is free software: you can redistribute it and/or modify it under the terms
 
 * Lab site: http://lia.univ-avignon.fr/
 * GitHub repo: https://github.com/CompNet/NaNet
-* Data: https://doi.org/... **(not available yet)**
+* Data: https://doi.org/10.5281/zenodo.6395874
 * Contact: Vincent Labatut <[email protected]>
 
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@@ -24,7 +24,7 @@ The script does the following:
 
 
 # Data
-The raw dataset was manually for bande dessinée [Thorgal](https://en.wikipedia.org/wiki/Thorgal). The files produced by the scripts (graphs, plots, tables...) can be obtained by running these scripts, but they are also directly available on [Zenodo](https://doi.org/...) **(not available yet)**.
+The raw dataset was manually for bande dessinée [Thorgal](https://en.wikipedia.org/wiki/Thorgal). The files produced by the scripts (graphs, plots, tables...) can be obtained by running these scripts, but they are also directly available on [Zenodo](https://doi.org/10.5281/zenodo.6395874).
 
 
 # Organization
@@ -66,7 +66,7 @@ In order to extract the networks from the raw data, compute the statistics, and
 
 The scripts will produce a number of files in the subfolders of folder `nets`. They are grouped in subsubfolders, each one corresponding to a specific topological measure (degree, closeness, etc.). 
 
-The script `src/Labatut2022.R` reproduces the computations described in article [[L'22](#references)]. Please, use [v1](https://github.com/CompNet/NaNet/releases/tag/v1) of the source code in the *Releases* page. Be warned that this will take a while (possibly several days). You can directly retrieve the data resulting from this process on [Zenodo](https://doi.org/...) **(not available yet)**. 
+The script `src/Labatut2022.R` reproduces the computations described in article [[L'22](#references)]. Please, use [v1](https://github.com/CompNet/NaNet/releases/tag/v1) of the source code in the *Releases* page. Be warned that this will take a while (possibly several days). You can directly retrieve the data resulting from this process on [Zenodo](https://doi.org/10.5281/zenodo.6395875). 
 
 
 # Dependencies

TODO.txt

@@ -81,26 +81,19 @@ Extraction and Analysis of Character Networks from Graphic Novels EACNGN
 
 -------------------------------
 Clean code:
-- separate the "window" part from the "scene" part
-- networks and their stats: move "filt"/"unfilt" in "scenes"
-- put no stat from "scene" in "comparison"
-- in the logs, systematically print the filenames of the plots and csv (including post scripts)
 - automate the comparison of node measure vs. node measures (same for edges) (in both directions n1 vs n2 and n2 vs n1)
 - define a unique function to plot distributions
   > systematize histo & ccdf?
-- narrative smoothing is slow, and possibly bugged
+- narrative smoothing is so slow, and possibly bugged
 
 - make names larger in volume- and arc-dependent net plots (PDF)
 
 - use nbr of occurrences (scenes) to plot vertex size (by default)? (instead of betweenness)
 
-- check that we record the data used in plots that are costly to compute (e.g. reg sim)
-
 TODO
 - should separate the extraction of the static graph vs. graph sequence
   >> maybe not a good idea: better to mutualize this code (or maybe separate the core loop in a different function ?)
 
 - mutualize distribution plots
   - put common processing in single function
   - systematically produce histos and ccdf, distinguish file names with suffixes 
-