## Découvrir ensemble

24 février 2012

The isolated man does not develop any intellectual power. It is necessary for him to be immersed in an environment […] He may then perhaps do a little research of his own and make a very few discoveries […] the search for new techniques must be regarded as carried out by the human community as a whole, rather than by individuals.

Alan Turing, 1948, quoted in Nature v482

## What is science?

8 juillet 2011

Science isn’t about what’s known. Nor is it about what isn’t known. At its most basic level, SCIENCE is nothing more than a process of playing games and making puzzles that may or may not tell us something about the world or ourselves in that world. When thought of in this way, it’s obvious that we all ‘do science‘ hundreds of times a day every day, which is about discovering and exploring through interaction. When interaction is made conscious and combined with reflection, that is ‘science‘.

Beau Lotto (on « Street Science« )

## Uncertainty and collective science

22 mai 2011

There are some things that you, the reader of this preface, know to be true, and others that you know to be false; yet, despite this extensive knowledge that you have, there remain many things whose truth or falsity is not known to you. We say that you are uncertain about them. You are uncertain, to varying degrees, about everything in the future; much of the past is hidden from you; and there is a lot of the present about which you do not have full information. Uncertainty is everywhere and you cannot escape from it. Truth and falsity are the subjects of logic, which has a long history going back at least to classical Greece. The object of this book is to tell you about work that has been done in the twentieth century about uncertainty.

[…]

Research is carried out by individuals and often the best research is the product of one person thinking deeply on their own. For example, relativity is essentially the result of Einstein’s thoughts. Yet, in a sense, the person is irrelevant, for most scientists feel that if he had not discovered relativity, then someone else would; that relativity is somehow ‘‘out there’’ waiting to be revealed, the revelation necessarily being made by human beings but not necessarily by that human being. This may not be true in the arts so that, for example, if Shakespeare had not written his plays it would not follow that someone else would have produced equivalent writing. Science is a collective activity, much more so than art, and although some scientists stand out from the rest, the character of science depends to only a very small extent on individuals and what little effect they have disappears over time as their work is absorbed into the work of others.

Dennis Lindley, Understanding uncertainty

Je ne sais pas pour vous, mais moi, quand je lis ça, j’ai envie de m’installer dans un fauteuil et lire toute la journée.

## Do you know what heritability is, do you?

9 mai 2011

Genetics is the study of genes. When they were « invented » (i.e. conceptualized) in the 19th century, genes were defined as units of heredity. Thanks to the revolution of molecular biology, we now know that a gene is a section of DNA (although it is still possible to debate on the precise meaning of this statement). But what I am interested here, is to ask a few questions about quantitative genetics, and especially heritability.

Indeed, this notion of heritability is key in genetics (and, by extension, in biology as a whole) but it is often obscure for many biologists (notably for me, until I decided to seriously read some papers, and thus write this post to be sure I understood properly). And you, the reader, even if you think you know everything about heritability, I hope it can still be worth the reading.

* * *

But let’s start with basics: Gregor Mendel and the birth of genetics. This Austrian monk was interested in inheritance (passing of traits from parents to offsprings). To study this phenomenon, he worked with pea plants and chose seven characters to study, but let’s focus on one: seed color.

Mendel observed that some pea plants had green seeds while others had yellow seeds, we will speak of a « green » phenotype and a « yellow » phenotype. Intrigued about what would happen with offsprings, he started by crossing the peas having green seeds among themselves during several generations, idem for the peas with yellow seeds (selfing is possible in this species). After doing this for several generations, he always observed that peas with the « green » phenotype always had offsprings with the « green » phenotype, and reciprocally for the « yellow » phenotype. These offsprings resulting of several generations of selfing were called « pure lines« , as they were obtained by crossing plants with the same phenotype.

Then, Mendel went on to cross a yellow male plant (denoted as P1) with a green female plant (denoted as P2), and named the offsprings the F1 generation. He observed that all F1 plants had green seeds, as if the yellow material disappeared. He also observed such a result when doing the reciprocal cross: a green male plant crossed with yellow female plant gave green offsprings. He therefore decided to qualify the « green » phenotype as dominant and the « yellow » phenotype as recessive.

Then, he continued his experiment, and crossed F1 plants together to obtain F2 plants. And here, what is interesting, is that some F2 plants had the « yellow » phenotype (although most had the « green » one): as if the « yellow » material, somehow, jumped from the P generation to the F2 generation…! At this point, Mendel had the genial idea of counting the plants: he found that 3/4 had the « green » phenotype and 1/4 had the « yellow » phenotype, i.e. a 3:1 ratio.

In front of such a striking observation, Mendel went on to characterize the F2 generation. And here again, he observed something strange: when crossing « green » F2 with themselves, some had only « green » offsprings but some had a mix of « green » and « yellow » offsprings, here also in a 3:1 ratio. This was not the case when selfing the F2 « yellow » plants. Therefore, the F2 « yellow » plant seemed to be « pure » as the P « yellow » plants, but 2/3 of the F2 « green » plants were like the F1 plants, and 1/3 were « pure » like the P « green » plants.

Mendel then realized that the behind the 3:1 ratio was a more fundamental 1:2:1 ratio, 3/4 F2 green is in fact 1/4 « pure » green and 2/4 « impure » green, whereas 1/4 F2 yellow is 1/4 « pure » yellow:

From all this, Mendel was able to develop his theory, summarized in the 5 points below:

1. existence of genes, i.e. discrete units (« atomic particles ») of heredity;
2. genes are in pairs, i.e. a gene may have different forms called alleles;
3. each gamete carries only one member of each gene pair;
4. the members of each gene pair segregate equally into the gametes;
5. random fertilization, i.e. gametes combine together to form an organism without regard to which allele is carried.

Now let’s recapitulate by noting « A » the « green » allele, and « a » the « yellow » allele. In the case of the pea described above, Mendel hypothesized that the P « green » plants had an « AA » gene pair, called their genotype, while the P « yellow » plants had the « aa » genotype. As each parent can make only one kind of gametes, « A » for the green and « a » for the yellow, the F1 plants must have the « Aa » genotype. But these plants can make « A » gametes as well as « a » gametes, in equal proportions. This gives rise to F2 plants, 1/4 have the « AA » genotype and thus have the green phenotype, 1/2 have the « Aa » genotype and thus the green phenotype also, and 1/4 have the « aa » genotype and therefore the yellow phenotype:

This is Mendel’s explanation of the 1:2:1 ratio. It seems likely that he got it by imagining that F1 needed to have a « bit » of yellow as well as a « bit » of green somewhere. But still, this will be, forever, one of the greatest scientific discovery…

* * *

Ok, now, let’s look at another classical example which, at the end, introduces quantitative genetics, and from there on, heritability. We are now in the early 20th century and the American geneticist, Edward M. East, also has fun with crossing plants. He worked with a species related to tobacco, some of the plants having a long corolla, ~90mm, some having a short one, ~40mm. As Mendel, he started by selfing plants with a long corolla, idem for the plants with a short corolla. Thus, after several round of selfing, he got two « pure » lines: one pure line with a long corolla (we will call them the P1), and one with a short one (P2). He crossed plants from P1 with plants from P2 and got F1 plants having a medium-size corolla, ~65mm.

Until here, everything seemed fine. But when he crossed F1 together to obtain F2 plants, he didn’t get the 3:1 ratio of corolla length. Instead, he got plants with a corolla of ~65mm on average, and a much larger variability (the appropriate mathematical term is variance). To understand a bit more what happened, he chose F2 plants with a small corolla, crossed them, and obtained F3 plants also with a small corolla. When he crossed F2 plants with a medium corolla, he also obtained F3 plants with a medium corolla. And so on (see the picture below).

This means that the inheritance of the corolla length has indeed a genetic component, but not as simple as the Mendelian case seen above. It is very likely that, instead of a single one, it is several genes that influence the length of the corolla.

Let’s imagine that 5 genes are involved, each with two alleles, + and -, and that the each + allele lengthens the corolla by 1mm, whereas each – allele shortens the corolla by 1mm. The P1 plants with a small corolla are likely to have only the – allele at each of the 5 genes, whereas the P2 plants with the long corolla have the + allele at each of the 5 gene. As usual, the F1 plants have 5 – alleles, coming from their P1 parent, and 5 + alleles from their P2 parent. However, when producing the F2 generation, recombination occurs in all gametes from the F1 parents, and thus F2 offsprings will have different numbers of + and – alleles. Consequently, F2 offsprings will have a wider range of corolla lengths than their F1 parents.

There comes quantitative genetics: a trait is called quantitative if it varies continuously. The seed color of Mendel was a discrete trait (qualitative), while the corolla length is a continuous trait (quantitative). And therefore, quantitative genetics is the study of the genetic basis of quantitative traits. Why is it so important? Well, the overwhelming majority of traits are quantitative…

Here are the questions that quantitative geneticists try to answer:

• Is the observed variation in a character influenced at all by genetic variation? (versus environmental variation)
• If there is genetic variation, what are the norms of reaction of the various genotypes?
• How important is genetic variation as a source of total phenotypic variation?
• Do many loci (or only a few) contribute to the variation in the character? How are they distributed throughout the genome?

* * *

I won’t answer all of them because it would be way too long (!), and in these times of whole-genome sequencing, lots of ongoing research is still underway… But I can still say a few words more.

Misconception: what differentiates a Mendelian trait from a quantitative one is the number of genes involved.

Although, the earlier part may suggest it, this is false. The critical difference between Mendelian and quantitative traits is not the number of segregating loci, but the size of phenotypic differences between genotypes compared with the individual variation within genotypic classes. The scheme below should make this clear. It represents the phenotypic distribution according to the genotype at a given bi-allelic locus, height being the phenotype of interest.

Hence, the definition of a quantitative character becomes: a quantitative character is one for which the average phenotypic differences between genotypes are small compared with the variation between individuals within genotypes.

* * *

After all this, what is heritability? Well, it’s simple. Let’s take a given phenotype, whatever it is (seed color, corolla length, hair color, disease status, growth rate, milk production…). The phenotype is the result of the interaction between genotype and environment. That is, a given genotype in a given environment may not lead to the same phenotype as the same genotype in a different environment, or as a different genotype in the same environment. That’s why the notion of reaction norm is essential.

But still, people always want to know how much genes contribute to some phenotypes. Is it possible? Here again, it’s tricky. Let’s take the example of two bricklayers building a wall. If both of them work in parallel, that is one builds the left of the wall and the other builds the right, it is possible to assess their respective contribution: we just have to count the number of bricks each made. But if now one makes mortar, and the other lays bricks, it is not possible anymore to compare their work (it would be absurd to do it). And it is the same for genes.

Thus, what do we do? Instead of trying to assess the contribution of genes to the phenotype (compare to the contribution of the environment), we can try to assess the contribution of genes to variations of the phenotype, using what statisticians call the analysis of variance. When looking at a given phenotype among many individuals, we try to partition the variability of their phenotype ($\sigma^2_P$) into a variability due to the fact that they have different alleles for the genes involved in the phenotype ($\sigma^2_G$), and into a variability due to the fact that they live in different environments ($\sigma^2_E$):

$\sigma^2_P = \sigma^2_G + \sigma^2_E$

Last but not least, the heritability $H^2$ can now be defined:

$H^2 = \sigma^2_G / \sigma^2_P$

The question “Is a trait heritable?” is a question about the role that differences in genes play in the phenotypic differences between individuals or groups of individuals.

Misconception: a high heritability means that a character is unaffected by the environment.

Hell, no! Because genotype and environment interact to produce the phenotype, no partition of variation into its genetic and environmental components can actually separate causes of variation. But a highly heritable trait means that the genetic component is much more important than the environmental component in contributing to the variation in phenotype.

* * *

There is still much to say of course, and one can find lots of very well explained details in the reference book Introduction to Genetic Analysis from Griffiths and colleagues. Indeed, I even allowed myself to scan some of its figures as they are very self-explanatory. (But  due to copyright issues, I will remove them if the authors and/or publisher ask me to do so.)

## Proof that P-values under the null are uniformly distributed

22 avril 2011

I often hear in talks from statisticians that P-values are uniformly distributed under the null. But how can this be? And what does it mean? As the demonstration is pretty straightforward but nonetheless hard to find on the Internet, here it is.

Everything starts with an experiment (or at least with the observation of a natural phenomenon, be it part of an experiment or not). The aim is to assess whether or not the hypothesis we have about this phenomenon seems to be true. But first, let’s recall that a parametric test (see Wikipedia) is constituted of:

• data: the $n$ observations $x_1$, $x_2$, …, $x_n$ are realizations of $n$ random variables $X_1$, $X_2$, …, $X_n$ assumed to be identically distributed;
• statistical model: the probability distribution of the $X_1$, $X_2$, …, $X_n$ depends on parameter(s) $\theta$;
• hypothesis: an assertion concerning $\theta$, noted $H_0$ for the null (e.g. $\theta=a$), and $H_1$ for the alternative (e.g. $\theta=b$ with $b > a$);
• decision rule: given a test statistic $T$, if it belongs to the critical region $C$, the null hypothesis $H_0$ is rejected.

In practice, $T$ follows a given distribution under $H_0$ (e.g. a Normal distribution, or a Student distribution) that does not depend on $\theta$ but on $n$. We use the observations to compute a realization, noted $t$, of $T$.

The P-value, noted $P$, can be seen as a random variable, and its realization, noted $p$, depends on the observations. According to the notations, the formal definition of the P-value for the given observations is:

$p = \mathbb{P} ( T \ge t | H_0 )$

Therefore, according to Wikipedia, a P-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. According to Matthew Stephens (source), a p value is the proportion of times that you would see evidence stronger than what was observed, against the null hypothesis, if the null hypothesis were true and you hypothetically repeated the experiment (sampling of individuals from a population) a large number of times.

Very importantly, note that the 2nd definition emphasizes the fact that, although it is computed from the data, a P-value does not correspond to the probability that $H_0$ is true given the data we actually observed!!

$p \ne \mathbb{P} ( H_0 | x_1, x_2,..., x_n )$

A P-value simply gives information in the case we would repeat the experiment a large number of times… (That’s why P-values are often decried.)

Ok, back on topic now. From the formula above, we can also write:

$p = 1 - \mathbb{P} ( T < t | H_0 )$

By noting $F_0$ the cumulative distribution function (cdf, fonction de répartition in French) of $T$ under $H_0$, we obtain:

$p = 1 - F_0( t )$

And here is the trick, thanks to the fact that the cdf is monotonic, increasing and (left-)continuous:

$\mathbb{P} ( T \ge t | H_0 ) = \mathbb{P} ( F_0(T) \ge F_0(t) ) = 1 - \mathbb{P} ( F_0(T) < F_0(t) )$

Therefore, we have:

$\mathbb{P} ( F_0(T) < F_0(t) ) = F_0( t )$

Which means that $F_0(T)$ is following a uniform distribution. And, as this means also that $1 - F_0(T)$ is uniformly distributed, then we can conclude that P-values are uniformly distributed under the null hypothesis.

cqfd.

But what does it mean? Well, we usually consider a significance level, noted $alpha$ (small, e.g. 5%, 1%, 0.1%…), and if the P-value falls below this threshold, we reject the null and decide that the alternative is significant. However, let’s say we re-do the same experiment $N$ times and compute a P-value for each of them. Since P-values are uniformly distributed under the null, it is as likely to find some of them between 0.8 and 0.85 than to find some of them below 0.05, if $H_0$ is indeed true. That is, some of them will fall below the significance threshold, just by chance. The experiments corresponding to these P-values are called false-positives: we think they are positives, i.e. we decide to accept $H_1$, while in fact they are really false, i.e. $H_0$ is true and should not be rejected.

Last but not least, if we re-do the same experiment 100 times and consider a threshold of 5%:

• if $H_0$ is false (although we are not supposed to know it before doing the experiment), how many P-values will fall below this threshold just by chance? 5, on average;
• if now $H_0$ is supposed to be true 50% of the time, what proportion of P-values will be around $5\% +- \epsilon$? at least 23%, and typically 50% (see the paper of Sellke et al in 2001). In other words, when $H_0$ is true 50% of the time, a P-value of 5% doesn’t tell us anything, as half of the experiments from which they were calculated correspond to a true $H_0$, and half to a false $H_0$

## Le pari de Pascal à la sauce anglo-saxonne

15 mars 2011

Very likely he got more out of his faith than I out of my doubt. And so, pragmatism is true, he was right and I wrong.

Granville Stanley Hall

(the first to be awarded a PhD in psychology in the US, by Harvard’s philosophy department in 1878)

## Just waiting for the future to come?

16 janvier 2011

Prediction is very difficult, especially about the future.

Niels Bohr

Ok, but:

The best way to predict the future is to invent it.

Alan Kay

Why should we draw a line between a researcher (describe, explain, predict) and a social actor (involve, convince, transform)? Aren’t they the same (recombine, share and enjoy)?!

## « Learning about nature through provisional knowledge » as universal legacy

11 janvier 2011

Science is one of the pillars of civilization and liberal democracy, as that eminent philosopher of science, Karl Popper, convincingly argued. It is, he said, « one of the greatest spiritual adventures man has yet known ». Because science rejects claims to truth based on authority and depends on the criticism of established ideas, it is the enemy of autocracy. Because scientific knowledge is tentative and provisional, it is the enemy of dogma. Because it is the most effective way of learning about the physical world, it erodes superstition, ignorance and prejudice, which have been at the root of the denial of human rights throughout history, whether through racism, chauvinism or the suppression of the rights of women.

Dick Tavern, Nature v459, 2009

Indeed…

Yet it is important, when exposing such a view, to distinguish between science and its consequences (which are not « science » although they can be studied scientifically). I know some people  (me included) who acknowledge that things start to complicate once knowledge derived from science exits the inner circle of so-called « appointed scientists » to enter mass media, political cenacles, private companies, school/university teaching, etc, and this for numerous reasons (divergent interests, lack of time, environment pressure).

However, the reverse is true also. Miseries and vagaries of scientific understanding among people should not let us forgot that science is about going on doubting, aware of our ignorance but determined to expand our knowledge, ever and ever, through experiments and conceptualizations. And the words of Taverne, e.g. « tentative and provisional », are very much appealing to me. In the same vein, Richard Feynman once said that « science is the belief in the ignorance of experts » (talk here).

The challenge is all about conciliating science and its consequences, and being aware of what science is should not be too damaging.

Why writing all this? Because this topics was quite hot a few weeks ago…

## Utiliser LaTeX pour une thèse en SHS franco-vietnamienne

3 janvier 2011

De tout temps, des artisans ont produit de magnifiques objets, mais tous les métiers ne s’y prêtent pas. Certaines professions sont nettement plus « scribouillardes » que d’autres, notamment dans le monde académique où l’on produit (est censé produire) des articles à la chaine.

Ces idées, résultats, interprétations, discussions, conclusions, méritent d’être diffusés avec une mise en page respectant autant que faire se peut les règles typographiques en vigueur. De plus, à notre époque numérique, il est important de s’assurer de la pérennité de ces documents académiques, c’est-à-dire de leur préservation sous une forme permettant de les convertir facilement à l’avenir.

Dans un un tel contexte, nous pouvons donc tous nous réjouir du travail de Donald Knuth à qui nous devons, entre autre, l’existence de TeX, un système de composition de documents. Au passage, remercions également Leslie Lamport qui a grandement facilité l’utilisation de TeX en créant LaTeX. Mais passons plutôt aux aspects pratiques…

L’intérêt de LaTeX réside dans le fait qu’il exige du rédacteur de se concentrer sur la structure logique de son document, son contenu, tandis que la mise en page du document est laissée au logiciel lors d’une compilation ultérieure. LaTeX sépare ainsi en deux phases la forme du contenu. Ce système est libre, gratuit et multi-plateforme (en gros, il fonctionne sur GNU/Linux, Windows et Mac).

Ce système est utilisé relativement fréquemment par les chercheurs en sciences « naturelles », mais très peu par les chercheurs en sciences humaines et sociales. Dans la suite de ce billet, je présente un exemple de comment écrire une thèse mêlant français et vietnamien pour en convaincre quelques uns de sauter le pas.

Commencez par enregistrer dans les favoris de votre navigateur l’adresse du wikibook sur LaTeX, http://fr.wikibooks.org/wiki/LaTeX, et téléchargez-le au format pdf afin de ne pas être bloqué si vous n’avez pas de connexion internet. Puis, lisez la première partie intitulée « Premiers pas ». Une fois cela fait, vous êtes prêt à écrire votre thèse en LaTeX !

Créez un répertoire « thèse_latex » sur votre ordinateur à l’intérieur duquel vous créez un document texte « fichier_maitre.tex ». C’est ce fichier que vous compilerez afin d’obtenir votre thèse au format pdf. Comme un tel document est généralement très gros, surtout en SHS, il est pertinent de créer un fichier « chapitre1.tex », un fichier « chapitre2.tex » et ainsi de suite pour chaque chapitre. Le fichier maître se chargera d’inclure le contenu de ces fichiers lors de la création du pdf final.

Voici le fichier maître, les lignes commencées par « % » étant des commentaires:


% !TEX encoding = UTF-8 Unicode

% sous Mac, pour l'antislash, faire Alt + Maj + /
% sous Mac, pour l'accolade, faire Alt + ( ou )
% sous Mac, pour le crochet, faire Alt + Maj + ( ou )
% sous Mac, pour le le tilde, faire Alt + n

% définir le format des pages, la taille de la police et le type de document
\documentclass[a4paper,12pt]{book}

% package pour l'encodage des langues
\usepackage[vietnam,francais]{babel}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}

% package pour les liens hypertexte et les propriétés du pdf
\usepackage{hyperref}
\hypersetup{
unicode=true,
pdfauthor={M G},
pdftitle={Ma thèse},
pdfkeywords={géographie, urbanisme},
}

% package pour inclure des images
\usepackage{graphicx}

% package pour inclure un document pdf à l'intérieur du pdf final
\usepackage[final]{pdfpages}

% package pour faire un index
\usepackage{makeidx}
\makeindex

% package pour faire un glossaire
% (on peut aussi rajouter l'option "acronym" pour avoir une liste d'acronymes)
\usepackage{glossaries}
\makeglossaries
\newglossaryentry{sample}{name={sample}, description={a sample entry}}
% à terme, on peut regrouper toutes les entrées dans un seul fichier et faire un "include"

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{document}

\title{Ma thèse}
\author{M G}
\date{14/12/2010}
\maketitle

\listoffigures

% inclusion du contenu du chapitre 1
\include{chapitre1}

% inclusion du contenu du chapitre 2
\include{chapitre2}

% créer l'index
\printindex

% créer le glossaire
\printglossaries

% créer la bibliographie
\nocite{durand_villes_1995}   % ajout à la bibliographie sans besoin d'être cité dans le texte lui-même
\bibliographystyle{plain-fr}   % spécifie le style selon lequel la bibliographie va être représentée
\bibliography{Mabibliotheque.bib}   % spécifie le fichier au format "bibtex" dans lequel se trouvent les références bibliographiques

% insérer la table des matières
\tableofcontents

\end{document}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



Voici le contenu du fichier « chapitre1.tex »:

% !TEX encoding = UTF-8 Unicode

\chapter{Voici mon titre de premier chapitre}

\section{Et voici ma première section}

\subsection{Ainsi que ma première sous-section}

Salut, voici un premier essai de lettres accentuées: é à ç. Et voici du remplissage en latin: Legi, Patres colendissimi, in Arabum monumentis, interrogatum Abdalam 1 Sarracenum, quid in hac quasi mundana scaena admirandum maxime spectaretur, nihil spectari homine admirabilius respondisse. Cui sententiae illud Mercurii adstipulatur: Magnum, o Asclepi, miraculum est homo.

\subsection{Deuxième sous-section}

Maintenant un peu de vietnamien: \foreignlanguage{vietnam}{đưỡng lô ghè}. Puis un terme à mettre dans l'index: toto\index{toto}. Mais cela peut également être un ensemble de mots en vietnamien:
<pre>\foreignlanguage{vietnam}{đưỡng lô ghè}</pre>
\index{\foreignlanguage{vietnam}{đưỡng lô ghè}}.

\section{Deuxième section}

Horum dictorum rationem cogitanti mihi non satis illa faciebant, quae multa de humanae naturae praestantia afferuntur a multis: esse hominem creaturarum internuntium, superis familiarem, regem inferiorum; sensuum perspicacia...

% insertion d'une figure
\begin{figure}[!t]
\centering
\includegraphics[width=\textwidth]{images/IMG_1426_small}
\caption{Le marché}
\label{Marché}
\end{figure}

Magna haec quidem, sed non principalia, idest quae summae admirationis privilegium sibi iure vendicent. Cur enim non ipsos angelos et beatissimos caeli choros magis admiremur?

% commande pour ajouter un espace vertical
\vspace{0.5cm}

Je vais maintenant testé l'ajout d'un terme dans le glossaire, commençons avec \gls{sample}. Cela a l'air de marcher. Rajoutons un peu de vietnamier pour rigoler encore: \foreignlanguage{vietnam}{đưỡng lô ghè}.


Et voici le contenu du fichier « chapitre2.tex »:

% !TEX encoding = UTF-8 Unicode

\chapter{Deuxième chapitre}

\section{Première section}

Voici une entrée pour le glossaire: \newglossaryentry{Toto_1}{description={un toto qui rit bien}}. Et maintenant une citation bibliographique: \cite{hue_litteratures_1999}.

\section{Deuxième section}

Mais n'oublions pas les listes:
\begin{itemize}
\item Toto1: bciovzgiuzbe
\item Toto2: cnfivgu
\end{itemize}

Enfin ajoutons une note de bas de page\footnote{toto}, et une deuxième\footnote{\cite{hue_litteratures_1999}}, avec une référence cette fois.

Sed non erat paternae potestatis in extrema fetura quasi effeta defecisse; non erat sapientiae, consilii inopia in re necessaria fluctuasse; non erat benefici amoris, ut qui in aliis esset divinam liberalitatem laudaturus in se illam damnare cogeretur.



Et enfin voici le fichier de bibliographie au format Bibtex:


% !TEX encoding = UTF-8 Unicode

@book{hue_litteratures_1999,
series = {Histoire littéraire de la francophonie, {ISSN} 1258-0368 ; {1999Universités} francophones, {ISSN} 0993-3948},
title = {Littératures de la Péninsule indochinoise},
isbn = {{2-86537-968-X}},
abstract = {{AUF} = Agence universitaire de la francophonie. - Coll. principale : Universités francophones. - 9782865379682},
publisher = {Ed. Karthala},
author = {Bernard Hue and {Pierre-Richard} Féray},
year = {1999},
keywords = {24288, Francophonie -- Histoire, French language -- Indo China, Indo China -- Literature, Indochine -- Dans la littérature, Indochine -- Littératures, Literature -- Indo China -- 19th century, Literature -- Indo China -- 20th century, Littérature francophone -- Histoire et critique}
},

@article{durand_villes_1995,
title = {Villes et urbanisation au Vietnam, une esquisse d'état des lieux bibliographique},
volume = {2},
number = {31},
journal = {Péninsule},
author = {Frédéric Durand},
year = {1995},
pages = {141--162}
},



Avec cet exemple, nous aurons vu à peu près toutes les bases nécessaires pour écrire sa thèse avec LaTeX: inclusion de documents, bibliographie, index, glossaire, images, notes de bas de page, etc. Le pdf final ressemble à ça: pas mal, non ?

Pour aller plus loin, personnaliser la mise en page, ajouter des annexes, le mieux est de farfouiller dans le wikibooks ou sur le site des tuteurs de l’ENS. Et puis on apprend en essayant… A noter le package « classic thesis » qui permet d’obtenir une belle mise en page sans beaucoup d’efforts (disponible ici).

## Notes de lecture et cerises de crise

3 décembre 2010
• Beaucoup craignent cette obligation d’inventer, on ne peut leur donner tort. J’ai l’audace de m’en réjouir. Pourquoi ?
• Comment mesurer la nouveauté d’un évènement ? Elle est proportionnelle à la longueur de l’ère précédente, que cet évènement clôt.
• Pour l’avoir lu mille fois dans les livre d’histoire, nous croyons, d’autre part et naïvement, que la vieille conduite du peuple romain, réclamant sans cesse panem et circenses, du pain et des jeux, résultait de son état de décadence ou, du moins, le faisait voir. Pas du tout: elle la causait. Croire, en effet, qu’une société ne vie que de pain et de jeux, d’économie et de spectacle, de pouvoir d’achat et de médias, de banque et de télés, comme nous subsistons aujourd’hui, constitue un tel contresens sur le fonctionnement réel de toute collectivité que ce choix exclusif, erroné, la précipite vers sa fin pur et simple, comme on l’a vu pour la Rome Antique.
• Nous dépendons enfin des choses qui dépendent de nous. Étrange boucle, difficile à gérer. Nous dépendons, en effet, d’un monde dont nous sommes en partie responsable de la production. Nous entrons dans cette ère anthropocène.
• Qui va parler au nom de la Biogée ? Ceux qui la connaissent et lui ont consacré leur vie.
• […] les six grands bouleversements cités proviennent tous, sans aucune exception, de la recherche scientifique et de ses applications: agronomie, médecine, pharmacie, biochimie, physique nucléaire, sciences de la vie et de la Terre… Les scientifiques ont donc déjà manifesté le pouvoir de transformer la face du monde et la maison des hommes. […] De plus, et contrairement aux industries et aux instances financières, seule la science a l’intuition et le souci du long terme.
• […] qui prendra la parole en Biogée ? Les savants. […] Qu’ils disent le bien commun. […] Qu’ils définissent un nouveau travail, orienté vers la reconstruction. Que, d’après ses codes propres, ils énoncent les lois de la Biogée. […] Pourraient-ils se séparer du complexe militaro-industriel et couper toute relation avec les secteurs de l’économie qui détruisent le monde et affament les hommes ?
• Les Sciences de la Vie et de la Terre parlent la langue propre à la Biogée. Elles réinventent, aujourd’hui, une pluridisciplinarité, fédérée autour d’elles, fédération qui peut conduire à un enseignement propre à passionner tout le monde, donc à susciter une autre société.
• […] les choses de la Terre et de la vie, comme nous codées, savent et peuvent recevoir de l’information, en émettre, la stocker, la traiter.
• Signer un contrat naturel paraît aujourd’hui moins une obligation juridique et morale qu’une évidence de fait.
• […] aucune règle éthique ne sait ni ne peut interdire, au préalable, l’exercice libre de la recherche collective du vrai; que telle recommandation morale intervienne après l’invention, l’innovation ou la réalisation nouvelles, et elle se rend inefficace par là même. Comment s’y prendre pour qu’une loi morale s’exerce avant, pendant et après toute recherche ?
• Premier et ancien serment: Pour ce qui dépend de moi, je jure: de ne point faire servir mes connaissances, mes inventions et les applications que je pourrai tirer de celles-ci à la violence, à la destruction ou à la mort, à la croissance de la misère ou de l’ignorance, à l’asservissement ou à l’inégalité, mais à les dévouer, au contraire, à l’égalité entre les hommes, à leur survie, à leur élévation et à leur liberté.
• La religion géra les hommes; en prétendant les défendre, l’armée les gouverna et, souvent, les asservit; enfin, l’économie se mit à régir leurs vies, parfois implacablement. […] Qui prend aujourd’hui le relais ? Le savoir, aux accès désormais faciles, une pédagogie accessible, la démocratie de l’accès général. […] Que les savants puissent parler au nom de la Biogée exige qu’ils prêtent un Serment dont les termes les libèrent de toute inféodation aux trois classes précédentes. Pour devenir plausibles, il faut que, laïques, ils jurent ne servir aucun intérêt militaire ni économique.
• […] toute hiérarchie précédente se fondait sur la rétention de l’information, sur le verrouillage d’une rareté […] La hiérarchie, c’est ce vol. […] Aujourd’hui, ô paradoxe, la plus belle mine d’or réside dans les données, je veux dire vraiment données: à la disposition de tous et partagées. Cet accès universel change la nature même du pouvoir. […]  La liberté, c’est l’accès.
• Non seulement l’accès, possible, mais l’intervention, active. Le nouvel habitat permet à chacun, ignorant, inexpert, indigent, pauvre ou misérable, mineur en tous ordres, de s’y instruire, de s’y investir, d’y donner son opinion, de participer aux décisions, de partager l’expertise, bref, de rester attentif à son propre destin et actif dans celui de la collectivité. Voici que vient un vote en temps réel et généralisé, qui permet de rêver à une authentique démocratie de participation, puisque l’égalité règne, ici, aussi bien pour l’intervention, libre, que par l’accès, facile.
• Quel drame pour la pensée que la vieille morale de l’engagement politique ! […] Urgent à entreprendre, voici un petit travail par lequel les engagés pourraient recommencer: travailler à la Réforme de l’Entendement.
• Douces, les trois révolutions de l’écriture, de l’imprimerie et de l’ordinateur ont bouleversé l’histoire , les conduites, les institutions et le pouvoir de nos sociétés, de manière beaucoup plus fondamentale que les changements durs, ceux des techniques de travail, par exemple.
• Je le répète, dur se dit du travail à l’échelle entropique: coups de marteau sur un burin, fonte de l’acier, moteurs, bombes nucléaires. Doux se dit des actes d’échelle informationnelle: traces, marques, signes, codes et leur sens.
• Je promets, pour demain, un long livre sur ce Doux.

Michel Serres, Temps des crises (2009)

Un court livre (80p.) le long duquel j’ai oscillé entre de nombreux acquiescements manifestes et quelques désaccords tranchés, plutôt des déceptions réalistes.

Quelques questions pour l’avenir: comment quantifier le potentiel d’inventions d’un système (dans le sens, sa capacité à produire de l’information) ? (cela nécessitant de définir ce qu’on entend par information…); comment créer les conditions pour qu’un message soit sélectionné, du type « que les savants puissent parler au nom de la Biogée exige qu’ils prêtent un Serment » ? (quand on sait les difficultés actuelles); comment favoriser l’accès aux données ? (en parlant d’accès aux données…); comment, au delà de l’accès aux données, insuffler le « don d’information » among our fellow citizens ? (par « don d’information », j’entends « enseignement », « pédagogie », etc)