Presentation (English)

Société de Philosophie des Sciences

7th biennal meeting in Nantes (France) July 4-6, 2018

Call for papers

The Société de Philosophie des Sciences is holding its 7th biennal meeting in Nantes (France) on July 4-6, 2018. The call for papers is open until February 1st. The main theme is Theories and data in the era of big data, but any topic in philosophy of science is welcome.

Deadline for submission: February 1st 2018

Submission via web-site

Submission of individual papers:

- In English   or in French 

- Abstract: 1000 words

- About the theme of the meeting – Theories and data in the era of big data – or any other topic in philosophy of science.

Symposium (3 people):

- In English or in French  

- 2000 words for the session as a whole (~500 words for a general introduction and ~500 words per paper)

- About the theme of the meeting – Theories and data in the era of big data – or any other topic in philosophy of science.

Notification of acceptance: February 22, 2018

Registration from March 1, 2018  to May 31, 2018

For any inquiry, please contact Karine Le Jeune (

Plenary invited speakers

Serge Abiteboul (INRIA, ENS Paris)
Les sciences questionnées par le numérique

Anouk Barberousse (Paris IV, Sorbonne)
Les bases de données de la biodiversité

Sonia Desmoulin-Canselier (DCS, CNRS, Université de Nantes)
L'évaluation à l'ère de la médecine des données

Sabina Leonelli (Egenis, Exeter University)
Research in the age of big and open data

Marco Panza (IHPST, CNRS, Université Paris 1)
Understanding science without understanding

Description of the theme

Theories and data in the era of big data

The increasing use of big data in numerous scientific and technical fields, from climate science to health science through data mining of social networks, in order to explain, predict, or decide, raises new questions for philosophy of science. What does philosophy of science have to say about the sudden appearance of big data in all these scientific domains? In particular, how does it change scientific practice?

Big data and data. What is the difference between big data and data in general? The first question may be whether there is a critical mass of data beyond which they qualify as ‘big’ and how to defined this threshold, or whether it is because of a certain way of treating the same body of data that they qualify as massive (or not). One can also ask whether the availability of a great amount of data has triggered some change in scientific thinking, or whether the search for a great amount of data has followed from such a change.

Big data and algorithms. Big data are characterized by automated processing and by the constitution of one or several algorithm(s), particularly learning algorithms. What is an algorithm? What are the potentialities and limits of these algorithms?

Intelligibility and big data. Even though algorithms are not new, their generalization, complexification and performance raise the question of whether the relationship between scientific thinking and its objects has changed: less direct, characterized by a supervising rather than a checking role, this relationship now must manage the black boxes of algorithms. What is the impact on the nature of scientific thinking? Does science get rid of theories? What does the expression ‘data-driven science’ mean?

Quality of big data. By becoming ‘big’, data limit the possibility of counterchecking. Does the great amount of data compensate the inevitable problem of their quality, or does the quality of data remain the same throughout their processing, according to the principle: garbage in – garbage out? What does moving to big data change to the way evidence is provided in the various sciences?

Structure and curation of database. Beyond the question of which piece of information is conserved in a database, the question of the structure of this information can also be addressed: which ontology is needed for big data? How these choices for standards are done, and should t be done? How can one mine data with different structures? Questions of storage can also be raised, for instance, how data are preserved in the face of the threat of obsolescence of the format in which they are preserved, e.g. because of changes of software or even of software versions?

Scientific Committee

Daniel Andler
Anouk Barberousse
Denis Bonnay
Jean-Paul Delahaye
Isabelle Drouet
Denis Forest
Alexandre Guay
Xavier Guchet
Paul Humphreys
Philippe Huneman
Cyrille Imbert
Vincent Israel-Jost
Maël Lemoine
Pierre-Michel Menger
Francesca Merlin
Pierre-Olivier Méthot
Marco Panza
Cédric Paternotte
Carlo Ratti
Christian Sachse
Jonathan Sholl
Cristian Saborido
Stéphane Tirard
Franck Varenne
Marion Vorms

Karine Le Jeune
Maël Lemoine
Stéphane Tirard


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