diff --git a/paper.bib b/paper.bib
index 76d214a4967e5e59bbcbed3469f39533283920d2..5aef0bea01d048814225d5d466f032fc8b01bcc3 100644
--- a/paper.bib
+++ b/paper.bib
@@ -15,8 +15,12 @@
   organization={IEEE}
 }
 
-@article{barretpredicting,
-  title={Predicting the enviornment of a neighbourhood: a use case for France},
-  author={Barret, Nelly and Duchateau, Fabien and Favetta, Franck and Bonneval, Loic},
-  year={2020}
+@INPROCEEDINGS{data20,
+  title = {Predicting the Environment of a Neighborhood: a Use Case for France},
+  publisher = {SciTePress},
+  year = {2020},
+  isbn = {978-989-758-440-4},
+  author = {Nelly Barret and Fabien Duchateau and Franck Favetta and Loïc Bonneval},
+  booktitle = {International Conference on Data Management Technologies and Applications (DATA)},
+  pages = {294-301},
 }
\ No newline at end of file
diff --git a/paper.md b/paper.md
index 48c7857fdb7a46ead1488460edccf4cf30c5abf1..55e6cc98841663912b511d47fd4d35f3720dddba 100644
--- a/paper.md
+++ b/paper.md
@@ -35,7 +35,7 @@ Several projects focus on qualifying neighbourhoods using social networks. For i
 
 # Methodology
 
-In order to describe in the most accurate way the environment of a neighbourhood, social science researchers have defined six environment variables, each with a limited number of values [@barretpredicting]. These six variables are the _building type_, the _building usage_, the _landscape_, the _social class_, the _morphological position_ and the _geographical position_. As an example, the _landscape_ can be evaluated as _urban_, _green areas_, _forest_ or _countryside_ while the _social class_ have values from _lower_ to _upper_. These variables are commonly accepted and easily understandable.
+In order to describe in the most accurate way the environment of a neighbourhood, social science researchers have defined six environment variables, each with a limited number of values [@data2020]. These six variables are the _building type_, the _building usage_, the _landscape_, the _social class_, the _morphological position_ and the _geographical position_. As an example, the _landscape_ can be evaluated as _urban_, _green areas_, _forest_ or _countryside_ while the _social class_ have values from _lower_ to _upper_. These variables are commonly accepted and easily understandable.
 
 Predihood provides the following functionnalities:
 - adding new neighbourhoods and indicators to describe them;
@@ -111,7 +111,7 @@ In addition, Predihood provides an interface for easily tuning and testing algor
 
 # Mentions of Predihood
 
-Our Predihood tool has been presented during the DATA conference [@barretpredicting]. Prediction results using 6 algorithms from Scikit-learn range from 30% to 65% depending on the environment variable, and designing new algorithms could help improving these results.
+Our Predihood tool has been presented during the DATA conference [@data2020]. Prediction results using 6 algorithms from Scikit-learn range from 30% to 65% depending on the environment variable, and designing new algorithms could help improving these results.
 
 The project is available here: [https://gitlab.liris.cnrs.fr/fduchate/predihood](https://gitlab.liris.cnrs.fr/fduchate/predihood).