diff --git a/paper.md b/paper.md
index 5b489dedc3972d569060770ef4dc87fb3abe8c8a..ec8679c9fcdb0d96a9aa5e773b2626bfde3f36b4 100644
--- a/paper.md
+++ b/paper.md
@@ -14,7 +14,7 @@ authors:
     orcid: 0000-0003-2039-3481
     affiliation: 1
 affiliations:
- - name: LIRIS, UMR5205 Université Claude Bernard Lyon 1, Lyon, France
+ - name: LIRIS UMR5205, Université Claude Bernard Lyon 1, Lyon, France
    index: 1
 date: 11 August 2019
 bibliography: paper.bib
@@ -26,7 +26,7 @@ When studying geographical areas such as neighborhoods, it is necessary to colle
 For instance, social science researchers study the relationship between citizens and their living area [@preteceille2009segregation;@authier2008citadins] or how they describe their neighborhood [@airbnb2017]. Computer science researchers are interested in recommending the most relevant neighborhood when buying a house [@RealEstate2013], in predicting price and types of neighborhoods [@tang2015neighborhood] or in detecting similar areas between different cities [@le2015soho].
 
 National institutions (e.g., Open Data initiatives, INSEE in France) may produce data about neighborhoods, but they are usually spread in heterogenous files (databases, spreadsheets). Initiatives such as DataFrance [@datafrance] enable their visualization on a map, but their authors do not share collected data.
-Thus, researchers have to manually collect and integrate raw data from national institutions, a challenging issue refered to as `data integration` [@christen2012data]. Although some tools such as OpenRefine or Talend facilitates this integration, they require expert knowledge and programming skills.
+Thus, researchers have to manually collect and integrate raw data from national institutions, a challenging issue refered to as `data integration` [@christen2012data]. Although some tools such as OpenRefine or Talend facilitates this integration, they require expert knowledge and programming skills. Besides, spatial queries (e.g., neighborhoods located within a close distance), which are useful in a research context, are usually not directly available.
 The French administration provides data about IRIS [@insee-iris], a small division unit of the national territory for statistical purposes (mostly with the same number of residents, thus mainly small-sized in cities and wider in rural areas). 
 To ease the exploitation of IRIS, we propose the package Mongiris, which includes integrated data about these neighborhoods (IRIS) and an API for manipulating them.
 
@@ -36,12 +36,12 @@ The Python package is composed of two modules: integration and API.
 
 The `integration module` is responsible for extracting information from data sources. The module currently supports spreadsheets produced by [INSEE](https://www.insee.fr/).
 Since data evolve (e.g., statistics from INSEE are updated every few years), the integration module may be run. Note that new data may be stored in different database or collections so that the evolution can be studied.
-For most users, there is no need to use the integration module since the dump of the database is provided. It is mainly based on INSEE files from 2014 and 2016.
+For most users, there is no need to use the integration module since a recent dump of the database is provided. It is mainly based on INSEE files from 2014 and 2016.
 The current dump contains roughly 37,000 IRIS with 375 indicators and 12,800 IRIS with 640 indicators.
 <!-- {362: 36530, 650: 11738, 627: 1057, 385: 79} -->
 
 The `API module` includes common operations such as searching for an IRIS (by IRIS code or according to any field value), inserting, updating or deleting an IRIS. 
-It also provides geospatial operations useful in a research context: get IRIS given coordinates, get all adjacent or close IRIS from a given IRIS, find all IRIS in a given area, etc.
+It also provides geospatial operations: get IRIS from coordinates, get all adjacent or close IRIS from a given IRIS, find all IRIS in a given area, etc.
 
 The Mongiris package is currently used in Mapiris, a tool for visualizing and searching for IRIS.
 
diff --git a/paper.pdf b/paper.pdf
index e842848952959a356ffff863df8aa0a5d7e08b2d..d4c30ffffb7e54cdf5675a481015adebe4783b2e 100644
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