diff --git a/_projects/1_project.md b/_projects/1_project.md
index 9aff0bd..7b5614d 100644
--- a/_projects/1_project.md
+++ b/_projects/1_project.md
@@ -4,7 +4,7 @@ title: Point pattern analysis (PPA) for neuroscience
 description: Point pattern analysis (PPA) to examine the spatial arrangement/distribution of cells. 
 img: assets/img/Pointanalysis.png
 importance: 2
-category: Image analysis
+category: Image and data analysis
 ---
 
 I am exploring ways to incorporate Point patterns analysis (PPA) for the analysis of microscopy images in neuroscience. PPA allows the investigation of the spatial arrangement/distribution of cells, like neurons and neuroglia, in the healthy and disease central nervous system. Indeed, it has application in multiple biomedical fields. I perform point pattern analysis using the [spatstat](https://spatstat.org/download.html) package in R-programming language. 
diff --git a/_projects/2_project.md b/_projects/2_project.md
index cc15a22..5e46cf2 100644
--- a/_projects/2_project.md
+++ b/_projects/2_project.md
@@ -4,7 +4,7 @@ title: Topological data analysis for neuroscience
 description: Topological Data Analysis (TDA) to examine topological features of point clouds. 
 img: assets/img/TDAimg.jpg
 importance: 3
-category: Image analysis
+category: Image and data analysis
 ---
 
 Topological data analysis (TDA) is an approach that uses algebraic topology to analyze complex data sets, including point clouds. With TDA, the user can evaluate the degree of noise, variability, and complexity, as well as identifying topological features such as holes, loops, and voids in the point clouds at different scales. This approach is based on tools like vietoris-rips complexes and persistent homology that allow to visualize complex topological structures.