diff --git a/README.md b/README.md
index 251b0deccf..015fb9b9f2 100644
--- a/README.md
+++ b/README.md
@@ -38,7 +38,7 @@
 
 ## Statement of Need
 
-There are a plethora of Python packages for geospatial analysis, such as geopandas for vector data analysis and xarray for raster data analysis. However, few Python packages provide interactive GUIs for loading and visualizing geospatial data in a Jupyter environment. It might take many lines to code to load and display geospatial data with various file formats on an interactive map, which can be a challenging task for novice users with limited coding skills. Leafmap provides many convenient functions for loading and visualizing geospatial datasets with only one line of code. Users can also use the interactive GUI to load geospatial datasets without coding. Anyone with a web browser and Internet connection can use leafmap to perform geospatial analysis and data visualization in the cloud with minimal coding.
+There are a plethora of Python packages for geospatial analysis, such as [geopandas](https://github.com/geopandas/geopandas) for vector data analysis and [xarray](https://github.com/pydata/xarray) for raster data analysis. However, few Python packages provide interactive GUIs for loading and visualizing geospatial data in a Jupyter environment. It might take many lines to code to load and display geospatial data with various file formats on an interactive map, which can be a challenging task for novice users with limited coding skills. Leafmap provides many convenient functions for loading and visualizing geospatial datasets with only one line of code. Users can also use the interactive GUI to load geospatial datasets without coding. Anyone with a web browser and Internet connection can use leafmap to perform geospatial analysis and data visualization in the cloud with minimal coding.
 
 Launch the interactive notebook tutorial for the **leafmap** Python package with Google Colab or Binder now:
 
diff --git a/docs/changelog.md b/docs/changelog.md
index 5085b8ec5a..7dc1e9496c 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -1,5 +1,13 @@
 # Changelog
 
+## v0.3.2 - June 22, 2021
+
+**New Features**:
+
+-   Added time slider [#42](https://github.com/giswqs/leafmap/issues/42)
+-   Added JOSS manuscript
+-   Added unittests
+
 ## v0.3.1 - June 20, 2021
 
 **New Features**:
diff --git a/docs/index.md b/docs/index.md
index 5083b23990..32cef1b4e8 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -38,7 +38,7 @@
 
 ## Statement of Need
 
-There are a plethora of Python packages for geospatial analysis, such as geopandas for vector data analysis and xarray for raster data analysis. However, few Python packages provide interactive GUIs for loading and visualizing geospatial data in a Jupyter environment. It might take many lines to code to load and display geospatial data with various file formats on an interactive map, which can be a challenging task for novice users with limited coding skills. Leafmap provides many convenient functions for loading and visualizing geospatial datasets with only one line of code. Users can also use the interactive GUI to load geospatial datasets without coding. Anyone with a web browser and Internet connection can use leafmap to perform geospatial analysis and data visualization in the cloud with minimal coding.
+There are a plethora of Python packages for geospatial analysis, such as [geopandas](https://github.com/geopandas/geopandas) for vector data analysis and [xarray](https://github.com/pydata/xarray) for raster data analysis. However, few Python packages provide interactive GUIs for loading and visualizing geospatial data in a Jupyter environment. It might take many lines to code to load and display geospatial data with various file formats on an interactive map, which can be a challenging task for novice users with limited coding skills. Leafmap provides many convenient functions for loading and visualizing geospatial datasets with only one line of code. Users can also use the interactive GUI to load geospatial datasets without coding. Anyone with a web browser and Internet connection can use leafmap to perform geospatial analysis and data visualization in the cloud with minimal coding.
 Launch the interactive notebook tutorial for the **leafmap** Python package with Google Colab or Binder now:
 
 [![image](https://colab.research.google.com/assets/colab-badge.svg)](https://gishub.org/leafmap-colab)
diff --git a/paper/paper.bib b/paper/paper.bib
index 5daf40aabe..2f7f8f960a 100644
--- a/paper/paper.bib
+++ b/paper/paper.bib
@@ -34,11 +34,9 @@ @ARTICLE{Wu2020
 @MISC{Lindsay2018,
   title        = "{WhiteboxTools User Manual}",
   author       = "Lindsay, John B",
-  publisher    = "GitHub.com",
   month        =  "4~" # mar,
   year         =  2018,
   url          = "https://jblindsay.github.io/wbt_book",
-  howpublished = "\url{https://jblindsay.github.io/wbt_book}",
   note         = "Accessed: 2021-1-7"
 }
 
@@ -54,10 +52,12 @@ @ARTICLE{Hoyer2017
   doi       = "10.5334/jors.148"
 }
 
-@MISC{Jordahl2014,
-  title   = "{GeoPandas: Python tools for geographic data}",
-  author  = "Jordahl, K",
-  journal = "URL: https://github.com/geopandas/geopandas",
-  year    =  2014,
-  doi     = "10.5281/zenodo.4569086"
+@MISC{Jordahl2021,
+  title  = "{geopandas/geopandas: v0.9.0}",
+  author = "Jordahl, Kelsey and Van den Bossche, Joris and Fleischmann, Martin
+            and McBride, James and Wasserman, Jacob and Gerard, Jeffrey",
+  month  =  "28~" # feb,
+  year   =  2021,
+  url    = "https://zenodo.org/record/4569086",
+  doi    = "10.5281/zenodo.4569086"
 }
diff --git a/paper/paper.md b/paper/paper.md
index 3ee0913519..1a66c73e7c 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -33,7 +33,7 @@ bibliography: paper.bib
 
 # Statement of Need
 
-There are a plethora of Python packages for geospatial analysis, such as geopandas for vector data analysis [@Jordahl2014] and xarray for raster data analysis [@Hoyer2017]. However, few Python packages provide interactive GUIs for loading and visualizing geospatial data in a Jupyter environment. It might take many lines to code to load and display geospatial data with various file formats on an interactive map, which can be a challenging task for novice users with limited coding skills. Leafmap provides many convenient functions for loading and visualizing geospatial datasets with only one line of code. Users can also use the interactive GUI to load geospatial datasets without coding. Anyone with a web browser and Internet connection can use leafmap to perform geospatial analysis and data visualization in the cloud with minimal coding.
+There are a plethora of Python packages for geospatial analysis, such as [geopandas](https://github.com/geopandas/geopandas) for vector data analysis [@Jordahl2021] and [xarray](https://github.com/pydata/xarray) for raster data analysis [@Hoyer2017]. However, few Python packages provide interactive GUIs for loading and visualizing geospatial data in a Jupyter environment. It might take many lines to code to load and display geospatial data with various file formats on an interactive map, which can be a challenging task for novice users with limited coding skills. Leafmap provides many convenient functions for loading and visualizing geospatial datasets with only one line of code. Users can also use the interactive GUI to load geospatial datasets without coding. Anyone with a web browser and Internet connection can use leafmap to perform geospatial analysis and data visualization in the cloud with minimal coding.
 
 # Leafmap Tutorials