Added JOSS banner and small fix in paper text

README.rst

@@ -9,6 +9,10 @@
     :target: https://microbenthos.readthedocs.io/en/latest/?badge=latest
     :alt: Documentation Status
 
+.. image:: http://joss.theoj.org/papers/5daae2e14258034d77cb694370cbae23/status.svg
+    :target: http://joss.theoj.org/papers/5daae2e14258034d77cb694370cbae23
+    :alt: Journal of Open Source Software
+
 MicroBenthos is a modeling framework (in python) useful for studies of benthic microbial systems,
 such as microbial mats, marine sediments, etc. It enables in silico experiments in microbial
 ecophysiology and biogeochemistry through computational simulations of geochemical processes,

joss/paper.md

@@ -14,24 +14,25 @@ authors:
  - name: Arjun Chennu
    orcid: 0000-0002-0389-5589
    affiliation: 1
- 
+
 affiliations:
  - name: Max Planck Institute for Marine Microbiology
    index: 1
- 
+
 date: 4 April 2018
 bibliography: paper.bib
 ---
 
 # Summary
 
-Microbial benthic habitats, such as microbial mats and sediments, exhibit extremely steep gradients in the physical, chemical and biotic parameters within the space of a few millimeters. These micro-environments drive the localization and exploitation of physico-chemical niches by a variety of microbial groups, such as cyanobacteria, sulfur-oxidizing bacteria, etc [@VanGemerden-1993]. Studies of biogeochemistry and microbial ecology in these systems use various sensors to profile micro-environments and infer the local budgets and productivities of the microbial groups and metabolisms [@Revsbech-1983]. Microbenthic habitats are typically modeled as diffusive-reactive systems [@deWit-1995], i.e. the dominant mass transport mode is physical diffusion of solutes within the porespaces of the sediment matrix. The “reactive” aspect refers to the presence of a large number of local sources and sinks within the mat system. 
+Microbial benthic habitats, such as microbial mats and sediments, exhibit extremely steep gradients in the physical, chemical and biotic parameters within the space of a few millimeters. These micro-environments drive the localization and exploitation of physico-chemical niches by a variety of microbial groups, such as cyanobacteria, sulfur-oxidizing bacteria, etc [@VanGemerden-1993]. Studies of biogeochemistry and microbial ecology in these systems use various sensors to profile micro-environments and infer the local budgets and productivities of the microbial groups and metabolisms [@Revsbech-1983]. Microbenthic habitats are typically modeled as diffusive-reactive systems [@deWit-1995], i.e. the dominant mass transport mode is physical diffusion of solutes within the porespaces of the sediment matrix. The “reactive” aspect refers to the presence of a large number of local sources and sinks within the mat system.
 
-MicroBenthos is a modeling framework to study *in silico* microbenthic habitats. The main perspective is to recognize that while modeling physical diffusion is straightforward, the larger challenge is to have a flexible way to define, compose and study various microbial metabolisms under dynamic conditions. MicroBenthos enables this by providing a high-level abstraction to compose and simulate microbenthic systems in terms of solar irradiance, chemical solutes, microbial groups and chemical or metabolic processes. While the software is written in python, with a modular structure for ease of extensibility, it can be used without programming through a (YAML) structured text file as the interface. This allows the user to focus on  specifying the constitutive relations between environmental parameters and processes as a simple mathematical formula, which is then symbolically cast (using sympy [@Meurer-2017]) into a set of coupled partial differential equations for the full model. Using a simple command, the equations can be numerically solved (using fipy [@Guyer-2009]) to study the evolution of the various model variables.
+MicroBenthos is a modeling framework created to study *in silico* microbenthic habitats. The main
+perspective is to recognize that while modeling physical diffusion is straightforward, the larger challenge is to have a flexible way to define, compose and study various microbial metabolisms under dynamic conditions. MicroBenthos enables this by providing a high-level abstraction to compose and simulate microbenthic systems in terms of solar irradiance, chemical solutes, microbial groups and chemical or metabolic processes. While the software is written in python, with a modular structure for ease of extensibility, it can be used without programming through a (YAML) structured text file as the interface. This allows the user to focus on  specifying the constitutive relations between environmental parameters and processes as a simple mathematical formula, which is then symbolically cast (using sympy [@Meurer-2017]) into a set of coupled partial differential equations for the full model. Using a simple command, the equations can be numerically solved (using fipy [@Guyer-2009]) to study the evolution of the various model variables.
 
-MicroBenthos provides several useful features: 
+MicroBenthos provides several useful features:
 
-* Modular and extensible abstractions to create microbenthic systems	
+* Modular and extensible abstractions to create microbenthic systems
 * Non-programming interface to define processes and model structure
 * On-line visualization of running simulations and video export
 * Stateful simulations that can be interrupted and resumed
@@ -41,4 +42,4 @@ MicroBenthos provides several useful features:
 
 
 # References
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+