Fixed typo

Author: DamienIrving

paper.md

@@ -19,7 +19,7 @@ bibliography: paper.bib
 
 Python is rapidly emerging as the programming language of choice for data analysis in the atmosphere and ocean sciences. By consulting online tutorials and help pages, most researchers in this community are able to pick up the basic syntax and programming constructs (e.g. loops, lists and conditionals). This self-taught knowledge is sufficient to get work done, but it often involves spending hours to do things that should take minutes, reinventing a lot of wheels, and a nagging uncertainty at the end of it all regarding the reliability and reproducibility of the results. To help address these issues, the [Python for Atmosphere and Ocean Scientists](https://carpentrieslab.github.io/python-aos-lesson/) educational materials cover a suite of programming and data management best practices that are not so easy to glean from a quick Google search.
 
-The materials contain everything required to run a one-day workshop. This includes data files, software installation instructions and lesson plans (complete with exercises and solutions), which double as teaching notes for instructors and a reference for learners to refer back to. For those unable to attend a workshop, it is also possible work through the lessons independently. The skills covered in the lessons are presented in the context of a typical data analysis task: creating a command line program that plots the average rainfall for any given month, so that the output from two different global climate models can be compared visually. After giving an overview of the PyAOS stack (i.e. the ecosystem of libraries used in the atmosphere and ocean sciences) and the management of software environments using conda, the lessons introduce the basic Python commands required to create the plot. Those commands are then refactored to be more modular/reusable (using functions) before being transferred to a stand-alone script that can be executed from the command line. Changes to that script are then tracked using version control as further edits are made to implement common defensive programming strategies and to record the provenance of the input data files and output figures. Along the way, the basics of the Network Common Data Form (netCDF) file format and associated “climate and forecasting” metadata convention are introduced. The raster (or “gridded”) output from weather, climate and/or ocean models is almost universally archived using this format.
+The materials contain everything required to run a one-day workshop. This includes data files, software installation instructions and lesson plans (complete with exercises and solutions), which double as teaching notes for instructors and a reference for learners to refer back to. For those unable to attend a workshop, it is also possible to work through the lessons independently. The skills covered in the lessons are presented in the context of a typical data analysis task: creating a command line program that plots the average rainfall for any given month, so that the output from two different global climate models can be compared visually. After giving an overview of the PyAOS stack (i.e. the ecosystem of libraries used in the atmosphere and ocean sciences) and the management of software environments using conda, the lessons introduce the basic Python commands required to create the plot. Those commands are then refactored to be more modular/reusable (using functions) before being transferred to a stand-alone script that can be executed from the command line. Changes to that script are then tracked using version control as further edits are made to implement common defensive programming strategies and to record the provenance of the input data files and output figures. Along the way, the basics of the Network Common Data Form (netCDF) file format and associated “climate and forecasting” metadata convention are introduced. The raster (or “gridded”) output from weather, climate and/or ocean models is almost universally archived using this format.
 
 ## Statement of need