Renamed to and corrected a few typos

README.md

@@ -8,20 +8,23 @@ To use this template for your research, fork this repository, change the name
 to something descriptive for your project, and adjust the licensing as you
 see fit.
 
-To use this repository for your own research, simply clone the repo using the following:
-
-```
-git clone https://github.com/gchure/reproducible_research your_repo_title
-```
+To use this repository for your own research, simply click "Use this template" 
+at the top of this page
 
 > :warning: :warning: :warning: I wouldn't advise forking this repository. As you can only fork a given repository once, there is little utility in forking this repo if you hope to use it again in your future projects :warning: :warning: :warning:
 
+Alternatively, you can clone the repository and restructure as necessary. Via 
+the command line,
+
+```
+> git clone git@github.com:gchure/reproducible_research
+```
 ## Layout
 
 The repository is split into seven main directories, many of which have subdirectories. This structure has been designed to be easily navigable by humans and computers alike, allowing for rapid location of specific files and instructions. Within each directory is a `README.md` file which summarizes the purpose of that directory as well as some examples where necessary. This structure may not be perfect for your intended us and may need to be modified. Each section is briefly described below. 
 
-### **`code`** 
-Where all of the *executed* code lives. This includes pipelines, scripts, and figure files. 
+### **`experiments`** 
+Where all of the experimental information lives, including any *executed* code. lives. This includes pipelines, scripts, and figure files. 
  * **`processing`**: Any code used to *transform* the data into another type should live here. This can include everything from parsing of text data, image segmentation/filtering, or simulations.
  * **`analysis`**: Any code to to *draw conclusions* from an experiment or data set. This may include regression, dimensionality reduction, or calculation of various quantities.
  * **`exploratory`**: A sandbox where you keep a record of your different approaches to transformation, interpretation, cleaning, or generation of data.
@@ -52,7 +55,6 @@ There are some files which I consider to be mandatory for any project.
 
 2. **`README.md`**: A descriptive yet succinct description of your research project and information regarding the structure outlined below.
 
-
 # License Information
 
 <p xmlns:dct="http://purl.org/dc/terms/" xmlns:vcard="http://www.w3.org/2001/vcard-rdf/3.0#">

data/README.md

@@ -1,7 +1,8 @@
 ## `data`
-
-This directory houses all small (< 50 MB) data sets that are a result of individual experiments and/or simulations. Depending on the type of data collected, you may want to split them up based on file type.
+This directory houses all small (< 100 MB) data sets that are a result of individual experiments and/or simulations. Depending on the type of data collected, you may want to split them up based on file type. Data larger than 
+100 MB can be stored using [Git LFS](https://git-lfs.github.com/), though I personally try to avoid saving large files under version control and have them 
+redundantly backed up elsewhere.
 
 If possible, data sets from individual experiments should be compiled in a long-form tidy format. This is important not only for your analysis, but for others who wish to reproduce your work. While you may have an intimate knowledge of your data and experimental structure, it may not be obvious to anyone else. It is much easier if you can combine the individual data sets into as few files as possible so only one or two files have to be read to perform the analysis and generate the figures. 
 
-This is **not** a place to store all of your large (> 50 MB) data files, such as images. For accessibility of these large data sets, there are myriad online data repositories such as [Zenodo](https://zenodo.org) which provide free storage and DOI generation. In addition, you should have all of your data backed up locally with redundancy.
+This is **not** a place to store all of your large (> 1000 MB) data files, such as images. For accessibility of these large data sets, there are myriad online data repositories such as [Zenodo](https://zenodo.org) which provide free storage and DOI generation. In addition, you should have all of your data backed up locally with redundancy.

code/README.md → experiments/README.md

@@ -1,4 +1,7 @@
-# `code`
+# `experiments`
+
+This subdirectory serves houses all research associated with the project, including code used in experimental processing, analysis, exploration, and 
+visualization.
 
 It's important to keep the code that is executed separated from that which is called. The distinct difference here is that executed code should change from day-to-day or analysis to analysis. You're not necessarily making all measurements at the same time, or at the exact same concentration, or even on the same materials. As your experiments change, your code should also change to make sure you a transforming or interpreting the data correctly. 
 

code/processing/YEAR-MONTH-DATE_experiment_description/README.md → experiments/processing/YEAR-MONTH-DATE_experiment_description/README.md

@@ -24,6 +24,7 @@ Set up a short table that includes vital information about the materials used. F
 Briefly summarize the results of the experiment and report data validation outcomes here (if possible). For example,  in order to verify that an experiment didn't have any pathological results you could generate a specific plot. You can then include that plot in this section along with some comments. For example,
 
 ![](output/validation_plot.png)
+
 Highlighted distribution shows oversampling relative to other samples.
 
 

miscellaneous/software_information/README.md

@@ -16,6 +16,6 @@ Just as knowing what chemicals, instruments, or samples were used, knowing the c
 
 ## Required Software
 
-* Python:  v3.7
-* `numpy`: 1.11.2
-* `scipy`: 0.18.1
+* Python:  v3.9
+* `numpy`: 1.23.0
+* `scipy`: 1.9.0