Update readme with getting started section

README.md

@@ -1,11 +1,6 @@
 # DIMA: Data Integration and Metadata Annotation
 
-DIMA (Data Integration and Metadata Annotation) is a Python package designed for the **Laboratory of Atmospheric Chemistry** to support the integration of multi-instrument data in HDF5 format. It is tailored for data from diverse experimental campaigns, including:
+DIMA (Data Integration and Metadata Annotation) is a Python package designed for the **Laboratory of Atmospheric Chemistry** to support the integration of multi-instrument data in HDF5 format. It is tailored for data from diverse experimental campaigns, including **beamtimes**, **kinetic flowtube studies**, **smog chamber experiments**, and **field campaigns**.
-
-- **beamtimes**, 
-- **kinetic flowtube studies**, 
-- **smog chamber experiments**, and
-- **field campaigns**.
 
 ## Key Features
 
@@ -21,44 +16,63 @@ DIMA provides reusable operations for data integration, manipulation, and extrac
 4. **Jupyter notebooks**
   Demonstrates DIMA’s core functionalities, such as data integration, HDF5 file creation, visualization, and metadata annotation. Key notebooks include examples for data sharing, OpenBis ETL, and workflow demos. 
 
-## Adaptability to Experimental Campaign Needs 
-
-The `instruments/` module is designed to be highly adaptable, accommodating new instrument types or file reading capabilities with minimal code refactoring. The module is complemented by instrument-specific dictionaries of terms in YAML format, which facilitate automated annotation of observed variables with:
-  - `standard_name`
-  - `units`
-  - `description`  
-
-  as suggested by [CF metadata conventions](http://cfconventions.org/).
-### Versioning and Community Collaboration
-   The instrument-specific dictionaries in YAML format provide a human readable interface for community-based development of instrument vocabularies. These descriptions can potentially be enhanced with semantic annotations for interoperability across research domains.  
-
 ## Repository Structure
 
 
+
+## Requirements
+
+For **Windows** users, the following are required:
+
+1. **Git Bash**: Install [Git Bash](https://git-scm.com/downloads) to run shell scripts (`.sh` files).
+
+2. **Conda**: Install [Anaconda](https://www.anaconda.com/products/individual) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html).  
+
+3. **PSI Network Access**: Ensure access to PSI’s network and access rights to source drives for retrieving campaign data from YAML files in the `input_files/` folder.  
+
+:bulb: **Tip**: Editing your system’s PATH variable ensures both Conda and Git are available in the terminal environment used by Git Bash.
+
+
+## Getting Started
+
+### Download DIMA
+
+Open a **Git Bash** terminal.
+
+Navigate to your `GitLab` folder, clone the repository, and navigate to the `dima` folder as follows:
+
+   ```bash
+   cd path/to/GitLab
+   git clone --recurse-submodules https://gitlab.psi.ch/5505/dima.git
+   cd dima
+   ```
+
+### Install Python Interpreter
+
+Open **Git Bash** terminal. 
+
+**Option 1**: Install a suitable conda environment `pyenv5505` inside the repository `dima` as follows:
+
+   ```bash
+   cd path/to/GitLab/dima
+   Bash setup_env.sh
+   ```
+
+Open **Anaconda Prompt** or a terminal with access to conda.
+
+**Option 2**: Install conda enviroment from YAML file as follows:
+   ```bash
+   cd path/to/GitLab/dima
+   conda env create --file environment.yml
+   ```
+
 ## Software arquitecture
 
 <p align="center">
   <img src="docs/software_arquitecture_diagram.svg" alt="Alt Text">
 </p>
 
-## Installation
+## Working with Jupyter Notebook on the `multiphase_chemistry_env`
-
-Follow these steps to install and set up the project:
-
-1. Download our GitLab repository in your GitLab folder, or alternatively open a Git Bash terminal and run the following commands: 
-  ```
-  cd Path/to/GitLab
-  git clone https://gitlab.psi.ch/5505/data-integration-and-metadata-annotation.git
-  ```
-
-2. Open an Anaconda Prompt (Anaconda3) as administrator, and set the current directory to the path of the project's folder.
-
-3. Create the project's environment `multiphase_chemistry_env` by running the following command:
-  ```
-  conda env create -f environment.yml
-  ```
-
-### Working with Jupyter Notebook on the `multiphase_chemistry_env`
 
 1. Open an Anaconda Prompt as a regular user, ensure that `multiphase_chemistry_env` is in the list of available enviroments and activate it by running the following commands:
   ```
@@ -99,7 +113,16 @@ and select the `multiphase_chemistry_env` environment from the kernel options.
 | processing_filename     | - | Denotes the name of the file used to process an initial version (e.g, original version) of the dataset into a processed dataset. | 
 | processing_date         | -                | The date when the data processing was  completed.                                                                                                                    |                                 |
 
+## Adaptability to Experimental Campaign Needs 
 
+The `instruments/` module is designed to be highly adaptable, accommodating new instrument types or file reading capabilities with minimal code refactoring. The module is complemented by instrument-specific dictionaries of terms in YAML format, which facilitate automated annotation of observed variables with:
+  - `standard_name`
+  - `units`
+  - `description`  
+
+  as suggested by [CF metadata conventions](http://cfconventions.org/).
+### Versioning and Community Collaboration
+   The instrument-specific dictionaries in YAML format provide a human readable interface for community-based development of instrument vocabularies. These descriptions can potentially be enhanced with semantic annotations for interoperability across research domains.
     
 ### Specifying a compound attribute in yaml language.
 Consider the compound attribute *relative_humidity*, which has subattributes *value*, *units*, *range*, and *definition*. The yaml description of
@@ -126,7 +149,7 @@ relative_humidity:
 
 ```
 
-# How to Extend DIMA’s File Reading Capabilities for New Instruments
+# Extend DIMA’s file reading capabilities for new instruments
 
 We now explain how to extend DIMA's file-reading capabilities by adding support for a new instrument. The process involves adding instrument-specific files and registering the new instrument's file reader.
 
@@ -158,7 +181,6 @@ file_extensions.append('.json')
 file_readers.update({'ACSM_TOFWARE_flags_json' : lambda x: read_jsonflag_as_dict(x)})
 ```
 
-## -------------------
 ## Getting started
 
 To make it easy for you to get started with GitLab, here's a list of recommended next steps.