raw2ready

Table of Contents

1. Introduction
2. Deployment
3. Containerized Deployment (Docker)
4. Troubleshooting
5. Support

1. Introduction

raw2ready is a locally deployed, open-source web app for the harmonization, processing, visualization, semantic annotation, and intelligent exploration of bioprocess data.

The framework automates the transformation of heterogeneous raw data files originating from bioprocess-related devices (e.g., bioreactors, gas analyzers) into standardized, machine-readable datasets through a modular and extensible architecture. It provides an interactive web-based graphical user interface that enables users to perform data curation, preprocessing, visualization, and AI-assisted exploration directly through the browser.

The framework supports:

• Raw data parsing and harmonization
• Time-series synchronization and dataset merging
• Dynamic calculation of user-defined variables
• Interactive visualization
• Metadata annotation using MIFE
• Agentic AI assisted exploration of data, experimental conditions, microbial information (BacDive) and literature (CrossRef, DuckDuckGo).

Currently Supported Data Formats

• Sartorius formatted MS Excel files
• BioLectorXT formatted MS Excel files
• Gas analyzer formatted text files
• Generic text (csv, tsv etc) or MS Excel files with tabular data

2. Deployment

The framework has been tested on Linux environments (Ubuntu, Mint) and Windows via WSL.

Manual Deployment (Conda)

You can deploy raw2ready manually using Conda (requires installation of miniconda - see installation). This creates an isolated virtual environment and installs all dependencies specified in the conda.yml configuration.

1. Clone the Repository

Download and extract the repository manually, or clone it using Git.

git clone https://github.com/GeorgakilasLab/raw2ready.git
cd raw2ready

2. Create the Conda Environment

Run the following command in the root of the project to create the raw2ready Conda environment:

conda env create -f conda.yml

3. Activate the Environment

Once the environment has been successfully created, activate it using:

conda activate raw2ready

4. Launch the Web Application

To start the raw2ready web interface, run the entry script:

python run_app.py

Then, open your browser and navigate to:

http://localhost:8081

5. Optional: Install Ollama

raw2ready supports Agentic AI assisted exploration of data, experimental conditions, microbial information and literature using local Large Language Models via Ollama.

To install Ollama, run the following command from a directory in which you want to download it:

cd ~
mkdir ollama
cd ollama
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3

3. Containerized Deployment (Docker)

Coming soon...

4. Troubleshooting

Common Issues

The files of my equipment are not recognized

Verify that the equipment is currently supported.

Contact us for support in adding support for your equipment.

Ollama model unavailable

Verify Ollama installation:

ollama list

If the required models are not available, download them:

ollama pull [MODEL_NAME] # e.g. llama3

5. Support

Getting Help

GitHub Issues:

https://github.com/GeorgakilasLab/raw2ready/-/issues

Email Support:

ggeorgakilas@athenarc.gr antoniades000michael@gmail.com

Citing raw2ready

If you use raw2ready in your research, please cite:

Coming soon...