This is the dashboard for the international flow battery reproducibility study. It shows electrochemical data collected by participating institutions and allows you to explore and compare results interactively.
No installation needed — just open the link below in any web browser:
All experimental data from this study is stored in a database called Google BigQuery and is freely available to anyone.
The study involves hundreds of thousands of rows of electrochemical measurements from dozens of institutions. A database is the most efficient and reliable way to store, query, and share this volume of data — far better than sharing individual Excel files. It also means the data is always up to date as new participants submit results.
BigQuery is Google's cloud database service. Think of it like a very powerful spreadsheet in the cloud that anyone can query using a language called SQL. You don't need to download anything — you just ask it questions and it returns the data you need.
The dataset is public — you just need a free Google account to access it.
- Project:
flow-battery-data-ingestion - Dataset:
electrochem - Tables:
charge-discharge-data— raw time-series cycling datacharge-discharge-metrics— per-cycle efficiency and capacity metricspolarisation-metrics— polarisation pulse dataeis-data— electrochemical impedance spectraparticipant-metadata— experimental protocol details submitted by participants
To browse the data in BigQuery:
- Go to console.cloud.google.com/bigquery
- Sign in with a Google account
- In the Explorer panel on the left, search for
flow-battery-data-ingestion - Browse the tables and run queries
Example query — get average coulombic efficiency by participant:
SELECT ParticipantID, Phase, AVG(CoulombicEfficiency_pct) as avg_CE
FROM `flow-battery-data-ingestion.electrochem.charge-discharge-metrics`
GROUP BY ParticipantID, Phase
ORDER BY ParticipantIDNo problem. You can export any table as a CSV file directly from BigQuery by running a query and clicking Save Results → CSV. You can then open it in Excel, Python, R, or any other tool you prefer.
Alternatively, you can use the BigQuery API to access the data programmatically from any language.
You can download and run this dashboard locally on your own computer — useful if you want to explore the data offline, modify the charts, or adapt it for your own research.
The dashboard is built using Marimo, an open-source Python tool for building interactive data dashboards. It runs in your web browser but is powered by Python running on your computer. You don't need to know Python to run it — just follow the steps below.
A terminal (also called a command prompt or shell) is a text-based window where you type instructions for your computer. It sounds intimidating but you only need to type a few simple commands — we'll walk you through each one.
uv is a fast Python package manager that will handle dependencies for this project.
On Windows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"On Mac:
curl -LsSf https://astral.sh/uv/install.sh | shOn Linux:
curl -LsSf https://astral.sh/uv/install.sh | shThis lets your computer connect to the BigQuery database.
- Go to cloud.google.com/sdk/docs/install
- Download the installer for your operating system (Windows, Mac, or Linux)
- Run the installer and follow the prompts
- Click the green Code button at the top of this GitHub page
- Select Download ZIP
- Extract the ZIP file to a folder on your computer (e.g. your Desktop)
On Windows:
- Press the
Windowskey, typePowerShell, and press Enter
On Mac:
- Press
Cmd + Space, typeTerminal, and press Enter
In the terminal, type cd followed by the path to the folder where you extracted the files. For example:
Windows:
cd C:\Users\YourName\Desktop\flow-battery-dashboard
Mac:
cd ~/Desktop/flow-battery-dashboard
Type this and press Enter:
uv sync
This downloads and installs everything the dashboard needs. It may take a few minutes — you'll see a lot of text scrolling by, that's normal.
Type this and press Enter:
gcloud auth application-default login
A browser window will open — sign in with your Google account. This gives the dashboard permission to read the public dataset. You only need to do this once.
Type this and press Enter:
uv run marimo run dashboard.py
Then open your web browser and go to:
http://localhost:2718
The dashboard should appear. It connects to the same live data as the online version.
If you want to edit the charts or add new features, run this instead of Step 8:
uv run marimo edit dashboard.py
This opens an editable version in your browser where you can modify any part of the dashboard interactively.
If you want to host your own version of this dashboard online, you can deploy it to Google Cloud Run using Docker. This requires a Google Cloud account. See the Google Cloud Run documentation for guidance, or open a GitHub issue and we can help.
If you have any questions about the data, the dashboard, or the study, please open a GitHub issue or contact the study coordinators.