Sprecher
Beschreibung
We show a cloud-based platform for experimental data storage, management, and sharing. The platform has a UI that runs on a containerized Pythonic web application hosted by a container service. It is fronted by a lightweight authentication portal for a username and password. The experimental data is stored on a database service and object storage service. The container server, database, object storage, and authentication is managed by a cloud provider and therefore, requires minimal intervention and configuration, and can rapidly scale to accommodate high throughput and large volumes of data. The platform is accessible through a web browser where one can perform web-based data entry as well as data visualization and download. The lightweight web-app can be customized to include more functionality by scientists comfortable with Python. Data is available for further post-processing and machine learning through the cloud platform's high-speed internal internet backbone and its SDKs and APIs. We show an ad-hoc use-case where experimental data stored on the platform is post-processed using a hosted Jupyter Notebook and used for downstream machine learning. This platform is built using infrastructure-as-code for version control and extensibility.
