Clowder is a research data management system designed to support any data format and multiple research domains. When new data is added to the system, whether it is via the web front-end or through its Web service API, a cluster of extraction services process the data to extract interesting metadata and create web based data visualizations.
Support for both user-defined and machine-defined metadata. System accepts metadata in a flexible representation based on JSON-LD. Users can add metadata entries directly from the UI. Extractors and external clients can attach metadata to files and datasets using the Web service API.
Extend the system by creating new extractors to analyze data. Using the publish-subscribe model and the RabbitMQ broker, when certain events occur in Clowder, such as the uploading of a new file, a message is published notifying any listening metadata extractors that a new file is available. Each extractor can then use the public Web Service API to analize the data and write back to Clowder any relevant information.
A short list of publications related to Clowder:
Please cite the following in any published research that uses Clowder:
Luigi Marini, Indira Gutierrez-Polo, Rob Kooper, Sandeep Puthanveetil Satheesan, Maxwell Burnette, Jong Lee, Todd Nicholson, Yan Zhao, and Kenton McHenry. 2018. Clowder: Open Source Data Management for Long Tail Data. In Proceedings of the Practice and Experience on Advanced Research Computing (PEARC '18). ACM, New York, NY, USA, Article 40, 8 pages. DOI: https://doi.org/10.1145/3219104.3219159