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Tuesday, June 28 • 2:12pm - 2:30pm
RosettaHUB-Sheets, a programmable, collaborative web-based spreadsheet for R, Python and Spark

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RosettaHUB-Sheets combine the flexibility of the bi-dimensional data representation model of classic spreadsheets with the power of R, Python, Spark and SQL. RosettaHUB-Sheets are web based, they can be created programmatically on any cloud. They enable Google-docs like real-time collaboration while preserving the user's data privacy. They have no limitation of size and can leverage the cloud for performance and scalability. RosettaHUB-Sheets act as highly flexible bi-dimensional notebook as they make it possible to create powerful mash-ups of multi-language scripts and results. RosettaHUB-Sheets are combined with an interactive widgets framework with the ability to overlay advanced interactive widgets and visualizations including 3D Paraview ones. RosettaHUB-Sheets are fully programmable in R, Python and JavaScript, macros similar to Excel VBA's can be triggered by various cells and variables states changes events. RosettaHUB-sheets can be shared to allow real-time collaboration, interactive teaching, etc. RosettaHUB-Sheets' are represented by an SQL database and can be queried and updated in pure SQL. The RosettaHUB Excel add-in makes it possible to synchronize a local Excel sheet with a RosettaHUB-Sheet on the cloud: Excel becomes capable of accessing any R or Python function as a formula and can interact seamlessly with powerful cloud-based capabilities, likewise, any Excel VBA function or data can be seamlessly exposed and shared to the web. RosettaHUB-sheet are the first bi-dimensional data science notebooks they give access to the most popular data-science tools and aim to contribute to the democratization and pervasiveness of data science.

avatar for Ioannis  Kosmidis

Ioannis Kosmidis

Associate Professor, Department of Statistical Science, University College London
I am a Senior Lecturer at the Department of Statistical Science in University College London. My theoretical and methodological research focuses on optimal estimation and inference from complex statistical models, penalized likelihood methods and clustering. A particular focus of my work is the development of efficient, in terms of computational complexity and implementation, algorithms for applying the methods I develop to prominent... Read More →


Latifa Bouabdillah