Features and workflows
Researchers gain access to a JupyterLab instance and can analyze available data in Python or R, including custom Python libraries. Pre-installed libraries allow researchers to perform common statistics such as data processing, data analysis, machine learning, and data visualization.
Qualified academics are given their own private research environment with free compute and, in some instances, the ability to upload their own data.
Researcher Platform is a controlled environment. We include specific rules around downloading or exporting data out of the environment, copying, and reverse engineering the data in order to optimize for transparency while maintaining privacy.
See the following sections for features and workflows supported by Researcher Platform.
GPU server option
While CPUs handle a wide variety of tasks quickly, GPUs are better for applications that require parallel processing of large amounts of data, including mathematical computation. For your Jupyter notebook server, you can choose the type of server that is best for your work.
Asynchronous queries
The asynchronous queries feature decouples query submission from the fetching of query results, enabling you to run queries in the background while you continue working in your Jupyter notebooks. This feature is for use with static datasets.
Export Jupyter notebooks
You can export your Jupyter notebooks in a way that keeps the code intact, but scrubs the outputs in conformance with privacy rules.
Install Python packages yourself
You can install Python packages available in the Python Package Index (PyPi) into your Jupyter environment using the Pip package manager class.
Install R packages yourself
You can install R packages into your Jupyter environment using our Conda and CRAN package manager classes.
Download machine learning models
You can download approved machine learning (ML) models from the Hugging Face Hub for translation, semantic analysis and more.
Upload files to an S3 bucket
You can upload your own files to your Amazon Simple Storage Service (Amazon S3) bucket using Researcher Platform.
Share R notebooks
Two collaborating researchers with access to the same environment can share code in R (not currently in Python).
Compare Researcher Platform to RStudio
View side by side comparisons of various analyses and outputs in RStudio and Researcher Platform to illustrate the similarities and differences.