Eaglescope

Eaglescope is a configurable code-free interactive visualization and cohort definition and analysis tool designed for biomedical data exploration. It is designed to be hosted flexibly without the need for a dedicated server, and creates an interactive dashboard based upon a configuration file and either an API or data file. It uses visualizations of sets of features to describe and enable contextual filtering of the data. This allows for users to understand deeper patterns or anomalies within the data, and to create datasets specifically tuned to their requirements effortlessly. Eaglescope is typically utilized either as a tool to create refined datasets tailored for training and validating machine learning AI models, or as a central hub for further exploration, allowing users to seamlessly navigate to biomedical viewers such as DICOM or whole slide imaging (WSI) platforms.

Development

Reporting Issues: If you find a bug or have a feature request, please open an issue on Eaglescope's GitHub Issues page. Be sure to provide a clear description of the problem, steps to reproduce, and any relevant screenshots.

Pull Requests: If you’d like to propose additions, changes, or improvements, please submit a pull request against the main branch. Make sure to provide a clear description of your changes and any relevant context. Screenshots for visual changes are appreicated (before and after), as well as any tests performed.

Please see our code of conduct for the expectations for all open source community members.

Install dependencies by running npm install

Use develop mode by running npm run dev

Build ./dist for use with a static web server by running npm run build

To run code style checks, run npm run lint or to also try to automatically fix some issues, run npm run lint:fix.

To run unit and functional tests, which are also run automatically using github actions, run npm run test which runs the tests in this folder.

Setup and Configuration

The simplest way to set up eaglescope is to build (npm run build) then host the contents of ./dist/ using a static web server such as rapache or python -m http.server. Additionally, configuration and data resources are already on the web with appropriate cross origin headers, you can simply navigate to https://sharmalab.github.io/eaglescope/?configurl=(configuration json file).

Each Eaglescope dashboard needs a configuration url which contains global information for the dashboard, information about the data resource and how to read it, and configuration for each visualization.

Additionally, the field “VISUALIZATIONVIEWCONFIGURATION” contains a list of visualization config, where each element in the list represents one visualization. Most visualizations support the following fields:

Visualization Types

Chart Type	Use for	Photo	Fields
PIE_CHART	Proportions for distinct values of x.		x
BAR_CHART	Total count of distinct values of x.		x
HISTOGRAM	Binned continuous values of x, with binsCount bins.		x
SCATTER_CHART	The relationship between two generally continuous variables.		x and y for the spatial axes values
PARALLEL_COORDINATES	Data points as lines going through a set of variables.		y: an array of each field to render in order.
KM_CURVE	Shows heatmap-like density in a 2D space of x, y.		Event, time, and group as objects including field and title. Also, eventValue, and censoredValue.
DENSITY_2D	Shows observation density in a 2D space of x, y.		x and y for the spatial axes values
HEATMAP	Uses color to show mean value of z for discrete-grouped observations by x and y.		x and y for spatial arrangement of values, z for colors
HORIZONTAL_BAR_CHART	A horizontal bar chart.		x and y for the spatial axes values
VIS_DATA_TABLE	A tabular representation of data for columns supplied.		(list of objects representing fields to show in table)

Interactive Demo Site

Eaglescope Demo