The Data INCITE Lab

Data Informatics Challenges in Technology Education

The Rensselaer Data Informatics Challenges in Technology Education (INCITE) Pipeline recruits and prepares students at Rensselaer and worldwide to be data scientists using early data analytics courses and experiential research projects centered on real-world problems.

Rensselaer's Data INCITE pipeline for undergraduate data science education consists of an early data analytics course followed by applied data science research experiences on real-world problems. Data INCITE results in data science skills and prompts students to pursue further coursework and careers in data science.

From 2018-2023 the Data INCITE lab focused on health informatics, during which time it became the Rensselaer Health INCITE Pipeline:

Produced students skilled in health informatics.
Created novel, low-barrier pathways into health informatics for students from a wide array of majors, including pre-med, biology, biomedical engineering, computer science, and mathematics.
Enabled health informatics education at many institutions by creating shared health informatics instructional project resources, including publicly-available, web-based, open source data analytics applications.
Recruited students to pursue health informatics careers.

The Rensselaer Health INCITE Pipeline was generously funded by the United Health Foundation

Publications about Data INCITE:

Bennett, Kristin P.; Erickson, John S.; Svirsky, Amy; and Seddon, Josephine C. (2022) "A Mathematics Pipeline to Student Success in Data Analytics through Course-Based Undergraduate Research," The Mathematics Enthusiast: Vol. 19 : No. 3 , Article 5. DOI: https://doi.org/10.54870/1551-3440.1573

Check out the Project Gallery (BELOW) to see research products (interactive apps, papers, and presentations) created by students in the Data INCITE Lab.

Project Resources

Summary Video: Tackling COVID-19 Using Data Analytics (NOV 2020)
Highlights from 112 Undergraduate Research Experiences in the Rensselaer Data INCITE Lab During COVID-19 Pandemic (Kristin P. Bennett)

Project Gallery

Mars Mission Minder

Integrated, interactive data analysis of the Mars 2020 mission

Mars Mission Minder is a data visualization and analysis application designed to support NASA's Mars missions, currently focused on the Mars 2020 Perseverance Rover. The app enables users to analyze three key datasets: PIXL (Planetary Instrument for X-ray Lithochemistry), LIBS (Laser Induced Breakdown Spectroscopy), and SHERLOC (Scanning Habitable Environments with Raman & Luminescence for Organics and Chemicals). These datasets provide insights into elemental composition, mineral abundance, and potential biosignatures on Mars. Analytical tools such as heatmaps, principal component analyses, and ternary diagrams help visualize the data, while Mars Explorer, an interactive map, tracks the rover's path and data collection sites.

Risky Business? Deep Dives into DeFi

Studying the emerging blockchain financial ecosystem

We seek to investigate current patterns of usage in DeFi lending protocols, and quantify risk and user behaviors across various protocols. Our strategy is to exploit powerful AI models and technology developed for transaction data such as those arising in health and commerce. For example, we can utilize temporal clustering to characterize different types of users and then use these in a dashboard to understand how usage of lending protocols changes over time.

COVID Back-to-School

A tool for generating actionable information on how to reopen schools, universities, and workplaces

To control spread of COVID, we must implement social distancing measures. Rather than arbitrarily implementing measures against COVID spread, we have built a tool that gives you a quantitative approach to controlling spread. COVID Back-to-School is a tool for generating actionable information on how to reopen schools (elementary, secondary, boarding), universities, workplaces, etc. For different settings of the social distancing "knobs,: you can find out how the infection will spread in your school/university/organization. You can tune the knobs until the spread is a tolerable level for you. The settings for these knobs will then tell you what social distancing protocols you need in place to accomplish that level of tolerable spread.

COVID Twitter NLP

Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse

In this exploratory study, we scrutinize a database of over one million tweets collected from March to July 2020 to illustrate public attitudes towards mask usage during the COVID-19 pandemic. We employ natural language processing, clustering and sentiment analysis techniques to organize tweets relating to mask-wearing into high-level themes, then relay narratives for each theme using automatic text summarization. In recent months, a body of literature has highlighted the robustness of trends in online activity as proxies for the sociological impact of COVID-19.

COVID WarRoom

Aiding the development of institutional re-opening strategies based on location and selected Social Distancing models

COVID WarRoom has been designed to aid in the development of re-opening strategies as we begin the re-opening process. Presently, COVID WarRoom allows the user to select a location for analysis, and then define the parameters by using one of our four predefined Social Distancing models: Linear Auto-SD, Linear Default-SD, Quadratic Auto-SD, and Quadratic Default-SD.

COVIDMINDER

Revealing the regional disparities in outcomes, determinants, and mediations of the COVID-19 pandemic

COVIDMINDER reveals the regional disparities in outcomes, determinants, and mediations of the COVID-19 pandemic. Outcomes are the direct effects of COVID-19. Social and Economic Determinants are pre-existing risk factors that impact COVID-19 outcomes. Mediations are resources and programs used to combat the pandemic. COVIDMINDER analysis and visualizations are by students and staff of The Rensselaer Institute for Data Exploration and Applications at Rensselaer Polytechnic Institute with generous support from the United Health Foundation. COVIDMINDER is an open source project implemented on the R Shiny platform.

Eat4Genes: A Bioinformatic Rational Gene Targeting App to Address Pathologies using Healthy Diet

Eat4Genes is a prototype diet recommendation web app for patients, healthcare providers, and researchers that aids in the selection of a healthy diet to help treat and prevent numerous health conditions. Our approach is focused on the strategic use of diet to regulate key risk gene expression, which we call dietary rational gene targeting (DRGT).

Health INCITE

Health Informatics Challenges in Technology Education

The Rensselaer Health Informatics Challenges in Technology Education (INCITE) Pipeline recruits and prepares students at Rensselaer and worldwide to be data scientists in healthcare using early data analytics courses and experiential research projects centered on real-world health challenges. With the advent of electronic healthcare records (EHR) and precision medicine, healthcare increasingly relies on health informatics (HI), the philosophy and tools of data science (DS) and their application in healthcare. Rensselaer Health INCITE is a innovative, replicable program that directly expands the health informatics workforce pipeline at the early undergraduate level for students at RPI and worldwide.

MORTALITYMINDER

Enabling healthcare researchers, providers, payers, and policy makers to gain actionable insights into how, where, and why midlife mortality rates are rising in the United States

MortalityMinder (MM) is a web-based visualization tool that enables interactive exploration of social, economic and geographic factors associated with premature mortality among mid-life adults ages 25-64 across the United States. Using authoritative data from the CDC and other sources, MM is a freely available, publicly-accessible, open source, and easily maintained tool. The goal of MortalityMinder (MM) is to enable healthcare researchers, providers, payers, and policy makers to gain actionable insights into how, where, and why midlife mortality rates are rising in the United States (US). It is designed to help healthcare payers, providers and policymakers at the national, state, county and community levels identify and address unmet healthcare needs, healthcare costs, and healthcare utilization.

RPI SafeCampus

WiFi access point usage on the RPI campus

RPI SafeCampus reveals WiFi access point usage and aggregations of wireless users on the campus network at Rensselaer Polytechnic Institute.

RPI StudySafe

Find a safe place to study!

RPI StudySafe reveals the anonymous usage of Wi-Fi access points and aggregations of wireless users on the campus network at Rensselaer Polytechnic Institute

Temporal Analysis of Social Determinants Associated with COVID-19 Mortality

This study examines how social determinants associated with COVID-19 mortality change over time. Using US county-level data from July 5 and December 28, 2020, the effect of 19 high-risk factors on COVID-19 mortality rate was quantified at each time point with negative binomial mixed models. Then, these high-risk factors were used as controls in two association studies between 40 social determinants and COVID-19 mortality rates using data from the same time points. The results indicate that counties with certain ethnic minorities and age groups, immigrants, prevalence of diseases like pediatric asthma and diabetes and cardiovascular disease, socioeconomic inequalities, and higher social association are associated with increased COVID-19 mortality rates.

Twitter Time Machine

A Multidimensional Immersive Exploration Environment for the #BlackLivesMatter TwitterSphere

Twitter Time Machine (TTM) is an immersive environment for discursive analysis of social media crisis response through multidimensional, interactive analysis.

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