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Harnessing Data Science for Health Discovery and Innovation in Africa: NIH joins Africa Movement Thinking initiative

We are experiencing an explosion of data relevant to human health, from both biological and non-health sources, that currently exceeds our ability to capture and interpret. This gap applies to every field of biomedical and behavioral research. If we don’t invest in the data itself, applying known and developing new Data Science (DS) approaches, we are wasting an incredible opportunity to improve Human Health. This set of initiatives addresses that gap for Global Health research and Public Health application in low resource settings.

In the next decade, rapid advances in DS, including new approaches to the description, collection, storage, integration, and analysis of large, heterogeneous, structured and unstructured data sets, and new computational methods such as advanced deep learning, digital phenotypes, machine/artificial intelligence, and 3D imaging are expected to transform biomedical and behavioral research and lead to improved health for individuals and populations. Traditional datasets (e.g. national health systems, surveillance, surveys) are becoming deeper and richer while new sources of data based on new technologies and sensors (e.g. social media, geospatial data, mobile phones, wearables, electronic medical records, bioimaging, and genomics) are emerging that may be of greatest value when linked to DS. Progress in development of huge new data sets and advanced methods for mining them underpin advances in diagnostics, technology development, and the potential for precision public health. This initiative examines whether advances in DS developed and/or applied in the African context can be used to spur discoveries and innovations that ultimately promote significant improvements in health for African individuals, communities, and populations. We define DS as “the interdisciplinary field of inquiry in which quantitative and analytical approaches, processes, and systems are developed and used to extract knowledge and insights from increasingly large and/or complex sets of data” (NIH Strategic Plan for Data Science).

While advances in the United States and other high-income countries (HICs) are being actively explored and increasingly supported in both the public and private sectors, applications that are relevant, affordable, acceptable, and scalable in low- and middle-income countries (LMICs) are largely undeveloped. Moreover, in most cases applications and tools cannot simply be adapted from HICs for use in LMICS as they are generated from data collections biased towards European ancestry populations and may not apply to other genetic backgrounds (this is true, for example, for GWAS studies, clinical imaging data used as a basis for machine learning, and major disease biomarkers). However, we can leverage the momentum and knowledge generated in this field in HICs to stimulate new investment, knowledge generation and innovations for LMIC populations.

What is InventXR is solving for?

While advances in the United States and other high-income countries (HICs) are being actively explored and increasingly supported in both the public and private sectors, applications that are: relevant, affordable, acceptable, and scalable in low- and middle-income countries (LMICs) are largely undeveloped. 

  • We focus this initiative in Africa for several reasons: 
    • First, despite recent progress, Africa carries a disproportionate share of the global burden of disease. DS has the potential to significantly impact both quantitative and qualitative research and health on the continent. 
    • Second, the African population is growing faster than other world regions and some African leaders are eager to transition to knowledge-based economies that InventXR infrastructure is well designed for. Extensive mobile phone coverage in Africa has led to major innovations in banking, agriculture, and other sectors and has the potential to “leopard frog” in health care delivery systems, bringing the clinic to the patient through Point of Care (POC) technologies and self-management systems, with applications to rural and underserved populations worldwide. 
    • Third, this initiative is synergistic with and leverages the substantial investments NIH has already made in research and research training in Africa.
AwardsMovement ThinkingUniversity Affairs

The Stanford Data Science Collaboratory

Sponsor: Stanford University
Award Number: 1934578
Awarded Amount to Date: $1,000,000.00
Emmanuel Candes [email protected] (Principal Investigator)
Fiorenza Micheli (Co-Principal Investigator)
Chiara Sabatti (Co-Principal Investigator)
Jurij Leskovec (Co-Principal Investigator)


Data-driven inquiry is key to all aspects of science and discovery, and data-based decisions are becoming integral to society. The challenges and the importance of meeting them are especially critical when the goal is to obtain relevant, valid, reproducible scientific insights. The Stanford Data Science Collaboratory will confront these challenges by creating a community of faculty, postdoctoral scholars, students, and research fellows that leverage data science methods and domain knowledge to tackle pressing problems. In the Collaboratory, data scientists will work closely with scholars from other fields who rely on large, accurate, dependable datasets and data science techniques. The Collaboratory will foster the work of researchers who study the ethical issues related to data collection and use, and will use data to solve societal and scientific problems. A hallmark will be thorough validation of data and a careful statistical calibration of the evidence to avoid misinterpretations that could have adverse consequences. A second major goal of the Collaboratory is the growth of a citizenry literate in data science: universities have an obligation to ensure the next generation understands how to interpret and learn from data, and how to collect and manage it.

The Collaboratory identifies a set of five high-profile, high-impact projects that domain scientists deem important, and where they believe they are unable to make progress without a paradigm shift in the way they approach data sets. The first two concern a sustainable relation between humans and the environment, namely, (1) the problem of managing coral reefs in a changing climate and (2) reducing illegal fishing and forced labor in tuna supply chains. To make progress, the project will leverage new data sources: satellite remote sensing, ground monitoring stations based on soundscape, and genomic measurements that track biodiversity and evolution. The other three projects are about fractures in society and steps towards a sustainable one: (3) How to understand the determinants of poverty in the U.S.; (4) How to detect and track political framing in digital media; and (5) How to develop data science tools that support equitable treatment between individuals. Public data streams (e.g., social media apps, Wikipedia and Wikidata and moderate-resolution satellite imagery) as well as private-sector data (e.g., cell phone records, Facebook activity, internet search queries, drone imagery and fine-resolution satellite data) will inform understanding of the mechanisms causing poverty. To meet the research goals, the Collaboratory will incentivize faculty, students and postdocs to come together to find new data science solutions by supporting collaborative research teams and brainstorming working groups. The Collaboratory will also engage the undergraduate community by providing hands-on guided scientific research experience. To enlarge collaboration beyond Stanford, the Collaboratory will host outside visitors and invite scientists to campus for an annual symposium.

This project is part of the National Science Foundation’s Harnessing the Data Revolution (HDR) Big Idea activity.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

Please report errors in award information by writing to: [email protected]

AwardsInventXRUniversity Affairs

Stanford University Collaborative Research: Advancing Ocean Literacy through Immersive Virtual Reality (National Science Foundation Award)

Jeremy Bailenson [email protected] (Principal Investigator)
Sponsor: Stanford University
Award Number: 1906728


As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative resources for use in a variety of settings. The project will develop and research how an emerging technology, immersive virtual reality (IVR) using head mounted displays (HMDs), can enhance ocean literacy and generate empathy towards environmental issues. Recent advances in design have resulted in HMDs that provide viscerally realistic and immersive experiences that situate participants in underwater or other remote environments. IVR can provide many people with virtual access to these environments, including persons with disabilities, people living away from coastal areas, or those who lack access for other reasons (e.g., low-income families, underserved/underrepresented communities, persons untrained in diving). The project will develop a high quality 360-degree underwater film that includes live action footage, animation, and interactive elements. The IVR experience will take the participant through an immersive underwater journey of a Pacific reef, using realistic visualizations, narrative, and a compelling story to engage participants in learning the ecology and biology of coral reefs, as well as the impacts of climate change and human disturbances on ocean ecosystems. In addition to the IVR ocean journey, the project will integrate interactive functionality of being on a reef during mass coral spawning, an annual natural phenomenon through which coral reefs replenish their populations. With hand-held controllers, participants will be able to “swim” through the water, watch the degraded reef recover and grow and will have the ability to change the rate of coral recovery and learn how increases in temperature impede coral recovery. While research has been conducted on early, desk-top versions of IVR, the potential impact of IVR on learning is still unclear. The research findings will help guide the development of IVR for use in informal STEM environments such as aquariums, zoos, science museums, and others. The IVR experience will be shared on online platforms for home viewing, at film festivals and conferences, and in informal learning environments.

The project addresses the need for research on the impacts of IVR devices as it become more affordable and more widely used at home and in other informal and formal environments. Few studies have investigated how design elements impact the user in IVR, in which the increased immersion affects the stimuli perception and cognitive processing. The research will assess the learning affordances and impacts of the IVR experience on participant ocean literacy (adapting items from an existing ocean literacy survey), environmental empathy/feelings of presence (naturalistic observations and post-experience interviews), and perceived self-efficacy (pre-post survey, post-interview interviews). In addition, the project will research how segmentation (i.e., a continuous experience vs. an experience with breaks), generative learning tasks (hands-on experiences and interactive during IVR), and gender of the narrator in an IVR experience supports learning about ocean environments. Researchers will collect data from students attending high schools with predominantly minority student enrollments. Research findings will be widely shared through peer-reviewed publications, conference presentations, and publications for educators and designers.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

Please report errors in award information by writing to: [email protected]

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