Open Science

Our goal is universal and immediate open sharing of all scientific knowledge and outputs. With our Open Science program, we empower more people to engage in research practices that accelerate the pace, robustness, and reproducibility of science through partnerships, policies, and grants. Helping scientists build on each others’ work can dramatically accelerate the pace of discovery, and in turn, our understanding of health and disease.

We support our grantees and the broader scientific community to deposit software code to open repositories, make experimental protocols openly accessible, and submit manuscripts to preprint servers to communicate results more quickly.

A group of seven people talking to each other while gathered around a table filled with computers.
Grantees collaborate during a working session at the CZI Essential Open Source Software for Science meeting. Photo by Scott Murphy, CZI.
Grantees collaborate during a working session at the CZI Essential Open Source Software for Science meeting. Photo by Scott Murphy, CZI.

Supporting Platforms and Infrastructure for Open, Collaborative Science

Our open source science work aims to accelerate the pace of scientific progress. Part of that is making sure researchers have access to reliable information and sustainable open source infrastructure. We support platforms where researchers can quickly and openly disseminate methods, tools, preprints (draft scientific papers), and other research outputs. We also partner with organizations contributing to the infrastructure underlying open science and serving the needs of communities around the world.

Enabling Knowledge Discovery

We aim to reduce barriers to knowledge discovery and access. We support platforms, tools, and research to represent, summarize, and discover our collective biomedical knowledge. We believe that fostering a more open and interoperable knowledge infrastructure will help accelerate the pace of research.

Meta

Meta uses machine learning to map the latest biomedical research in real time. It enables scientists to keep up to date with the latest research findings and to more quickly spot emerging trends through personalized feeds.

Computable Knowledge

We collaborate with Andrew McCallum and the Center for Data Science at UMass Amherst to accelerate science and medicine through research and development of novel approaches to knowledge representation and automated knowledge base construction. The goal of the Computable Knowledge project is to enable an intelligent and navigable map of scientific knowledge using a branch of artificial intelligence known as knowledge representation and reasoning.

The Computable Knowledge project will facilitate new ways for scientists to explore, navigate, and discover potential connections between millions of new and historical scientific research articles.

Blue dots, symbolizing viral particles of Covid-19, against a grey background.
Transmission electron microscopic image of an isolate from the first U.S. case of COVID-19. The spherical viral particles, colorized blue, contain cross-sections through the viral genome, seen as black dots. | Photograph by CDC / Hannah A Bullock; Azaibi Tamin.
Transmission electron microscopic image of an isolate from the first U.S. case of COVID-19. The spherical viral particles, colorized blue, contain cross-sections through the viral genome, seen as black dots. | Photograph by CDC / Hannah A Bullock; Azaibi Tamin.

Open Research Data

Researchers and leaders from the Chan Zuckerberg Initiative, the Allen Institute for Artificial Intelligence, Georgetown University’s Center for Security and Emerging Technology, Microsoft Research, and the National Library of Medicine of the National Institutes of Health have assembled and released a dataset of preprints, clinical reports, and published research papers about COVID-19, SARS-CoV-2, and the coronavirus group.

The COVID-19 Open Research Dataset (CORD-19) includes machine-readable full text of tens of thousands of papers, and is updated daily as new insights are released. With these machine-readable resources accessible and available for data analysis, the data science community has the opportunity to apply recent advances in natural language processing to find answers to questions within, and connect insights across, this content in support of the ongoing fight against this infectious disease.

In addition, to promote reuse and reproducibility, we encourage the open sharing of research data through services such as Dryad, a data publishing platform.

Building Computational Capacity

We believe in a future in which everyone in the biomedical community has the skills to make use of computational methods and engage in open science practices. The Chan Zuckerberg Initiative is focused on accelerating science by providing researchers with the resources and tools needed to do their best work.

Graphic on a blue background featuring an image of a microscope, computer, books and DNA strain.
CZI’s Essential Open Source Software for Science program supports software maintenance, growth, development, and community engagement. Graphic by CZI.
CZI’s Essential Open Source Software for Science program supports software maintenance, growth, development, and community engagement. Graphic by CZI.

Essential Open Source Software for Science

Open source software is crucial to modern scientific research, advancing biology and medicine while providing reproducibility and transparency. Yet even the most widely-used research software often lacks dedicated funding. CZI’s Essential Open Source Software for Science program supports software maintenance, growth, development, and community engagement for critical open source tools.

Open Science Team

Dario Taraborelli PROGRAM OFFICER, OPEN SCIENCE
Carly Strasser PROGRAM MANAGER, OPEN SCIENCE
Alex Wade GROUP TECHNICAL PROGRAM MANAGER, META

Advisory Board

Chonnettia Jones Michael Smith Foundation for Health Research
Molly Maleckar Simula Research Laboratory
Manu Prakash Stanford University
Emily Sena University of Edinburgh
Andrew Su Scripps Research
Carol Willing Python
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