D³b Center’s vision and mission are supported through the D³b Data-Driven Discovery Methodology, which defines the processes required to transform data into impact. Through the use of data-driven platforms and tools, which were developed using innovative design principles, the D³b Center is empowering convergent research. The D³b Methodology enables accelerated translational research and exponential growth and output in the number of data-driven discoveries, which, in turn, contribute to enhanced impact through improved patient outcomes. The D³b Methodology provides new routes to breakthroughs more rapidly than has ever been possible under previous processes and models of siloed research.
D³b Data-Driven Discovery Model
Since the completion of the first human genome at the turn of the 21st century, large-scale biomedical and genomic data generation have exponentially accelerated via new technologies, supporting the increased scale and diversity of molecular data generation, while lowering the costs of computation and data storage. To date, such “big data” efforts have primarily focused on data generation, while lacking the required infrastructure needed to translate discoveries into clinical impact during a child’s lifetime.
We hypothesize that when processes and platforms support the rapid conversion of “big data” into information, or organized, harmonized datasets, which can be shared and accessed in real time for use by the worldwide scientific community, there will be exponential, accelerated growth in the discovery landscape and increased diversity of knowledge for disease mechanisms across pediatrics.
D³b’s data platforms and tools are specifically designed to allow for collaborative team science. Each of these platforms are built to support the diversity and expertise of researchers everywhere. By continuing to scale and develop these platforms, D³b is supporting the research community’s simultaneous translation to clinical impact.
from data to information to knowledge to impact
The model begins with and requires harnessing new technologies and resources that define the Data Phase, where large and continuously-growing collections of high-quality, multi-dimensional data are generated, localized, or connected, but exist in a raw format that is not yet useable or queryable for hypothesis-driven research. These raw data must be harmonized and structured to allow for the application of data science approaches, including modeling, mapping, and algorithmic processing during the Information Phase.
These harmonized data are next moved into the Knowledge Phase, where hypothesis-driven questions can be formulated on the harmonized dataset. Through iterative hypothesis testing, new data testing, and repeated scientific analysis of the data, information is distilled and further transitioned into scientific knowledge. During the Knowledge Phase, new knowledge of a disease type, potential therapeutics, mechanisms, patient populations, outcomes, predictions, and so much more is discovered. Once new knowledge is gained, it can be transformed and translated into the clinic via clinical trials, decision support, and novel therapeutic development for patient care, leading to the Impact Phase, and ultimately alleviating the suffering of children and their families.