Science progresses when researchers build on prior work to extend, test, and apply theories. Aggregating the quantitative findings from prior research – meta-analysis — plays a significant role in advancing science, however current techniques have limitations. They assume prior studies share similar substantive factors and designs, yet many studies are heterogenous. A new method, co-created by MIT Sloan School of Management Prof. Hazhir Rahmandad, solves this problem by aggregating the results of prior studies with different designs and variables into a single meta-model.
Innovative drug development organizations are leveraging scientific and technical advances to data capture data that is increasingly multidimensional and information rich. However, existing data capture and sharing processes are often unable to support efficient integration and interpretation of this data. To learn more about available technologies available for data capture and relation science strategy download this white paper.
This technology guide is geared toward scientific researchers working at universities and other research institutions (e.g. NASA, JPL, NIH, etc.) who may benefit from learning more about how big data is meaningfully transformative in the way it can be applied to the data collection and analysis part of their projects. Further, we’ll illustrate how Dell big data technology solutions powered by Intel are actively helping scientists who are focused on their data, on their models and on their research results. Learn more by downloading this guide.