Getting useful information from “messy” data requires a lot of expertise and people. As Harvard Biostatistics Professor Francesca Dominici explains, “We need professionals who can work closely with the faculty and with the students to unlock bottlenecks.”
That’s why Harvard and Elsevier have embarked on a long-term collaboration to significantly advance data science research, recognizing Elsevier’s strengths as an information analytics business with a product ecosystem supporting predictive analytics, enhanced research performance and information access and sharing.
The Harvard data Science Initiative (HDSI) is focusing on several research areas that Elsevier has been developing, including the use of machine reading and machine learning to process huge volumes of research literature and create a knowledge base that can be used to empower decisions. As Co-Director of the HDSI, Prof. Dominici notes that they are only able to take on such complex problems because of a convergence of several key factors:
We have more data than ever before, and of different kinds. We also have very mature computational platforms made for processing large amounts of high-dimensional data in a very parallel way, and new algorithms are being developed that can bring all these elements together.
Elsevier is in a unique position to provide access to relevant datasets that present great potential for accelerating Harvard’s research scope. Used effectively, data can have a huge positive impact on society. It can boost economies, transform businesses, cure diseases and shape government policies. Both parties are interested in problems that have groundbreaking potential related to inspiring new lines of inquiry and development, and Elsevier is proud to share with Harvard its professional expertise massive data sets, as well as a sizable donation to support the effort.
In practical terms, the two institutions are working on a series of research project streams as well as grant applications and co-authored papers, and a cross-pollination of skills and knowledge. One initiative involves the development of a career track whereby professional data scientists can lend their industry expertise to help accelerate the research process.
The Elsevier and Harvard collaboration includes:
- Collaborative research among professional and academic data scientists.
- Sharing of data sources.
- Skills and knowledge sharing.
- Use of Elsevier’s research productivity tools, including data mining and predictive analytics.
- Knowledge sharing via co-publication, seminars, events and meetings.
In the words of Prof. Dominici:
Bringing together such an unusually broad range of expertise is how we will use data science to tackle some of the world’s most vexing problems. A lot can be done because the time is right and because there is such support of the university.
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