The concept of data-driven springs from the modern technological advances that continue to bring about mountains of systematic, comprehensive, and deep data. When flipping the paradigm, data drives novel scientific endeavors rather than the other way around. Researchers that can utilize the data available are able to mine it for unexpected, unpredictable relationships and new knowledge. At the same time, computing power, machine learning, AI, and other technologies available to crunch that data, have dramatically improved and present great opportunities for those who successfully link it up to their own work.
In episode 3 of the SciLifeLab Talk Show, Emma Lundberg (SciLifeLab Group leader and Director of the Cell Profiling unit at SciLifeLab), and Sebastian DiLorenzo (bioinformatician and community coordinator at NBIS), share their reflections.
We look closer at the data lifecycle, data handling and data sharing in all the steps of the process, and we visit SciLifeLab Group leader Ola Spjuth to hear about his research.
We sit down with Olli Kallioniemi (Director of SciLifeLab), to talk about what DDLS means for SciLifeLab, and with Annika Stensson Trigell (board member, KTH Royal Institute of Technology) and Lotta Ljungqvist (board member and CEO of Testa Center, Cytiva) to talk about what DDLS means for Sweden.
What’s the concept of open science, and how are researchers and units at SciLifeLab working with it? We take a trip around SciLifeLab to find out.
Experiments generate data, which can be analyzed to address specific hypotheses. The data can also be used and combined with other data, into larger and more complex sets of information, and generate new discoveries and new scientific models. Which, in their turn, can be addressed with new experiments.
Researchers in life science often collect large amounts of data to answer their research questions. Often, the same data can be used to answer other research questions, posed by other research groups. Perhaps many, many other questions, and many, many other research groups. This means that data analysis, data management and data sharing is central to each step of the research process, which can be illustrated with the data life cycle, a concept at the center of the DDLS program.