About the DDLS program

Accelerating the data-driven life science paradigm shift

Never before has so much data been produced within the life sciences. The cost of reading the genome from humans, species or individual cells has dropped, while the speed has increased, to the point that what initially took ten years, is now done in one day, and similar revolutions are taking place in several research areas. At the same time, computing power, artificial intelligence and other technology necessary to handle data have been greatly improved.

The mountains of data that are now emerging must be handled in a correct and ethical way. Among other things, they must be accessible and reusable, for researchers everywhere. Today, only a small part of all data is handled correctly, which means we miss out on opportunities to make new scientific discoveries, find patterns and investigate relationships. At the same time, many researchers lack the tools and knowledge needed to conduct data-driven research, and we need to strengthen the Swedish research community’s competencies.

This is the basis for SciLifeLab and Wallenberg national program for data-driven life science (DDLS), a 12-year initiative funded with a total of SEK 3.1 billion from the Knut and Alice Wallenberg Foundation. The purpose of the program is to train and recruit the next generation of life scientists, to create a strong computational and data science base, and to strengthen the competencies in today’s research society, thereby enabling every scientist to better analyze data patterns and integrate their data with the global data flows in life sciences. Furthermore, the program aims to strengthen national collaborations between universities, bridge the research communities of life sciences and data sciences, and create partnerships with industry, healthcare and other national and international actors.

SciLifeLab, which today conducts research activities at all major Swedish universities, provides a national infrastructure and functions as a hub for life sciences in various disciplines, is the main host of the program.

Data-driven strategic research areas

The program focuses on four strategic areas for data-driven research, all of which are essential for improving the lives of people as well as animals and nature, detecting and treating diseases, protecting biodiversity and creating sustainability:

Data-driven Cell and molecular biology
The subject area concerns research that fundamentally transforms our knowledge about how cells function by analyzing data from their molecular components in time and space, from single molecules to native tissue environments. This research subject area aims to lead the development or application of novel data- driven methods relying on machine learning, artificial intelligence, or other computational techniques to analyze, integrate and make sense of cell and molecular data.

Research Area lead: Erik Lindahl, Professor at Stockholm University

News article: DDLS Data Driven Cell and Molecular Biology expert group appointed

Data-driven Precision medicine and diagnostics
The subject area concerns research that will make use of computational tools to integrate molecular and clinical data for precision medicine and diagnostic development. The focus is on data integration, analysis, visualization, and data interpretation for patient stratification, discovery of biomarkers for disease risks, diagnosis, drug response and monitoring of health. The precision medicine research is expected to contribute with strong capabilities in machine learning and AI and other computational tools to make use of existing strong assets in Sweden, such as molecular data (e.g. omics), imaging, electronic health care records, longitudinal patient and population registries, biobanks and digital monitoring data.

Research Area lead: Janne Lehtiö, Professor at Karolinska Institutet

News article: DDLS Data Driven Precision Medicine and Diagnostics expert group appointed

Data-driven Evolution and biodiversity
The subject area concerns research that takes advantage of the massive data streams offered by techniques such as high-throughput sequencing of genomes and biomes, continuous recording of video and audio in the wild, high-throughput imaging of biological specimens, and large-scale remote monitoring of organisms or habitats. This research subject area aims to lead the development or application of novel methods relying on machine learning, artificial intelligence, or other computational techniques to analyze these data and take advantage of such methods in addressing major scientific questions in evolution and biodiversity.

Research Area lead: Fredrik Ronquist, Professor at Museum of Natural History

News article: DDLS Evolution and biodiversity research area specific expert group appointed

Data-driven Epidemiology and biology of infections
The subject area concerns research that will use big experimental, clinical, or pathogen surveillance data in innovative ways to transform our understanding of pathogens, their interactions with hosts and the environment, and how they are transmitted through populations. The priority area covers computational analysis or predictive modelling of pathogen-host systems for which No changes suggested multidimensional, genome-scale experimental data are now available and extends to using population-scale genetic, clinical, or public health data from pathogen surveillance efforts and biobanks.

Research Area lead: Oliver Billker, Professor at Umeå University

New article: DDLS Data Driven Epidemiology and biology of Infection RA expert group appointed

Upcoming recruitments

Over the years, the program aims to:

• Recruit 39 future research leaders as DDLS Fellows
• Establish a graduate school for 260 PhD students in academia and industry
• Create 210 postdoctoral positions in academia and industry.

The DDLS Fellows will be recruited to the participating organizations, enabling them to utilize the strong local research environments. At the same time, they will be connected to the national DDLS program, cultivating a strong, interdisciplinary community of researchers working with the rapidly expanding resources and needs of open data in life sciences. The Fellows will be recruited in two rounds, 2021 and 2024. Distribution of DDLS Fellow positions to the participating organizations was pre-defined in the donation letter from KAW.