Planning
Collection
Storage and Processing
Interpretation and Analysis
Long-term Access and Archiving
Publishing and Research Output
Meta-analysis and Data Reuse
PLANNING
Data sharing
Planning
Data re-use
F.A.I.R.
Planning is the backbone of every research project. Equally important is Good Data Management practices—deciding how data will be managed during and after the project. Read more
Planning
Data collection includes a wide range of information gathering activities that result in accumulating the data necessary for analysis. Read more
Collection
Storage and Processing
At the Storage and Processing step, data is processed in order to prepare it for analysis. This could include format conversion, quality checks, and data cleaning and filtering. Read more
Interpretation and Analysis
Interpretation and Analysis the collected data—whether it is new or reused—involves deep exploration of the information. This is often done by a group of researchers, and access to the data needs to be established for everyone involved. Read more
Long-term Access and Archiving
Scientific projects need to address data access, storage, and security to avoid data loss and ensure that the data is safely archived. Read more
PLANNING
Publishing and Research Output
Open access publishing, preprints, and research data repositories are all excellent ways to share research output. Read more
Meta-analysis and Data Reuse
Planning is the backbone of every research project. Equally important is Good Data Management practices—deciding how data will be managed during and after the project. Setting up a Data Management Plan (DMP) is an essential step, which includes ensuring that your data sharing adheres to Open Science and FAIR (findable, accessible, interoperable, reusable) practices. In the planning stage, researchers may reuse information such as DMPs or protocols shared by others. Read less
Additional Resources
Data Stewardship Wizard, a DMP tool
Guidelines for planning
Intro to Good Data Management practices
SciLifeLab supports Research Data Management (RDM) planning. Contact data-management@scilifelab.se
Planning is the backbone of every research project. Equally important is Good Data Management practices—deciding how data will be managed during and after the project. Read more
Data collection includes a wide range of information gathering activities that result in accumulating the data necessary for analysis. Depending on the study, this could involve describing biological specimens, generating data from tissue samples, or accessing data from registries and previous studies. Regardless of where the data come from, documentation on how they were created and procedures to assure their quality are key factors in making them usable in the analysis.
SciLifeLab provides access to pioneering technologies and high-quality data. In addition, staff at SciLifeLab can provide expertise in data-driven research, assist with finding existing data, and support researchers with metadata and data curation. Read less
Additional Resources
Explore the SciLifeLab infrastructure services
Find Data to reuse - SciLifeLab Data Repository
Guidelines for data collection
SciLifeLab technology services
Planning is the backbone of every research project. Equally important is Good Data Management practices—deciding how data will be managed during and after the project. Read more
At the Storage and Processing step, data is processed in order to prepare it for analysis. This could include format conversion, quality checks, and data cleaning and filtering. Accurate documentation of all steps is crucial for future data reuse. Data-driven research often involves analysis of large datasets that may require extra resources for storage and computation.
SciLifeLab offers support, tools, and resources to facilitate sharing and publishing of data as they are produced, and also provides support for metadata and curation. Furthermore, support for processing and storage of both non-sensitive and sensitive data is provided. Read less
Additional Resources
SciLifeLab Data Platform collates services for data-driven research
SciLifeLab Data Centre provides services for IT and data management
Guidelines for storage and processing
Interpretation and Analysis the collected data—whether it is new or reused—involves deep exploration of the information. This is often done by a group of researchers, and access to the data needs to be established for everyone involved. Maintaining good documentation practices is important for reproducibility and to increase the value of the data. Data-driven research often involves analysis of large datasets that requires specialised tools and computational resources.
SciLifeLab facilities provide data-analysis services, as well as services and tools for data sharing, communication, and collaboration both within and between research groups.
Read less
Additional Resources
National Bioinformatics Infrastructure Sweden (NBIS) offers bioinformatic support for a wide range of areas e.g. NGS, proteomics, metabolomics and biostatistics
Bioimage support is offered from https://www.scilifelab.se/units/bioimage-informatics/
Guidelines data analysis
SciLifeLab Data Centre services and tools
Scientific projects need to address data access, storage, and security to avoid data loss and ensure that the data is safely archived. According to Swedish law, universities must archive all research data. Researchers need to preserve both data and tools, as well as the documentation needed for data interpretation. The Long-term Access and Archiving step may include re-examination of ethics permits and access agreements to ensure that others are allowed to access and reuse the data.
The SciLifeLab Data Centre and National Bioinformatics Infrastructure Sweden (NBIS) can offer guidance, tools, and support for data sharing, storage, and access. Read less
Additional Resources
SciLifeLab Data Centre offers support for IT, storage, RDM, and data sharing
The National Academic Infrastructure for Supercomputing in Sweden (NAISS)
Data preservation guidelines
Open access publishing, preprints, and research data repositories are all excellent ways to share research output. A trusted data repository should be used, relevant metadata added, and suitable licences chosen. Data should be made findable, accessible, interoperable, and reusable (FAIR). Published data should be as open as possible, while simultaneously only as closed as necessary. Data sharing increases the impact and value of research data, is in line with Open Science goals, and permits data reuse.
SciLifeLab promotes Open Science and FAIR data sharing and provides support for data publishing. Read less
Additional Resources
SciLifeLab Data Guidelines guideline and dedicated support
SciLifeLab Data Repository, an all-purpose data repository with dedicated support
Guidelines for data sharing
Planning
Collection
Storage and Processing
Interpretation and Analysis
Long-term access and Archiving
Publishing and Research Output
Meta-analysis and Data Reuse
Meta-analysis and Reuse
Data reuse can occur in each step of a data life cycle, e.g., by reusing the protocols or analysis workflows of others, and is the core of each data life cycle. Each instance of data reuse can be thought of as the initiation of a new cycle.
Data reuse is a critical part of data-driven life science and a cornerstone in the European ambition to realise a “web of FAIR data and services” for science. Read more
Data reuse is a critical part of data-driven life science and a cornerstone in the European ambition to realise a “web of FAIR data and services” for science. Deliberate reuse increases the impact of studies that produce high-quality data, enabling new and more efficient research, innovation, and learning. Reuse is fuelled by networks of research communities converging on common practices for describing, curating, and sharing their data.
SciLifeLab promotes Good Data Management, Open Science, and FAIR data sharing practices. Read less
Additional Resources
Learn more about the data life cycle
Data reuse guidelines
But it doesn't end there…
The proliferation of modern technological advances has continued to create mountains of systematic, comprehensive, and deep data. Data has now become the driver for novel scientific endeavours, rather than the other way around. Researchers are mining data for unexpected relationships and new knowledge. Computing power, machine learning, AI, and other data-processing technologies are opening up exciting research opportunities.
Data analysis, management, and sharing are now central to almost every step of the research process, which can be illustrated as a data life cycle. From each step, new cycles could potentially emerge, driving life science research and advances forward.
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Discovering the Data Driven Life Cycle
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This area covers research that will transform our understanding of pathogens, their interactions with hosts and the environment, and how they are transmitted through populations. The research will focus on computational analysis or predictive modeling of pathogen biology or host-microbe systems.
Epidemiology & biology of infection
This area covers data integration, analysis, visualization, and data interpretation for patient stratification, discovery of biomarkers for disease risks, diagnosis, drug response, and monitoring of health. The research is expected to make use of existing strong assets in Sweden and abroad.
Precision medicine & diagnostics
This area concerns research that leverages the massive data streams offered by techniques such as high-throughput sequencing of genomes/biomes, recording of video/audio in the wild, high-throughput imaging of biological specimens, and large-scale remote monitoring of organisms or habitats.
Evolution & biodiversity
This area covers research that transforms our knowledge about how cells function by peering into their molecular components, from single molecules to native tissue environments. Researchers in this area leverage multiscale simulations, integrative predictive-experimental methods, and deep learning.
Cell & molecular biology
Four strategic areas for data-driven life sciences research
Erik Lindahl, former research area lead, Stockholm University
Fredrik Ronquist, research area lead,
The Swedish Museum of Natural History
Janne Lehtiö, research area lead, Karolinska Institutet
Oliver Billker, research area lead, Umeå University