Research Data Management Support
Following best practices in Research Data Management (RDM) ensures that you can manage your data efficiently throughout the data lifecycle, comply with ethical and legal frameworks, and maximise the impact of your work. Importantly, well-curated and shared research outputs (data and code) are key for enabling reuse and improving reproducibility. Research outputs should be as Open and nature.com/articles/sdata201618 FAIR (Findable, Accessible,Interoperable, and Reusable) as possible.
On this page, you will find information relevant to the management of infectious disease/pandemic preparedness data generated in Sweden. Read the national guidelines for more background information about Open Science in Sweden.
Getting further support with RDM
The Swedish Pathogens Portal offers considerable support for research data management, in the form of guidance and provide access to relevant tools. Direct one-on-one support is also available for tailored advice and can be accessed by sending a request to pathogens@scilifelab.se or via the contact form. You can typically expect a response within one working day.
For more general RDM advice relevant to Swedish research, we recommend visiting SciLifeLab Research Data Management Guidelines, and emailing data-management@scilifelab.se for direct support.
For information about where to share your data, please refer to our page on data sharing
For available datasets then refer to get data.
If you need anaylses, specific instruments or analyses then see the SciLifeLab Service Catalog.
Infectious Disease Resources & Tools
Effective research data management in infection biology relies not only on good planning but also on access to the right infrastructure and expert support. Below are some key services and tools available to Swedish researchers that help ensure data are managed according to FAIR principles.
Effective research data management in infection biology relies not only on good planning but also on access to the right infrastructure and expert support.
Research Data Management Kit (RDMkit)
The Research Data Management Kit (RDMkit) is similar to the Disease Toolkit (IDTk) , except that it contains information on RDM related to life science data in general, rather than just infectious disease data. It contains information relevant for all stages of the research data lifecycle, from planning to preservation.ELLIS against COVID-19
ELLIS against COVID-19 is a collection of recordings from online workshops organised by ELLIS, showcasing AI and machine learning projects tackling COVID-19. Topics include outbreak prediction, epidemiological modelling, drug discovery, and healthcare management.Metadata Standards
The following table provides an overview of key metadata standards separated by methodological areas.
| Metadata | ||
|---|---|---|
| Standards | Description | |
| Genomics | MIxS (Minimum Information about any (x) Sequence) | Developed by Genomic Standards Consortium ; used for describing sequences from different environments (e.g., host-associated, environmental). |
| MINSEQE (Minimum Information about a High-Throughput Nucleotide Sequencing Experiment) | Recommended by FGED for RNA-seq and other sequencing metadata. | |
| ENA Checklists | Specific checklists for submission to European Nucleotide Archive (e.g., pathogen, human, metagenome). | |
| ISA-Tab / ISA-JSON | Framework for describing experimental metadata, often used with bioinformatics tools and databases. | |
| Proteomics | MIAPE (Minimum Information About a Proteomics Experiment) | Developed by HUPO-PSI; covers mass spectrometry, sample processing, informatics. |
| PSI-MI XML / MITAB | For molecular interaction data formats (used in interaction databases). | |
| mzML / mzIdentML / mzTab | Standard formats for raw data, identifications, vocabulary and quantification results in the field of mass spectrometry-based proteomics. | |
| Imaging | OME-TIFF / OME-XML | Developed by Open Microscopy Environment; widely used for storing microscopy images and associated metadata. |
| REMBI (Recommended Metadata for Biological Images) | Designed to enable reproducibility and data reuse for imaging datasets. | |
| DICOM (Digital Imaging and Communications in Medicine) | Standard for handling, storing, and transmitting medical imaging information (e.g., CT, MRI). | |
| Bioassays / Experimental Data | MIACA (Minimum Information About a Cellular Assay) | For reporting cellular assays, including experimental context and protocols. |
| MIABE (Minimum Information About a Bioactive Entity) | For small molecule screening and bioactivity reporting. | |
| BAO (BioAssay Ontology) | Ontology that enables uniform annotation of bioassays and protocols. | |
| Clinical & Health Data | CDISC (e.g., SDTM, ADaM, SEND) | Industry standard for clinical trial data exchange and analysis. (not so open) |
| HL7 / FHIR (Fast Healthcare Interoperability Resources) | Widely adopted in EHR systems for structured health data. | |
| LOINC / SNOMED CT / ICD-10 | Controlled vocabularies for lab tests, symptoms, diagnoses. | |
| MIMIC-IV Metadata Guidelines | For structured ICU/clinical datasets in open research. | |
| Dublin Core + DCAT-AP-SE | Metadata cataloging for health data in national repositories. | |
| Omics Imaging (e.g., Spatial Transcriptomics, Multi-modal) | STOMIC (Spatial Transcriptomics Open Metadata and Image Convention) | A proposed standard for organizing spatial omics data. |
| ISA-Tab + OME-XML | For integrating omics and imaging data. | |
| HUPO-B/D Standards | For multimodal single-cell data and proteogenomics metadata. | |
| Metabolomics Data | ISA-Tab / ISA-JSON | Describes experimental design, sample preparation, and data files. |
| Metabolomics Standards Initiative (MSI) | Offers domain-specific guidelines for metadata and reporting. | |