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CLUSTER data - for researchers

  • Metadata

    • What is metadata?

    • Data that provides information about a dataset, which does not disclose the content of the dataset

      • Study-level examples: project title, aims, dataset identifiers, file naming conventions, versioning systems

      • Data-level examples: variable names (age_at_diagnosis), labels (0=No, 1=Yes), variable types (integer, text), units (mg, cm, kg)

    • Why is metadata important?

    • It provides a description of everything a future user of your data would need to know to independently find, understand, reproduce, and reuse your data

    • Generating your metadata as early as possible in the research process and maintaining it as your project continues is essential to have accurate, complete metadata

    • Documentation is required when publishing data, both at the study and data-level

    • How do I create a data dictionary/codebook?

    • If you want to publish your data in a specific repository, check for specific standards and guides

    • Find specific help on documentation in the note on this branch

    • There may be existing metadata standards and guides in your research field

    • Make sure your data dictionary is published in a machine readable format (.xml, .json, .csv)

  • Data sharing

    • What are CLUSTER's policies for data sharing?

    • CLUSTER is producing and harmonising existing clinical and biological data on patients with JIA and JIA-uveitis. We are open to collaborating and sharing data with researchers who have research questions that may be answered by our data. Access to CLUSTER data is regulated according to the conditions of patient consent, study ethics and the CLUSTER Consortium’s policies. Access is available to researchers via an application to the CLUSTER data access committee as detailed in the flow chart on the website. Please make any initial enquiries for data access via our contact email address: clusterconsortium@ucl.ac.uk 

    • Will CLUSTER share all of its data publicly?

    • No

      • Our first duty and priority is to protect the rights and privacy of the patients who have given us their data

      • We are conducting research on rare paediatric diseases, meaning we have smaller datasets than more common diseases, which could make it easier to identify individuals

      • Some of our data is sensitive and will not be publicly shared, or alternatives will be published (e.g. summary statistics of genetic sequencing data)

      • As CLUSTER is combining multiple data sources, that increases the opportunities to identify individuals - before each dataset is published, this will be discussed and fully reviewed to ensure we are not compromising individual data rights and privacy

  • Data access

    • How to I access CLUSTER data?

    • An outline of the data access process is available on the website

    • When they are finalised, a data catalogue, descriptions and data dictionaries for the datasets we have available will be located here: https://zenodo.org/communities/cluster-consortium?page=1&size=20

    • Apply for access using the form available, which will be reviewed by the Consortium Management Board

    •  Please make any initial enquiries for data access via our contact email address: clusterconsortium@ucl.ac.uk

  • FAIR principles

    • What are the FAIR principles?

    • Findable

      • Metadata and data should be discoverable by both humans and machines

    • Accessible

      • Data are accessible in that they can always be obtained by machines and humans

    • Interoperable

      • Data should be interoperable with applications or workflows for analysis, storage, and processing

    • Reusable

      • Metadata and data should be well described so that they can be used, replicated, and combined in different settings

    • Why should I apply the FAIR principles to my work?

    • The European Commission estimates that the cost of not having FAIR data in Europe is a minimum of €10.2 billion per year, with a further €16.9 billion per year for likely costs, such as the impact on innovation

    • Managing research data well can generate value beyond initial research purposes

    • Data resources are being extensively generated across the life sciences - this is resource intensive and time consuming. The ability to discover and reuse data effectively could reduce this burden and open up more research opportunities

    • How is CLUSTER applying the FAIR principles?

    • Every CLUSTER dataset has detailed metadata that clearly explains each aspect of the dataset

    • CLUSTER plans to publish its (meta)data where appropriate to make our data available to the scientific community and to assign persistent identifiers to individual datasets

    • All CLUSTER publications are open access and linked on our website

    • The CLUSTER website clearly states the steps a researcher needs to take to apply for access to our non-public data

    • CLUSTER has agreed file naming conventions and is documenting how participants from each dataset are linked to all other datasets

    • Where possible, biological data will be deposited in public repositories with associated metadata

    • All CLUSTER data on the tranSMART platform contains CLUSTER IDs - these allow researchers to link the same participant across multiple datasets, even if their data originated from different studies. tranSMART is a secure data warehouse that allows researchers to explore and analyse data through a browser.

  • CLUSTER studies

    • CHARMS

    • A study of the psychological, immunological, and genetic mechanisms of response to standard disease management in juvenile idiopathic arthritis (JIA)

    • CAPS

    • Cohort study that assesses longitudinal patient outcomes in JIA from initial diagnosis which includes psychology (particularly pain), pre and post treatment COVs, clinical, and genetic factors

    • BSR JIA registers

    • BCRD

      • Longitudinal drug effectiveness and safety study investigating biologic treatment in children with JIA (any biologic except Etanercept)

    • BSPAR-Et

      • Drug effectiveness and safety study investigating Etanercept and its biosimilars in children with JIA

    • SYCAMORE

    • Randomised controlled trial of the clinical effectiveness, safety, and cost effectiveness of Adalimumab for the treatment of JIA-uveitis

    • APTITUDE

    • Trial to establish the efficacy of Tocilizumab for treating JIA-uveitis in children who have not improved with Methotrexate or anti-TNF treatments

    • JIA-Pathogenesis

    • To improve understanding of what causes JIA and through this knowledge to improve the clinical management of JIA

    • EYE-CHART

    • Detailing and mapping JIA-uveitis datasets nationally towards building a common data model

  • Anonymisation/Pseudonymisation

    • What is anonymisation/pseudonymisation?

    • Anonymisation is the process of turning personal data into anonymous information so that an individual is not/no longer identifiable. E.g. the removal of your date of birth, email address, and phone number from a dataset about online shopping orders from Amazon.

    • Pseudonymisation is the process of reducing the risk of identifying someone from personal data in a dataset, without eliminating the risk entirely. E.g., replacing your date of birth with your age in a dataset.

    • https://ico.org.uk/about-the-ico/ico-and-stakeholder-consultations/ico-call-for-views-anonymisation-pseudonymisation-and-privacy-enhancing-technologies-guidance/

    • How is CLUSTER anonymising/pseudonymising its data?

    • Minimising the amount of personal data used/shared (e.g. replacing date of birth with age)

    • Using OpenPseudonymiser to pseudonymise NHS numbers to be able to allocate a unique CLUSTER ID for each patient across all studies contributing to CLUSTER

      • The CLUSTER ID means an extra layer of privacy for each patient when their data is analysed/shared as researchers do not know their original study IDs or which studies they participated in