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Good documentation and organisation strategies will make your processes more efficient and will improve the accessibility, interoperability and reusability of your data for the long term (FAIR).

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Research systems and software

The university maintains a number of systems and software to support researchers with collecting, managing and enabling safe access to data.

Survey tools

LimeSurvey allows you to develop, publish and collect responses to surveys. LimeSurvey includes a wide range of built-in question types and a wide range of flexible options. Your surveys can include branching, your own preferred layout and design (using a templating system), and can provide basic statistical analysis of survey results. Surveys can be public, or can be strictly controlled through the use of ‘once-only’ tokens for each survey participant. Results can be exported in many common formats: Excel, CSV, SPSS, PASW, PDF and Word. For more information about using LimeSurvey, please email the IT/AV team.

If your survey is considered very low risk and you will not be collecting any personal data or sensitive information then you can use Microsoft Forms on SGUL’s Office 365 to collect data for your study. Training on Microsoft Forms is available from our IT Training Manager via email.

Data Safe Haven (DaSH)

DaSH facilitates safe access to population health data for research while ensuring adherence to the highest standards of security. The Data Safe Haven has been certified to the ISO27001 information security standard and conforms to NHS Digital's Information Governance Toolkit. Built using a walled garden approach, where the data is stored, processed and managed within the security of the system, avoiding the complexity of assured end-point encryption. A file transfer mechanism enables information to be transferred into the walled garden simply and securely. For more information, access and training, please email the IT/AV team.

Research Data Collection Service (REDCap)

REDCap is a mature, secure web application for building and managing online surveys and databases. While REDCap can be used to collect virtually any type of data, it is specifically geared to support data capture for research studies. The REDCap service is run by IT/AV to provide a simple web-based tool for creating online surveys, electronic case report forms (eCRFs) and other research data collection needs. Its interface makes it straightforward for anybody to create and manage data collection tools, while also supporting more complex data management activities (double entry, randomisation, data cleansing, anonymisation etc). For more information about using REDCap, please email the IT/AV team.

High performance computing (HPC)

St George’s offers an on-site high performance computing (HPC) service that can be accessed by academic/research staff and research students. The service consists of a large high performance computing resource consisting of several clusters which are used for all forms of predictive modelling, data analysis and simulation which are too large, too complex or too time-consuming to be performed on a desktop PC. For more information about our HPC please email the IT/AV team.

ItemTracker

ItemTracker aids in the tracking and management of samples held at St George’s, University of London. All research groups using human samples which are stored under our Human Tissue Authority (HTA) licence must use ItemTracker – this includes samples stored in freezers, fridges, in liquid nitrogen or at ambient temperature. Studies using other kinds of laboratory samples or those where there is current ethics approval are also encouraged to use ItemTracker. There is an existing SOP (PDF) for managing your samples using the system. For more information and to request access, please email us.
Storage
It is advisable to store your data on a St George’s network or shared drive, which you will be allocated by Computing Services. As your project develops, you might find you need more storage space. Email the IT/AV team to discuss your needs for more active data storage.
Documenting data

Good documentation improves the quality, interoperability and re-usability of your data. It also makes your research processes more efficient, particularly over time.

Research can be documented with study-level documentation, data-level documentation and catalogue metadata. The UK Data Service provides a valuable summary of each of these levels of documentation with examples of the kinds of information you should include in your own documentation. If you’re writing source code as part of your research you should also document your code. The Software Sustainability Institute provides useful advice for making your source code readable.

You should use community-agreed documentation standards, controlled vocabularies, protocols, or good practice where these exist. Documentation standards for some common St George’s data types include:

  • CDISC, which maintains a range of data standards for clinical research
  • BIDS, which is a standard to organise and describe neuroimaging and behavioural data
  • the OME Model – a specification for storing (meta)data on biological imaging
  • the UK Data Service metadata guidelines will help researchers in the social, behavioural, economic and health sciences to document their data. 

FAIRsharing.org maintains a searchable list of documentation standards for a range of St George’s data types.

Contact the research data management service if you need help locating standards that best suit your research areas.

Quality

St George’s researchers are responsible for managing their data according to community standards for quality assurance and quality control (QA/QC).

Generally, producing clear documentation and working to standard operating procedures (SOPs) and protocols will help you to create better quality data regardless of your field.

The UK Data Service provides good examples of QA/QC processes that could be applied to a range of data types.

The World Health Organization’s (WHO) Quality practices in basic biomedical research handbook offers good guidance for addressing quality in non-regulated, basic biomedical research.

There are a number of quality systems in place for regulated research, including Good Laboratory Practice (PDF) and Good Clinical Practice (PDF) for pre-clinical and clinical studies respectively. Researchers conducting regulated research should be aware of their responsibilities regarding data quality and reliability. Visit the JRES governance pages for more information.

 

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