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Collecting data effectively is essential for accurate analysis and informed decision-making. Here are some key tips for successful data collection:

  1. Define clear objectives: Before collecting data, ensure you have a clear understanding of why you need the data and what you plan to achieve with it. This will help guide the type of data you collect and the methods you use.

  2. Ensure you have the right data: Collect only the data that is relevant to your objectives. Gathering unnecessary data can lead to clutter, confusion, and increased time for processing and analysis. Focus on quality over quantity.

  3. Choose the right data collection method: Depending on your needs, data can be collected through surveys, interviews, sensors, databases, or automated tools. Select the most appropriate method that aligns with your goals and resources.

  4. Consider the system the data needs to be collected from: Be mindful of the data sources and systems involved. Data can come from various systems like databases, cloud services, the EMR, or paper logs, and it’s crucial to understand the structure and compatibility of these sources. Ensure that the systems are reliable and secure.

  5. Validate your data sources: Always verify that the data sources are credible and accurate. This includes ensuring that the data is up-to-date and that you have proper permissions to access and use it.

  6. Standardise data collection processes: Use consistent formats and protocols when collecting data to avoid discrepancies. Having standardised methods helps ensure that data is comparable, reliable, and easier to analyse later.

  7. Protect data privacy: When collecting personal or sensitive information, ensure compliance with relevant data protection laws and policies. Use encryption, anonymisation, or de-identification techniques where needed to safeguard privacy.

  8. Test your data collection process: Before fully implementing your data collection, conduct a test run to identify any potential issues. This can help ensure that your methods are capturing the intended data efficiently.

Australian Data Archive (ADA)

  • A national service for the collection and preservation of digital research data. ADA holds over 6,000 datasets from more than 1500 projects and studies and includes health studies

Australian Institute of Health and Welfare

  • The AIHW is an independent statutory Australian Government agency with more than 30 years of experience working with health and welfare data.

Australian Research Data Commons (ARDC)           

  • Enables researchers to access, contribute and use quality research data.

Health Data Australia          

  • Health Data Australia is a national catalogue of Australian health data designed for researchers who want to discover and request access to data for their research.

Maiam Nayri Wingara               

  • Maiam nayri Wingara Aboriginal and Torres Strait Islander Data Sovereignty Collective develops Aboriginal and Torres Strait Islander data sovereignty principles and also focuses on identifying Aboriginal and Torres Strait Islander strategic data assets. This includes development of an Australian set of Indigenous Data Governance protocols.

Research Data Australia           

  • Helps find, access and reuse data for research. Data is not stored at Research Data Australia but provides descriptions of and links to the data from data publishing partners.

Clinical Study Data Request           

  •  Facilitates the sharing of patient level data from clinical study sponsors and funders. 

DANS                 

  • Data Archiving and Networked Services to deposit research data, search for datasets and research projects and provide education on RDM. 

Figshare           

  • A flexible open access repository where any file format may be uploaded and shared either privately or made public. 

Harvard Dataverse                 

  • Repository which includes medicine, health and life sciences.

Health and Medical Care Archive              

  • HMCA preserves and disseminates data collected by health and healthcare research projects funded by the Robert Wood Johnson Foundation (RWJF).  

Physionet           

  • Established under the National Institutes of Health (NIH) PhysioNet offers free access to large collections of physiological and clinical data and related open-source software.

Sicas Medical Image Repository      

  • Swiss based SICAS acquires and stores medical images and processes data for research and applications in medicine.

WHO Global Health Observatory Data Repository           

  • Provides access to over 1,000 indicators on priority health topics.

Case Study: Data Extraction for Audit on Severe Hypercalcaemia

In a healthcare setting, a Fellow is conducting an audit as part of a project examining cases of severe hypercalcaemia that presented at a specific health service. The project, approved by an ethics board, aims to identify patients with corrected calcium levels greater than or equal to 3.5 mmol/L between 1 June 2018 and 1 June 2023.

The Fellow has encountered delays in acquiring the necessary data from the biochemistry department and is seeking assistance from another department to access a list of patients who meet the study’s criteria. The audit requires a comprehensive list of cases with severe hypercalcaemia for analysis.

Discussion Questions:

  1. Data Collection

    • What specific data points are required for this audit, and how will they be collected?
    • Are there standardised protocols in place to ensure consistent and accurate data extraction?
    • How will data accuracy be verified, particularly for sensitive biochemical measurements like calcium levels?

  2. Privacy and Confidentiality

    • What measures are in place to ensure patient confidentiality during data extraction and analysis?
    • Who will have access to the extracted data, and how is access controlled?
    • How will patient data be stored securely to prevent unauthorised access?

  3. Ethics and Consent

    • Has the project obtained appropriate ethics approval, and does it cover the scope of data being collected?
    • Are patients informed of the use of their data in audits like this, and if not, is a waiver of consent justified?
    • Are there mechanisms in place to address potential ethical concerns raised during the audit?

  4. Data Usage and Reporting

    • How will the extracted data be used in the audit, and what are the intended outcomes?
    • Will findings from this audit be published or shared, and if so, how will patient anonymity be maintained?

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