Research Lifecycle Support

In addition to our ongoing data skills and publishing workshops, this page lists asynchronous resources and workshops to support you at every stage of the research lifecycle.

The research lifecycle guides your project through seven stages: plan, create, process, analyze, disseminate, preserve, and reuse, so you can develop a management plan, collect and clean data, apply the right analytic methods, share and preserve your findings, and enable future reuse.

Find the stage that matches where you are in your project to build hands‑on skills at your own pace:

1. Plan

Develop a research data management plan that addresses each of the components of the research lifecycle. A well thought-out plan will save you time and ensure your data remains organized, secure, and reusable.

Resources to support data planning

• Dig deeper into all things Research Data Management
• Check out our Organizing Your Data guide to learn the best practices for organizing research data
• Learn more about using Copyright protected works

2. Find / Create

The creation phase focuses on generating or collecting data through experiments, surveys, observations, digitizing analogue data through scanning and transcription, or other methods. It’s essential to document your methodology and data formats clearly to ensure data integrity and future usability.

Key questions to consider:

• What type of data does your research involve?
• Does your data exist already, or will you need to create it?
• How will you collect your data?

Choosing the right methodology and workflow ensures you gather high-quality, well-structured data.

Resources to support finding or creating data

• If you’re using Qualtrics survey software, XM Basecamp offers a variety of free tutorials
• Are you looking for data, statistics or another type of resource needed for your research? The library has access to many resources:
  • Find primary, secondary, and tertiary sources in the science
  • Find data and statistics
  • Find geospatial data
  • Find open data
  • Find environmental, social and governance data
  • Find market research
  • Find country info
  • Find industry info
  • Find government publications
• Learn more about Indigenous data and Indigenous data sovereignty
• Do you have a dataset with postal code data? Find out how using the Postal Code Conversion File (PCCF) may be beneficial for your research

3. Process

The process stage involves cleaning, organizing, and transforming raw data into a format that’s suitable for analysis. This step ensures consistency, accuracy, and readiness for deeper exploration.

Key questions to consider:

• How does your data need to be prepared for analysis?
• Are there any errors (misspellings, duplications, improper data formatting, etc.) that need to be cleaned?
• How will you handle missing values?

This step ensures your data is accurate, consistent, and ready for analysis.

Resources to support data processing

• Consult the Clean and Prepare Your Data guidefor steps and software which can help you process your data
• Dig deeper into the importance and various tasks associated with data cleaning during the Research Data Management process

4. Analyze

The analysis stage is where insights are drawn from the data using quantitative, qualitative, or geospatial methods. Proper documentation and reproducibility practices are key to validating findings and supporting future research.

Key questions to consider:

• What is your research question(s)?
• What types of statistical, geospatial, or textual analyses will you be conducting?
• Are there any biases or limitations in the analyses or the underlying data that could affect results?
• What software might you use to conduct your analysis?

With high-quality data, you can apply a range of analytic techniques, but it is important to understand and document the decisions that you make during your analysis.

Resources to support data analysis

• Using quantitative analysis software SPSS? Check out our SPSS Video series
• Using quantitative analysis software R? Check out our R / RStudio guide
• Are you working with geospatial data?
  • If you’re using ESRI, check out their tutorials
  • If you’re using QGIS, check out their training manual

5. Disseminate

Dissemination is the sharing of research outputs with others through publications, presentations, or repositories. This stage promotes transparency, collaboration, and broader impact of the research.

Key questions to consider:

• How will you share your research and data?
• Do you need to visualize your data in a chart, graph or map?
• What kind of data visualizations might best communicate your results to others?

Sharing and communicating your findings effectively is critical, this step can help you choose the right format to communicate your research.

Resources to support dissemination

• Consult our Data Visualization guide for best practices and determining which chart to use
• Our Indigenous Data Sovereignty guide can help you identify frameworks for responsible data use and sharing
• Our Open Access Publishing page provides an overview of supports and resources available to help researchers share their work openly
• Consult the Using Open Access Repositories guide to assess which repository is right for you

6. Preserve

Preservation ensures that research remains accessible and usable over time. It involves selecting a repository, appropriate formats, metadata, and storage solutions to safeguard data for future use.

Key questions to consider:

• Do you need to deposit your research in a repository?
• Have you received research funding that requires you to deposit and preserve your research?
• Do you know how to prepare your files, metadata, and documentation for deposit?

Resources to support preservation

• Use the Data Repositories guide to learn how to deposit and preserve your research data
• Use the Atrium guide to learn how to deposit and preserve your scholarly and creative works such as articles and theses/dissertations

7. Reuse

Reuse allows data and other scholarly outputs to be repurposed for new research, teaching, or policy-making. Well-documented and accessible outputs can inspire innovation and extend the value of the original output.

Key questions to consider:

• Have you thought about sharing your research and data under an open access license?
• What are the legal obligations associated with that?

Strong consideration of how you can enable reuse can accelerate discovery.

Resources to support reuse

• To help you determine if an open license is right for you, read our Licensing Datasets guide or watch the Creative Commons License video
• Learn more about Copyright at the University of Guelph

License