Glossary

In machine learning and data science, random state is a parameter that controls the randomness of algorithms, ensuring they are deterministic and reproducible. By assigning a specific integer to a random state, you guarantee that the same sequence of random numbers will be generated each time the code is executed.

A variable that stores responses given to a question in the survey in their original form. Contrast with derived variables.

A document placed at the top level of a project folder that explains what the project is, how it is organised, and how to use it. A typical GitHub ReadMe might include sections like an introduction, installation instructions, usage examples, and a list of contributors. For example, a Readme for a data analysis project could start with a summary of the purpose, followed by setup steps and code snippets showing how to run analyses. It is typically the first thing a collaborator or reviewer will read when they encounter the project.

A statistical model used to estimate how changes in one or more variables are associated with changes in another variable.

A method in which an agent (a system that can make decisions and take actions) learns by trial and error, receiving feedback in the form of rewards or penalties. Over time, the agent learns which actions lead to the best outcomes and uses this experience to improve its future decisions. For example, an AI writing assistant might suggest a synonym to a frequently used word based on the text surrounding the target word and is given a score of 1 to 5 based on how well the author likes the suggestion.

A centralised folder or storage location used in version-controlled environments (such as GitHub, GitLab, or Bitbucket) for keeping code, data, and documentation related to a research project. A version-controlled repository automatically saves a history of every change, allowing users to track revisions, collaborate, and revert to previous versions if needed. These repositories can be stored locally or on cloud-based platforms, enabling multiple contributors to access and update the project efficiently.

A representative sample is one that replicates the characteristics of the population.

In the context of scientific research, reproducibility refers to the ability of an independent researcher or team to recreate the results of a study using the same methods and data as the original study. This concept hinges on the provision of detailed methodology, context, and background information.

Reproducible research involves making data, code, and computational steps publicly available, where possible, so that others can verify, replicate, and build upon findings. This practice enhances the reliability of research, promotes transparency, and facilitates the extension of findings by making the original inputs available, where possible, ensuring consistent results.

Research data management refers to the systematic organisation, storage, preservation and sharing of data resulting from a research project. It involves practices that span the entire data lifecycle, from planning the collection to wider data sharing.

Source: CODATA RDM Terminology Working Group. (2024). CODATA RDM Terminology (2023, v0001): overview (2023, v0001). Zenodo. DOI: https://doi.org/10.5281/zenodo.10626170

Research methodology is a description of the approach followed to complete a research project; the ‘how’ that helps the researcher address the research aims, objectives and research questions.

A person, or other entity, who responds to a survey.