# ΒΆ Research Data Lifecycle Research data has a "life cycle" that describes and identifies the steps to be taken at the different stages of the research cycle to ensure successful data curation and preservation. The research data lifecycle can be divided into two main parts, **Active Research Stage** and **Post-Active Research Stage**. During the active research stage, research activities mainly include **data planning**, **acquiring**, and **analysis**; while during the post-active research stage, the focus is on **long-term data preservation**, **sharing**, and **re-use** (Also see, [Data Management Best Practices](/handbook/digital-scholarship-methods/data/data-lifecycle/best-practices.md)). ![](/files/-MZJGyLsFlhmlHerTpJn) ### **Active Research Stage** **Planning** - The stage it is determined how data will be managed. Typical considerations include: * The type and format of data will be used. * Whether any collected data will involve human subjects. * Where the data will be stored and whether it will be re-used or shared at the end of the project. **Acquire (or "Find")** - The stage of when data is found or collected. There are a few steps that can help you develop your approach: * **Define your topic as specifically as possible.** For example: * *What is the average SAT score by race for the last 10 years?* * **Identify the unit of analysis, meaning what you will specifically be analyzing and by what measure.** For example: * Geographic unit, e.g., local, national, international * Frequency, e.g., annual, quarterly, daily * Unit of analysis, e.g., individual, institution * Time series, e.g., cross-sectional, longitudinal (or panel) * **Identify data sources.** For example: * Government agencies, e.g., census * Organization, e.g., International Monetary Fund (IMF) * Commercial Subscription Services, e.g., Inter-University Consortium for Political and Social Research (ICPSR), Statista **Collaborate and Analyze** - The stage of your (and your collaborators') acitve use of the research data. * What data processing tool(s) are you using? e.g., Excel, Stata, SPSS, Python, R * What kind of data are you working on? e.g., numerical, categorical, text * What kind of data tasks are you performing? e.g., data cleaning, descriptive statistics. * Are you working in a team and is there a designated project manager? * Are you looking for a web-based tool for working on your data? ### **Post-Active Research Stage** **Store and Preserve** - The planning stage for how the data will be archived for long-term preservation. Considerations include: * What archive/repository/database have you identified as a place to deposit data? e.g., Dataverse * How long will data be kept beyond the life of the project? * What metadata schema will you use? Established domain-specific repositories will usually only accept data that meet their standards for file formats, documentation and metadata, e.g., [DublinCore](https://dublincore.org/) **Share (or Publish)** - The stage in which data is shared (or re-used) after a project. Some considerations include: * Through what resources/platforms the data be made available, e.g., a server or data repository * When the data will be made available, e.g., immediately or after a 12 month embargo * If the dataset was collected by the researchers, how it will be licensed to others e.g., a Creative Commons licenses **Discovery and Re-Use** - this stage involves facilitating data sharing, which refers to publicly sharing data from completed (parts of) research, and having data reusable, i.e. outside your project or research team. * Whether any permission restrictions need to be placed on the data, e.g., non-commercial use * What are the intended or foreseeable uses of the data and who are the users The following video explains the data management activities that can take place at different stages of the research process. {% embed url="" %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://bcds.gitbook.io/handbook/digital-scholarship-methods/data/data-lifecycle.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.