Data life cycle & types

Research data have a long lifespan, often longer than the period between their creation and the writing of the scientific publication for which they were created. The function and value of the data changes from one phase of the cycle to the next. The concept of research data life cycle is a tool that can be used to map different phases and see how they connect to each other. The use of a life cycle makes it possible to move from a short-term perspective to a long-term perspective in data management.

Developed by UK Data Archive, the Research Data Lifecycle Reference Model defines 6 main steps : Data creation ; Data processing ; Data analysis ; Preparing data for preservation ; Data access ; Data reuse.

Each of these steps consists of several actions to be carried out to ensure proper management of research data.

Uniris has developed a similar vision based also on 6 phases :

1. Project planning management (DMP)
2. Data collection or creation
3. Organization and analysis
4. Preservation and curation
5. Archiving and sharing (publication)
6. Reuse of data

Taking these 6 phases into account allows the following aspects to be achieved :

• Secure storage during the active phase
• Long term storage of organized and documented data
• Archiving on a FAIR and non-commercial repository, and sharing (Open research Data)

A distinction is made between active research data, the preservation of part of this data (long-term preservation) and permanent archiving and data sharing.

• active research data are data in use by the researcher ;
• long-term stored data are data that have already been analysed and are available for consultation and/or use in other research, or that have not yet been used in the first research ;
• data that are permanently archived and shared via a non-commercial and FAIR data repository are archived to allow their accessibility and reuse over time and thus meet the challenges of Open research Data.

See the diagram on the right.

Research data are numerous, varied and very heterogeneous. They can be divided into five categories (André, 2014) :

•     Observation data
•     Experimental data
•     Computational data, models or simulations
•     Derived or compiled data
•     Reference or canonical data

Depending on their context of creation (capture or production), exploitation, analysis and processing, research data can be of different kinds :

• Raw, derived, formatted, cleaned, primary, secondary, processed, etc.

Contained in various media :

• Laboratory notebooks, electronic documents, paper, software, computer programs, etc.

All types :

•  Archives, audio, video, databases, source codes, geospatial, images, photographs, programming languages, hardware and physics, models, visualizations, 3D, digital, textual, digitization, scanning, qualitative, quantitative, statistical, etc.