## 2 Categorization Approach  
### Existing Data Lifecycle Models  
After reviewing several data lifecycle models used in scientific bibliography, the United States Geological Survey (USGS) data lifecycle model was found to be the most suitable for serving as a basis for the development of our categorization approach. This model was created to ensure that USGS research projects and data products are described, stored, accessed, and reused in an appropriate manner [13]. It consists of primary elements and cross-cutting elements. The primary elements are the following:  
1. Plan: This element refers to evaluation, addressing, and documentation of all the other elements of the model.
2. Acquire: The second element involves collection, generation, and evaluation for re-use of data.
3. Process: This element encompasses activities for preparing datasets (e.g., through extraction, transformation, and load operations) for their subsequent analysis and/or integration.
4. Analyze: The fourth element revolves around exploring, interpreting, and transforming to extract knowledge and/or new data.
5. Preserve: This element represents actions for storing data and ensuring that they can be accessed and used in the future.
6. Publish/Share: The sixth element involves activities for distribution and sharing of information.  
Cross-cutting elements are of vital importance, as they refer to certain actions that need to be performed for all of the primary elements on a continuous basis. These elements are:  
1. Describe: This element stresses the importance of metadata based on standards as well as sufficient documentation for all lifecycle stages. This leads to fewer errors and facilitates current and future use of data.
2. Manage Quality: The second cross-cutting element refers to quality assurance (QA) and quality control (QC) measures for all lifecycle stages.
3. Backup and Secure: The third cross-cutting element underlines the importance of preventing physical data losses and promotes secure data management methods.  
Another popular data lifecycle model has been developed by the Digital Curation Centre (DCC). This model offers a generic and highly adaptable approach, which can be used for various purposes (e.g., as a training solution for data creators, for planning and organizing resources, for digital assets risk management). This model consists of: full lifecycle actions (description and representation of information, preservation planning, community watch and participation, curation and preservation), sequential actions (conceptualizing, creating and receiving, appraising and selecting, ingesting, preserving, storing, accessing, using and reusing, transforming), and occasional actions (disposing, reappraising, migrating) [14]. The data lifecycle model proposed by the University of Deusto provides an interesting approach mainly focusing on data management in smart cities [15]. This model encompasses eight lifecycle stages, i.e., discovery, capture, curate, store, publish, linkage, exploit, and visualize. The so-called comprehensive scenario agnostic data (COSA) data lifecycle model, which can be used in many different scientific fields, stresses the importance of data quality in all lifecycle stages and aims to address several data-related challenges. It has three main pillars: (1) data acquisition (including data collection/filtering/quality/description), (2) data processing (encompassing data processing/quality/analysis), and (3) data preservation (including data classification/quality/archive/dissemination). Several other data lifecycle models can be found in scientific bibliography. However, further analysis of such models falls out of scope of this paper.