CAVAA Data Management Plan
The CAVAA project is required to define a Data Management Plan (DMP), aiming to describe the data management life cycle for data generated, collected, and processed by and during the project. Furthermore, it aims to explain how the used data will be handled and preserved after completion of the project.
Over the course of the project, depending on shortcomings, needs, etc., the DMP as described below will be updated. This will be done, for example, when significant changes to the original project plan arise. This might include (1) the generation/collection of (a) new (type of) data, (2) change(s) in the composition of the consortium, (3) change(s) in policies used within the consortium, and (4) external factors.
The size of the data generated/collected/processed throughout the project will be difficult to estimate. This is depending on the development of the project, as this will influence the data output. Furthermore, data sets often comprise large amount of data, requiring a lot of storage. Therefore, the expected data size required will be updated over the course of the project, with the aim of being able to better estimate the total amount of data required as the project progresses.
The data that will be generated/collected/processed during the project, can be classified into several categories. These categories include:
- (General) personal data. This includes the data of the researchers and administrative staff involved in the project. This involves contact information of the associated staff, and personal information required for the appointment of staff at local institutions. This last subset of data will not be stored by the consortium. Instead, this will be collected by the local beneficiaries, in line with the local General Data Protection Regulation (GDPR). The beneficiaries each have their own responsibility in properly handling this data.
- Administrative data. Linked to (General) personal data, this data output will also be generated/collected/processed over the course of the project. This includes, but is not limited to, data associated to meetings (like agendas and minutes), deliverables, and agreements.
- Experimental Data. This includes all data that will be generated/collected/processed during the project, that is required for the project. Examples include (measurable) outcomes of experiments, software/hardware created for the initiation of the experiments, and experimental procedures.
- Scientific data. As mentioned in the Grant Agreement, (a part of) the output produced during this project includes the publication of scientific articles in journals, project demos at prominent events, thematic schools and workshops, and the release of new tools and services that come forth from the project. We will define these into one data category.
- Outreach/Promotional data. Also this data output is described in the project’s Grant Agreement. This category of data includes the generation of promotional materials in any form for social media, events, and other occasions.
Of the categories that have been defined, Scientific data (4) and Outreach/Promotional data (5) will have the most evident significance and role outside of the consortium. Also for the defined categories hold that they will be updated over the course of the project.