Data Management Plan

EDIAQI project has started to conduct indoor air quality studies/pilots in several European cities, gather the data on the Know Data Platform (KDP) and share data on the IPCHEM Portal and within the IDEAL cluster. As part of the European Commission’s goal to advance Open Science policy and practices – it is a mandatory component in Horizon Europe that all projects involving data include a data management plan (DMP). Once a project is selected for funding an extensive DMP must be provided as a deliverable within 6 months from the project’s official launch. The plan is expected to be updated and adjusted regularly, in line with the progress of the project.

The DMP outlines the ways in which data is collected, generated and/or processed throughout the lifespan of a research project. It comprises these data sharing activities and is based on the Horizon Europe Data Management template and therefore also comprises the devised crucial data management sections to be developed and discussed within the EDIAQI project team, some of which are data summary, FAIR data, other research outputs, allocation of resources, data security, ethics, and other issues. The DMP is defined and implemented to ensure a high level of data quality and accessibility for final users and stakeholders, and to allow the application of data analytics techniques. This task is also responsible for the detection and assessment of the knowledge generated by the consortium. The plan outlines the measures that will be taken to maximize access and re-use of the data for further purposes and applications. Whenever personal data are being processed, compliance and security are important. In the cases where the datasets cannot be publicly shared, the reasons will be mentioned in their metadata descriptions (ethical, rules of personal data, intellectual property, commercial, privacy-related, security-related). As such, it is important to draw up a DMP that fits the specific characteristics of EDIAQI.

Data acquisition within EDIAQI is carried out on 3 levels:

i) Sensor (top) represents the conceptual layer where IoT (Internet of Things) and sensor equipment are provided

ii) Integration (middle) is the conceptual layer of the infrastructure where data must be harmonized to fit a common analytics and processing model. As a result, it will be possible to share all the data collected at pilots through web services based on common standards (WP4)

iii) Presentation (bottom) is the final conceptual layer of the infrastructure where harmonized data will be made visible and accessible through maps, tables, and diagrams.