MobiDataLab D5.3 Analysis and conclusions on the data exchange culture
Creators: name: HADJIDIMITRIOU, Selini
Publisher: name: Zenodo
Year: 2024
Subjects: transport cloud
data sharing culture
data security
GDPR
PaaS
TAM
UTAUT

description: This deliverable presents the results of the Transport Cloud acceptance and data-sharing cultureevaluation. The assessment of the Transport Cloud's acceptance through the MobiDataLab projectreveals valuable insights into its effectiveness, user perceptions, and areas for improvement. Thetransition from stakeholder expectations to practical experiences during the x-athons, involvingdatathon, hackathon, and codagon events, provides a comprehensive view of the platform'sevolution. The evaluation of the Transport Cloud reveals a dynamic landscape of user perceptions,emphasizing the need for continuous engagement, clear communication, and iterativeimprovements.

Concerning the data-sharing culture, data quality and completeness in the context of mobilityunderscore the intricate interplay between accuracy, representativity, and the open availability ofdata. Ensuring accurate data is a starting point, but anonymization without compromising quality iscrucial. Advanced data science skills are required to navigate potential pitfalls and make datasetsmeaningful for transportation analysis. Direct connections with data owners, awareness oflimitations, and clear expectations are vital for improving overall data quality. Legal responsibility indata-sharing for public services involves evolving regulations like GDPR. The collaborationbetween the public and private sectors emphasizes transparency, personal data control, and ashared goal of an interconnected and transparent transport system. Knowing how data is used iscrucial for building public trust. Establishing trust is key in collaborations, especially with diversestakeholders. Governmental authorities play a crucial role in building and maintaining trust. Trustcan be strained when partners do not respect research questions or provide low-quality data.Partner selection involves balancing fairness, encouraging participation, and diversifyingopportunities.

Previous experience and knowledge-sharing are crucial for creating long-term value in datasharing. Transparency about successes and challenges, adapting to evolving realities, and learning from past experiences contribute to a culture of collaboration. EU projects provide motivation, fostering commitment and satisfaction among participants.In the pursuit of sustainable transportation, data-sharing contributes to optimizing logistics andreducing environmental impact. Demonstrating the benefits of public transport through datapromotes environmental awareness. Machine learning plays a role in recommendingenvironmentally friendly transport modes. Rigorous measurement of environmental impact throughdata analytics is essential for achieving sustainability goals.

Sharing data and tools is essential for achieving common goals and building necessaryinfrastructure. Effective communication and information exchange are vital for promoting thepositive aspects of data-sharing. Encouraging experts to work in public institutions is crucial forleveraging analytical capabilities in public mobility planning.