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European Union's Transport policy's pivotal aim is to strengthen the existing Transport infrastructure, which is crucial to economic development. The improvement in the transport sector should provide efficient logistics of goods, better travel and commuting facilities, and accessibility of the European region. This report, as part of the first phase of the Leveraging Big Data to Manage Transport Operations (LeMO) project, provides an introduction to big data in the transport sector. It identifies untapped opportunities and challenges and describes numerous data sources. This report is a part of WP1 which is a cornerstone of the LeMO project. It aims to generate a shared understanding of current big data landscape in transport and identifies a holistic view on opportunities, challenges, and limitations. The remainder of this report is structured as follows: Chapter 2 explores the characteristic of big data and highlights the big data challenges in the transport sector. It covers six transportation modes (air, rail, road, urban, water and multi-modal) and two transportation sectors (passenger and freight). Chapter 3 identifies several opportunities and challenges of big data in transportation, by using: several subject matter expert interviews, nineteen applied cases, and a literature review. It also indicates that the combination of different means and approaches will enhance the opportunities for successful big data services in the transport sector. Chapter 4 offers an intensive survey of the various data sources, data producers, and service providers. In addition, cartography was modeled to visualize data flows intuitively. Cartography demonstrates where data originated from and where it is flowing to. Chapter 5 summarizes all findings and provides a conclusion.

One of the application fields for data analysis techniques and technologies gaining momentum is the area of social good or "common good", covering cases related to humanitarian crises, global health care, or ecology and environmental issues, among others. The promotion of data-driven projects in this field aims at increasing the efficacy and efficiency of social initiatives, improving the way these actions help humanity in general and people in need in particular. This application field, however, poses its own barriers and challenges when developing data-driven projects, lagging behind in comparison with other scenarios. These challenges derive from aspects such as the scope and scale of the social issue to solve, cultural and political barriers, the skills of main stakeholders and the technological resources available, the motivation to be engaged in such projects, or the ethical and legal issues related to sensitive data. This paper analyzes the application of data projects in the field of social good, reviewing its current state and noteworthy initiatives, and presenting a framework covering the key aspects to analyze in such projects. The goal is to provide guidelines to understand the main challenges and opportunities for this type of data project, as well as identifying the main differential issues compared to "classical" data projects in general. A case study is presented on the initial steps and stakeholder analysis of a data project for the inclusion of refugees in the city of Frankfurt, Germany, in order to empirically confront the framework with a real example.