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Every day a vast amount of misinformation and Fake News are repeated and infinitely shared, reaching millions of people in a short time. The large-scale dissemination of misinformation is one of the major challenges that current societies face, with long-lasting costs to individuals and governments. European Commission’s recent efforts in seeking advice from experts regarding measures to counteract disinformation attest to the urgency of addressing this issue. The fact that people tend to believe in information they repeatedly encounter and to reject claims that contradict what they heard before makes misinformation-correction very difficult. Since most correction strategies entail both a repetition of the false claims and their contradiction, they ironically end up strengthening the validity of the misinformation they attempt to correct. It is thus of the utmost importance to examine the mechanisms that may contribute to the development of effective misinformation-correction actions.
Population growth, increased prosperity and rapid urbanization are bringing global demand for natural resources to a point increasingly beyond the Earth’s carrying capacity. Together with climate change, those pressures are causing significant environmental degradation in many parts of the planet. Latin America is particularly vulnerable. The United Nations Sustainable Development Goals (SDGs) constitute urgent calls and drivers for higher education to be part of future generations of engaged citizens aware of their role in creating fair and healthy societies.
The Change the Climate project addresses three main needs: environmental management at all levels of higher education activities, integration of environmental management with sustainability strategies and institutional quality management, and customized strategies for sustainability in education. The project’s main goal is to increase Latin-American University’s contribution to Sustainable Development, through the implementation of environmental systematic practices and quality processes in alignment with the UN SDGs, improving the management and operations of higher education institutions.
The project will deliver tools and guides for environmental impact analysis and SDGs mapping in campus operations and educational activities. An environmental management system will be implemented in each partner university decreasing their environmental impact; sustainability awareness will be assessed thoughout the academic communities; strategies for sustainability in higher education will be developed for curricula improvement; and a common open online course on sustainability will be created in English, Spanish and Portuguese.
The project’s impact will reach stakeholders beyond the project partnership at local, regional and national levels contributing to behavioural change for sustainable futures.
Projecto internacional (envolvendo 5 países: Estónia, Portugal, Finlândia , Polónia, e Itália), “Be Competent in Entrepreneurship: Knowledge Alliances for Developing Entrepreneurship Competencies for the Benefit of Higher Education and Business(BeComE)” (612582-EPP-1-2019-1-EE-EPPKA2-KA), no âmbito da Call Erasmus+, KA - Knowledge Alliances, 2019 - EAC-A03-2018 - Cooperation for innovation and the exchange of good practices, financiado pela Comissão Europeia e coordenado porUrve Venesaar (Tallinn University of Technology, Estónia).
The objective of this project is to explore the use of visual programming paradigms to enable non-programmers to be part of the Data Science workforce.
In contrast to existing approaches, which require programming, Scientific Workflow Management Systems (SWMS) can become an alternative to support the visual programming of data science projects. Such systems (e.g. Taverna and Kepler) use a simple graphical, graph-based structure to develop applications.
This simplicity has shown to be suitable in several scientific areas such as bioinformatics, geophysics, and climate analysis. Despite the success of SWMS in data intensive research, they did not reach a state where non-programmers data scientists can use them. They still require some programming and scripting skills to code individual processing tasks. That is why research teams using those systems are usually composed of scientists and software developers.
We propose to extend current SWMS to support the parameterization of generic prebuild workflow templates. Workflow templates capture the processing tasks of data science projects. A template can be seen as a formalized best practice that data scientists can use to solve common data analysis challenges. Templates are developed by multidisciplinary teams of experts and reused by non-programmer data scientists. Parameterized workflows have been used successfully in the field of enterprise computing since 1970 to increase software reuse (e.g. SAP’s parameterized workflows to automate business process models). We claim that the same type of benefits can be obtained by parameterizing scientific workflow templates.