Projects

Use your data to inform decision making and better serve your beneficiaries and customers! Apply to the second edition of the “Data for Change” program and develop a Data Science project free of charge.

Applications until May 31!

“Data for Change” is a pioneering program in Portugal, which aims to identify Portuguese Social Impact Organizations that have a social/ environmental problem that can be solved through Data Science.

The projects will be developed in partnership with the Nova SBE Data Science Knowledge Center team. The proposed challenges can be as varied as possible, covering areas such as Health, Employment, Environment and Education.

See here the complete information about the “Data for Change” program (in Portuguese).

The “Data for Change” program is part of the Social Equity Initiative, a partnership between Nova SBE,  “la Caixa” Foundation and BPI.

Developing a predictive support system to understand which individuals are at risk of becoming long-term unemployed, in order to assist IEFP's Counsellors to determine how to personalize their professional development plans best. The tool allows Counsellors to understand which factors contribute to an individual's risk, giving them insight into the system's decision-making process, and granting the agency to override the tool in favor of an expert human judgment when appropriate. We are currently preparing a pilot study that will provide a critical evaluation of Counsellor’s opinions about the tool, to better understand the extent to which it facilitates their daily interactions with unemployed persons, and to support future research in unemployment prevention. This work was co-created with experts in labor policy and social work, including the Counsellors themselves in the design process.

Partner: IEFP, FEUP and FCT

# Social Good  # Unemployment  # Decision-Making

In the last few years, Portugal has been witnessing rapid growth in tourism which is ill-equipped to face hard to quantify challenges such as tourist congestions, loss of city identity, degradation of patrimony, etc. The project proposes a data-driven approach to the tourism management in the Portuguese territory through a set of tools to inform Portuguese policy-makers about the state of the tourism industry, going beyond the traditional survey data and research and providing complete analyses of this sector using Big Data sources. With the insights provided by the project, tourism decision-makers will have crucial information to design policies that foster sustainable and friendly cities, both for tourists and dwellers.

Partners: Turismo de Portugal and NOS

# Social Good  # Tourism Management  # Data Driven Policy

Data-driven management is increasingly on the agenda of many Municipalities. The amount of data that Municipalities generate themselves, plus the data available from local partners such as Police, Transportation Companies, Telecom, among many others, offer a huge range of opportunities to develop new knowledge that supports public policy. CCDE is the data hub for the region, aiming to provide meaningful insights for local challenges, to experiment new approaches and solutions within the region and to foster knowledge dissemination through the integration of youth and a series of events.  

Partner: Câmara Municipal de Cascais

# Social Good  # Data Driven Policy

The social sector represents an important sector of the Portuguese economy. However, the few data sources available provide little and disconnected pieces of information, a low level of interaction and poor visualizations. The goal of the project is to create a new and integrated database for the social sector to be at the disposal of the Portuguese society, serving NGOs, foundations, impact-driven companies, researchers, investors, Government agencies, among other stakeholders.

Partner: La Caixa Foundation

# Social Good   # Portuguese Social Sector

The project’s goal was to create a machine learning model using data from incidents recorded, road characteristics, speed and flow traffic data and weather data in order to predict, within a given time window, the probability of an accident. This project allows for a better allocation of road inspectors to road sections to patrol and decrease their response time to incidents.

Partner: Rijkswaterstaat

# Data Science for Social Good 2018  # Transportation  # Decision-Making

As Tuscany is one of the most popular tourist destinations in the world, the region, its resources and inhabitants have been put under extreme pressure. The project aimed to use machine-learning algorithms to group individuals and, thus, help to better understand the region’s tourists and improve tourism management.

Partners: Toscana Promozione Turistica and Vodafone Italy

# Data Science for Social Good 2018  # Tourism Management  # Data-Driven Policy

The purpose of the project is to use the data made available by the IEFP to build a modelled system whose objective is to empower the IEFP and its local offices to better identify individuals at high risk of long-term unemployment, and to provide data-driven insights which will allow for a more efficient allocation of IEFP’s resources to respond to the needs of unemployed individuals.

Partners: IEFP and EAPN

# Data Science for Social Good 2018  # Unemployment  # Data-Driven Policy

The ultimate goal of the project was to increase the vaccination rate for measles, mumps, and rubella in Croatia. In order to achieve this, the team predicted the risk level of each child of not receiving the required two doses of the MMR vaccine and develop clusters of the above-mentioned children, according to similar features that would allow for customized vaccination-promotion policies.

Partners: Croatian Institute of Public Health (CIPH), Institute of Public Health of Split-Dalmatia County, Andrija Stampar School of Public Health and Croatian Society for School and University Medicine

# Data Science for Social Good 2018  # Healthcare  # Data-Driven Policy

The project aimed to support local authorities in understanding and measuring tourism beyond traditional surveys and statistics so that they can explore and design solutions for sustainable tourism in the city.

Partners: Toscana Promozione Turistica and Vodafone Italy

# Data Science for Social Good 2017  # Tourism Management  # Data Driven Policy

Illegal fishing and over-fishing are posing great threats to our oceans. It is estimated that 70% of global fisheries are over-exploited, and many are now on the brink of collapse. Populations of tuna, a critical apex predator in many marine ecosystems, have declined by over 90% in the past 40 years. Unless serious action is taken, we may be on the brink of large scale fishery collapses, which could devastate the environment and the livelihoods of millions around the world that depend on such fisheries.

The project aimed to create an open-source Risk Tool combining multiple satellite data sources to help overcome illegal, unreported and unregulated fishing, and creating a risk score.

Partners: Spire, World Economic Forum, Planet and DigitalGlobe

# Data Science for Social Good 2017  # Sustainability

The goal of the project was to assist Cascais’ Municipality in understanding patterns of unemployment in the region and to develop a system to recommend the best type of interventions to bridge skills gaps and increase people’s likelihoods of employment.

Partners: Câmara Municipal de Cascais and IEFP

# Data Science for Social Good 2017  # Unemployment  # Data Driven Policy

The project focused on optimizing matchmaking between patients and doctors by developing an automated mediation service. Its main objective was to increase the likelihood of a long-lasting relationships and the quality of follow-ups, as well as the practice of preventive medicine.

Partner: José de Mello Saúde

# Data Science for Social Good 2017  # Healthcare  # Decision-Making

The project aimed to help Rijkswaterstaat, a division of the Dutch Ministry of Infrastructure and Environment, by developing a policy for optimizing stationary and patrolling locations of inspectors on duty to minimize the time it takes them to reach an incident site. This way, the project contributed to optimize safety and traffic flow for road users.

Partner: Rijkswaterstaat

# Data Science for Social Good 2017  # Transportation  # Decision-Making

The project aimed to figure out how improved use of its rooftops could help address challenges with water storage, green spaces, and energy generation. In order to achieve this, the team used aerial and satellite images to identify the usage of rooftops in Rotterdam.

Partner: City of Rotterdam

# Data Science for Social Good 2017  # Data-Driven Policy

Diabetic Nephropathy is a critical condition that can lead to irreversible damage to the kidneys. Chronic Kidney Disease describes the gradual loss of kidney function that greatly diminishes the qualify of life of those suffering from it, as well as reduces life expectancy. Early signs of diabetic neuropathy are critical to prevent or slow down disease progression. Our project with APDP aims to predict the probability of disease onset in twelve months to allow medical teams to proactively act and avoid progression. 

Partner: APDP (Associação Protectora dos Diabéticos de Portugal) and La Caixa Foundation

# Social Good # Healthcare # Predictive Analytics

Crowdfunding social projects is an innovative and inspiring way to foster cooperating and collaboration within our societies. This project aims to determine the key factors that influence the success of a crowdfunding campaign. The main goal is to support PPL guiding fundraisers how to set up campaigns, as well as proactively react to on-going ones in order to maximise the chance of raising the capital required for their projects.

Partner: PPL

# Social Good # Crowdfunding # Decision-Making