Over the past few years, there has been an explosion of data science as a profession and an academic field. The increasing impact and societal relevance of data science is accompanied by important questions that reflect this development: how can data science become more responsible and accountable while also responding to key challenges such as bias, fairness, and transparency in a rigorous and systematic manner? This Patterns special collection has brought together research and perspective from academia, the public and the private sector, showcasing original research articles and perspectives pertaining to responsible and accountable data science.
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It has become a topic at Dutch educational institutes to feel not only responsible for improvement of theoretical and practical skills, but also of 'competences' in a broader sense. The curriculum of the Electrical and Electronic (E&E) Department has been changed enormously in the past decade. Fewer lessons and many more projects were introduced. We have choosen to let the students work on competences especially in the projects they are in. With the introduction of competences and the aid of a student portfolio we have given the tools to the students to improve their competences in a broader way. At the E &E department we introduced two different ways of working on competences. In the first years of their study students choose different roles in our projects every time. We have described all the roles and the related tasks for each specific role. While working on a role, the students indirectly work on different competences. This way of working inforces a broader educational level (a student shouldn t work on things he already knows or is able to handle) and the hitch hiking behaviour is banned out. Students now do take responsibility while contributing to the project teams. Inquiries amongst the students confirm these results. The second way is working on the specific competences in their traineeship and thesis work in the last part of their study. This will be introduced in autumn 2004 in the E&E department. In this paper we will show you how we are implementing the integration of competences, like the E&E department did, for IPD projects as well. This implementation is planned to start in autumn 2004.
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Background: In implementation science, vast gaps exist between theoretical and practical knowledge. These gaps prevail in the process of getting from problem analysis to selecting implementation strategies while engaging stakeholders including care users. Objective: To describe a process of how to get from problem analysis to strategy selection, how to engage stakeholders, and to provide insights into stakeholders’ experiences. Design: A qualitative descriptive design. Setting and participants: The setting was a care organization providing long-term care to people with acquired brain injuries who are communication vulnerable. Fourteen stakeholders (care users, professionals and researchers) participated. Data were collected by a document review, five interviews and one focus group. Inductive content analysis and deductive framework analysis were applied. Intervention: Stakeholder engagement. Main outcome measures: A three-step process model and stakeholders experiences. Results and conclusion: We formulated a three-step process: (a) reaching consensus and prioritizing barriers; (b) categorizing the prioritized barriers and idealization; and (c) composing strategies. Two subthemes continuously played a role in how stakeholders were engaged during the process: communication supportive strategies and continuous contact. The experiences of stakeholder participation resulted in the following themes: stakeholders and their roles, use of co-creation methods and communication supportive strategies, building relationships, stimulus of stakeholders to engage, sharing power, empowerment of stakeholders, feeling a shared responsibility and learning from one another. We conclude that the inclusion of communicationvulnerable care users is possible if meetings are prepared, communication-friendly presentations and reports are used, and relationship building is prioritized.
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Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
