Academic design research often fails to contribute to design practice. This dissertation explores how design research collaborations can provide knowledge that design professionals will use in practice. The research shows that design professionals are not addressed as an important audience between the many audiences of collaborative research projects. The research provides insight in the learning process by design professionals in design research collaborations and it identifies opportunities for even more learning. It shows that design professionals can learn about more than designing, but also about application domains or project organization.
Many studies report changes taking place in the field of higher education, changes which present considerable challenges to educational practice. Educational science should contribute to developing design guidance, enabling practitioners to respond to these challenges. Design patterns, as a form of design guidance, show potential since they promise to facilitate the design process and provide common ground for communication. However, the potential of patterns has not been fully exploited yet. We have proposed the introduction of a task conceptualization as an abstract view of the concept chosen as central: the task. The choice of the constituting elements of the task conceptualization has established an analytical perspective for analysis and (re)design of (e)learning environments. One of the constituting elements is that of ‘boundary objects’, which has added a focus on objects facilitating the coordination, alignment and integration of collaborative activities. The presented task conceptualization is deliberately generic in nature, to ease the portability between schools of thought and make it suitable for a wide target audience. The conceptualization and the accompanying graphical and textual representations have shown much promise in supporting the process of analysis and (re)design and add innovative insights to the domain of facilitating the creation of design patterns.
Many quality aspects of software systems are addressed in the existing literature on software architecture patterns. But the aspect of system administration seems to be a bit overlooked, even though it is an important aspect too. In this work we present three software architecture patterns that, when applied by software architects, support the work of system administrators: PROVIDE AN ADMINISTRATION API, SINGLE FILE LOCATION, and CENTRALIZED SYSTEM LOGGING. PROVIDE AN ADMINISTRATION API should solve problems encountered when trying to automate administration tasks. The SINGLE FILE LOCATION pattern should help system administrators to find the files of an application in one (hierarchical) place. CENTRALIZED SYSTEM LOGGING is useful to prevent coming up with several logging formats and locations. Abstract provided by the authors. Published in PLoP '13: Proceedings of the 20th Conference on Pattern Languages of Programs ACM.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
Design, Design Thinking, and Co-design have gained global recognition as powerful approaches for innovation and transformation. These methodologies foster stakeholder engagement, empathy, and collective sense-making, and are increasingly applied to tackle complex societal and institutional challenges. However, despite their collaborative potential, many initiatives encounter resistance, participation fatigue, or only result in superficial change. A key reason lies in the overlooked undercurrent—the hidden systemic dynamics that shape transitions. This one-year exploratory research project, initiated by the Expertise Network Systemic Co-design (ESC), aims to make systemic work accessible to creative professionals and companies working in social and transition design. It focuses on the development of a Toolkit for Systemic Work, enabling professionals to recognize underlying patterns, power structures, and behavioral dynamics that can block or accelerate innovation. The research builds on the shared learning agenda of the ESC network, which brings together universities of applied sciences, design practitioners, and organizations such as the Design Thinkers Group, Mindpact, and Vonken van Vernieuwing. By integrating systemic insights—drawing from fields like systemic therapy, constellation work, and behavioral sciences—into co-design practices, the project strengthens the capacity to not only design solutions but also navigate the forces that shape sustainable change. The central research question is: How can we make systemic work accessible to creative professionals, to support its application in social and transition design? Through the development and testing of practical tools and methods, this project bridges the gap between academic insights and the concrete needs of practitioners. It contributes to the professionalization of design for social innovation by embedding systemic awareness and collective learning into design processes, offering a foundation for deeper impact in societal transitions.
