This paper introduces and contextualises Climate Futures, an experiment in which AI was repurposed as a ‘co-author’ of climate stories and a co-designer of climate-related images that facilitate reflections on present and future(s) of living with climate change. It converses with histories of writing and computation, including surrealistic ‘algorithmic writing’, recombinatory poems and ‘electronic literature’. At the core lies a reflection about how machine learning’s associative, predictive and regenerative capacities can be employed in playful, critical and contemplative goals. Our goal is not automating writing (as in product-oriented applications of AI). Instead, as poet Charles Hartman argues, ‘the question isn’t exactly whether a poet or a computer writes the poem, but what kinds of collaboration might be interesting’ (1996, p. 5). STS scholars critique labs as future-making sites and machine learning modelling practices and, for example, describe them also as fictions. Building on these critiques and in line with ‘critical technical practice’ (Agre, 1997), we embed our critique of ‘making the future’ in how we employ machine learning to design a tool for looking ahead and telling stories on life with climate change. This has involved engaging with climate narratives and machine learning from the critical and practical perspectives of artistic research. We trained machine learning algorithms (i.e. GPT-2 and AttnGAN) using climate fiction novels (as a dataset of cultural imaginaries of the future). We prompted them to produce new climate fiction stories and images, which we edited to create a tarot-like deck and a story-book, thus also playfully engaging with machine learning’s predictive associations. The tarot deck is designed to facilitate conversations about climate change. How to imagine the future beyond scenarios of resilience and the dystopian? How to aid our transition into different ways of caring for the planet and each other?
BackgroundScientific software incorporates models that capture fundamental domain knowledge. This software is becoming increasingly more relevant as an instrument for food research. However, scientific software is currently hardly shared among and (re-)used by stakeholders in the food domain, which hampers effective dissemination of knowledge, i.e. knowledge transfer.Scope and approachThis paper reviews selected approaches, best practices, hurdles and limitations regarding knowledge transfer via software and the mathematical models embedded in it to provide points of reference for the food community.Key findings and conclusionsThe paper focusses on three aspects. Firstly, the publication of digital objects on the web, which offers valorisation software as a scientific asset. Secondly, building transferrable software as way to share knowledge through collaboration with experts and stakeholders. Thirdly, developing food engineers' modelling skills through the use of food models and software in education and training.
This paper presents a mixed methods study in which 77 students and 3 teachers took part, that investigated the practice of Learning by Design (LBD). The study is part of a series of studies, funded by the Netherlands Organisation for Scientific Research (NWO), that aims to improve student learning, teaching skills and teacher training. LBD uses the context of design challenges to learn, among other things, science. Previous research showed that this approach to subject integration is quite successful but provides little profit regarding scientific concept learning. Perhaps, when the process of concept learning is better understood, LBD is a suitable method for integration. Through pre- and post-exams we measured, like others, a medium gain in the mastery of scientific concepts. Qualitative data revealed important focus-related issues that impede concept learning. As a result, mainly implicit learning of loose facts and incomplete concepts occurs. More transparency of the learning situation and a stronger focus on underlying concepts should make concept learning more explicit and coherent.
Climate change adaptation has influenced river management through an anticipatory governance paradigm. As such, futures and the power of knowing the future has become increasingly influential in water management. Yet, multiple future imaginaries co-exist, where some are more dominant that others. In this PhD research, I focus on deconstructing the future making process in climate change adaptation by asking ‘What river imaginaries exist and what future imaginaries dominate climate change adaptation in riverine infrastructure projects of the Meuse and Magdalena river?’. I firstly explore existing river imaginaries in a case study of the river Meuse. Secondly, I explore imaginaries as materialised in numerical models for the Meuse and Magdalena river. Thirdly, I explore the integration and negotiation of imaginaries in participatory modelling practices in the Magdalena river. Fourthly, I explore contesting and alternative imaginaries and look at how these are mobilised in climate change adaptation for the Magdalena and Meuse river. Multiple concepts stemming from Science and Technology Studies and Political Ecology will guide me to theorise the case study findings. Finally, I reflect on my own positionality in action-research which will be an iterative process of learning and unlearning while navigating between the natural and social sciences.
The SPRONG-collaboration “Collective process development for an innovative chemical industry” (CONNECT) aims to accelerate the chemical industry’s climate/sustainability transition by process development of innovative chemical processes. The CONNECT SPRONG-group integrates the expertise of the research groups “Material Sciences” (Zuyd Hogeschool), “Making Industry Sustainable” (Hogeschool Rotterdam), “Innovative Testing in Life Sciences & Chemistry” and “Circular Water” (both Hogeschool Utrecht) and affiliated knowledge centres (Centres of Expertise CHILL [affiliated to Zuyd] and HRTech, and Utrecht Science Park InnovationLab). The combined CONNECT-expertise generates critical mass to facilitate process development of necessary energy-/material-efficient processes for the 2050 goals of the Knowledge and Innovation Agenda (KIA) Climate and Energy (mission C) using Chemical Key Technologies. CONNECT focuses on process development/chemical engineering. We will collaborate with SPRONG-groups centred on chemistry and other non-SPRONG initiatives. The CONNECT-consortium will generate a Learning Community of the core group (universities of applied science and knowledge centres), companies (high-tech equipment, engineering and chemical end-users), secondary vocational training, universities, sustainability institutes and regional network organizations that will facilitate research, demand articulation and professionalization of students and professionals. In the CONNECT-trajectory, four field labs will be integrated and strengthened with necessary coordination, organisation, expertise and equipment to facilitate chemical innovations to bridge the innovation valley-of-death between feasibility studies and high technology-readiness-level pilot plant infrastructure. The CONNECT-field labs will combine experimental and theoretical approaches to generate high-quality data that can be used for modelling and predict the impact of flow chemical technologies. The CONNECT-trajectory will optimize research quality systems (e.g. PDCA, data management, impact). At the end of the CONNECT-trajectory, the SPRONG-group will have become the process development/chemical engineering SPRONG-group in the Netherlands. We can then meaningfully contribute to further integrate the (inter)national research ecosystem to valorise innovative chemical processes for the KIA Climate and Energy.
The objective of DIGIREAL-XL is to build a Research, Development & Innovation (RD&I) Center (SPRONG GROUP, level 4) on Digital Realities (DR) for Societal-Economic Impact. DR are intelligent, interactive, and immersive digital environments that seamlessly integrate Data, Artificial Intelligence/Machine Learning, Modelling-Simulation, and Visualization by using Game and Media Technologies (Game platforms/VR/AR/MR). Examples of these DR disruptive innovations can be seen in many domains, such as in the entertainment and service industries (Digital Humans); in the entertainment, leisure, learning, and culture domain (Virtual Museums and Music festivals) and within the decision making and spatial planning domain (Digital Twins). There are many well-recognized innovations in each of the enabling technologies (Data, AI,V/AR). However, DIGIREAL-XL goes beyond these disconnected state-of-the-art developments and technologies in its focus on DR as an integrated socio-technical concept. This requires pre-commercial, interdisciplinary RD&I, in cross-sectoral and inter-organizational networks. There is a need for integrating theories, methodologies, smart tools, and cross-disciplinary field labs for the effective and efficient design and production of DR. In doing so, DIGIREAL-XL addresses the challenges formulated under the KIA-Enabling Technologies / Key Methodologies for sectoral and societal transformation. BUas (lead partner) and FONTYS built a SPRONG group level 4 based on four pillars: RD&I-Program, Field Labs, Lab-Infrastructure, and Organizational Excellence Program. This provides a solid foundation to initiate and execute challenging, externally funded RD&I projects with partners in SPRONG stage one ('21-'25) and beyond (until' 29). DIGIREAL-XL is organized in a coherent set of Work Packages with clear objectives, tasks, deliverables, and milestones. The SPRONG group is well-positioned within the emerging MINDLABS Interactive Technologies eco-system and strengthens the regional (North-Brabant) digitalization agenda. Field labs on DR work with support and co-funding by many network organizations such as Digishape and Chronosphere and public, private, and societal organizations.
