Can city administrations benefit from the entrepreneurial spirit of startups, and create better urban solutions with their help? In this paper, we critically assess the interplay between startups and city administrations for city-driven innovative public procurement or “challenge-based procurement” policy, taking Amsterdam’s Startup in Residence (SiR) programme as a case study. We describe and analyse this programme from two perspectives: i) the economic development perspective, i.e. does it promote startups and does it bring them new business opportunities, and ii) a governance perspective, i.e. does it bridge the gap between startups and the city bureaucracy; does it lead to a more innovative culture within city government.
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What is this publication about?In this publication on ‘New urban economies’, we search for answers and insights to a key question: how can cities foster economic development and develop ‘new urban economies’. And, importantly, how can they do that:◗ in concertation with different urban stakeholders, ◗ responding adequately to key challenges and developments beyond their control, ◗ building on the cities’ own identity, industries and competences, ◗ in a sustainable way, ◗ and without compromising weaker groups.
from the article: Abstract Based on a review of recent literature, this paper addresses the question of how urban planners can steer urban environmental quality, given the fact that it is multidimensional in character, is assessed largely in subjective terms and varies across time. The paper explores three questions that are at the core of planning and designing cities: ‘quality of what?’, ‘quality for whom?’ and ‘quality at what time?’ and illustrates the dilemmas that urban planners face in answering these questions. The three questions provide a novel framework that offers urban planners perspectives for action in finding their way out of the dilemmas identified. Rather than further detailing the exact nature of urban quality, these perspectives call for an approach to urban planning that is integrated, participative and adaptive. ; ; sustainable urban development; trade-offs; quality dimensions
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).
In De Haagse Hogeschool werken de lectoraten vanuit faculteiten, dicht bij het onderwijs, nauw samen in zeven kenniscentra. Deze kenniscentra zijn de verbinding tussen de regio, met zijn actuele thema’s (vaak gelinkt aan het missiegedreven innovatiebeleid van de overheid) en het onderwijs en onderzoek van de Haagse Hogeschool. De zeven kenniscentra van De Haagse Hogeschool zijn: Cybersecurity, Digital Operations & Finance, Global & Inclusive Learning, Global Governance, Health Innovation, Governance of Urban Transitions & Mission Zero. Deze kenniscentra zijn in opstartende fase en worden ondersteund door centrale diensten. De Haagse Hogeschool kiest voor versterking van de onderzoeksinfrastructuur die centraal staat in de kenniscentra: ‘de Haagse Labs’. Praktijkgericht onderzoek vindt in deze omgevingen plaats als een vervlechting van onderwijs (studenten en docenten), onderzoek, het werkveld en maatschappelijke partners. Sommige labs hebben een tijdelijk karakter, andere, zoals de hogeschool zelf, zijn continu een omgeving waarbinnen onderzoek gedaan wordt. De Haagse Labs zijn bij uitstek de plek waarin nauw samengewerkt wordt met andere hogescholen of kennisinstellingen (veelal zijn ze ontstaan uit een samenwerking zoals The Green Village, of het Basalt SmartLab). De keuze voor de Haagse Labs geeft verdieping aan regionale samenwerkingen en bijbehorende speerpunten. De huidige, meer informele inrichting, kan met behulp van Impuls 2020, verder structuur krijgen, leiden tot een betere kennisdeling tussen de kenniscentra heen en de regionale netwerkvorming versterken. Naast het formaliseren van ‘de Haagse Labs’ zetten we in op zichtbaarheid van de Hogeschool in de regio door te investeren in communicatie (denk bijvoorbeeld aan het opzetten van podcasts, en digitale middelen in Corona-tijd). Die profilering van ons onderzoek wordt verder ondersteunt door een traject rond visievorming en strategische positionering. De kenniscentra zullen begeleid worden om einde 2021 een visie te ontwikkelen met bijbehorende acties om de rol van de hogeschool in de regio te versterken.
Climate change is increasing the challenges for water management worldwide. Extreme weather conditions, such as droughts and heavy rainfall, are increasingly limiting the availability of water, especially for agriculture. Nature-Based Solutions (NBS) offer potential solutions. They help to collect and infiltrate rainwater and thus play an important role in climate adaptation.Green infrastructure, such as rain gardens (sunken plant beds) and wadis (sunken grass fields for temporary storage of rainwater), help to restore the urban water balance. They reduce rainwater runoff, stabilize groundwater levels and solve problems with soil moisture and temperature. Despite these advantages, there is still much ignorance in practice about the possibilities of NBS. To remedy this, freely accessible knowledge modules are being developed that can help governments and future employees to better understand the application of these solutions. This research, called GINA (Green Infrastructure in Urban Areas), aims to create more sustainable and climate-resilient cities by developing and sharing knowledge about NBS, and supports local governments and students in effectively deploying these green infrastructures.
