During the 2024 Open Science Retreat, the Measuring Open Science team collected, reviewed, and analyzed existing research into open science practices. As a team, we developed an interactive overview of open science surveys, which may be used e.g. to reuse questionnaire items on different open science practices.
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Out-of-school science educational activities, such as school visits to a science center, aim at stimulating pupils’ science talent. Science talent is a developmental potential that takes the form of talented behaviors such as curiosity and conceptual understanding. This dissertation investigates whether and how out-of-school science activities contribute to the elicitation, emergence, and development of pupils’ science talent. The context of this thesis is the Northern Netherlands Science Network, an alliance of primary schools, out-of-school science facilities, the university of Groningen, and the Hanze University of Applied Sciences (www.wknn.nl). Interviews with the schools on their starting position showed that adequate communication between schools and out-of-school facilities is necessary to coordinate the participants’ educational goals. Secondly, the elicitation and expression of science talent was studied in the micro-interactions between pupils and their educator (classroom teacher or facility instructor). To do so, a multivariate coding scheme was developed to measure Pedagogical Content Knowledge expressed in real-time interaction (EPCK). The interaction shows a variable pattern over time. Sometimes episodes of high-level EPCK — so-called talent moments — emerge, in which talented pupil behavior in the form of pupils’ conceptual understanding, and talent elicitation by the educator in the form of open teaching focused on conceptual understanding, determine one another. These talent moments only occur in activities that were prepared in the classroom and with educators who were trained to evoke conceptual understanding. Under these conditions, out of school science activities can contribute to the elicitation and development of science talent in primary school pupils.AB - Out-of-school science educational activities, such as school visits to a science center, aim at stimulating pupils’ science talent. Science talent is a developmental potential that takes the form of talented behaviors such as curiosity and conceptual understanding. This dissertation investigates whether and how out-of-school science activities contribute to the elicitation, emergence, and development of pupils’ science talent. The context of this thesis is the Northern Netherlands Science Network, an alliance of primary schools, out-of-school science facilities, the university of Groningen, and the Hanze University of Applied Sciences (www.wknn.nl). Interviews with the schools on their starting position showed that adequate communication between schools and out-of-school facilities is necessary to coordinate the participants’ educational goals. Secondly, the elicitation and expression of science talent was studied in the micro-interactions between pupils and their educator (classroom teacher or facility instructor). To do so, a multivariate coding scheme was developed to measure Pedagogical Content Knowledge expressed in real-time interaction (EPCK). The interaction shows a variable pattern over time. Sometimes episodes of high-level EPCK — so-called talent moments — emerge, in which talented pupil behavior in the form of pupils’ conceptual understanding, and talent elicitation by the educator in the form of open teaching focused on conceptual understanding, determine one another. These talent moments only occur in activities that were prepared in the classroom and with educators who were trained to evoke conceptual understanding. Under these conditions, out of school science activities can contribute to the elicitation and development of science talent in primary school pupils.
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Nederland zet koers richting een open vorm van wetenschapsbeoefening, getuige onder andere de lancering van het Nationaal Plan Open Science (NPOS) afgelopen februari en het nieuwe regeerakkoord dat stelt dat open access en open science de norm worden in wetenschappelijk onderzoek. Open science heeft als doel om wetenschappelijke kennis op transparante wijze en voor een breed publiek te publiceren. Dat vergt een herijking van onderzoek doen, samenwerking tussen onderzoekers en de wijze waarop kennis wordt gedeeld en de wetenschap wordt georganiseerd. Informatieprofessionals kunnen hierbij een rol van betekenis spelen, zoals blijkt uit cases van de Universiteit Utrecht, Hogeschool van Arnhem en Nijmegen en KNMI. Maar eerst een korte toelichting op open science en het NPOS. http://www.informatieprofessional.nl
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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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The evolving landscape of science communication highlights a shift from traditional dissemination to participatory engagement. This study explores Dutch citizens’ perspectives on science communication, focusing on science capital, public engagement, and communication goals. Using a mixed-methods approach, it combines survey data (n = 376) with focus group (n = 66) insights. Findings show increasing public interest in participating in science, though barriers like knowledge gaps persist. Trust-building, engaging adolescents, and integrating science into society were identified as key goals. These insights support the development of the Netherlands’ National Centre of Expertise on Science and Society and provide guidance for inclusive, effective science communication practices.
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Abstract Despite the numerous business benefits of data science, the number of data science models in production is limited. Data science model deployment presents many challenges and many organisations have little model deployment knowledge. This research studied five model deployments in a Dutch government organisation. The study revealed that as a result of model deployment a data science subprocess is added into the target business process, the model itself can be adapted, model maintenance is incorporated in the model development process and a feedback loop is established between the target business process and the model development process. These model deployment effects and the related deployment challenges are different in strategic and operational target business processes. Based on these findings, guidelines are formulated which can form a basis for future principles how to successfully deploy data science models. Organisations can use these guidelines as suggestions to solve their own model deployment challenges.
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Poster presentation on work package Open Science within RUN-EU PLUS
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Uit het voorwoord van Erlijn Eweg: Het bruist op De Uithof. Tienduizenden studenten en medewerkers van Hogeschool Utrecht, de Universiteit Utrecht en het Universitair Medisch Centrum Utrecht zijn er aan het werk. Er zijn grote plannen met het gebied, dat zich ontwikkelt van universiteitscentrum De Uithof tot het Utrecht Science Park, waar bedrijven en kennisinstellingen samen werken aan kennisvermeerdering. Een duurzame aanpak staat daarbij hoog op de agenda. De omslag naar een duurzame samenleving is een speerpunt van Hogeschool Utrecht (HU). In het speerpuntprogramma ‘De Omslag – duurzame ontwikkeling met Utrechtse energie’ versterken we duurzaamheid in onderzoek en onderwijs, samen met partners en externe partijen. Met het programma De Omslag als katalysator werken studenten en medewerkers van de HU aan duurzame oplossingen. De HU maakt samen met andere ‘bewoners’ van De Uithof deel uit van de kerngroep Duurzame Uithof. Deze kerngroep wil De Uithof verduurzamen op de terreinen energie, gebouwen, mobiliteit en water. Daarom is de technische faculteit van de HU in het collegejaar 2010-2011 van start gegaan met het project De Duurzame Uithof, onder leiding van Wilko Planje, projectleider. Het project is mogelijk gemaakt door de Gemeente Utrecht. Er zijn vier werkgroepen ingericht, voor ieder thema één. Iedere werkgroep wordt begeleid door een docent. Studentenprojecten en toepassingsgericht onderzoek leidden zo tot bruikbare aanbevelingen voor onze partners. In dit boekje vertellen we u er meer over.
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This chapter explores the legal and moral implications of the use of data science in criminal justice at two levels: police surveillance and the criminal trial of a defendant. At the first level, police surveillance, data science is used to identify places and people at high risk of criminal activity, allowing police officers to target surveillance and take proactive measures to try to prevent crime (predictive policing). At the second level, the criminal trial of a defendant, data science is used to make risk assessments to support decisions about bail, sentencing, probation, and supervision and detention orders for high-risk offenders. The use of data science at these levels has one thing in common: it is about predicting risk. The uncertainty associated with risk prediction raises specific related legal and ethical dilemmas, for example in the areas of reasonable suspicion, presumption of innocence, privacy, and the principle of non-discrimination.
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This video offers a concise exploration of the distinctions between Data Science, AI, Machine Learning, and Deep Learning. Starting with the foundational role of Data Science, it navigates through the various machine learning categories and touches upon the capabilities and constraints of Deep Learning. The discussion culminates in understanding the nuances of AI, differentiating between narrow and general AI. Through insightful examples, viewers are guided on selecting the right technique for specific projects, ensuring both clarity and cost-effectiveness in the realm of data science.
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