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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This research paper looks at a selection of science-fiction films and its connection with the progression of the use of television, telephone and print media. It also analyzes statistical data obtained from a questionnaire conducted by the research group regarding the use of communication media.
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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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This review of meta-analyses of outcome studies of adults receiving Computer-Based Health Education (CBHE) has two goals. The first is to provide an overview of the efficacy of CBHE interventions, and the second is to identify moderators of these effects. A systematic literature search resulted in 15 meta-analyses of 278 controlled outcome studies. The meta-analyses were analysed with regard to reported (overall) effect sizes, heterogeneity and interaction effects. The results indicate a positive relationship between CBHE interventions and improvements in health-related outcomes, with small overall effect sizes compared to non-computer-based interventions. The sustainability of the effects was observed for up to six months. Outcome moderators (31 variables) were studied in 12 meta-analyses and were clustered into three categories: intervention features (20 variables), participant characteristics (five variables) and study features (six variables). No relationship with effectiveness was found for four intervention features, theoretical background, use of internet and e-mail, intervention setting and self-monitoring; two participant features, age and gender; and one study feature, the type of analysis. Regarding the other 24 identified features, no consistent results were observed across meta-analyses. To enhance the effectiveness of CBHE interventions, moderators of effects should be studied as single constructs in high-quality study designs. http://www.journalofinterdisciplinarysciences.com/ https://www.linkedin.com/in/leontienvreeburg/
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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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In de agro-chemie industrie wordt tegenwoordig een grote hoeveelheid data gegenereerd. De inzet van sensoren voor het monitoren van productieprocessen, het sequensen van gewassen en het karakteriseren van bodemmonsters zijn voorbeelden van activiteiten die veel data opleveren. Tegelijkertijd dalen de kosten voor dataopslag en -verwerking sterk. Bedrijven die hier gebruik van weten te maken, hebben goud in handen.
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Computer security incident response teams (CSIRTs) respond to a computer security incident when the need arises. Failure of these teams can have far-reaching effects for the economy and national security. CSIRTs often have to work on an ad hoc basis, in close cooperation with other teams, and in time constrained environments. It could be argued that under these working conditions CSIRTs would be likely to encounter problems. A needs assessment was done to see to which extent this argument holds true. We constructed an incident response needs model to assist in identifying areas that require improvement. We envisioned a model consisting of four assessment categories: Organization, Team, Individual and Instrumental. Central to this is the idea that both problems and needs can have an organizational, team, individual, or technical origin or a combination of these levels. To gather data we conducted a literature review. This resulted in a comprehensive list of challenges and needs that could hinder or improve, respectively, the performance of CSIRTs. Then, semi-structured in depth interviews were held with team coordinators and team members of five public and private sector Dutch CSIRTs to ground these findings in practice and to identify gaps between current and desired incident handling practices. This paper presents the findings of our needs assessment and ends with a discussion of potential solutions to problems with performance in incident response. https://doi.org/10.3389/fpsyg.2017.02179 LinkedIn: https://www.linkedin.com/in/rickvanderkleij1/
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Brochure from the Inauguration of Klaas Dijkstra, professor Computer Vision and Data Science
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Background: The strain on health care services is increasing due to an ageing population and the increasing prevalence of chronic health conditions. eHealth could contribute to optimise effective and efficient care to older adults with one or more chronic health conditions in the general practice. Aim: The aim of this study was to identify the needs, barriers and facilitators amongst community-dwelling older adults (60þ) suffering from one or more chronic health conditions, in using online eHealth applications to support general practice services. Methods: A qualitative study, using semi-structured followed by think-aloud interviews, was conducted in the Netherlands. The semi-structured interviews, supported by an interview guide were conducted and analysed thematically. The think-aloud method was used to collect data about the cognitive process while the participant was completing a task within online eHealth applications. Verbal analysis according to the Chi approach was conducted to analyse the think-aloud interviews. Findings: A total of n = 19 older adults with a mean age of 73 years participated. The ability to have immediate contact with the GP on important health issues was identified as an important need. Identified barriers were non-familiarity with the online eHealth applications and a mismatch of user health needs. The low computer experience resulted in non-familiarity with the online eHealth applications. Faltering applications resulted in participants refusing to participate in the use of online eHealth applications. Convenience, efficiency and the instant availability of eHealth via applications were identified as important facilitators. Conclusion: To improve the use and acceptability of eHealth applications amongst older adults in the general practice, the applications should be tailored to meet individual needs. More attention should be given to improving the user-friendliness of these applications and to the promotion of the benefits such as facilitating older adults independent living for longer.
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This extended abstract introduces the work of the Netherlands AI Media and Democracy lab, especially focusing on the research performed from an AI/computer science perspective at CWI, the Netherlands National Research Center for Mathematics and Computer Science in Amsterdam. We first provide an overview of the general aims and set-up of the lab, and then focuses in on the research areas of the 3 research groups at CWI, outlining there are of research and expected research contributions in the areas between AI and media & democracy
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