Vanuit Fontys Hogescholen wordt veel onderzoek gedaan, met name door onderzoekers van de verschillende lectoraten. Vanzelfsprekend worden er binnen deze onderzoeken veel data verzameld en verwerkt. Fontys onderschrijft het belang van zorgvuldige omgang met onderzoeksdata en vraagt daarom van onderzoekers dat zij hun Research Data Management (RDM) op orde hebben. Denk hierbij aan veilige opslag en duurzame toegankelijkheid van data. Maar ook (open access) publiceren en archiveren van onderzoeksdata maken onderdeel uit van RDM. Hoe je hier als onderzoeker invulling aan geeft kan soms best een zoektocht zijn, mede doordat nog niet iedereen even bekend is met het onderwerp RDM. Met dit boek hopen we onderzoekers binnen Fontys de belangrijkste informatie te bieden die nodig is om goed invulling te geven aan Research Data Management en daarbij ook te wijzen op de ondersteuning die op dit gebied voorhanden is.
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This study explores the evaluation of research pathways of self-management health innovations from discovery to implementation in the context of practice-based research. The aim is to understand how a new process model for evaluating practice-based research provides insights into the implementation success of innovations. Data were collected from nine research projects in the Netherlands. Through document analysis and semi-structured interviews, we analysed how the projects start, evolve, and contribute to the healthcare practice. Building on previous researchevaluation approaches to monitor knowledge utilization, we developed a Research Pathway Model. The model’s process character enables us to include and evaluate the incremental work required throughout the lifespan of an innovation project and it helps to foreground that innovation continues during implementation in real-life settings. We found that in each researchproject, pathways are followed that include activities to explore a new solution, deliver a prototype and contribute to theory. Only three projects explored the solution in real life and included activities to create the necessary changes for the solutions to be adopted. These three projects were associated with successful implementation. The exploration of the solution in a real-life environment in which users test a prototype in their own context seems to be a necessaryresearch activity for the successful implementation of self-management health innovations.
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How come Open Science is a well-shared vision among research communities, while the prerequisite practice of research data management (RDM) is lagging? This research sheds light on RDM adoption in the Dutch context of universities of applied sciences, by studying influencing technological, organizational, and environmental factors using the TOE-framework. A survey was sent out to researchers of universities of applied sciences in the Netherlands. The analyses thereof showed no significant relation between the influencing factors and the intention to comply with the RDM guidelines (p-value of ≤ .10 and a 90% confidence level). Results did show a significant influence of the factor Management Support towards compliance with a p-value of 0.078. This research contributes towards the knowledge on RDM adoption with the new insight that the factors used in this research do not seem to significantly influence RDM adoption in the Dutch context of universities of applied sciences. The research does show that the respondents have a positive attitude in their intention to change, increase or invest time and effort towards RDM compliance. More research is advised to uncover factors that do significantly influence RDM adoption among universities of applied sciences in the Netherland for stakeholders in Open Science and RDM to enhance their strategies.
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This matrix is generic. It is a tool for data stewards or other research supporters to assist researchers in taking appropriate measures for the safe use and protection of data about people in scientific research. It is a template that you can adjust to the context of your own institution, faculty and / or department by taking into consideration your setting’s own policies, guidelines, infrastructure and technical solutions. In this way you can more effectively determine the appropriate technical and organizational measures to protect the data based on the context of the research and the risks associated with the data.
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Deze publicatie richt zich vooral op het concept Design Based Research,gezien vanuit het perspectief van de bijna 40 lectoren die de hogeschool rijk is. Dit lectoratenoverzicht kan worden beschouwd als een atlas of reisgids waarmee de lezer een route kan afleggen langs de verschillende lectoraten. De lectoraten die actief zijn op het gebied van de Service Economy worden beschreven in hoofdstuk 2. De lectoraten die actief zijn op het gebied van Vitale Regio worden beschreven in hoofdstuk 3. De lectoraten die actief zijn op het gebied van Smart Sustainable Industries worden beschreven in hoofdstuk 4. De lectoraten die actief zijn op het gebied van de hogeschoolbrede thema’s Design Based Education en Research worden beschreven in hoofdstuk 5. Tenslotte wordt er in hoofdstuk 6 een eerste aanzet gedaan om één of meer verbindende thema’s of werkwijzen te ontdekken in de aanpak van de verschillende lectoraten. Het is niet de bedoeling van deze publicatie om een definitief antwoord te geven op de vraag wat NHL Stenden precies bedoelt met het concept Design Based Research. Het doel van deze publicatie is wel om een indruk te krijgen van wat er allemaal gebeurt binnnen de lectoraten van NHL Stenden, en om nieuwsgierig te worden naar meer.
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Deze casestudie geeft inzicht in verschillende soorten kennis die kenmerkend zijn voor applied design research. Er wordt onderscheid gemaakt tussen kennis over de huidige situatie, over wenselijke alternatieven en over effectieve oplossingen om daar te komen. Ofwel, kennis hoe het is, kennis over hoe het kan zijn en kennis over hoe het zal zijn als we effectieve oplossingen toepassen. Elk van deze soorten kennis heeft andere kwaliteitscriteria.
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Hoe meer data er beschikbaar komt, des te meer de beslissing verbeterd kan worden. Hoe beter (slimmer) de beslissing wordt gemaakt, des te meer waarde de beslissing heeft voor uw organisatie. Dit wordt het data-netwerk-effect genoemd. Vaak wordt het data-netwerk-effect gerealiseerd door het gebruik van data van onbewuste data-donoren. In dit artikel wordt een ander soort data-donor belicht: de bewuste data-donor.
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This data set contains the results of a survey about reviewing Data Management Plans (DMPs). The survey was carried out by the Research Support and Advice working group of the Dutch National Coordination Point Research Data Management (LCRDM). Sixty people shared their experiences and feedback on DMPs by responding to the survey. The data set contains a concise report, the survey questions and the anonymised data.
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Over the last two decades, institutions for higher education such as universities and colleges have rapidly expanded and as a result have experienced profound changes in processes of research and organization. However, the rapid expansion and change has fuelled concerns about issues such as educators' technology professional development. Despite the educational value of emerging technologies in schools, the introduction has not yet enjoyed much success. Effective use of information and communication technologies requires a substantial change in pedagogical practice. Traditional training and learning approaches cannot cope with the rising demand on educators to make use of innovative technologies in their teaching. As a result, educational institutions as well as the public are more and more aware of the need for adequate technology professional development. The focus of this paper is to look at action research as a qualitative research methodology for studying technology professional development in HE in order to improve teaching and learning with ICTs at the tertiary level. The data discussed in this paper have been drawn from a cross institutional setting at Fontys University of Applied Sciences, The Netherlands. The data were collected and analysed according to a qualitative approach.
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In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By ‘novice’ we mean Master’s students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of qualitative research papers. The second article focused on context, research questions and designs, and referred to publications for further reading. This third article addresses FAQs about sampling, data collection and analysis. The data collection plan needs to be broadly defined and open at first, and become flexible during data collection. Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used. Data saturation determines sample size and will be different for each study. The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions. Analyses in ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory, and a descriptive summary, respectively. The fourth and final article will focus on trustworthiness and publishing qualitative research.
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