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Series: Practical guidance to qualitative research

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.

Impacts of floating urbanization on water quality and aquatic ecosystems:

Floating urbanization is a promising solution to reduce the vulnerability of cities against climate change, population growth or land scarcity. Although this type of construction introduces changes to aquatic systems, there is a lack of research studies addressing potential impacts. Water quality data collected under/near floating structures were compared with the corresponding parameters measured at the same depth at open water locations by (i) performing scans with underwater drones equipped with in situ sensors and video cameras and (ii) fixing two sets of continuous measuring in situ sensors for a period of several days/months at both positions. A total of 18 locations with different types of floating structures were considered in this study. Results show small differences in the measured parameters, such as lower dissolved oxygen concentrations or higher temperature measured underneath the floating structures. The magnitudes of these differences seem to be linked with the characteristics and type of water system. Given the wide variety and types of water bodies considered in this study, results suggest that water quality is not critically affected by the presence of the floating houses. Underwater images of biofouling and filter feeders illustrate the lively ecosystems that can emerge shortly after the construction of floating buildings.

QualiTePE digital web-based evaluation tool QualiTePE-Tool

The Technical Manual for the digital evaluation tool QualiTePE supports users of the QualiTePE tool in creating, conducting and analysing evaluations to record the quality of teaching in physical education. The information on the General Data Protection Regulation (GDPR) instructs users on how to anonymise the data collection of evaluations and which legal bases apply with regard to the collection of personal data. The technical manual for the digital evaluation tool QualiTePE and the information on the General Data Protection Regulation (GDPR) are available in English, German, French, Italian, Spanish, Dutch, Swedish, Slovenian, Czech and Greek.

What quality in Data Journalism? Assessing quality parameters in data-driven stories.

Coming to grips with urban environmental quality in planning sustainable cities

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

Towards trustworthy data-driven gas turbine prognostics

Trustworthy data-driven prognostics in gas turbine engines are crucial for safety, cost-efficiency, and sustainability. Accurate predictions depend on data quality, model accuracy, uncertainty estimation, and practical implementation. This work discusses data quality attributes to build trust using anonymized real-world engine data, focusing on traceability, completeness, and representativeness. A significant challenge is handling missing data, which introduces bias and affects training and predictions. The study compares the accuracy of predictions using Exhaust Gas Temperature (EGT) margin, a key health indicator, by keeping missing values, using KNN-imputation, and employing a Generalized Additive Model (GAM). Preliminary results indicate that while KNN-imputation can be useful for identifying general trends, it may not be as effective for specific predictions compared to GAM, which considers the context of missing data. The choice of method depends on the study’s objective: broad trend forecasting or specific event prediction, each requiring different approaches to manage missing data.