Strengthening research integrity: which topic areas should organisations focus on?

The widespread problems with scientific fraud, questionable research practices, and the reliability of scientific results have led to an increased focus on research integrity (RI). International organisations and networks have been established, declarations have been issued, and codes of conducts have been formed. The abstract principles of these documents are now also being translated into concrete topic areas that Research Performing organisations (RPOs) and Research Funding organisations (RFOs) should focus on. However, so far, we know very little about disciplinary differences in the need for RI support from RPOs and RFOs. The paper attempts to fill this knowledge gap. It reports on a comprehensive focus group study with 30 focus group interviews carried out in eight different countries across Europe focusing on the following research question: “Which RI topics would researchers and stakeholders from the four main areas of research (humanities, social science, natural science incl. technical science, and medical science incl. biomedicine) prioritise for RPOs and RFOs?” The paper reports on the results of these focus group interviews and gives an overview of the priorities of the four main areas of research. The paper ends with six policy recommendations and a reflection on how the results of the study can be used in RPOs and RFOs.Modern and Contemporary Studie

A lack of mess? Advice on undertaking video-mediated participant observations

Purpose: This article offers practical advice to ethnographers venturing into doing participant observations through, but not about, videoconferencing applications such as Zoom, for which the methods literature offers little guidance. Design/methodology/approach The article stems from a research project about a BioMedical Design Fellowship. As the COVID-19 pandemic hit, the Fellowship converted all teaching activities to online learning via Zoom, and the participant observations followed along. Taking an autoethnographic approach, the authors present and discuss concrete examples of encountered obstacles produced by the video-mediated format, such as limited access and interactions, technical glitches and changing experiences of embodiment. Findings: Changing embodiment in particular initially led the authors to believe that the “messiness” of ethnography (i.e. misunderstandings, emotions, politics, self-doubts etc.) was lost online. However, over time the authors realized that the mess was still there, albeit in new manifestations, because Zoom shaped the interactions of the people the authors observed, the observations the authors could make and how the authors related to research participants and vice versa. Practical implications: The article succinctly summarizes the key advice offered by the researchers (see Section 5) based on their experiences of converting on-site ethnographic observations into video-mediated observations enabling easy use by other researchers in relation to other projects and contexts. Originality/value The article positions video-mediated observations, via e.g. Zoom, which are distinctly characterised by happening in real time and having an object of study other than the online sphere itself, vis-à-vis other "online ethnography" methods. The article further aims to enable researchers to more rapidly rediscover and re-incite the new manifestations of the messiness of ethnography online, which is key to ensuring high-quality research

Multi-neighborhood search for discrimination of signal peptides and transmembrane segments

Abstract. A key step in study of biosynthesis of membrane proteins is to look for the code that could be used to explain and predict which proteins would eventually be inserted in the membrane and which proteins would be secreted into the ER lumen when they cross the translocon channel. The aim of this work is to present an improvement of a previous method based on a local search approach. The proposed method relies on new in-depth biological observations to design a new search space for the local search algorithm. Experiments conducted on a dedicated dataset show that our new approach leads to improved outcomes in terms of prediction rates