Our Work

This dataset contains the following supplementary data of Wagner and Köster, Systematic parameter study in an agent-based exposure model reveals complex effects of airflow on pathogen transmissio…

This dataset contains the following supplementary data of Wagner and Köster, Systematic parameter study in an agent-based exposure model reveals complex effects of airflow on pathogen transmission, 2026:  Videos of the simulated scenarios:  Restaurant outbreak scenario used for validation Choir scenario used for uncertainty quantification, specifically videos for the three different seating configurations and videos for the three different airflow configuration for the first seating configuration Scenario files of the restaurant (validation), close contact and choir (parameter study) scenarios Output files of the validation (restaurant), close contact scenario, and the parameter study for the three airflow configurations and for the setting without airflow The validation and uncertainty quantification study were conducted with Vadere 4.1 (https://gitlab.lrz.de/vadere/vadere, commit: 4c53f1cc4811959e49a6f079c88d9612ffbd7583) and the uncertainty quantification project https://github.com/pedestrian-dynamics-HM/airflow-parameter-study (commit: 864d39e71e38156909336caef07dce365a821149). 

Effective clinical communication is one of the most important foundations of healthcare. Medicine is not only about diagnosing diseases, prescribing treatments, or performing procedures; it is also ab…

Effective clinical communication is one of the most important foundations of healthcare. Medicine is not only about diagnosing diseases, prescribing treatments, or performing procedures; it is also about understanding people, listening to their concerns, and building relationships based on trust, empathy, and respect. The Patient Whisperer: The Art and Science of Clinical Communication explores the essential role of communication in creating meaningful and effective interactions between healthcare professionals and patients. Every patient brings a unique combination of symptoms, experiences, expectations, fears, beliefs, and personal circumstances. A clinically accurate diagnosis may not always lead to successful care if the patient's concerns are not properly understood or communicated. Clear explanations, active listening, appropriate questioning, empathy, and respectful dialogue can significantly improve the quality of the patient experience and support better healthcare outcomes. This book presents clinical communication as both an art and a science. The scientific dimension involves structured communication techniques, clinical interviewing, information gathering, patient education, informed decision-making, and effective documentation. The human dimension involves compassion, emotional awareness, cultural sensitivity, non-verbal communication, and the ability to recognize what patients may find difficult to express directly. The chapters discuss important aspects of communication across different stages of healthcare, from the first patient encounter and history-taking to diagnosis, treatment discussions, follow-up, and difficult conversations. Particular attention is given to communicating complex medical information in simple and understandable language. The book also highlights the importance of shared decision-making, confidentiality, professionalism, and respect for patient autonomy. Modern healthcare is increasingly collaborative, involving doctors, nurses, pharmacists, therapists, technicians, counselors, and other healthcare professionals. Effective communication among members of the healthcare team is therefore equally important. Miscommunication can contribute to misunderstandings, delays, errors, and dissatisfaction, whereas clear communication can strengthen teamwork and continuity of care. Technology has also transformed clinical communication. Electronic health records, telemedicine, digital consultations, and online communication platforms have created new opportunities as well as new challenges. Healthcare professionals must learn how to maintain empathy, clarity, privacy, and professionalism even when communication takes place through digital channels. The concept of the “patient whisperer” in this book does not refer to a special ability or talent. Rather, it represents the practice of listening carefully, observing thoughtfully, asking meaningful questions, and responding with humanity. It emphasizes that effective communication is a skill that can be learned, practiced, evaluated, and continuously improved. This book is intended for medical students, nursing students, allied health professionals, healthcare practitioners, educators, researchers, and anyone interested in improving patient-centered care. It aims to provide practical knowledge that can be applied in classrooms, clinical settings, healthcare institutions, and everyday professional interactions. Ultimately, good communication is more than exchanging information. It is about creating understanding. When healthcare professionals listen with attention and communicate with clarity and compassion, patients are more likely to feel heard, respected, and involved in their care. The Patient Whisperer: The Art and Science of Clinical Communication is an invitation to recognize communication not as an additional part of healthcare, but as an essential component of quality clinical practice.

This is data used for simulation research by Nevalainen et al. 2026. Includes data from Suomen Metsäkeskus (2024).

This release contains the analysis code used to prepare the plots and tables included in Symplectic lattice gauge theories in the Grid framework: domain wall fermions and continuum extrapolations. Fo…

This release contains the analysis code used to prepare the plots and tables included in Symplectic lattice gauge theories in the Grid framework: domain wall fermions and continuum extrapolations. For more information see the README.md file.

This dataset contains behavioural and pupillometry data collected during an experiment investigating the temporal deployment of attention in response to rapid visual stimulation and its…

This dataset contains behavioural and pupillometry data collected during an experiment investigating the temporal deployment of attention in response to rapid visual stimulation and its modulation by the emotional content of successive targets. The experiment used a rapid serial visual presentation (RSVP) paradigm in which participants viewed a sequence of emotional faces containing two task-relevant targets (T1 and T2). The temporal separation between the two targets was manipulated across different lags, and the emotional content of the targets was varied. Participants reported the emotional category of the targets after each trial. Pupil responses were recorded during the task. The dataset provides the processed behavioural and pupil data used to examine how temporal separation and emotional content influence attentional processing. Participants viewed a rapid sequence of face stimuli presented using an RSVP paradigm. Each stimulus was presented for 100 ms, followed by a 50 ms blank interval, resulting in a stimulus onset asynchrony (SOA) of 150 ms. Two task-relevant targets, T1 and T2, were embedded within the RSVP stream. T1 appeared at serial positions 6–8, while T2 appeared at one of three temporal lags following T1: Lag 1, 2, 3 and 5. The target faces were either happy or angry, producing four possible target-pair conditions: HH: Happy T1 – Happy T2, HA: Happy T1 – Angry T2, AH: Angry T1 – Happy T2 and  AA: Angry T1 – Angry T2. The remaining stimuli in the RSVP stream were neutral faces. Trials included both two-target and single-target conditions. The dataset contains data from 37 participants, out of which 32 were included in the final analysis. Participants were between 18 and 24 years of age. Participant-level identifiers in the dataset are pseudonymised and do not contain directly identifying information. Pupil data were recorded using an EyeLink 1000 eye tracker at a sampling rate of 500 Hz, using the right eye. The recorded pupil signal was processed before analysis. Processing included blink handling, interpolation of missing samples, downsampling, and baseline correction. The resulting pupil time series were aligned to the RSVP onset and used for subsequent analysis. File Format: The data are provided in DataMatrix (.dm) format. DataMatrix is a Python-based data structure and file format designed for behavioural and experimental research data. The .dm files can be read using the DataMatrix Python package. The dataset is provided in its original .dm format to preserve the structure used during data preprocessing and analysis. Here is a direct link to DataMatrix Package