The effect of crowding on the reading of program code for programmers with dyslexia

Eye track recording data and meta data for 30 participants. There were 14 participants with dyslexia, consisting of 3 female and 11 male. Dyslexia group mean age was 21.50 years (SD = 1.61) and mean programming experience was 3.67 years (SD = 1.87). There were 16 participants in the control group, 2 female and 14 male. Control group mean age was 22.19 years (SD = 2.99) and mean programming experience was 4.75 years (SD = 1.96)

Analysis of General Practitioner Prescribing Behaviours Local Data Store

Open Prescription data used in Postgraduate project into Northern Ireland General Practice prescribing

Dataset for the ReSolve Project

The RE:Solve project aim was to develop a process/proof of concept (POC), and to tackle the issue of contaminated plastic packaging from the food waste management sector in order to divert this substantial (and potentially valuable and useful) waste stream from landfill via effective separation of the material contaminated with organic residues from the plastic packaging

InSync

Version 1.0 ====================================== Abstract: ====================================== The InSync data set was collected at the Pervasive Computing lab at Ulster University. It consists of subjects performing activities of daily living (ADLs) in an atmosphere that mimics a real-life environment while data is collected using three different sensing technologies: inertial, image, and audio. The data set can be used to research human activity recognition algorithms to tackle problems on classification, transfer learning, data fusion, data segmentation, feature extraction, so on and so forth. Number of instances: ====================================== 16,959 (inertial data points) + 650 (thermal images) + 16,986 (audio files) Relevant information: ====================================== InSync contains 12 hours of data from ten subjects, consisting of 78 runs (times that a subject performed the scripted protocol). Sensor data from three different technologies (inertial, images and audio) captured the performance (not simulation) of the subjects performing ADLs. All the activities were annotated a posteriori using a video stream. ***** ACTIVITIES OF DAILY LIVING As the data set aimed at recording the subject's physical activity performance. The tasks consisted of ADLs and well-known scenarios. Three general scenarios were chosen, a bedroom-related scenario in which the subjects performed two of the ADLs, namely, personal hygiene and dressing, a breakfast-related scenario was chosen to embrace the ADL of feeding as it has extensively been used in literature, and free of obstacle scenario in which the subjects can walk alongside to demonstrate their transferring capabilities. The script was designed with nine high-level activities: Bedroom: (1) Napping (2) Wearing joggers (3) Combing hair (4) Brushing teeth Corridor: (5) Operating door Kitchen: (6) Drinking water (7) Eating cereal Livingroom: (8) Transporting (i.e. walking) (9) Resting (i.e. sitting in a chair) Details of the room's dimensions and sensor locations are available in the Relevant Papers. ***** SENSING TECHNOLOGY The deployed sensing technology included thirteen shimmer devices enabled with 3-axis accelerometers, four Matrix Voice ESP32 consisting of eight embedded microphones and four Thermal Vision Sensor (TVS). The sensing technology was placed as described next: Shimmers wore by the subject: - Right wrist - Left wrist - Lower back - Upper back - Right shoe Shimmers mounted on everyday items: - Comb - Toothbrush - Glass - Spoon - Jogger - Belt - Strap to mimic a watch - Strap to mimic smart shoe Matrix Voice ESP32 (one located in each room): - Bedroom - Corridor - Kitchen - Livingroom Thermal sensor (one located in each room): - Bedroom - Corridor - Kitchen - Livingroom Attribute information: ====================================== The data set comprises the readings of inertial sensors, thermal images, and audio files to recorded performed ADLs. There is a total of 60 attributes for the inertial data which includes the mean value and root-mean-square (RMS) from x, y, and z-axis. The thermal data consists of grayscale images in 32x32 pixels, and the audio data consists of 44.1 kHz waveform audio files. A list of videos of the experiment can be seen in the following links. Bedroom: (1) napping: https://youtu.be/IqWLKsgch6A (2) wearing joggers: https://youtu.be/FJBjO9C4Q4U (3) combing hair: https://youtu.be/bYKrKbVBNos (4) brushing teeth: https://youtu.be/wuVkrWlsmSs Corridor: (5) operating door: https://youtu.be/pWJjx3TH6Q4 Kitchen: (6) drinking water: https://youtu.be/wS9OBKK_LFY (7) eating cereal: https://youtu.be/nOK8TuyCXBA Livingroom: (8) transporting (i.e. walking): https://youtu.be/45MGsYS9cYg (9) resting (i.e. sitting in a chair): https://youtu.be/45MGsYS9cYg IMPORTANT: The videos previously provided were recorded using conventional webcams. The videos were used as ground truth; they were not used for training nor testing purposes. Note that the participant's identity has been considered by blurring their face. The speed of the videos varies as different sampling rates were used when recording the videos

EEG and psychological assessment datasets: Neurofeeedback for the treatment of PTSD

Psychological assessments were conducted through clinical interviews, to collect psychometric data for twenty-nine female survivors of the 1994 genocide against the Tutsi in Rwanda, before and after an intervention aimed at reducing Post-Traumatic Stress Disorder (PTSD) symptom severity. Three measures of trauma and four measures of wellbeing were assessed using empirically validated standardised assessments. The participants were assigned to a control group (n = 9), a motor-imagery group (MI, n = 10), and a neurofeedback group (NF, n = 10). Participants in the latter two groups received a Brain-Computer Interface (BCI) based training as a treatment intervention over a period of two weeks between the pre- and post- clinical interviews. The training involved presenting feedback visually via a game, based on real-time analysis of the EEG recorded data during the BCI-based treatment session. Participants were asked to regulate (NF) or intentionally modulate (MI) brain activity to affect/control the game.

Assessment of the volumetric solar-to-thermal energy conversion performance of carbon nanotubes decorated with gold nanoparticles as a multi-component nanofluid Dataset

The dataset that corresponds to the results reported in the paper are included within this record as an Excel file and with tabs corresponding to each figure. Additional results and raw data underlying this work (theoretical calculations) are available in the Supporting Information (in PDF format) or on request following instructions provided here. This work was supported by EPSRC (award n.EP/M024938/1, EP/V055232/1, EP/R008841/1)

The Views and Needs of People With Parkinson Disease Regarding Wearable Devices for Disease Monitoring: Mixed Methods Exploration

Background: Wearable devices can diagnose, monitor, and manage neurological disorders such as Parkinson disease. With a growing number of wearable devices, it is no longer a case of whether a wearable device can measure Parkinson disease motor symptoms, but rather which features suit the user. Concurrent with continued device development, it is important to generate insights on the nuanced needs of the user in the modern era of wearable device capabilities. Objective: This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. Methods: This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. Results: Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. Conclusions: Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease-related health barriers and the users' confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management

Chatpal Chatbot dialogue data set

The scripts used in the ChatPal chatbot are freely available as an output from the ChatPal project. The datasets contain the chatbot utterances in English, Swedish, Finnish and Scottish Gaelic. Any replies collected from users through the ChatPal chatbot are not included in these data. Datasets are available in csv format and contain Unicode character encodings (UTF-8). Disclaimer: The datasets are open access, should be used appropriately and can be repurposed. However, the ChatPal project team are not responsible for how you chose to use the data or repurpose the content