1056 research outputs found
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Data sets for "Bond counting strategies in an oxygen centric perspective on the structure of oxide glasses"
No full textData sets used to prepare Figures 2-10 in the Journal of the Ceramic Society of Japan article entitled "Bond counting strategies in an oxygen centric perspective on the structure of oxide glasses." The data sets describe the structures of several oxide glasses and provide the results obtained from bond counting strategies for examining the connectivity and nature of the network forming motifs. It is noted that the oxygen packing fraction acts as a marker for structural change in network-forming oxides under high-pressure conditions.The data sets were collected using the methods described in the published paper.The figures were prepared using QtGrace (https://sourceforge.net/projects/qtgrace/). The data set corresponding to a plotted curve within a QtGrace file can be identified by clicking on that curve.The files are labelled according to the corresponding figure numbers. The units for each axis are identified on the plots
Dataset for "Assessing the susceptibility to mould growth of mycelium-based composite insulation"
No full textThis dataset contains the raw experimental results generated in the characterisation of mycelium-based composite (MBC) insulation materials. It includes primary measurement data for laboratory-produced specimens (MBC A) and two commercially sourced materials (MBC B and MBC C), covering thermal conductivity measurements, liquid water absorption by immersion, surface wettability (contact angle) measurements, and mould susceptibility assessments. The mould dataset includes individual specimen ratings after 28 days of incubation across five temperature and relative humidity conditions, as well as ratings after subsequent liquid-water exposure. All files report unprocessed specimen-level results used to generate the figures and statistical summaries in the associated publication.This dataset was generated through a series of controlled laboratory experiments on mycelium-based composite (MBC) insulation materials, including one material produced at laboratory scale and two commercially sourced products. Laboratory specimens were manufactured using a hemp-shiv substrate inoculated with Ganoderma curtisii, grown under controlled incubation conditions, and oven-dried prior to testing.
The dataset includes raw results from four test methods. Thermal conductivity was measured using a heat flow meter in accordance with ASTM C518, with five repeat measurements performed on each of five specimens per material type. Liquid water absorption was measured by full immersion following ASTM C1763, using five specimens per material type, with mass recorded before and after 2 h immersion to calculate percentage mass increase. Surface wettability was assessed using sessile-drop contact angle measurements with deionised water following the principles of ISO 19403-2; three specimens per material were tested with three measurements per specimen.
Mould susceptibility testing was conducted with reference to BS EN 17886 using a mixed spore suspension containing six fungal species (Aspergillus niger, Trichoderma viride, Talaromyces pinophilus, Chaetomium globosum, Paecilomyces variotii, and Aspergillus versicolor). Specimens were inoculated at a defined spore density and incubated for 28 days in an environmental chamber under five controlled temperature and relative humidity conditions (80–90% RH and 25–30 °C). After incubation, mould growth was assessed visually and microscopically using a standardised 0-3 rating scale. Specimens that did not show visible growth were subsequently wetted with sterile water and returned to the same environmental conditions for a further 14 days, after which mould growth was re-assessed.
All files in the dataset contain specimen-level, unprocessed measurements and ratings recorded directly during these experiments, which were used to generate the figures, statistics, and mould risk charts reported in the associated publication
Dataset for: Multi-Fidelity deep learning for predicting the nonlinear buckling behaviour of concrete thin-shells
No full textThis dataset contains scripts and data supporting the research article, "Multi-Fidelity deep learning for predicting the nonlinear buckling behaviour of concrete thin-shells".
Concrete thin-shells are materially efficient structures, which can be used to reduce the environmental impact of concrete structures. Their shape is typically determined iteratively and evaluated through Finite Element Analysis (FEA). This research proposes the use of surrogate models as faster alternatives to FEA, thus enabling wider design space exploration.
This dataset contains deep learning models – Multi-Fidelity Multilayer Perceptrons – that have been trained to predict the nonlinear buckling factor of concrete thin-shells of various shapes under design loads. It also contains the Python scripts that were used to train these models and assess their performance. Running these scripts necessitates the associated ConcreteShellFEA dataset to be downloaded. Further details about this data can be found in the related research article.The methods used to generate this data can be found in the related article.The data in the models and results folders was generated using the Python code in scripts folder. These scripts rely on the dependencies listed in requirements.txt.The original folder structure is given in README.md. To reproduce it, create a new folder and extract the "models.zip" and "results.zip" folders inside. Additionally, create a "scripts" folder and store all Python scripts inside
Experimental data and code for "Consensus formation and change are enhanced by neutrality"
No full textThis dataset contains raw and processed data on a number of experiments on marching locust nymphs in a ring-shaped arena. With sufficiently large densities the locusts exhibit coherent motion and directional switching.
The code accompanying that data is for processing the raw data, analysing the processed data and simulating both spatial and non-spatial models of the data.
This dataset also contains raw and processed data on a number of experiments into consensus formation in which human participants played an iterated voting game.
The code accompanying the data is for processing the raw data, analysing the processed data and simulating mathematical models of the process.
This dataset also contains code to simulate a model of nucleosome modification. The model describes the modifications of nucleosomes by recruitment of modifying and unmodifying enzymes from their neighbours or in a ‘recruitment-independent’ (spontaneous) manner. In the model, nucleosomes can be acetylated (A), unmodified (U) or methylated (M), hence A and M represent active states, while U is a neutral state.
Effective collective decision-making in human and animal groups requires robust mechanisms for consensus formation and change, typically via feedback loops in which individuals adapt their behaviour and opinions based on their perception of others. Such processes have been observed in the onset of motion in insect swarms and is believed to manifest across scales from nucleosomes to entire societies. However, levels of participation can be highly variable over time, with individuals sometimes adopting neutral positions such as moving to the back of a group or abstaining from a vote.
In this work we present a new theoretical and experimental analysis showing that neutrality has two important and hitherto unreported benefits to collective decision making. First, it enables the robust formation of consensus in groups of individuals applying simple linear reasoning, updating their state after consideration of at most one other individual at a time. Second, we find that neutral actors can facilitate efficient consensus change by reducing the effective population size during transitions. These findings are derived from a new general mathematical model of collective binary decision problems, and validated against experiments with insect and human populations. Our results provide a parsimonious explanation of how groups of animals and humans quickly reach and overturn consensus, suggesting efficient solutions to collective decision-making problems.The methodology can be found in the associated paper
Dataset and code for: Expanding scenario diversity in prospective LCA: Coupling the TIAM-UCL integrated assessment model with Premise and ecoinvent
No full textThis dataset contains TIAM-UCL scenario and mapping files designed for use with Premise, a Python-based tool for prospective life cycle assessment (LCA). TIAM-UCL is an integrated assessment model (IAM) that projects future scenarios for energy systems and their environmental impacts. The dataset includes four climate change mitigation scenarios, ranging from limiting global warming to 1.5°C to 3.0°C, across 16 global regions. These scenarios cover key sectors such as electricity, fuels, and steel, projecting production volumes, technology mixes, and efficiencies. Examples include the phase-out of fossil fuels and the increased adoption of renewable energies. While primarily developed for LCA applications within Premise, these data can be utilized in other contexts as well. The dataset, code, and additional figures also serve as supplementary information 1-4 for the associated paper.The data was collected directly from the TIAM-UCL model at University College London as several Excel-based outputs. The data defined multiple technology variables across various sectors, regions, and year periods.These outputs were combined and processed using Python to fulfil the mapping formatting of the intended Premise software and obtain the final files published in this data set.The data can be viewed and used with Microsoft Excel. If needed, raw, unprocessed data can be obtained directly from TIAM-UCL by contacting the relevant authors
Dataset for "Low-Cost, Multi-Sensor Non-Destructive Banana Ripeness Estimation Using Machine Learning"
No full textProcessed datasets containing all numerical sensor data used for training and testing the ML algorithms discussed in the associated publication. Data from temperature, pressure, humidity, VOC and spectral sensors is included. The data is split into four datasets (as defined in Table V of the associated publication), each containing a different combination of sensor data and each subdivided into data ("x") and labels ("y") for both testing and training data. 30% of the cleaned data is randomly taken to form the testing data, while the remaining 70% forms the training data. Each data subset is balanced, as discussed in section 3.E.3 in the associated publication.The data collection methodology can be found in the associated publication.The data preparation & processing methodology can be found in the associated publication.The datasets were created with Python 3.10.13, with libraries Numpy 1.26.0 and Pandas 2.1.2. The data is saved in CSV format and does not require specialist software to read.Data organisation and encoding is described in the associated ReadMe files
Dataset for "Understanding Freehand Cursorless Pointing Variability and Its Impact on Selection Performance"
Full text linkThis dataset supports the journal entry "Understanding Freehand Cursorless Pointing Variability and Its Impact on Selection Performance" (TOCHI, 2025), containing motion capture data, of the body and hands, captured during a range of pointing gestures from 23 participants.
The user study that captured this data systematically explored how target position (3 rows by 5 columns), task focus (Pointing as a Primary Task vs Secondary Task), and user effort (Accurate pointing vs Casual pointing), affect pointing behaviour and performance.
The dataset includes:
- Motion capture data for each trial (grouped by participant). This contains body landmarks – captured via a markerless motion capture system and finger landmarks – tracked with infrared markers.
- Trial Annotations. Metadata for each trial, such as the target position, labels for when pointing occurs, and observed behaviour labels.
- Encoded gesture statistics. For each trial, for which a valid pointing gesture could be extracted, an encoding of the gesture performed, derived from the medians for body pose features (e.g. elbow flexion), fatigue measures (e.g. consumed endurance), and rays (e.g. vector and accuracy).
- Self-reported user data. Including participant age, hand dominance, and fatigue measures (obtained after completing pointing within each condition).
- Code for visualising the trials, including a subset of the rays used in our subsequent analysis, code for generating our encoded gestures, using the motion capture data and annotations, and the script used to perform the analysis over our encoded gestures.
This dataset has been provided for two purposes:
1. For further investigation into pointing behaviour and for the development of pointing interaction systems. For this, please refer to the Pointing Dataset section of the README to understand the structure and dataset contents, and the Trial Visualiser section of the README for usage of a script for visualising the motion capture data.
2. For reproduction of data used in the analysis of the accompanying paper (Understanding Freehand Cursorless Pointing Variability and Its Impact on Selection Performance), for which please see Pointing Dataset section of the README to understand the structure and dataset contents, along with the Gesture Encoder and Analysis Script sections of the README for the code used to perform our analysis.For the complete methodology used in the data collection, please refer to the paper.
We used a repeated measures within-subject design with Pointing Style (accurate or casual) and Focus (focused or distracted) as independent variables (IV).
We asked participants to "point as accurately and precisely as possible" for the accurate pointing condition. In contrast, in the casual pointing condition participants were instructed to "point as casually and relaxed as possible...".
The distracted condition involved participants completing a Stroop effect test while pointing to targets, while the focused condition had pointing as the primary and only task.Please refer to the README file for instructions to visualise the recorded pointing gestures or use any of the other provided scripts.
Please refer to the README file for an explanation of the dataset structure and fields within specific files.
For the technical details of the study setup and data collection, please refer to the paper.
In summary, we utilised a set of 135 targets, which were grouped into 15 clusters of 9 (3×3), arranged into a 3 × 5 (rows × columns) array, with each target within a cluster spaced 8.4cm apart. The middle row was located 1.4 m from the floor and 2 m away from the participant, and the top and bottom rows were pitched ±25° from the middle row. Each column was yawed ±35° relative to the adjacent column.
We used 12 Arqus infrared (IR) tracking cameras, and 10 Miqus cameras
capturing RGB images at 1080p (4:3), with recording and marker tracking managed by QTM. The cameras provided coverage of a 4 m wide × 3 m deep × 2.5 m tall volume, within which the participant would be placed 2 m from the shorter edges, and ∼1 m from the long edge. The system was calibrated at the start of each day, with an average residual of 0.732 mm and standard deviation of 0.174 mm. We used 28 6.5 mm IR reflective markers to track all fingers on both hands; two for each finger and four to capture the palm and wrist. To aid in the tracking of the hands, we employed QTM’s AIM models and skeleton-assisted labelling.
All sensing apparatus sampled data at 100Hz.Motion capture data is grouped into zips for each participant, where each participant has been given a unique ID (GUUID). Each zip contains files representing each captured trial for the given participant, where the name of the file details the condition and trial number, e.g. ACCURATE_FOCUSED_3.csv, where 'ACCURATE_FOCUSED' is the condition and '3' is the trial number within that condition. Metadata for the trials is located within the GestureAnnotations.json. Additionally, and encoding of each valid gesture can be located within the encoded_gestures.csv
Please refer to the dataset README file for an explanation of the dataset structure and fields within specific files
Dataset for, "An RCT study showing few weeks of music lessons enhance audio-visual temporal processing"
No full textThis dataset includes data on behavioural outcomes for the audio-visual simultaneity judgement task and emotion recognition task used in the publication, "An RCT study showing few weeks of music lessons enhance audio-visual temporal processing". In this study, the authors investigated the effect of eleven weeks of piano lessons on audio-visual temporal processing and emotion recognition abilities in adults. The data is organised to facilitate replication of the analyses carried out in this study, which includes the raw data of the two tasks mentioned above collected from each participant over seven data-collection sessions. A 'Read-me-first' file is included in both data folders that introduce the structure of the data, the meaning of the file names, and how to interpret the raw data.This dataset contains data for behavioural outcomes from the audio-visual simultaneity judgement (SJ) and emotion recognition (ER) tasks described in the paper: "An RCT study showing few weeks of music lessons enhance audio-visual temporal processing". In the SJ task, participants judged whether the presented auditory and visual cues were synchronised by making a key press. The SJ task included two types of audio-visual cues, which are the flash and beep, and the face and voice. In the ER task, participants made emotional judgements about dynamic facial expression stimuli, having to classify them as being either joy, sadness, fear, anger, disgust, surprise, or neutral by making a speeded mouse click on the target emotion. Three levels of emotional intensity (low, medium, and high) were included for all the emotions except the neutral.
Participants were screened before being recruited in the study so that only non-musician adults with normal or adjust-to-normal vision and hearing were included. This study used a parallel group RCT design. We did not include blinding in this study as the design required participants’ active involvement in certain conditions. The experimenter had to know and run the sessions, with the experimenter also serving as the trainer. However, the experimenter had no control over the group allocation process as the participants were randomly assigned to their group at the beginning of the study.All the data in this dataset has been anonymised prior to sharing.The simultaneity judgement task data is stored as .txt files therefore no special software is required to view them.
The emotion recognition task data is also stored as .txt files. However, a MATLAB program is required to process these files and get further data of interest (average accuracy and reaction time for correct responses). The details of data processing have been provided in the Read-me-first.txt under the emotion recognition task folder. The code of the MATLAB program used for data processing (Emotion_analysis.m) is also included in the same folder.Information on the layout of the data has been provided in the Read-me-first.txt under each folders
Dataset for "Does independent regulation of MPs’ pay and expenses improve political trust? Evidence from a survey experiment"
Full text linkThe data was collected as part of a political science research project on political trust.
The two data files contain the raw data from two online survey experiments which sought to test the effect of pay and expenses information on political trust. The surveys together received 1957 responses.
The R code used to analyse the data files is also made available.The experiments were pre-registered with the Open Science Framework, where they are explained in full.
Briefly, in each survey, participants were randomly allocated into one of four groups: a control group and 3 treatment conditions, which received varying levels of information about pay or expenses. Participants were also asked a battery of questions about political trust, their views on whether pay/expenses were set at the right level, and questions about their level of interest in politics and party allegiance. Additional demographic information, such as age, was provided by the survey platform (Prolific) and is also included in the spreadsheet.Participants were recruited through Prolific. Survey data was collected via QuestionPro. The data can be viewed in MS Excel. The code was written in R Studio using R version 4.2.2.See documents attached
Dataset for "Greater tolerance of uncertainty facilitates thriving in doctors entering postgraduate training"
No full textQuestionnaire data from 66 doctors entering UK foundation training after graduation from medical school. The questionnaire gathered data using validated measures for perceived stress, wellbeing, career success, tolerance of uncertainty, and adverse childhood experiences. Additional items enquired about lifetime stress, age, sex, and disability.Cross-sectional online survey using validated measures. Participants recruited by email from medical school graduates entering a postgraduate foundation training programme in a training region (postgraduate deanery) of the United Kingdom.Data are anonymised
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