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Determinants of wellbeing in students
No full textQuantitative survey data collected by a group of students on the MSc Psychology of Mental Health Conversion programme in 2024/25. Contains a number of determinants of wellbeing in students, alongside demographics and the PERMA-profiler measure of wellbeing. University students aged 18-30 were recruited from across the world, predominantly UK and Ireland and North America but with representation from most other continents.
Each of the three students put forward measures for inclusion and wrote up the results independently of one another. Two of the subsequent dissertations are being submitted for publication (Boylan et al and Frankenfield et al) and these two data sets will be deposited here
A pedagogical research on use of an online learning platform by final year medical students on under five pneumonia
No full textAn online portal was developed for final year medical students from five medical universities in Rawalpindi and Islamabad cities of Pakistan on case management of under-five pneumonia. This was a 6 month intervention which had a pre and post intervention survey
Trypanocide resistance in Uganda
No full textThe dataset are quantitative observations from a survey conducted in Uganda among 557 dairy farmers in 8 districts of southwestern Uganda. The communities are characterized by a high antimicrobial consumption rate although quantitative evidence was lacking through a search of any available scholarly resources. Entries are on farm structure, livestock densities, and income earned from livestock sales and associated expenditures. We subsequently assess farmer knowledge, opinions and practices on trypanocide usage within the community and entries are recorded as categories. Columns describing scores were made on a scale of 1-5 with 1 being the lowest and 5 the highest. This was done with the goal of quantifying the severity of the previous response
SSD_AMC_Open-set_CODE
No full textAbstract: Increased crowding on the radio frequency spectrum has resulted in a greater risk of radar interference, creating the demand for cognitive radars that can dynamically adapt to avoid interference. A technique that benefits cognitive radar performance is automatic modulation classification, which is the task of identifying the modulation scheme used to encode received digital communications without prior knowledge. Current approaches fail to address the challenges of wideband radio frequency environments that simultaneously contain multiple transmitters and previously unseen modulation schemes. To address the issue of numerous transmitters, this research proposes a novel singleshot detector architecture for detecting and classifying radio frequency communications in a single forward pass through the model. The second issue of previously unseen modulation schemes is also addressed through the incorporation of open-set recognition. The results demonstrate that the proposed model achieves high accuracy in detection, classification, and open-set recognition. This research helps adapt automatic modulation classification to more realistic scenarios by addressing the joint detection and classification in wideband operation with previously unseen modulation schemes. In turn, this framework can improve the performance of downstream tasks such as spectrum sensing for cognitive rada
Disrupting Understandings of Disruptive Behaviour
No full textThis DataShare item relates to a qualitative study funded by the Spencer Foundation, titled ‘Disrupting Understandings of Disruptive Behaviour’. The study sought to investigate the school experiences of pupils who had been receiving support at school before COVID-19 to help them manage their behaviour in school.
The aim was to investigate whether, as the literature below would predict, some aspects of education under lockdown were experienced positively by pupils with a history of disruptive behaviour (PHDB), and to work with families and schools in Scotland to identify ways in which these positive aspects may be replicated in the post COVID classroom
Major element zonation following rapid heating of homogeneous glass in wire-loop experiments_full dataset
No full textData used for the publication "Major element zonation following rapid heating of homogeneous glass in wire-loop experiments" [https://doi.org/10.1016/j.chemer.2025.126383]. Data obtained as part of a systematic investigation of magmatic degassing process in analogue lunar magma, funded by the Leverhulme Trust through Research Project Grant RPG-2021-015, and the Carnegie Trust through PhD Scholarship award PHD01069
Species diversity and risk factors of gastrointestinal nematodes in smallholder dairy calves in Kenya
No full textGastrointestinal nematodes (GIN) are of major concern in dairy farming, particularly
in smallholder systems, because of their impact on the health of the calves and
later on their productivity. These infections often occur as co-infections, which
can complicate their prevention and treatment. The aim of this study was to
conduct fecal egg counts (FEC), genetically identify GIN species, assess species
diversity, and identify associated risk factors for GIN infections in dairy calves.
Fecal samples were collected from 532 dairy calves across 289 small holder dairy
farms. Species identification was achieved through deep amplicon sequencing of
the Internal Transcribed Spacer-2 rDNA locus (ITS-2) of first-stage larvae (L1). The
mean eggs per gram (EPG) was 62.0 ± 93.0. Most of the calves 64.2% had lowintensity
infections (<50 EPG), 28.6% had medium-intensity infections (50–200 EPG),
and 7.2% had high-intensity infections (>200 EPG). Next Generation Sequencing
analysis identified nine GIN species, with Cooperia punctata (27.8%), Haemonchus
placei (26.3%), and Haemonchus contortus (23.6%) being the most prevalent.
Co-infections were common, accounting for 69.5% of all infections, with two
(40.1%), three (26.9%), and four-species combinations (19.8%) predominating. Male
calves showed a significant association with both increased FEC and smaller heart
girth, while FEC decreased with age. H. placei and C. punctata were associated
with increased FEC, whereas Ostertagia ostertagi (14.5%) and Trichostrongylus
colubriformis (8.0%) were associated with decreased heart girth. Calves managed
under pasture systems had higher odds of co-infection. This study reveals that GIN
infections are highly prevalent in dairy calves, with co-infections being common,
and that GIN burden is significantly influenced by calf age, sex, and management
system. The Nemabiome tool offers a promising approach to assessing GIN burden
and guiding the selection of anthelmintic protocols as part of sustainable farming
strategies in tropical regions.This dataset is the test results from 579 calves sampled as part of a larger study on smallholder dairy cattle in Kenya. The data capture the key animal features such as age sex etc plus the results of screening for gastrointestinal nematodes using the nemabiome tool (a sequence based unbiased tool)
Climate change increases ammonia emissions, reducing the efficacy of mitigation actions (AMCLIM modelling results)
No full textTemperature sensitivity of agricultural ammonia emissions modelled by the AMCLIM model. Projected agricultural ammonia emissions in 2041-2050 and 2091-2100 under four different shared socio-economic pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.
Mitigation of agricultural ammonia emissions for the year 2010. Base run results are deposited under:
https://datashare.ed.ac.uk/handle/10283/8753
https://datashare.ed.ac.uk/handle/10283/895
Data Stories dataset 2020-2022
No full textFictional stories in text and image form, submitted anonymously through a research project web site and inspired by prompts designed to explore possible surveillance futures in higher education. The stories include educational settings, characters, technologies, processes and wider social and political contexts
Achieving Robust Channel Estimation Neural Networks by Designed Training Data
No full textCode for Luan, Dianxin, and John Thompson. "Achieving Robust Channel Estimation Neural Networks by Designed Training Data.", Accepted for Publication in IEEE Transactions on Cognitive Communications and Networking (2025).
This dataset is uploaded from this Github page, maintained by Dianxin Luan:
https://github.com/dianixn/Achieving-Robust-Channel-Estimation-Neural-Networks-by-Designed-Training-Data
Paper Abstract: Channel estimation is crucial in wireless communications. However, in many papers neural networks are frequently tested by training and testing on one example channel or similar channels. This is because data-driven methods often degrade on new data which they are not trained on, as they cannot extrapolate their training knowledge. This is despite the fact physical channels are often assumed to be time-variant. However, due to the low latency requirements and limited computing resources, neural networks may not have enough time and computing resources to execute online training to fine-tune the parameters. This motivates us to design offline-trained neural networks that can perform robustly over wireless channels, but without any actual channel information being known at design time. In this paper, we propose design criteria to generate synthetic training datasets for neural networks, which guarantee that after training the resulting networks achieve a certain mean squared error (MSE) on new and previously unseen channels. Therefore, trained neural networks require no prior channel information or parameters update for real-world implementations. Based on the proposed design criteria, we further propose a benchmark design which ensures intelligent operation for different channel profiles. To demonstrate general applicability, we use neural networks with different levels of complexity to show that the generalization achieved appears to be independent of neural network architecture. From simulations, neural networks achieve robust generalization to wireless channels with both fixed channel profiles and variable delay spreads.
Funding Acknowledgement:
This research is supported by EPSRC projects EP/X04047X/2 and EP/Y037243/1 (TITAN Extension project).COPY OF README.TXT FILE IN THE ZIP FILE:
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# Achieving Robust Channel Estimation Neural Networks by Designed Training Data
Code for Luan, Dianxin, and John Thompson. "Achieving Robust Channel Estimation Neural Networks by Designed Training Data." IEEE Transactions on Cognitive Communications and Networking (2025).
Abstract:
Channel estimation is crucial in wireless communications. However, in many papers neural networks are frequently tested by training and testing on one example channel or similar channels. This is because data-driven methods often degrade on new data which they are not trained on, as they cannot extrapolate their training knowledge. This is despite the fact physical channels are often assumed to be time-variant. However, due to the low latency requirements and limited computing resources, neural networks may not have enough time and computing resources to execute online training to fine-tune the parameters. This motivates us to design offline-trained neural networks that can perform robustly over wireless channels, but without any actual channel information being known at design time. In this paper, we propose design criteria to generate synthetic training datasets for neural networks, which guarantee that after training the resulting networks achieve a certain mean squared error (MSE) on new and previously unseen channels. Therefore, trained neural networks require no prior channel information or parameters update for real-world implementations. Based on the proposed design criteria, we further propose a benchmark design which ensures intelligent operation for different channel profiles. To demonstrate general applicability, we use neural networks with different levels of complexity to show that the generalization achieved appears to be independent of neural network architecture. From simulations, neural networks achieve robust generalization to wireless channels with both fixed channel profiles and variable delay spreads.
%%%
Run Demonstration_of_H_Rayleigh_Propogation_Channel, Demonstration_of_H_Rayleigh_Propogation_Channel_Alternative and Demonstration_of_H_Rayleigh_Propogation_Channel_batch files to test for sub-6 band results.
Run Demonstration_of_H files to test for Millimeter-wave band results.
Run Demonstration_of_H_Appendix to get the first three figures of Simulation Section.
%% File +Training has
ResNN_pilot_regression to train the InterpolateNet and SimpleNet for default pilot pattern.
ResNN_pilot_regression_Alternative to train the InterpolateNet and SimpleNet for alternative pilot pattern.
Training_hybrid_offline to train Channelformer.
Training_hybrid_offline_Alternative to train Channelformer for alternative pilot pattern.
%% File +parameter has
parameters contains the system parameters for generating the training data and testing on the default pilot pattern on sub-6 band.
parameters_alternative contains the system parameters for generating the training data and testing on the alternative pilot pattern on sub-6 band.
parameters_hybrid contains the hyperparameters for Channelformer.
parameters_Millimeterwave contains the hyperparameters for CDL/TDL channels operating on Millimeter-wave band (39GHz).
%% File +Channel contains
Propagation_Channel_Model is a LTEfading channel developed by MATLAB specificed in https://uk.mathworks.com/help/lte/ref/ltefadingchannel.html.
CDL_Channel contains 3GPP TS38.901 CDL channel - nrCDLChannel object.
TDL_Channel contains 3GPP TS38.901 TDL channel - nrTDLChannel object.
%% File +CSI has
LS - It is the implementation of the LS method and the time interpolation method is bilinear method.
MMSE - It is the linear MMSE method and the time interpolation method is bilinear method.
%% File +Data_Generation contains
Data_Generation - used to generate the training data for online Channelformer offline
Data_Generation_Online - used to generate the training data for online training
Data_generation_offline_version - used to generate the training data for offline Channelformer and HA02.
Data_Generation_Residual - used to generate the training data for InterpolateNet and ReEsNet
Data_Generation_Transformer - used to generate the training data for TR method.
%% File +OFDM contains
OFDM_Receiver - OFDM receiver
OFDM_Transmitter - OFDM transmitter
Pilot_extract - extract the pilot
Pilot_Insert - insert the pilot
QPSK_Modualtor - generate QPSK symbols
QPSK_Demodulator - decode the received QPSK signals
%% File +Performance_plot contains
Plot_Alternative - plot function.
Plot_Appendix - plot function.
Plot_BER - plot function.
Plot_HA02 - plot function.
Plot_InterpolateNet - plot function.
Plot_N_f - plot function.
Plot_SimpleNet - plot function.
Plot_batch - plot function.
Plot_generalization - plot function.
%% File Residual_NN contains
Interpolation_ResNet - Untrained InterpolateNet (WSA paper)
SimpleNet - 882 parameters neural networks
%% File +transformer contains Channelformer code
model - system model for Channelformer
+HA03 - The encoder and decoder architecture of Channelformer
Encoder_block - the encoder of Channelformer
Decoder_block - the decoder of Channelformer
+layer contains the layer modules for attanetion mechanism and residual convolutional neural network
normalization - layer normalization
FC1 - fully-connected layer
gelu - Activation function of GeLu
multiheadAttention - multihead attention module, which calcualte the attention from Q, K and V
attention - main control unite of the multiohead attention module, designed by tranformer encoder
FeedforwardNN - feedforward neural network designed by tranformer encoder
%%% Comments
Run with MATLAB 2023B, with fully-installed deep learning toolbox because it requires customized training.
%%% Acknowledgement
This research is supported by UK Engineering and Physical Sciences Research Council (EPSRC) projects EP/X04047X/2 and EP/Y037243/1 for the TITAN Telecoms Hub