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Efficient Tactile Perception with Soft Electrical Impedance Tomography and Pre-trained Transformer
No full textThis dataset is for publication "Efficient Tactile Perception with Soft Electrical Impedance Tomography and Pre-trained Transformer".
ABSTRACT: Tactile sensing is fundamental to robotic systems, enabling interactions through physical contact in multiple tasks. Despite its importance, achieving high-resolution, large-area tactile sensing remains challenging. Electrical Impedance Tomography (EIT) has emerged as a promising approach for large-area, distributed tactile sensing with minimal electrode requirements which can lend itself to addressing complex contact problems in robotics. However, existing EIT-based tactile reconstruction methods often suffer from high computational costs or depend on extensive annotated simulation datasets, hindering its viability in real-world settings. To address this shortcoming, here we propose a Pre-trained Transformer for EIT-based Tactile Reconstruction (PTET), a learning-based framework that bridges the simulation-to-reality gap by leveraging self-supervised pretraining on simulation data and fine-tuning with limited real-world data. In simulations, PTET requires 99.44% fewer annotated samples than equivalent state-of-the-art approaches (2,500 vs. 450,000 samples) while achieving reconstruction performance improvements of up to 43.57% under identical data conditions. Fine-tuning with real-world data further enables PTET to overcome discrepancies between simulated and experimental datasets, achieving superior reconstruction and detail recovery in practical scenarios. PTET’s improved reconstruction accuracy, data efficiency, and robustness in real-world tasks establish it as a scalable and practical solution for tactile sensing systems in robotics, especially for object handling and adaptive grasping under varying pressure conditions
DNS Kolmogorov flow data for Re 90
No full textThe dataset contains a three dimensional (3-D) Direct Numerical Simulation (DNS) of a Kolmogorov flow with the unit Reynolds number defined as Re=1/nu and a sinusoidal forcing on streamwise axis and one wavenumber. It is an extended shear flow simulation from 2-D simulation into 3-D with periodic boundary on the side walls, input on the left and output on the right walls. It was generated in python using the spectral solver Dedalus. The data can be used to train machine learning algorithm such as an autoencoder for reduce-order modelling of turbulent flow. This is a subset of three increasing Reynolds numbers of Kolmogorov flow set to Re= 30, 50 and 90. You can find the other dataset on the Datashare
DNS Kolmogorov flow data for Re 30
No full textThe dataset contains a three dimensional (3-D) Direct Numerical Simulation (DNS) of a Kolmogorov flow with the unit Reynolds number defined as Re=1/nu and a sinusoidal forcing on streamwise axis and one wavenumber. It is an extended shear flow simulation from 2-D simulation into 3-D with periodic boundary on the side walls, input on the left and output on the right walls. It was generated in python using the spectral solver Dedalus. The data can be used to train machine learning algorithm such as an autoencoder for reduce-order modelling of turbulent flow. This is a subset of three increasing Reynolds numbers of Kolmogorov flow set to Re= 30, 50 and 90. You can find the other dataset on the Datashare
Assessment of perivascular space filtering methods using a three-dimensional computational model: source code
No full textPublication available; If you use our computational model in your research, please cite the following paper: Bernal J, Valdés-Hernández MD, Escudero J, Duarte R, Ballerini L, Bastin ME, Deary IJ, Thrippleton MJ, Touyz RM, Wardlaw JM. Assessment of perivascular space filtering methods using a three-dimensional computational model. Magnetic resonance imaging. 2022 Nov 1;93:33-51. Introduction ------------ Perivascular spaces (PVS) are fluid-filled tubular passageways surrounding cerebral microvessels, thought to facilitate waste clearance and fluid exchange. PVS can become visible on structural MRI and, while a few may be normal, increased number or size is linked to ageing, hypertension, blood-brain barrier dysfunction, and other small vessel disease (SVD) markers. Their accurate quantification is important for understanding SVD and broader neurological health. Materials and methods ------------ Computational model for assessing PVS quantification. We generated maps with PVS-like structures in different orientations and locations throughout the brain. We used a high-resolution model of the head and the resulting “PVS” maps to synthesise T2-weighted like images. We then sampled these high-resolution digital reference objects (DRO) and incorporated motion and Rician noise to generate the “acquired” T2-weighted images. We finally segmented the “PVS” on these DRO and compared the results against the ground truth. The closer the results to the ground truth, the more resilient the “vesselness” filtering method against distortion. Result ------------ Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO's performance does not deteriorate as PVS volume increases. Second, the performance of all “vesselness” filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). Conclusions ------------ Our work reveals appropriate processing of MRI signals is necessary to maximise PVS measurement reliability. While filters to enhance and facilitate PVS detection can correctly achieve these goals, they are sensitive to imaging artefacts, such as ringing and motion, and ineffective at distinguishing between PVS from other lesions with similar contrast, shapes and overlapping size. These issues ultimately highlight the importance of masking out other neuroradiological features and of prospective or retrospective image enhancement for better PVS quantification. Computational model from the article: Bernal, Jose, Maria DC Valdés-Hernández, Javier Escudero, Roberto Duarte, Lucia Ballerini, Mark E. Bastin, Ian J. Deary, Michael J. Thrippleton, Rhian M. Touyz, and Joanna M. Wardlaw. "Assessment of perivascular space filtering methods using a three-dimensional computational model." Magnetic resonance imaging 93 (2022): 33-51. Acknowledgements ------------ This work was supported by: MRC Doctoral Training Programme in Precision Medicine (JB - Award Reference No. 2096671); The Galen and Hilary Weston Foundation under the Novel Biomarkers 2019 scheme (ref UB190097) administered by the Weston Brain Institute; the UK Dementia Research Institute which receives its funding from DRI Ltd., funded by the UK MRC, Alzheimer's Society and Alzheimer's Research UK; the Fondation Leducq Network for the Study of Perivascular Spaces in Small Vessel Disease (16 CVD 05); Stroke Association ‘Small Vessel Disease-Spotlight on Symptoms (SVD-SOS)’ (SAPG 19\100068); The Row Fogo Charitable Trust Centre for Research into Ageing and the Brain (MVH) (BRO-D.FID3668413); British Heart Foundation Edinburgh Centre for Research Excellence (RE/18/5/34216); NHS Lothian Research and Development Office (MJT).
The LBC1936 study was funded by Age UK and the UK Medical Research Council (http://www.disconnectedmind.ed.ac.uk/) (including the Sidney De Haan Award for Vascular Dementia). LBC1936 MRI brain imaging was supported by Medical Research Council (MRC) grants G0701120, G1001245, MR/M013111/1 and MR/R024065/1. Funds were also received from The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1), and the Biotechnology and Biological Sciences Research Council (BBSRC).
We thank the LBC1936 study participants, their families and radiographers at Edinburgh Imaging Facilities. We thank the LBC1936 study team members who recruited the participants of the study.* Step_01_createHRSegMap.m: creates the high-resolution anatomical segmentation map
* Step_02_createHRPVSMap.m: generates synthetic PVS structures taking into account the HR atlas
* Step_03_createHRSI.m: generates high-resolution synthetic MR images (e.g., T1w, T2w, FLAIR
* Step_04_createLRPVSMaps.m: creates low-resolution versions of PVS masks
* Step_05_createLRSI.m: generates low-resolution synthetic MRI images
* artefacts: contains scripts for simulating artefacts:
- add_motion_artifacts_rotation_kspace.m: Adds motion artefacts to images by simulating rotation-induced changes in the k-space domain.
- add_noise.m: Adds realistic noise (probably Gaussian or Rician) to simulate scanner noise in MR images.
* inputs: stores the input data for the pipeline.
- Atlas/: contains high-resolution and low-resolution anatomical atlases used as priors or templates for generating synthetic data.
* output: contains all output data generated by the pipeline:
- HR_PVS/: high-resolution PVS masks.
- HR_SI/: high-resolution synthetic signal intensity (e.g., T1w, T2w, FLAIR) generated from tissue labels.
- LR_PVS/: Low-resolution versions of PVS masks.
- LR_SI/: Low-resolution versions of signal intensity maps, with added motion/noise artefacts.
* utils: contains core utility function
High-speed imaging of non-photochemical laser-induced nucleation in aqueous cesium chloride
Get PDFThe contents of this archive are described in detail in the manuscript “High-speed imaging of non-photochemical laser-induced nucleation in aqueous cesium chloride” by Eleanor R. Barber and Andrew J. Alexander, published in Physical Chemistry Chemical Physics, 2025 (doi:10.1039/D4CP04147J). The designation Fn in the folders refer to specific figures in that work (e.g., F1 means Figure 1). The folders contain raw images and edited montages from recordings of nucleation of CsCl crystals from a supersaturated aqueous solution using a single, nanosecond laser pulse
National Security and Defence Documents Dataset (1987-2024) v2.0
No full textVersion 2.0 of this dataset consists of 607 national security and defence documents from 119 countries, covering the period from 1987 to 2024. It includes national security strategies, defence white papers, and other top-level security documents, all available as machine-readable PDFs and txt files. Non-English texts are accompanied by translations. The corpus is complemented by metadata providing context for each document and country, including variables such as UN region, regime type, and economic data. This collection enables comparative analysis, trend identification, and research across security studies, international relations, and related fields. The corpus serves as a resource for researchers, policymakers, civil society, and educational purposes, offering a compilation of global security priorities spanning nearly four decades. Additionally, the team provides code for reproducing their computational text analysis, allowing other researchers to replicate and build upon their work.pdf_documents.zip - machine-readable national security and defence documents for 119 countries, translated where necessary.
Country_metadata.csv (14.29Kb) - data about all countries in dataset, one row per country
document_metadata.csv (112.7Kb) - data about all documents in dataset, one row per document
Original_language_documents.zip - original untranslated national security and defence documents
en_core_web_lg-3.4.1.zip (560.4Mb) - data tools
model.zip (3.092Gb) - semantic embedding model
use-4.zip (916.5Mb) - semantic model encoder
ns_codebase.zip (933.5Kb) - codebase for topic search, analysis, document processing, and model building
ns_codebase_installation.txt (1.073Kb) - installation instructions
readme.txt (6.821Kb)
license_text (17.00Kb)
institutional_membership_data.zip - additional country metadata
txt_files.zip - national security and defence documents extracted and cleaned from pdf documents as plain tex
Supplementary data for the manuscript "Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science: Incremental Templates in Year 1"
No full textThe Vera C. Rubin Observatory is due to commence the 10-year Legacy Survey of Space and Time (LSST) at the end of 2025. To detect transient/variable sources and identify solar system objects (SSOs), the processing pipelines require templates of the static sky to perform difference imaging. During the first year of the LSST, templates must be generated as the survey progresses, otherwise SSOs cannot be discovered nightly. The incremental template generation strategy has not been finalized; therefore, we use the Metric Analysis Framework (MAF) and a simulation of the survey cadence (one snap_v4.0_10yrs) to explore template generation in Year 1. We have assessed the effects of generating templates over timescales of days-weeks, when at least four images of sufficient quality are available for ≥ 90% of the visit. We predict that SSO discoveries will begin ∼2-3 months after the start of the survey. We find that the ability of the LSST to discover SSOs in real-time is reduced in Year 1. This is especially true for detections in areas of the sky that receive fewer visits, such as the North Ecliptic Spur (NES), and in less commonly used filters, such as the u and g-bands. The lack of templates in the NES dominates the loss of real-time SSO discoveries; across the whole sky the MAF Main-Belt asteroid (MBA) discovery metric decreases by up to 63% compared to the baseline observing strategy, whereas the metric decreases by up to 79% for MBAs in the NES alone.README.txt - Overview of dataset
first_year_baseline_v3_3_10yrs_n_visits_4_noDD_noTwi.csv - Database of template coverage per visit for the baseline_v3.3_10yrs LSST pointing simulation
first_year_baseline_v3_4_10yrs_n_visits_4_noDD_noTwi.csv - Database of template coverage per visit for the baseline_v3.4_10yrs LSST pointing simulation
first_year_baseline_v3_5_10yrs_n_visits_4_noDD_noTwi.csv - Database of template coverage per visit for the baseline_v3.5_10yrs LSST pointing simulation
first_year_baseline_v3_6_10yrs_n_visits_4_noDD_noTwi.csv - Database of template coverage per visit for the baseline_v3.6_10yrs LSST pointing simulation
first_year_baseline_v4_0_10yrs_n_visits_4_noDD_noTwi.csv - Database of template coverage per visit for the baseline_v4.0_10yrs LSST pointing simulation
first_year_one_snap_v4_0_10yrs_n_visits_4_noDD_noTwi.csv - Database of template coverage per visit for the one_snap_v4.0_10yrs LSST pointing simulation
licence_text.txt - Dataset licence
read_data.ipynb - Example jupyter notebook to demonstrate opening a database file with panda
Simulation cases for 'Assessment of coarse grained CFD--DEM modelling strategies in horizontal plug flow pneumatic conveying' paper
No full textOpenFOAM and Aspherix simulation data for the paper, 'Assessment of coarse grained CFD--DEM modelling strategies in horizontal plug flow pneumatic conveying' The study examines the coarse grained (CG) CFD--DEM methodologies for plug flow pneumatic conveying within a horizontal, rigid and cylindrical pipe with periodic boundaries in the axial direction. We use coupled Computational Fluid Dynamics (CFD) -- Discrete Element Method (DEM) simulations to investigate the effect of different CG rules on four plug characteristics: velocity, length, porosity, and particle exchange rate with the stationary bed.
Spherical particles are used, and the CG rules are applied via the input scripts of the simulation cases. 9 simulation cases are provided, and these are at CG ratio=6. All the simulation cases in the paper can be created from the uploaded cases by varying the CG ratio or the smoothing length accordingly.
The simulation was run using 'CFDEMcoupling-Nonspherical-6.1.0' and 'Aspherix-6.1.0.' Aspherix is a proprietary software from DCS Computing and well recognized within Discrete Element Method (DEM) research community.
Each case folder includes the data produced from that simulation and the input files to re-run. The other simulation cases in the publication can be created by varying either the CG ratio from the .asx files in the DEM folders or the smoothing length from the couplingProperties dictionary in the CFD/constant folders.
In all simulations, plug flow pneumatic conveying is simulated in a horizontal and circular pipe using periodic boundary conditions. The flow solver is a slightly modified version of the "cfdemSolverPiso" of CFDEMcoupling. The implementation was done according to Eq. (21) of Hilton and Cleary (2011). The equation is also given in the submitted manuscript, so an interested reader can refer to the manuscript for more detail. This is done to enable us to input the pressure gradient as the momentum source in the periodic system.Please refer to the README.txt file in the dataset
DNA methylation-based predictors of metabolic traits in Scottish and Singaporean cohorts
No full textThis dataset corresponds to the study: "DNA methylation-based predictors of metabolic traits in Scottish and Singaporean cohorts". Files within this dataset include summary statistics from epigenome-wide association studies (EWASs) of six metabolic traits in Generation Scotland using two distinct methods OSCA and BayesR+. For the OSCA files, DNAm principal component (PC) adjusted and non-PC-adjusted results are included. Weights for six metabolic epigenetic scores (EpiScores) are included. These weights were generated using two methods: Elnet and BayesR+. EpiScore weights for each trait (and each method) are included (two files per trait)
Data from: Sex ratio adjustment and kin discrimination in malaria parasites.
No full textThis manuscript demonstrates that evolutionary theory developed to explain the biology of multicellular organisms can be applied to reveal how ‘sophisticated’ parasite strategies are. Specifically, reveals that Plasmodium can discriminate the relatedness of co-infecting genotypes and use this information to plastically adjust investment into male versus female transmission stages to maximise fitness