cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/sks3d6sd6f.2
title: Mechanical load applied by Intraosseous Transcutaneous Amputation Prosthesis (ITAP)

description: Walking on level and sloped treadmill M, N and P data sets. Also included are data from Sitting and rising from stool (Q) and Step over block (R). Data collected from one participant: The participant was a 50 year old male with a left transfemoral amputation (residuum = 181 mm measured from greater trochanter proximal ridge) as a result of trauma in 1984, he provided full informed consent for this study. He had used a prosthetic socket until he received an ITAP in 2012 as part of a clinical trial (Identifier = NCT02491424). The ITAP spigot (the percutaneous part of the POI) connected to a failsafe release device, which protects the bone from overload (now withdrawn Fail-Safe Attachment for Prosthetic Limb, patent GB2479532A, Stanmore Implants Worldwide, Hertfordshire, UK). The failsafe connected to a microprocessor controlled Genium knee, which connected to a mechanical carbon fibre Taleo foot (both Ottobock, Duderstadt, Germany) using standard prosthetic fittings. He was 1.89 m tall and weighed 102.8 kg (including the 1.4 kg artificial leg and all prosthetic components) with a K3 prosthetic activity level. Kinetic data collection: Uphill, level and downhill gait data was collected on a treadmill (GRAIL Motek, Amsterdam, The Netherlands) at a self-selected speed and gradient. The uphill and downhill gradients were 8.5 ° and 7.0 ° respectively. Level and downhill walking was at 1.0 m/s; uphill walking was at 0.8 m/s. The participant walked untethered without the use of handrails in level walking and intermittently on the slopes, and after fitting the load cell, acclimatised to the treadmill conditions for 20 minutes prior to measurement. Load cell construction: A prosthetic tube connecting the participant’s artificial knee to a failsafe device was replaced with an instrumented tube (load cell). The load cell contained 20 thin film strain gauges oriented such as to be sensitive to the DOF to be measured, having five thin film strain gauges located on each of four sides. Gauges (20 kOhms) were wire bonded to a flexible printed circuit which interconnected the four quadrants. Electrically, these formed six half bridges of gauges wired for primary sensitivity to axial compression, AP bending and ML bending. Four gauges at 45 ° to the long axis were wired into four channels of quarter bridge action only, for primary sensitivity to shear force and torque. It was accepted that there would be cross-talk between channels wired primarily for one DOF, as is usually the case where gauges are shared between DOF’s, and the matrix method of calibration was therefore used to identify and appropriately combine channels sensitive to more than one applied load type. Each channel was wired to a printed circuit for amplification, A-D conversion, serial data streaming and radio transmission
DOI: 10.25582/data.2021-08.2296081/2145564
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-07.2291651/2145565
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-07.2285305/2145566
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-07.2287019/2145567
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-07.2293406/2145568
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2296707/2145569
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2295128/2145570
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-07.2285285/2145571
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2293539/2145572
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-07.2291626/2145573
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2294004/2145574
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2295051/2145575
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2302244/2145576
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2295018/2145577
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2296328/2145578
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-07.2291848/2145579
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2294991/2145580
title: Data for EMSL Project 51287 from August 2021
DOI: 10.17632/bympr3p3p2.1
title: Integrated Machine Learning and Multivariate Analysis for Quantifying the Relative Contribution of Terrestrial Organic Matter to Marine Sediments: A Case Study in Environmental Geochemistry.Mirzaei et al.

description: Data used to train the model
DOI: 10.25582/data.2021-07.2290731/2145581
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2295056/2145582
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2295058/2145583
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2293561/2145584
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2294880/2145585
title: Data for EMSL Project 51287 from August 2021
DOI: 10.17632/3skx9bzzt7.2
title: HSF1 and GAPDH Western Blot

description: Western blot data for HSF1 and GAPDH. Blot using chemiluminescence to show HSF1 and GAPDH. Blot using bright field to show ladder.
DOI: 10.25582/data.2021-07.2293138/2145586
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-07.2286534/2145587
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2296025/2145588
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2294997/2145589
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2296234/2145590
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2294993/2145591
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2295048/2145592
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2296036/2145593
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2296176/2145594
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2296232/2145595
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-07.2290238/2145596
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-07.2291826/2145597
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2295049/2145598
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2293573/2145599
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2295002/2145600
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-07.2282519/2145601
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2294963/2145602
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2294879/2145603
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-07.2292718/2145604
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2296238/2145605
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2293782/2145606
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-07.2283770/2145607
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-07.2286941/2145608
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2294396/2145609
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2295118/2145610
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2295240/2145611
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2296233/2145612
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-07.2292192/2145613
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2295179/2145614
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-08.2298117/2145615
title: Data for EMSL Project 51287 from August 2021
DOI: 10.25582/data.2021-07.2287428/2145616
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-07.2285535/2145617
title: Data for EMSL Project 51287 from July 2021
DOI: 10.25582/data.2021-08.2302247/2145618
title: Data for EMSL Project 51287 from August 2021
DOI: 10.5281/zenodo.15205026
title: Dataset and code for: Non-invasive diagnosis of chronic myocardial infarction via composite in-silico-human data learning

description: Non-invasive diagnosis of chronic myocardial infarction via composite in-silico-human data learning

This repository contains the codes and datasets used in our study on non-invasive myocardial infarction (MI) diagnosis through composite in-silico-human data learning. Our work combines simulations and limited human cardiac magnetic resonance (CMR) strain data to train machine learning (ML) models for predicting infarct regions in the left ventricle. Below are the highlights of the repository content:

Strain Transformation and Preprocessing



Strain_transformation/ES_strains.ipynb: Python code for extracting cardiac strains at the end-systole timepoint from simulation datasets provided in the Strain_transformation/reference_files folder.

Strain_transformation/strains_AHA.m: MATLAB script for transforming strains into American Heart Association (AHA) format for machine learning inputs.

Reference Files: Includes .dat, .sta, fiber orientations, elements, nodes, and short-axis slice files for strain computation.


Estimation of Cardiac Strains from CMR



Super-Resolution Reconstruction in Cardiac Magnetic Resonance (SRR in CMR): This in-house built tool is used for estimating the four-dimensional motion of the heart using super-resolution reconstruction (SRR) of CMR images. This code is publicly available on Github.


Datasets



In-silico simulated cardiac strain datasets generated from finite element models.

Human CMR-derived strain data and corresponding infarct labels, organized for model training and evaluation.


Machine Learning Models (Multifidelity_codes)



Single-fidelity UNet models trained exclusively on simulation data.

Multi-fidelity codes integrating in-silico (low-fidelity) and human (high-fidelity) strain data for robust infarct region prediction.


Performance Evaluation (Evaluation_metrics)



Codes and model weights for evaluating the ML model performance in predicting infarct regions using Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and other metrics.

The DSC validation experiment includes both ideal predictions (perfect overlap) and completely mismatched images (e.g., non-pathological pixels) to assess the model’s scoring behavior across best- and worst-case scenarios.
DOI: 10.5281/zenodo.15205027
title: Dataset and code for: Non-invasive diagnosis of chronic myocardial infarction via composite in-silico-human data learning

description: Non-invasive diagnosis of chronic myocardial infarction via composite in-silico-human data learning

This repository contains the codes and datasets used in our study on non-invasive myocardial infarction (MI) diagnosis through composite in-silico-human data learning. Our work combines simulations and limited human cardiac magnetic resonance (CMR) strain data to train machine learning (ML) models for predicting infarct regions in the left ventricle. Below are the highlights of the repository content:

Strain Transformation and Preprocessing



Strain_transformation/ES_strains.ipynb: Python code for extracting cardiac strains at the end-systole timepoint from simulation datasets provided in the Strain_transformation/reference_files folder.

Strain_transformation/strains_AHA.m: MATLAB script for transforming strains into American Heart Association (AHA) format for machine learning inputs.

Reference Files: Includes .dat, .sta, fiber orientations, elements, nodes, and short-axis slice files for strain computation.


Estimation of Cardiac Strains from CMR



Super-Resolution Reconstruction in Cardiac Magnetic Resonance (SRR in CMR): This in-house built tool is used for estimating the four-dimensional motion of the heart using super-resolution reconstruction (SRR) of CMR images. This code is publicly available on Github.


Datasets



In-silico simulated cardiac strain datasets generated from finite element models.

Human CMR-derived strain data and corresponding infarct labels, organized for model training and evaluation.


Machine Learning Models



Single-fidelity UNet models trained exclusively on simulation data.

Multi-fidelity codes integrating in-silico (low-fidelity) and human (high-fidelity) strain data for robust infarct region prediction.


Performance Evaluation



Codes and model weights for evaluating the ML model performance in predicting infarct regions using Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and other metrics.
DOI: 10.5281/zenodo.15208063
title: Dataset and code for: Non-invasive diagnosis of chronic myocardial infarction via composite in-silico-human data learning

description: Non-invasive diagnosis of chronic myocardial infarction via composite in-silico-human data learning

This repository contains the codes and datasets used in our study on non-invasive myocardial infarction (MI) diagnosis through composite in-silico-human data learning. Our work combines simulations and limited human cardiac magnetic resonance (CMR) strain data to train machine learning (ML) models for predicting infarct regions in the left ventricle. Below are the highlights of the repository content:

Strain Transformation and Preprocessing



Strain_transformation/ES_strains.ipynb: Python code for extracting cardiac strains at the end-systole timepoint from simulation datasets provided in the Strain_transformation/reference_files folder.

Strain_transformation/strains_AHA.m: MATLAB script for transforming strains into American Heart Association (AHA) format for machine learning inputs.

Reference Files: Includes .dat, .sta, fiber orientations, elements, nodes, and short-axis slice files for strain computation.


Estimation of Cardiac Strains from CMR



Super-Resolution Reconstruction in Cardiac Magnetic Resonance (SRR in CMR): This in-house built tool is used for estimating the four-dimensional motion of the heart using super-resolution reconstruction (SRR) of CMR images. This code is publicly available on Github.


Datasets



In-silico simulated cardiac strain datasets generated from finite element models.

Human CMR-derived strain data and corresponding infarct labels, organized for model training and evaluation.


Machine Learning Models (Multifidelity_codes)



Single-fidelity UNet models trained exclusively on simulation data.

Multi-fidelity codes integrating in-silico (low-fidelity) and human (high-fidelity) strain data for robust infarct region prediction.


Performance Evaluation (Evaluation_metrics)



Codes and model weights for evaluating the ML model performance in predicting infarct regions using Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and other metrics.

The DSC validation experiment includes both ideal predictions (perfect overlap) and completely mismatched images (e.g., non-pathological pixels) to assess the model’s scoring behavior across best- and worst-case scenarios.
DOI: 10.7925/drs1.duchas_4948291
title: Some Facts Connected with the Village of Adare

description: Story collected by Mary Bourke, a student at Shountrade, Adare school (Shantraud, Co. Limerick) from informant Patrick Brown.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Máire, Bean Uí Fhloinn.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5076544
title: There are but a few old roads in this parish. ...

description: Story collected by Chris Droney, a student at Turlough, Béal an Chloga school (Turlough, Co. Clare) from informant John Kerins.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Áine, Bean Uí Éalaidhe.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_4689656
title: Scéal Grinn

description: Story collected by Tomás O Néil, a student at Baile Uí Dhuibhne school (Ballyquin, Co. Kerry) from informant Eoghan Ó Neil.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Seán Ó Loingsigh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_4689657
title: Scéal

description: Story collected by a student at Baile an Ghóilín (Scoil N. Finghín) school (Burnham East, Co. Kerry) from informant Tomás Ó Maoleoin.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Dll. Ó Loingsigh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5076545
title: Cill Stuifín

description: Story collected by a student at Dubhloch, Cill Mháille school (Doolough, Co. Clare) from informant David Dillon.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Tadhg Ó Maoldhomhnaigh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_4948293
title: Weather-Lore

description: Story collected by Teresa Walsh, a student at Coolcappa, Árd-achadh school (Coolcappagh, Co. Limerick) (no informant identified).
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Bríd Ní Shíthigh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5076547
title: Mo Cheantar Féin

description: Story collected by Mícheál Ó Flanagáin, a student at Mullach, Sráid na Cathrach school (Mullagh, Co. Clare) from informant Séumas Ó Scannláin.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Proinnsias Ó Sandair.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_4689658
title: Cluichí - Hi-Ho Tippy Toe

description: Story collected by Bríd Ní Suilliobháin, a student at Killorglin (C.) school (Killorglin, Co. Kerry) (no informant identified).
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Úna Nic Coluin.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_4948294
title: Elm Hill - A Funny Story

description: Story collected by Bessie O' Donnell, a student at Coolcappa, Árd-achadh school (Coolcappagh, Co. Limerick) from informant an unidentified informant.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Bríd Ní Shíthigh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_4689659
title: Cistí Óir i bhFolach

description: Story collected by Pádruig Gruméil, a student at Baile Uí Dhuibhne school (Ballyquin, Co. Kerry) from informant Seán O Bric.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Seán Ó Loingsigh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5076548
title: There are many roads all over Ireland. ...

description: Story collected by John P. Kerin, a student at Turlough, Béal an Chloga school (Turlough, Co. Clare) from informant Tim Kerin.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Áine, Bean Uí Éalaidhe.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_4948295
title: Fairy Forts

description: Story collected by John Creed, a student at Domhnach Mór, Luimneach school (Donaghmore, Co. Limerick) (no informant identified).
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Aingeal Nic Aodha Bhuidhe.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_4948296
title: Scourge of Landlordism in Rour Mor and Tuagh in the Parish of Adare

description: Story collected by Thomas Mc Mahon, a student at Shountrade, Adare school (Shantraud, Co. Limerick) from informant Patrick Browne.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Máire, Bean Uí Fhloinn.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5076549
title: Mo Cheantar Féin

description: Story collected by Pádraig Mac Iomaire, a student at Mullach, Sráid na Cathrach school (Mullagh, Co. Clare) from informant Máirtín Ó Seanáin.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Proinnsias Ó Sandair.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_4689660
title: Scéal

description: Story collected by a student at Baile an Ghóilín (Scoil N. Finghín) school (Burnham East, Co. Kerry) from informant Tomás Ó Maoleoin.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Dll. Ó Loingsigh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.