cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10845085
title: Data to Support Predictive Models for Detrital Titanite Provenance with application to the Nanga Parbat syntaxial massif, western Himalaya."

description: The files published here are metadata that are being used to support a manuscript currently (Mar, 2024) undergoing final reviews in Journal of Geophysical Research: Earth Surface.

The intention of these data and code is to support a publication that is about generating a predictive categorisation scheme for the mineral titanite.

The code to generate the titanite classification schemes was created in Python3, using Jupyter Notebook. The files also provide more motivation for why a predictive categorisation scheme for the mineral titanite is desirable, and other similar context. Chiefly, the dataset and random forest models published here will allow us to trace titanite in detritus.

For info on running Jupyter Notebook, please visit (https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html) to seek instructions. We also provide a readme file with some instructions. If you get really stuck, just email the authors.

Our Model can be compared to similar previously published works (e.g. https://doi.org/10.1111/ter.12574).

The supplementary file "Table_S4_Merged.csv" was used to train and generate the model.

Your unknowns must contain the correct elements and labelling for the code to successfully run, these details are provided in the code (Titanite_Random_Forest_Model1_Mar24.ipynb). A template is also provided for you to paste your unknown data into (titanite_data_template.csv)

Any new published data are titanite compositional or isotopic data collected by LA-ICP-MS. Description of how those data were collected is given in "OSullivan_et_al_Supp..." file.

Some of the data, information and code in this submission has been subject to change after journal review, this is a second version of this content.

References for the dataset compilation are provided in File S3.

If you have any queries contact:Gary O'Sullivan, Trinity College Dublin
description: The authors acknowledge financial support from the Irish Research Council (Government of Ireland Postdoctoral Fellowship GOIPD/2019/906 to GO'S); Science Foundation Ireland (Starting Investigator Research Grant 18/SIRG/5559 to CM); the Australian Research Council (grants LP0991834 and FL160100168 to ES); and by a Robert and Maude Gledden Postgraduate Scholarship (to ES). NCIG equipment used in this study was funded by grants from Science Foundation Ireland (13/RC/2092) and the Irish Higher Education Authority through the Programme for Research at Third Level Institutions, Cycle 5 (PRTLI-5). GeoHistory Facility instruments at the JdLC, Curtin University were funded by an Australian Geophysical Observing System grant provided to AuScope Pty Ltd. by the AQ44 Australian Education Investment Fund program.
DOI: 10.5281/zenodo.8144345
title: Data to Support Predictive Models for Detrital Titanite Provenance with application to the Nanga Parbat syntaxial massif, western Himalaya."

description: The files published here are metadata that are being used to support a manuscript currently (Jul, 2023) submitted to Journal of Geophysical Research: Earth Surface. The intention of these data and code is to support a publication that is about generating a predictive categorisation scheme for the mineral titanite. The code to generate the titanite classification schemes (Titanite_Random_Forest_Model1.ipynb; Titanite_Random_Forest_Model2.ipynb; and Titanite_Random_Forest_Model3.ipynb) was created in Python3, using Jupyter Notebook. Each of those files also provides more motivation for why a predictive categorisation scheme for the mineral titanite is desirable, and other similar context. Chiefly, the dataset and random forest models published here will allow us to trace titanite in detritus. In general, Models 2 or 3 are probably the most useful models. Model 1 is mostly just provided to enable comparison to similar previously published works (e.g. https://doi.org/10.1111/ter.12574). Briefly, the supplementary file “Table_S4_Titanite_Machine_Learning_Dataset.csv” is required to generate the models and must be saved in the appropriate directory for the code to run. Your unknowns must contain the same elements and identical labelling for the code to successfully run, unless the files and code are modified. Broader descriptions of these matters are contained in each file. Any new published data are titanite compositional or isotopic data collected by LA-ICP-MS. Description of how those data were collected is given in "OSullivan_et_al_Supp..." file. Some of the data, information or code in this submission may be subject to change after journal review, in which case a new version may be published. References for the dataset compilation are provided in File S3. If you have any queries contact:<br> Gary O’Sullivan, Trinity College Dublin
description: The authors acknowledge financial support from the Irish Research Council (Government of Ireland Postdoctoral Fellowship GOIPD/2019/906 to GO'S); Science Foundation Ireland (Starting Investigator Research Grant 18/SIRG/5559 to CM); the Australian Research Council (grants LP0991834 and FL160100168 to ES); and by a Robert and Maude Gledden Postgraduate Scholarship (to ES). NCIG equipment used in this study was funded by grants from Science Foundation Ireland (13/RC/2092) and the Irish Higher Education Authority through the Programme for Research at Third Level Institutions, Cycle 5 (PRTLI-5). GeoHistory Facility instruments at the JdLC, Curtin University were funded by an Australian Geophysical Observing System grant provided to AuScope Pty Ltd. by the AQ44 Australian Education Investment Fund program.
DOI: 10.6084/m9.figshare.25469980
title: Supplementary Material for: Reticular Bullous Epithelial Corneal Edema after Netarsudil Use for Elevated Intraocular Pressure with Concurrent Fuchs Endothelial Corneal Dystrophy: A Case Report

description: Introduction: We describe a case of reticular bullous corneal epithelial edema associated with the use of netarsudil ophthalmic solution (0.02%) for elevated intraocular pressure. Case presentation: A 74-year-old man with a complex ocular medical history, including Fuchs dystrophy and primary open-angle glaucoma, developed progressively worsening loss of vision three weeks following the initiation of topical netarsudil for increased intraocular pressure. Visual acuity in the left eye was counting fingers at 3 feet and intraocular pressure in the left eye was 7 mmHg. A characteristic “honeycomb” pattern epitheliopathy was seen on ocular examination. Conclusion: Reticular bullous epithelial corneal edema is an uncommon finding associated with netarsudil use, which can be overlooked in favor of corneal edema associated with Fuchs dystrophy. This is especially relevant given Fuchs dystrophy itself is a predisposing risk factor for netarsudil-induced reticular bullous corneal epithelial edema. Improvement of both the corneal edema and visual acuity should be expected after discontinuing netarsudil and undergoing superficial keratectomy.
DOI: 10.57754/fdat.5zmx4-y4w22
title: Eye tracking data of participants with real and simulated tunnel vision in three Virtual Reality based visual tasks

description: This data set consists of eye tracking data samples from around 18.000 experimental trials in virtual reality, captured from two participant groups: One group of eight Retinitis Pigmentosa (tunnel vision) patients and another group of eight age-matched visually healthy participants with simulated tunnel vision. The trials are divided into three different virtual visual exercises: A motion tracking task, a visual search task, and a navigation and obstacle awareness task. Each participant carried out approximately 20 sessions of 20 trials each over the course of four weeks of training.
DOI: 10.5281/zenodo.10867937
title: R-XIMO experiment data

description: The data utilized in the R-XIMO article:

Misitano, G., Afsar, B., Lárraga, G. et al. Towards explainable interactive multiobjective optimization: R-XIMO. Auton Agent Multi-Agent Syst 36, 43 (2022). https://doi.org/10.1007/s10458-022-09577-3

Re-uploaded to Zenodo because the original link in the article does not work anymore.
DOI: 10.5281/zenodo.10867938
title: R-XIMO experiment data

description: The data utilized in the R-XIMO article:

Misitano, G., Afsar, B., Lárraga, G. et al. Towards explainable interactive multiobjective optimization: R-XIMO. Auton Agent Multi-Agent Syst 36, 43 (2022). https://doi.org/10.1007/s10458-022-09577-3

Re-uploaded to Zenodo because the original link in the article does not work anymore.
DOI: 10.5281/zenodo.10867939
title: Data to Support Predictive Models for Detrital Titanite Provenance with application to the Nanga Parbat syntaxial massif, western Himalaya."

description: The files published here are metadata that are being used to support a manuscript currently (Mar, 2024) undergoing final reviews in Journal of Geophysical Research: Earth Surface.

The intention of these data and code is to support a publication that is about generating a predictive categorisation scheme for the mineral titanite.

The code to generate the titanite classification schemes was created in Python3, using Jupyter Notebook. The files also provide more motivation for why a predictive categorisation scheme for the mineral titanite is desirable, and other similar context. Chiefly, the dataset and random forest models published here will allow us to trace titanite in detritus.

For info on running Jupyter Notebook, please visit (https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html) to seek instructions. We also provide a readme file with some instructions. If you get really stuck, just email the authors.

Our Model can be compared to similar previously published works (e.g. https://doi.org/10.1111/ter.12574). Model was trained using skikit-learn v1.41.

The supplementary file "Table_S4_Merged.csv" was used to train and generate the model.

Your unknowns must contain the correct elements and labelling for the code to successfully run, these details are provided in the code (Titanite_Random_Forest_Model1_Mar24.ipynb). A template is also provided for you to paste your unknown data into (titanite_data_template.csv)

Any new published data are titanite compositional or isotopic data collected by LA-ICP-MS. Description of how those data were collected is given in "OSullivan_et_al_Supp..." file.

Some of the data, information and code in this submission has been subject to change after journal review, this is a second version of this content.

References for the dataset compilation are provided in File S3.

If you have any queries contact:Gary O'Sullivan, Trinity College Dublin
description: The authors acknowledge financial support from the Irish Research Council (Government of Ireland Postdoctoral Fellowship GOIPD/2019/906 to GO'S); Science Foundation Ireland (Starting Investigator Research Grant 18/SIRG/5559 to CM); the Australian Research Council (grants LP0991834 and FL160100168 to ES); and by a Robert and Maude Gledden Postgraduate Scholarship (to ES). NCIG equipment used in this study was funded by grants from Science Foundation Ireland (13/RC/2092) and the Irish Higher Education Authority through the Programme for Research at Third Level Institutions, Cycle 5 (PRTLI-5). GeoHistory Facility instruments at the JdLC, Curtin University were funded by an Australian Geophysical Observing System grant provided to AuScope Pty Ltd. by the AQ44 Australian Education Investment Fund program.
DOI: 10.5281/zenodo.8144344
title: Data to Support Predictive Models for Detrital Titanite Provenance with application to the Nanga Parbat syntaxial massif, western Himalaya."

description: The files published here are metadata that are being used to support a manuscript currently (Mar, 2024) undergoing final reviews in Journal of Geophysical Research: Earth Surface.

The intention of these data and code is to support a publication that is about generating a predictive categorisation scheme for the mineral titanite.

The code to generate the titanite classification schemes was created in Python3, using Jupyter Notebook. The files also provide more motivation for why a predictive categorisation scheme for the mineral titanite is desirable, and other similar context. Chiefly, the dataset and random forest models published here will allow us to trace titanite in detritus.

For info on running Jupyter Notebook, please visit (https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html) to seek instructions. We also provide a readme file with some instructions. If you get really stuck, just email the authors.

Our Model can be compared to similar previously published works (e.g. https://doi.org/10.1111/ter.12574). Model was trained using skikit-learn v1.41.

The supplementary file "Table_S4_Merged.csv" was used to train and generate the model.

Your unknowns must contain the correct elements and labelling for the code to successfully run, these details are provided in the code (Titanite_Random_Forest_Model1_Mar24.ipynb). A template is also provided for you to paste your unknown data into (titanite_data_template.csv)

Any new published data are titanite compositional or isotopic data collected by LA-ICP-MS. Description of how those data were collected is given in "OSullivan_et_al_Supp..." file.

Some of the data, information and code in this submission has been subject to change after journal review, this is a second version of this content.

References for the dataset compilation are provided in File S3.

If you have any queries contact:Gary O'Sullivan, Trinity College Dublin
description: The authors acknowledge financial support from the Irish Research Council (Government of Ireland Postdoctoral Fellowship GOIPD/2019/906 to GO'S); Science Foundation Ireland (Starting Investigator Research Grant 18/SIRG/5559 to CM); the Australian Research Council (grants LP0991834 and FL160100168 to ES); and by a Robert and Maude Gledden Postgraduate Scholarship (to ES). NCIG equipment used in this study was funded by grants from Science Foundation Ireland (13/RC/2092) and the Irish Higher Education Authority through the Programme for Research at Third Level Institutions, Cycle 5 (PRTLI-5). GeoHistory Facility instruments at the JdLC, Curtin University were funded by an Australian Geophysical Observing System grant provided to AuScope Pty Ltd. by the AQ44 Australian Education Investment Fund program.
DOI: 10.6084/m9.figshare.25469971
title: Supplementary material for Chapter 2

description: This dataset contains the metadata for the species molecular phylogeny used for the macroevolutionary study in the second chapter of my thesis. More specifically, it provides information on how the species in my morphological dataset were associated with the phylogeny from Thornhill et al. (2019).
DOI: 10.6084/m9.figshare.25378510
title: Supplementary material for Chapter 4

description: Exploring the origins of discordance (introgression and ILS) in a phylogenomic dataset of ~1600 BUSCO genes in closely-related Eucalyptus species.
DOI: 10.6084/m9.figshare.25378510.v1
title: Supplementary material for Chapter 4

description: Exploring the origins of discordance (introgression and ILS) in a phylogenomic dataset of ~1600 BUSCO genes in closely-related Eucalyptus species.
DOI: 10.6084/m9.figshare.25470013
title: Supplementary material for Chapter 3

description: This dataset contains the appendix tables for the third chapter of my thesis. This includes the accessions metadata, quality control information, post-filtering summary statistics of BUSCO alignments and BUSCO IDs of the extracted sets of sect. Adnataria and Exsertaria.
DOI: 10.6084/m9.figshare.25470025
title: Supplementary Material for: Improving Accuracy of In-the-bag Intraocular Lens Power Calculation in Adult Eyes with Unilateral Subluxated Lenses by Using the Anterior Chamber Depth of the Unaffected Eye

description: Introduction: This study aimed to determine the interchangeability of bilateral anterior chamber depth (ACD) in intraocular lens (IOL) power calculations for cataractous eyes and refractive outcomes using the unaffected fellow-eye ACD in lens subluxation.
Methods: The predicted postoperative spherical equivalent (SE) calculated using the Kane formula with and without fellow-eye ACD in 202 cataract patients was compared. Refractive outcomes of the newer formulas (the Kane, Barrett Universal II (BUII), and Pearl-DGS formulas) with affected-eye ACD and with unaffected fellow-eye ACD were compared in 33 eyes with lens subluxation (the affected eye) undergoing in-the-bag IOL implantation. The SD of the prediction error (PE) was assessed using the heteroscedastic method.
Results: In 202 paired cataractous eyes, no marked ACD difference was found bilaterally; the predicted SE obtained without the fellow-eye ACD was comparable with that calculated with the fellow-eye one (P = .90), with a mean absolute difference of 0.03 ± 0.03D. With the affected-eye AL, keratometry, and ACD, the median absolute error (MedAE) was 0.38-0.64D, and the percentage of PE within ±0.50D was 30.30-57.58%. The unaffected-eye ACD improved the results (MedAE, 0.35-0.49D; the percentage of PE within ±0.50D, 54.55-63.64%). The SDs of the BUII (0.82D) and Pearl-DGS formulas (0.87D) with the affected-eye ACD were significantly larger than those of the Kane and Pearl-DGS formulas (both 0.69D) with the unaffected-eye ACD.
Conclusion: Bilateral ACD was interchangeable in IOL power calculation for cataractous eyes. Unaffected-eye ACD in lieu of affected-eye ACD can enhance the accuracy of newer formulas in patients with unilateral subluxated lenses undergoing in-the-bag IOL implantation.
DOI: 10.6084/m9.figshare.25470025.v1
title: Supplementary Material for: Improving Accuracy of In-the-bag Intraocular Lens Power Calculation in Adult Eyes with Unilateral Subluxated Lenses by Using the Anterior Chamber Depth of the Unaffected Eye

description: Introduction: This study aimed to determine the interchangeability of bilateral anterior chamber depth (ACD) in intraocular lens (IOL) power calculations for cataractous eyes and refractive outcomes using the unaffected fellow-eye ACD in lens subluxation.
Methods: The predicted postoperative spherical equivalent (SE) calculated using the Kane formula with and without fellow-eye ACD in 202 cataract patients was compared. Refractive outcomes of the newer formulas (the Kane, Barrett Universal II (BUII), and Pearl-DGS formulas) with affected-eye ACD and with unaffected fellow-eye ACD were compared in 33 eyes with lens subluxation (the affected eye) undergoing in-the-bag IOL implantation. The SD of the prediction error (PE) was assessed using the heteroscedastic method.
Results: In 202 paired cataractous eyes, no marked ACD difference was found bilaterally; the predicted SE obtained without the fellow-eye ACD was comparable with that calculated with the fellow-eye one (P = .90), with a mean absolute difference of 0.03 ± 0.03D. With the affected-eye AL, keratometry, and ACD, the median absolute error (MedAE) was 0.38-0.64D, and the percentage of PE within ±0.50D was 30.30-57.58%. The unaffected-eye ACD improved the results (MedAE, 0.35-0.49D; the percentage of PE within ±0.50D, 54.55-63.64%). The SDs of the BUII (0.82D) and Pearl-DGS formulas (0.87D) with the affected-eye ACD were significantly larger than those of the Kane and Pearl-DGS formulas (both 0.69D) with the unaffected-eye ACD.
Conclusion: Bilateral ACD was interchangeable in IOL power calculation for cataractous eyes. Unaffected-eye ACD in lieu of affected-eye ACD can enhance the accuracy of newer formulas in patients with unilateral subluxated lenses undergoing in-the-bag IOL implantation.
DOI: 10.6084/m9.figshare.25470028
title: Supplementary Material for: A Klotho-based Machine Learning Model for Prediction of both Kidney and Cardiovascular Outcomes in Chronic Kidney Disease

description: Background: This study aimed to develop and validate a machine learning (ML) model based on serum Klotho for predicting end-stage kidney disease (ESKD) and cardiovascular disease (CVD) in patients with chronic kidney disease (CKD).
Methods: Five different ML models were trained to predict the risk of ESKD and CVD at three different time points (3, 5, and 8-year) using a cohort of 400 non-dialysis CKD patients. The dataset was divided into a training set (70%) and an internal validation set (30%). These models were informed by data comprising 47 clinical features, including serum Klotho. The best-performing model was selected and used to identify risk factors for each outcome. Model performance was assessed using various metrics.
Results: The findings showed that the Lasso regression model had the highest accuracy (C-index=0.71) in predicting ESKD. The features mainly included in this model were estimated glomerular filtration rate (eGFR), 24-hour urinary microalbumin, serum albumin, phosphate, parathyroid hormone, and serum Klotho, which achieved the highest area under the curve (AUC) of 0.930 (95% CI: 0.897-0.962). In addition, for the CVD risk prediction, the Random Survival Forest (RSF) model with the highest accuracy (C-index=0.66) was selected and achieved the highest AUC of 0.782 (95% CI: 0.633-0.930). The features mainly included in this model were age, history of primary hypertension, calcium, tumor necrosis factor-alpha, and serum Klotho.
Conclusion: We successfully developed and validated Klotho-based ML risk prediction models for CVD and ESKD in CKD patients with good performance, indicating their high clinical utility.
DOI: 10.6084/m9.figshare.25467979
title: The genomes of Pseudomonas isolates XN05-1 and YE17

description: The whole genome of Pseudomonas strains XN05-1 and YE17 were sequenced using PacBio Sequel platform and Illumina NovaSeq PE150. The chemotaxis gene cheW in these two genomes were predicted.
DOI: 10.5281/zenodo.10669803
title: Example MRI Phantom Data for Mrpro

description: Data was obtained in ismrmr format and is used as example data for Mrpro package
DOI: 10.5281/zenodo.10664973
title: Example MRI data for mrpro

description: MRI data for the examples scripts of https://github.com/PTB-MR/mrpro

All data were acquired at a Siemens MRI scanner (Magnetom Cima.X, IDEA XA61) using either vendor-specific or vendor-agnostic pulseq sequences.
DOI: 10.5281/zenodo.10664974
title: Example MRI Phantom Data for Mrpro

description: Data was obtained in ismrmr format and is used as example data for Mrpro package
DOI: 10.5281/zenodo.10669837
title: Example MRI Phantom Data for Mrpro

description: Data was obtained in ismrmr format and is used as example data for Mrpro package
DOI: 10.5281/zenodo.10671597
title: Example MRI Phantom Data for Mrpro

description: Data was obtained in ismrmr format and is used as example data for Mrpro package
DOI: 10.5281/zenodo.10854057
title: Example MRI Data for mrpro

description: MRI data for the examples scripts of https://github.com/PTB-MR/mrpro

All data were acquired at a Siemens MRI scanner (Magnetom Cima.X, IDEA XA61) using either vendor-specific or vendor-agnostic pulseq sequences.
DOI: 10.5281/zenodo.10868061
title: Example MRI data for mrpro

description: MRI data for the examples scripts of https://github.com/PTB-MR/mrpro

All data were acquired at a Siemens MRI scanner (Magnetom Cima.X, IDEA XA61) using either vendor-specific or vendor-agnostic pulseq sequences.