cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e116306v1-138060
title: Replication data for: Testing for the Disposition Effect on Optimal Stopping Decisions

description: This paper develops a new laboratory test of the hypothesis that individual investors sell winners too early and ride losers too long. In the experiment, subjects invest in a risky asset, whose price evolves in near-continuous time, and they are provided with the option to liquidate it at a fixed salvage value. Optimal behavior is characterized by an upper and a lower stopping thresholds in the asset price space, thus producing a clear rational benchmark and eliminating known confounds. This design allows me to detect and quantify the disposition effect in a sample of 108 subjects.
DOI: 10.5525/gla.researchdata.1433
title: Consumer-grade UAV solid-state LiDAR accurately quantifies topography in a vegetated fluvial environment

description: To accompany a journal article in Earth Surface Processes & Landforms (ESPL) which details an investigation into the data quality and limitations of using the DJI L1 solid-state LiDAR system at both a controlled test location (Garscube Sports Fields, Glasgow, Scotland) and also in a geomorphic environment (River Feshie, Highlands, Scotland). Included are the following datasets (further detailed in readme file): • Point Clouds o Garscube – 4 x raw, unthinned o Feshie – 6 x thinned to 15cm density o Terrestrial Laser Scanning – 7 x gravel comparison patches • Digital Elevation Model o River Feshie only (see Figure 10 of article) • GNSS points o Garscube – GCPs & Football pitch markings o Feshie – GCPs, check points and vegetation
DOI: 10.6084/m9.figshare.18637732
title: Imputation of Missing Covariates in Randomized Controlled Trials with Continuous Outcomes: Simple, Unbiased and Efficient Methods

description: The literature on dealing with missing covariates in nonrandomized studies advocates the use of sophisticated methods like multiple imputation (MI) and maximum likelihood (ML)-based approaches over simple methods. However, these methods are not necessarily optimal in terms of bias and efficiency of treatment effect estimation in randomized studies, where the covariate of interest (treatment group) is independent of all baseline (pre-randomization) covariates due to randomization. This has been shown in the literature, but only for missingness on a single baseline covariate. Here, we extend the situation to multiple baseline covariates with missingness and evaluate the performance of MI and ML compared with simple alternative methods under various missingness scenarios in RCTs with a quantitative outcome. We first derive asymptotic relative efficiencies of the simple methods under the missing completely at random (MCAR) scenario and then perform a simulation study for non-MCAR scenarios. Finally, a trial on chronic low back pain is used to illustrate the implementation of the methods. The results show that all simple methods give unbiased treatment effect estimation but with increased mean squared residual. It also turns out that mean imputation and the missing-indicator method are most efficient under all covariate missingness scenarios and perform at least as well as MI and LM in each scenario.
DOI: 10.6084/m9.figshare.18637732.v1
title: Imputation of Missing Covariates in Randomized Controlled Trials with Continuous Outcomes: Simple, Unbiased and Efficient Methods

description: The literature on dealing with missing covariates in nonrandomized studies advocates the use of sophisticated methods like multiple imputation (MI) and maximum likelihood (ML)-based approaches over simple methods. However, these methods are not necessarily optimal in terms of bias and efficiency of treatment effect estimation in randomized studies, where the covariate of interest (treatment group) is independent of all baseline (pre-randomization) covariates due to randomization. This has been shown in the literature, but only for missingness on a single baseline covariate. Here, we extend the situation to multiple baseline covariates with missingness and evaluate the performance of MI and ML compared with simple alternative methods under various missingness scenarios in RCTs with a quantitative outcome. We first derive asymptotic relative efficiencies of the simple methods under the missing completely at random (MCAR) scenario and then perform a simulation study for non-MCAR scenarios. Finally, a trial on chronic low back pain is used to illustrate the implementation of the methods. The results show that all simple methods give unbiased treatment effect estimation but with increased mean squared residual. It also turns out that mean imputation and the missing-indicator method are most efficient under all covariate missingness scenarios and perform at least as well as MI and LM in each scenario.
DOI: 10.34740/kaggle/dsv/5600355
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.6084/m9.figshare.22760468
title: Data Set

description: Data Set
DOI: 10.4121/22117046
title: Data underlying dissertation: “The Causes of Regional Sea-level Change”

description: This repository contains data underlying the thesis “The Causes of Regional Sea-level Change” by Carolina M. L. Camargo. A copy of the dissertation can be found at the TU Delft repository. <br>The data is divided in folders following the chapters of the dissertations. When using any of this data, please cite the appropriate publications. In addition, all the produced results of this dissertation has underlying data of other publications and studies, so please also acknowledge those. <br><strong>Chapter 1 – Introduction</strong>In this folder you can find:- gmsl.xlsx – Excel table underlying Table 1.1, Table 1.2 and Figure 1.3- masks.nc – netcdf file with ocean basins underlying Figure 1.4- regional.xlsx – Excel table underlying bar plots in Figure 1.4<br><strong>Chapter 2 – Steric Sea-level Change</strong>In this folder you find: - Upper_ocean folder: 0-2000m steric time series and trends. A dedicated Readme on this folder explains in more detail the files.Note: The regional time series are only available at the 4tu link below. - Deep_ocean folder: Deep ocean (&gt;2000m) time series and trends, underlying Appendix of Chapter 2. The data of this (main) chapter is also available at: https://doi.org/10.4121/12764933<br><strong>Chapter 3 – Mass-driven Sea-level Change</strong>This folder contains files with regional barystatic (ocean mass) sea-level change trends and uncertainty, both absolute and relative fingerprints. A dedicated Readme on this folder explains in more detail the files. The data of this chapter is also available at: https://doi.org/10.4121/16778794<br><strong>Chapter 4 – Regional Sea-level Budget</strong>This folder contains files from the regional sea-level budget analysis. A dedicated Readme on this folder explains in more detail the files. The data of this chapter is also available at: https://doi.org/10.5281/zenodo.7007330<br>----In case of questions, please contact Carolina Camargo at carolina.camargo@nioz.nl or caromlcamargo@gmail.com
DOI: 10.4121/22117046.v2
title: Data underlying dissertation: “The Causes of Regional Sea-level Change”

description: This repository contains data underlying the thesis “The Causes of Regional Sea-level Change” by Carolina M. L. Camargo. A copy of the dissertation can be found at the TU Delft repository. <br>The data is divided in folders following the chapters of the dissertations. When using any of this data, please cite the appropriate publications. In addition, all the produced results of this dissertation has underlying data of other publications and studies, so please also acknowledge those. <br><strong>Chapter 1 – Introduction</strong>In this folder you can find:- gmsl.xlsx – Excel table underlying Table 1.1, Table 1.2 and Figure 1.3- masks.nc – netcdf file with ocean basins underlying Figure 1.4- regional.xlsx – Excel table underlying bar plots in Figure 1.4<br><strong>Chapter 2 – Steric Sea-level Change</strong>In this folder you find: - Upper_ocean folder: 0-2000m steric time series and trends. A dedicated Readme on this folder explains in more detail the files.Note: The regional time series are only available at the 4tu link below. - Deep_ocean folder: Deep ocean (&gt;2000m) time series and trends, underlying Appendix of Chapter 2. The data of this (main) chapter is also available at: https://doi.org/10.4121/12764933<br><strong>Chapter 3 – Mass-driven Sea-level Change</strong>This folder contains files with regional barystatic (ocean mass) sea-level change trends and uncertainty, both absolute and relative fingerprints. A dedicated Readme on this folder explains in more detail the files. The data of this chapter is also available at: https://doi.org/10.4121/16778794<br><strong>Chapter 4 – Regional Sea-level Budget</strong>This folder contains files from the regional sea-level budget analysis. A dedicated Readme on this folder explains in more detail the files. The data of this chapter is also available at: https://doi.org/10.5281/zenodo.7007330<br>----In case of questions, please contact Carolina Camargo at carolina.camargo@nioz.nl or caromlcamargo@gmail.com
DOI: 10.25403/upresearchdata.22101908
title: Infection prevention and control measures on neonatal due to COVID-19

description: This dataset is aimed at assessing the effects of enhanced infection prevention and control (IPC) measures due to COVID-19 on the incidence rate of neonatal infections in the Mankweng Hospital (MH) Neonatal Unit (NNU) from 2016–2021. Data was conducted through mixed method study using quatitative approaches which compromises of secondary data analysis and neonatal unit IPC audit.
DOI: 10.25403/upresearchdata.22101908.v1
title: Infection prevention and control measures on neonatal due to COVID-19

description: This dataset is aimed at assessing the effects of enhanced infection prevention and control (IPC) measures due to COVID-19 on the incidence rate of neonatal infections in the Mankweng Hospital (MH) Neonatal Unit (NNU) from 2016–2021. Data was conducted through mixed method study using quatitative approaches which compromises of secondary data analysis and neonatal unit IPC audit.
DOI: 10.25403/upresearchdata.22101674
title: Eastern Cape COVID-19 2020-2021 in-hospital surveillance data

description: In this dataset, data collection occurs through the DATCOV online platform available to health care workers or through the validated importation of data from pre-existing electronic health information systems into the DATCOV database. DATCOV is an active surveillance system that accounts for 100% of SARS-CoV-2 admissions in South Africa. It was established in March of 2020 by the National Institute for Communicable Diseases (NICD) and the National Department of Health. Retrospective cohort study was conducted using data on COVID-19 admissions in the Eastern Cape province from the COVID-19 hospital sentinel surveillance system. The surveillance system collects and collates data of all laboratory-confirmed SARS-CoV-2 cases (those with and without a diagnosis of COVID-19) with a duration of hospitalisation, irrespective of the reason for admission.
DOI: 10.25403/upresearchdata.22101674.v1
title: Eastern Cape COVID-19 2020-2021 in-hospital surveillance data

description: In this dataset, data collection occurs through the DATCOV online platform available to health care workers or through the validated importation of data from pre-existing electronic health information systems into the DATCOV database. DATCOV is an active surveillance system that accounts for 100% of SARS-CoV-2 admissions in South Africa. It was established in March of 2020 by the National Institute for Communicable Diseases (NICD) and the National Department of Health. Retrospective cohort study was conducted using data on COVID-19 admissions in the Eastern Cape province from the COVID-19 hospital sentinel surveillance system. The surveillance system collects and collates data of all laboratory-confirmed SARS-CoV-2 cases (those with and without a diagnosis of COVID-19) with a duration of hospitalisation, irrespective of the reason for admission.
DOI: 10.22024/unikent/01.01.479
title: Effect Traits for Human Wellbeing
DOI: 10.5281/zenodo.7874859
title: lean, DIO, Seno Hearts - WGA, Ly6c, CD45, F4/80

description: lean, DIO, Seno Hearts - WGA, Ly6c, CD45, F4/80 - Microscopy by Ash
DOI: 10.5281/zenodo.7874858
title: lean, DIO, Seno Hearts - WGA, Ly6c, CD45, F4/80

description: lean, DIO, Seno Hearts - WGA, Ly6c, CD45, F4/80 - Microscopy by Ash
DOI: 10.6084/m9.figshare.22565311.v3
title: TableS2:Heat-stress related mouse meta-data

description: Meta-information on mouse gene expression data collected from Gene Expression Omnibus (GEO).
DOI: 10.6084/m9.figshare.21084829.v22
title: List of native-alien populations in South Africa

description: Native-alien population is defined as a population that is within a country to which the species is native and founded by individuals moved by direct human agency (or substantial indirect human agency) over a biogeographical barrier to an area beyond the species’ native range (Nelufule et al. 2022). This dataset followed the data structure used for the species list of South Africa’s second national status report on biological invasions ‘The status of biological invasions and their management in South Africa in 2019' (Zengeya and Wilson 2020). This represents the first inventory of native-alien populations in South Africa, and is a step towards a greater understanding of native-alien populations and the biosecurity threat they pose. Reference Nelufule T, Robertson MP, Wilson JRU, Faulkner KT (2022) Native-alien populations—an apparent oxymoron that requires specific conservation attention. <em>NeoBiota</em> 74: 57–74, https://doi.org/10.3897/neobiota.74.81671 Zengeya, T. A. & Wilson, J. R. U. (eds.). The status of biological invasions and their management in South Africa in 2019. pp.71. South African National Biodiversity Institute, Kirstenbosch and DSI-NRFCentre of Excellence for Invasion Biology, Stellenbosch. (2020).
DOI: 10.25405/data.ncl.21588042.v7
title: Informing drug discovery: a systematic review of POLG-related disease models

description: Briefing. Overview of how the risk of bias was assessed <br> Supplementary Table 1. PRISMA checklist <br> Supplementary Figure 1. PRISMA flow diagram <br> Supplementary Table 2. Search strategy <br> Supplementary Table 3. Detailed article inclusion/exclusion criteria <br> Supplementary Table 4. Excluded full text articles <br> Supplementary Table 5. Data extracted from all included articles. <br> Supplementary Table 6A. The criteria for assessing reporting quality of each article. <br> Supplementary Table 6B. The assessment of the reporting quality of each article. <br> Supplementary Table 6C. The summarised Assessment of the reporting quality of each article. <br> Supplementary Table 6D. The assessment of risk of bias for each model system. <br> Updated versions are due to changes made during the review stages to ensure clarity of reporting.
DOI: 10.6084/m9.figshare.22313056
title: Bazedoxifene does not share estrogens effects on IgG sialylation

description: Data for "Bazedoxifene does not share estrogens effects on IgG sialylation" manuscript.
DOI: 10.6084/m9.figshare.22313056.v2
title: Bazedoxifene does not share estrogens effects on IgG sialylation

description: Data for "Bazedoxifene does not share estrogens effects on IgG sialylation" manuscript.
DOI: 10.6084/m9.figshare.22313056.v1
title: Bazedoxifene does not share estrogens effects on IgG sialylation

description: Data for "Bazedoxifene does not share estrogens effects on IgG sialylation" manuscript.
DOI: 10.5281/zenodo.7374155
title: Contamination pattern and risk assessment of polar compounds in snow melt: an integrative proxy of road runoffs

description: <strong>Abstract</strong> To assess the contamination and potential risk of snow melt with polar compounds, road and background snow was sampled during a melting event at 23 sites at the city of Leipzig and screened for more than 500 chemicals using LC-HRMS. Additionally, six 24 h composite samples were taken from the influent and effluent of the Leipzig WWTP during the snow melt event. 207 compounds were at least detected once (concentrations between 0.80 ng/L and 75 µg/L). A toxic unit-based assessment was performed to investigate the risk of adverse environmental effects in the receiving water. <strong>Description of the dataset</strong> The dataset contains the list of sampling points, the target compounds, the chemical findings, the results of the toxic unit assessment, the underlying ecotoxicity data, and the estimated compound removal rates in WWTP. The data is provided in xlsx and ods formats.
description: {"references": ["Schulze, 2022, https://zenodo.org/record/6137082"]}
DOI: 10.5281/zenodo.7374154
title: Contamination pattern and risk assessment of polar compounds in snow melt: an integrative proxy of road runoffs

description: <strong>Abstract</strong> To assess the contamination and potential risk of snow melt with polar compounds, road and background snow was sampled during a melting event at 23 sites at the city of Leipzig and screened for more than 500 chemicals using LC-HRMS. Additionally, six 24 h composite samples were taken from the influent and effluent of the Leipzig WWTP during the snow melt event. 207 compounds were at least detected once (concentrations between 0.80 ng/L and 75 µg/L). A toxic unit-based assessment was performed to investigate the risk of adverse environmental effects in the receiving water. <strong>Description of the dataset</strong> The dataset contains the list of sampling points, the target compounds, the chemical findings, the results of the toxic unit assessment, the underlying ecotoxicity data, and the estimated compound removal rates in WWTP. The data is provided in xlsx and ods formats.
description: {"references": ["Schulze, 2022, https://zenodo.org/record/6137082"]}
DOI: 10.6084/m9.figshare.22760711
title: zbc

description: possessed data
DOI: 10.6084/m9.figshare.22760711.v1
title: zbc

description: possessed data
DOI: 10.17863/cam.21736
title: Research data supporting "Study of g-factor variation in an ​array of split-gate transistors using a quantum multiplexer"

description: An integrated quantum circuit is demonstrated. The electrical measurements data at sub-Kelvin temperatures in presence/absence of magnetic fields are discussed. Data are taken in Cavendish Lab, University of Cambridge.
DOI: 10.6084/m9.figshare.22737269
title: <strong>Mixing and Spreading of Inclined Dense Jets with Submerged Aquatic Canopy</strong>

description: Flume experiments and numerical simulation were conducted to investigate the mixing and spreading processes of inclined dense jets. The dataset for the manuscript submitted to <em>Water Resources Research</em> includs the cross-sectional concentration profiles, concentration decay along the centerline, concentration fluctuations, and vorticity, so on.
DOI: 10.6084/m9.figshare.22737269.v1
title: <strong>Mixing and Spreading of Inclined Dense Jets with Submerged Aquatic Canopy</strong>

description: Flume experiments and numerical simulation were conducted to investigate the mixing and spreading processes of inclined dense jets. The dataset for the manuscript submitted to <em>Water Resources Research</em> includs the cross-sectional concentration profiles, concentration decay along the centerline, concentration fluctuations, and vorticity, so on.
DOI: 10.5281/zenodo.7499268
title: Red deer growth data and R code for analysis

description: Dataset and R code (Rmd-file) for analysis of seasonal growth of body weight in red deer. Supplementary material for the paper "Shifting seasonality of annual growth through ontogeny for red deer at northern latitudes". This study was part of the AgriDeer project (318575), funded by the Research Council of Norway.
DOI: 10.5281/zenodo.7896000
title: Red deer growth data and R code for analysis

description: Dataset and R code (Rmd-file) for analysis of seasonal growth of body weight in red deer. Supplementary material for the paper "Shifting seasonality of annual growth through ontogeny for red deer at northern latitudes". This study was part of the AgriDeer project (318575), funded by the Research Council of Norway.
description: Updated R code for analysis of data.
DOI: 10.5517/ccdc.csd.cc2czq47
title: CCDC 2204259: Experimental Crystal Structure Determination

description: Related Article: Tobias Brückner, Benedikt Ritschel, J. Oscar C. Jiménez-Halla, Felipe Fantuzzi, Dario Duwe, Rian D. Dewhurst, Holger Braunschweig|2023|Angew.Chem.,Int.Ed.|62|e202213284|doi:10.1002/anie.202213284
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2czq58
title: CCDC 2204260: Experimental Crystal Structure Determination

description: Related Article: Tobias Brückner, Benedikt Ritschel, J. Oscar C. Jiménez-Halla, Felipe Fantuzzi, Dario Duwe, Rian D. Dewhurst, Holger Braunschweig|2023|Angew.Chem.,Int.Ed.|62|e202213284|doi:10.1002/anie.202213284
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2czq69
title: CCDC 2204261: Experimental Crystal Structure Determination

description: Related Article: Tobias Brückner, Benedikt Ritschel, J. Oscar C. Jiménez-Halla, Felipe Fantuzzi, Dario Duwe, Rian D. Dewhurst, Holger Braunschweig|2023|Angew.Chem.,Int.Ed.|62|e202213284|doi:10.1002/anie.202213284
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2czq7b
title: CCDC 2204262: Experimental Crystal Structure Determination

description: Related Article: Tobias Brückner, Benedikt Ritschel, J. Oscar C. Jiménez-Halla, Felipe Fantuzzi, Dario Duwe, Rian D. Dewhurst, Holger Braunschweig|2023|Angew.Chem.,Int.Ed.|62|e202213284|doi:10.1002/anie.202213284
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d2z3l
title: CCDC 2207389: Experimental Crystal Structure Determination

description: Related Article: Miguel Angel Soto, Veronica Carta, Ingrid Suzana, Brian O Patrick, Francesco Lelj, Mark J. MacLachlan|2023|Angew.Chem.,Int.Ed.|62|e202216029|doi:10.1002/anie.202216029
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.