cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10432300
title: Decoding Pain: Uncovering the Factors that Affect Performance of Neuroimaging-Based Pain Models

description: This repository contains the codes and data which are used in the following paper.



Dong Hee Lee, Sungwoo Lee, Choong-Wan Woo, Decoding Pain: Uncovering the Factors that Affect Performance of Neuroimaging-Based Pain Models, 2023, bioRxiv (link)


Dependencies



CanlabCore toolbox (link)

Mediation toolbox (link)

SPM12 (link)

MATLAB Statistics and Machine Learning Toolbox (link)


Usage

Below are the codes that allow you to generate the main figures in the manuscript.



fig_3_litearture_survey.m

fig_4_literature_survey.R

fig_5_litearture_survey.m

fig_7_benchmarking_analysis.m

fig_8_benchmarking_analysis.m

fig_9_benchmarking_analysis.m

fig_10_benchmarking_analysis.m


To use these scripts, download all files and unzip the zipfile (i.e. data.zip). Open the script that you want to run in Matlab or R and set the current folder to the  working directory. Add the paths for the dependencies. The other functions (e.g., lpp_plot_boxplot.m) are necessary to create the main figures. Make sure that the functions have to be saved in the same directory with the main scripts.
DOI: 10.5281/zenodo.10432301
title: Decoding Pain: Uncovering the Factors that Affect Performance of Neuroimaging-Based Pain Models

description: This repository contains the codes and data which are used in the following paper.



Dong Hee Lee, Sungwoo Lee, Choong-Wan Woo, Decoding Pain: Uncovering the Factors that Affect Performance of Neuroimaging-Based Pain Models, 2023, bioRxiv (link)


Dependencies



CanlabCore toolbox (link)

Mediation toolbox (link)

SPM12 (link)

MATLAB Statistics and Machine Learning Toolbox (link)


Usage

Below are the codes that allow you to generate the main figures in the manuscript.



fig_3_litearture_survey.m

fig_4_literature_survey.R

fig_5_litearture_survey.m

fig_7_benchmarking_analysis.m

fig_8_benchmarking_analysis.m

fig_9_benchmarking_analysis.m

fig_10_benchmarking_analysis.m


To use these scripts, download all files and unzip the zipfile (i.e. data.zip). Open the script that you want to run in Matlab or R and set the current folder to the  working directory. Add the paths for the dependencies. The other functions (e.g., lpp_plot_boxplot.m) are necessary to create the main figures. Make sure that the functions have to be saved in the same directory with the main scripts.
DOI: 10.6084/m9.figshare.24904716
title: Proteomic results from PSD fractions of WT or APPPS1 mice

description: Proteomic results from PSD fractions of WT or APPPS1 mice
DOI: 10.6084/m9.figshare.24904721
title: Robust Validation: Confident Predictions Even When Distributions Shift*

description: While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy—coming from robust statistics and optimization—is thus to build a model robust to distributional perturbations. In this paper, we take a different approach to describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an <i>f</i>-divergence ball around the training population. The method, based on conformal inference, achieves (nearly) valid coverage in finite samples, under only the condition that the training data be exchangeable. An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it; we develop estimators and prove their consistency for protection and validity of uncertainty estimates under shifts. By experimenting on several large-scale benchmark datasets, including Recht et al.’s CIFAR-v4 and ImageNet-V2 datasets, we provide complementary empirical results that highlight the importance of robust predictive validity.
DOI: 10.6084/m9.figshare.24904721.v1
title: Robust Validation: Confident Predictions Even When Distributions Shift*

description: While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy—coming from robust statistics and optimization—is thus to build a model robust to distributional perturbations. In this paper, we take a different approach to describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an <i>f</i>-divergence ball around the training population. The method, based on conformal inference, achieves (nearly) valid coverage in finite samples, under only the condition that the training data be exchangeable. An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it; we develop estimators and prove their consistency for protection and validity of uncertainty estimates under shifts. By experimenting on several large-scale benchmark datasets, including Recht et al.’s CIFAR-v4 and ImageNet-V2 datasets, we provide complementary empirical results that highlight the importance of robust predictive validity.
DOI: 10.25345/c5dn4064x
title: MassIVE MSV000093728 - Large scale, quantitative top-down proteomic analysis of human frontal gyrus brain tissue samples in the context of Alzheimer's disease
title: 15-35 mg brain tissue plugs derived from dorsal lateral prefrontal cortex (DLPFC) were homogenized on ice with a pellet pestle homogenizer in a 1.5 mL LoBind Eppendorf tube after addition of 8 uL hexafluoroisopropanol (HFIP) / mg tissue. Once tissue was fully disrupted, an equivolume amount of homogenization buffer (HB, consisting of 8 M urea, 10 mM ammonium bicarbonate (ABC), 10 mM tris(2-carboxyethyl)phosphine (TCEP), 2 mM EDTA) was added and then vortexed for 1 min, before separation into soluble (referred to as "AB" in the datasets) and insoluble (referred to as "WO" in the datasets) fractions. Samples were analyzed using a Waters NanoACQUITY UPLC system with mobile phases consisting of 0.2% FA in H2O (Mobile Phase A) and 0.2% FA in ACN (Mobile Phase B). Both trapping-precolumn (200 um i.d., 5-cm length) and analytical column (100 um i.d., 50-cm length) were slurry-packed with C2 packing material (5 um and 3 um for trap/analytical respectively, 300 A, Separation Methods Technology). Samples were loaded into a 10 uL loop, corresponding to 2.5 or 5 ug of loaded material and injected onto the trapping column with an isocratic flow of 1% B at 5 uL/min over 15 min for desalting. Separation was performed with a 1% to 50% B gradient over 160 min at 300 nL/min. For MS/MS analysis of proteins, the NanoACQUITY system was coupled to a Thermo Scientific Orbitrap Fusion Lumos Tribrid mass spectrometer equipped with the FAIMS Pro interface. Source parameters included electrospray voltage of 2.2 kV, transfer capillary temperature of 275 C, and ion funnel RF amplitude of 30%. FAIMS was set to standard resolution without supplementary user-controlled carrier gas flow and a dispersion voltage (DV) of -5 kV (equivalent to a dispersion field of -33.3 kV/cm), while the compensation voltage (CV) switched between three voltages (-50,-40, and -30) throughout data collection (referred to as "internal CV stepping"). The Fusion Lumos was set to "Intact Protein" application mode, and data was collected as full profile. Proteoform identification was performed with TopPIC version 1.5.4. Settings for TopPIC included a precursor window of 3 m/z, mass error tolerance of 15 ppm, a proteoform cluster error tolerance of 0.8 Da, a mass shift upper bound of 4000 Da and lower bound of -150 Da, and a maximum number of allowed unknown modifications of 1. All databases were scrambled to generate decoys which were concatenated during the search. A list of 12 dynamic modifications were provided during the open modification search to reduce the number of unknown mass shifts. Downstream data analysis was performed with TopPICR.
DOI: 10.6084/m9.figshare.24078648
title: experiment_data.zip

description: This folder contains:<br><br>file: Data Analysis ANONYM.xlsx <br>folders: Student submissions to the version control systemArtemis filesData Analysis ANONYM.xlsxThis file contains on sheet 1:<br>The collected data extracted of the database e.g. Code, Feedback, User, Time and also some descriptive statistics, like how many times user x has asked the AI-Tutor and has submitted to the APAS and also his or her final score. <br><br>Sheet 2 contains:<br>The answers from the qualitative survey.<br><br>Sheet 3 contains:<br>An analysis of grouping the submissions and the current code when asking the AI-Tutor together. We analyzed whether the feedback was good and how the code changed after the feedback.<br> Student submissions to the version control system:These are multiple anonymized folders, each containing the code of the student submitted to the system.<br>This code has been enriched at the end with comments, whether the student had already asked the AI-Tutor and if yes the timestamps when he or she asked the AI-Tutor.<br><br><br>Artemis-FilesThese folder contains all files necessary to add to you public artemis project in order to enable the AI-Tutor functionality. We have chosen to not publish the full repository, because it would have been nearly impossible to anonymize it properly.
DOI: 10.6084/m9.figshare.24078648.v1
title: experiment_data.zip

description: This folder contains:<br><br>file: Data Analysis ANONYM.xlsx <br>folders: Student submissions to the version control systemArtemis filesData Analysis ANONYM.xlsxThis file contains on sheet 1:<br>The collected data extracted of the database e.g. Code, Feedback, User, Time and also some descriptive statistics, like how many times user x has asked the AI-Tutor and has submitted to the APAS and also his or her final score. <br><br>Sheet 2 contains:<br>The answers from the qualitative survey.<br><br>Sheet 3 contains:<br>An analysis of grouping the submissions and the current code when asking the AI-Tutor together. We analyzed whether the feedback was good and how the code changed after the feedback.<br> Student submissions to the version control system:These are multiple anonymized folders, each containing the code of the student submitted to the system.<br>This code has been enriched at the end with comments, whether the student had already asked the AI-Tutor and if yes the timestamps when he or she asked the AI-Tutor.<br><br><br>Artemis-FilesThese folder contains all files necessary to add to you public artemis project in order to enable the AI-Tutor functionality. We have chosen to not publish the full repository, because it would have been nearly impossible to anonymize it properly.
DOI: 10.5066/p95sa41w
title: Site data from observations of post-fire and thinned stands of piñon-juniper woodlands on the Colorado Plateau

description: These data were compiled to evaluate pinyon-juniper regeneration dynamics following stand-replacing wildfire and thinning treatments. Objectives of our study were to investigate vegetation community composition and tree recruitment in post-fire and post-thinning environments. These data represent plant and biological soil crust community composition and climatological records among intact, thinned, and burned pinyon–juniper woodlands. These data were collected in Mesa Verde National Park and Ute Mountain Ute Tribal Park from 6/1/2021 to 6/10/2021 and from 03/1/2022 to 11/30/2022 at two burned and two intact pinyon-juniper ecosystems in Mesa Verde National Park only. These data were collected by the U.S. Geological Survey, National Park Service, and Northern Arizona University through field observation and sensor arrays. These data can be used to interpret community composition and climatological differences among intact, thinned, and burned pinyon–juniper woodlands.
DOI: 10.5281/zenodo.10433195
title: Swift/BAT-GUANO upper limits fits map for S231224e
DOI: 10.5281/zenodo.10433196
title: Swift/BAT-GUANO upper limits fits map for S231224e
DOI: 10.5281/zenodo.6416118
title: Replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits"

description: This repository contains replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits". All non-confidential data inputs are included, as well as intermediate data outputs, final data outputs, and final tables and figures for all main text and supplementary tables and figures. Some input data are confidential (e.g., mortality records in some countries); therefore, intermediate regression results files are included in the upload to ensure all later stages of the analysis are fully replicable. The full data output files resulting from Monte Carlo simulations of future climate change impacts on mortality far exceed Zenodo file size limits; therefore, key aggregates of the raw output files are included here, which allow for replication of all tables and figures in the paper.



data.zip contains raw, intermediate, and final datasets

outputs.zip contains output tables and figures 


All replication code for the paper is available on a public Github repository, accessible here.The manuscript and supplementary information are available at the QJE, here.
DOI: 10.5281/zenodo.6416119
title: Replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits"

description: This repository contains replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits". All non-confidential data inputs are included, as well as intermediate data outputs, final data outputs, and final tables and figures for all main text and supplementary tables and figures. Some input data are confidential (e.g., mortality records in some countries); therefore, intermediate regression results files are included in the upload to ensure all later stages of the analysis are fully replicable. The full data output files resulting from Monte Carlo simulations of future climate change impacts on mortality far exceed Zenodo file size limits; therefore, key aggregates of the raw output files are included here, which allow for replication of all tables and figures in the paper.



data.zip contains raw, intermediate, and final datasets

outputs.zip contains output tables and figures 


All replication code for the paper is available on a public Github repository, accessible here.The manuscript and supplementary information are available at the QJE, here.
DOI: 10.5281/zenodo.3830096
title: Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (preprint, 2020), "Ultraviolet radiation decreases COVID-19 growth rates: Global causal estimates and seasonal implications"

description: This upload contains all replication material for "Ultraviolet radiation decreases COVID-19 growth rates: Global causal estimates and seasonal implications" (preprint). Please note that this manuscript is under review and the data and code are likely to change (updated versions will be uploaded to Zenodo as soon as they are available). <strong>Authors:</strong> Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor. <strong>Code is located within CCHMP_covid_climate_code_release.zip</strong>, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script. Please find the code needed to replicate the main findings of the paper described below: Plots of data: R and Stata scripts to make figures 1B, S1, S2, S3, S4 and S13 can be found within “code/analysis/data_plots/”. Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, S6, S7, S8 and S9 can be found within “code/analysis/regressions/” Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 3, S5 and S10 can be found within “code/analysis/seasonal_sim/”. SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S11 and S12 can be found within “code/analysis/SEIR/”. <strong>Data are located within CCHMP_covid_climate_data_release.zip.</strong>
description: {"references": ["Carleton et al., (preprint, 2020). Ultraviolet radiation decreases COVID-19 growth rates: Global causal estimates and seasonal implications. http://ssrn.com/abstract=3588601"]}
DOI: 10.5281/zenodo.3899594
title: Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (preprint, 2020), "Evidence for ultraviolet radiation decreasing COVID-19 growth rates: global estimates and seasonal implications"

description: This upload contains all replication material for "Evidence for ultraviolet radiation decreasing COVID-19 growth rates: global estimates and seasonal implications" (preprint). Please note that this manuscript is under review and the data and code are likely to change (updated versions will be uploaded as soon as they are available). <strong>Authors:</strong> Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor. <strong>Code is located within CCHMP_covid_climate_code_release.zip</strong>, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative). Please find the code needed to replicate the main findings of the paper described below: Plots of data: R and Stata scripts to make figures 1B, 2A/B, S1, S2, S3, and S9 can be found within “code/analysis/data_plots/”. Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, S5, S6, S7, S8, and S13, as well as Table S1, can be found within “code/analysis/regressions/” Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 3, S4 and S10 can be found within “code/analysis/seasonal_sim/”. SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S11 and S12 can be found within “code/analysis/SEIR/”. <strong>Data are located within CCHMP_covid_climate_data_release.zip.</strong>
description: {"references": ["Carleton et al., (preprint, 2020). Ultraviolet radiation decreases COVID-19 growth rates: Global causal estimates and seasonal implications. http://ssrn.com/abstract=3588601"]}
DOI: 10.5281/zenodo.3829621
title: Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (PNAS, 2020), "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates"

description: This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process. 

Authors: Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.

Code is located within CCHMP_covid_climate_code_release.zip, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative).

Please find the code needed to replicate the main findings of the paper described below:



Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.

Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".

Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.

SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.


Data are located within CCHMP_covid_climate_data_release.zip.
DOI: 10.5281/zenodo.3829622
title: Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (preprint, 2020), "Ultraviolet radiation decreases COVID-19 growth rates: Global causal estimates and seasonal implications"

description: This upload contains all replication material for "Ultraviolet radiation decreases COVID-19 growth rates: Global causal estimates and seasonal implications" (preprint). Please note that this manuscript is under review and the data and code are likely to change (updated versions will be uploaded to Zenodo as soon as they are available). <strong>Authors:</strong> Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor. <strong>Code is located within CCHMP_covid_climate_code_release.zip</strong>, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script. Please find the code needed to replicate the main findings of the paper described below: Plots of data: R and Stata scripts to make figures 1B, S1, S2, S3, S4 and S13 can be found within “code/analysis/data_plots/”. Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, S6, S7, S8 and S9 can be found within “code/analysis/regressions/” Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 3, S5 and S10 can be found within “code/analysis/seasonal_sim/”. SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S11 and S12 can be found within “code/analysis/SEIR/”. <strong>Data are located within CCHMP_covid_climate_data_release.zip.</strong>
description: {"references": ["Carleton et al., (preprint, 2020). Ultraviolet radiation decreases COVID-19 growth rates: Global causal estimates and seasonal implications. http://ssrn.com/abstract=3588601"]}
DOI: 10.5281/zenodo.4304620
title: Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (PNAS, 2020), "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates"

description: This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process. 

Authors: Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.

Code is located within CCHMP_covid_climate_code_release.zip, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative).

Please find the code needed to replicate the main findings of the paper described below:



Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.

Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".

Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.

SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.


Data are located within CCHMP_covid_climate_data_release.zip.