cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25810/fx6e-r462.56
title: European Newspaper Coverage of Climate Change or Global Warming, 2004-2023 - November 2023

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.34740/kaggle/dsv/7338462
title: Billboard
DOI: 10.34740/kaggle/dsv/7338464
title: Billboard
DOI: 10.34740/kaggle/dsv/7338466
title: Billboard
DOI: 10.34740/kaggle/dsv/7338468
title: Billboard
DOI: 10.34740/kaggle/dsv/7338469
title: Billboard
DOI: 10.34740/kaggle/dsv/7338470
title: Billboard
DOI: 10.34740/kaggle/dsv/7338472
title: Billboard
DOI: 10.34740/kaggle/dsv/7338473
title: Billboard
DOI: 10.25345/c5n01046g
title: MassIVE MSV000093765 - Identify PPARa ligands from human gut microbiome
title: A culture derived from fecal samples of multiple pilot donors (PDs) grown in modified Gifu Anaerobic Medium (mGAM), which yields a bacterial community similar in composition and diversity to the original PD microbiome. The culture supernatant was extracted using ethyl acetate. The initial His-tagged pull-downs were performed using a PPARa-LBD construct and an FXR-LBD construct was used as a negative control, and with each receptor pull-down performed in triplicate.
DOI: 10.5281/zenodo.10291936
title: FESOM-REcoM model data: Severe 21st-century ocean acidification in Antarctic Marine Protected Areas

description: This repository contains all post-processed model output used in the paper "Severe 21st-century ocean acidification in Antarctic Marine Protected Areas". It contains the data underlying the figures in the paper, such as regional averages, as well as masks for the marine protected areas and the grid information file of the original model output.
The data were created using python scripts provided at https://doi.org/10.5281/zenodo.10295920. 
Original model output, including full fields of computed pH and saturation states with respect to aragonite and calcite, is available at the World Data Center for Climate (WDCC) under the following DOIs:

simA, historical: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC
simA, ssp126: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC
simA, ssp245: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC
simA, ssp370: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC
simA, ssp585: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC
simB: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC
simC, historical: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC
simC, ssp245: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC
simC, ssp585: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC
simC, historical: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC
simC, ssp585: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC
DOI: 10.5281/zenodo.10291935
title: FESOM-REcoM model data: Severe 21st-century ocean acidification in Antarctic Marine Protected Areas

description: This repository contains all post-processed model output used in the paper "Severe 21st-century ocean acidification in Antarctic Marine Protected Areas". It contains the data underlying the figures in the paper, such as regional averages, as well as masks for the marine protected areas and the grid information file of the original model output.
The data were created using python scripts provided at https://doi.org/10.5281/zenodo.10295920. 
Original model output, including full fields of computed pH and saturation states with respect to aragonite and calcite, is available at the World Data Center for Climate (WDCC) under the following DOIs:

simA, historical: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC
simA, ssp126: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC
simA, ssp245: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC
simA, ssp370: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC
simA, ssp585: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC
simB: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC
simC, historical: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC
simC, ssp245: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC
simC, ssp585: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC
simC, historical: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC
simC, ssp585: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC
DOI: 10.5523/bris.eqed27cl4unl228g3tlwhjxvl
title: SrTi(1-x)V(x)O3 DFT+DMFT data

description: Density functional theory (DFT) + dynamical mean-field theory (DMFT) calculations of the electronic structure SrTi(1-x)V(x)O3 through the metal-insulator transition.
DOI: 10.25810/n3xc-9577.32
title: Finnish Newspaper Coverage of Climate Change or Global Warming, 2000-2023 - November 2023

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.6084/m9.figshare.24926535
title: genes.xlsx

description: Data from cell campaign
DOI: 10.6084/m9.figshare.24926535.v1
title: genes.xlsx

description: Data from cell campaign
DOI: 10.6073/pasta/0a62e0745371a7f51c0a8d01b2a71df7
title: Chemistry of rainfall and throughfall from El Verde and Bisley

description: Rain, throughfall, and stream water are collected weekly at the LEF sites listed below. Samples are collected by USDA Forest Service technicians Carlos Estrada in the field filtered in the lab by Miriam Salgado. These data sets begin as early as 1983; LTER sampling began in 1988. Rain and throughfall samples are the total catch for the week, and are exposed to field conditions for that time. No event sampling is conducted on a routine basis. Rain samples from WDEV are wet only from an automatically-closing collector that prevents any dry deposition (Aerochem Metrics NADP collector). RCEV and RCB are bulk or always-open collectors that receive dry deposition by sedimentation. All samples are measured for pH and conductivity, and then filtered (pre-combusted Whatman GF/F glass fiber filter) prior to further analysis. From 1983-1994 samples were cooled and returned to the San Juan chemistry laboratory for analysis. During those years, samples for NH4 and NO3 analyses were refrigerated continuously until analysis. Subsamples for NH4 analysis were also preserved with 1 molar H2SO4. From 1994 on, samples for NH4 and NO3 were frozen until analysis, were not acidified, and all analyses were conducted at the University of New Hampshire. Rain and Throughfall Sampling SitesDescriptions of LTER LUQ rain and throughfall weekly sample chemistry data from 1988 onwards. Chemical concentrations are recorded as mg/L or ug/L as appropriate. Values below detection limits are recorded as 1/2 the detection limit. Site Abbreviation Description Comments Rain collector Bisley. RCB Bulk collector. Rain collector El Verde  RCEV  Bulk collector. Wet/dry El Verde. WDEV. Wet only collector. Throughfall Bisley. TFB=TCB TF bulk 10-collector composite Bisley gap= BGAP, TF bulk 10-collector composite
 
 Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
DOI: 10.17909/p188-aj80
title: The outflow of the protostar in B335: I

description: The isolated globule B335 contains a single, low luminosity Class 0 protostar associated with a bipolar nebula and outflow system seen nearly perpendicular to its axis. We observed the innermost regions of this outflow as part of JWST/NIRCam GTO program 1187, primarily intended for wide-field slitless spectroscopy of background stars behind the globule. We find a system of expanding shock fronts with kinematic ages of only a few decades emerging 
symmetrically from the position of the embedded protostar, which is not directly detected at NIRCam wavelengths. 
The innermost and youngest of the shock fronts studied here shows strong emission from CO. The next older shock front shows less CO and the third shock front shows only H2 emission in our data. This third and most distant of these inner shock fronts shows substantial evolution of its shape since it was last observed with high spatial resolution in 1996 with Keck/NIRC. This may be evidence of a faster internal shock catching up with a slower one and of the two shocks merging.
DOI: 10.5281/zenodo.10079441
title: 1,237 Annotated Developer Apologies from GitHub

description: Software Developer Apologies

This dataset contains 1,237 GitHub comments with apology annotations (apology vs not apology), released as part of the following publication:



Benjamin S. Meyers. Human Error Assessment in Software Engineering. Rochester Institute of Technology. 2023. 


Included Files

The "github_apologies.csv" file contains the full dataset of 1,237 GitHub comments with apology annotations. In total, there are 365 comments containing an apology (872 non apologies). The comments themselves are a subset of those included in 88.6 Million Developer Comments from GitHub.

Annotation Details

Full details are provided in the above publication. We implemented a naive classifier (Precision: 41.7%, Recall: 99.7%, F1: 86.9%, Accuracy: 91.1%) using counts of apology lemmas. 91% of developer comments containing at least one apology lemma matched our manual annotations. Agreement between raters was almost perfect (Cohen's Kappa = 0.94).

CSV Fields



ID: Unique identifier for the comment.

SOURCE: Whether this comment originates from a commit, issue, or pull request.

COMMENT_URL: The URL linking to the comment.

COMMENT_TEXT: The raw comment text.

NUM_APOLOGY_LEMMAS: The count of apology lemmas present in the comment.

CLASSIFIER_LABEL: The automatically assigned label ("Apology" or "Not Apology").

RATER_1_LABEL: The manually assigned label ("Apology" or "Not Apology") from Rater 1.

RATER_2_LABEL: The manually assigned label ("Apology" or "Not Apology") from Rater 2.

AGREED_LABEL: The agreed upon label ("Apology" or "Not Apology") after Rater 1 and Rater 12 resolved disagreements.


Contact

Please contact Benjamin S. Meyers (email) with questions about this data and its collection.

Acknowledgments

Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).
DOI: 10.5281/zenodo.10079440
title: 1,237 Annotated Developer Apologies from GitHub

description: Software Developer Apologies

This dataset contains 1,237 GitHub comments with apology annotations (apology vs not apology), released as part of the following publication:



Benjamin S. Meyers. Human Error Assessment in Software Engineering. Rochester Institute of Technology. 2023. 


Included Files

The "github_apologies.csv" file contains the full dataset of 1,237 GitHub comments with apology annotations. In total, there are 365 comments containing an apology (872 non apologies). The comments themselves are a subset of those included in 88.6 Million Developer Comments from GitHub.

Annotation Details

Full details are provided in the above publication. We implemented a naive classifier (Precision: 41.7%, Recall: 99.7%, F1: 86.9%, Accuracy: 91.1%) using counts of apology lemmas. 91% of developer comments containing at least one apology lemma matched our manual annotations. Agreement between raters was almost perfect (Cohen's Kappa = 0.94).

CSV Fields



ID: Unique identifier for the comment.

SOURCE: Whether this comment originates from a commit, issue, or pull request.

COMMENT_URL: The URL linking to the comment.

COMMENT_TEXT: The raw comment text.

NUM_APOLOGY_LEMMAS: The count of apology lemmas present in the comment.

CLASSIFIER_LABEL: The automatically assigned label ("Apology" or "Not Apology").

RATER_1_LABEL: The manually assigned label ("Apology" or "Not Apology") from Rater 1.

RATER_2_LABEL: The manually assigned label ("Apology" or "Not Apology") from Rater 2.

AGREED_LABEL: The agreed upon label ("Apology" or "Not Apology") after Rater 1 and Rater 12 resolved disagreements.


Contact

Please contact Benjamin S. Meyers (email) with questions about this data and its collection.

Acknowledgments

Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).
DOI: 10.5281/zenodo.10080448
title: 200 Annotated Developer Human Errors from GitHub

description: Software Engineers' Human Errors

This dataset contains 200 GitHub comments with manual human error annotations, released as part of the following publication:



Benjamin S. Meyers. Human Error Assessment in Software Engineering. Rochester Institute of Technology. 2023.


Included Files

The "developer_human_errors.csv" file contains the full dataset of 200 software defect descriptions annotated with human error types (slips, lapses, mistakes) and T.H.E.S.E. categories.

CSV Fields



ID: Unique identifier for the comment.

SOURCE: Whether this comment originates from a commit, issue, or pull request.

COMMENT_URL: The URL linking to the comment.

COMMENT_TEXT: The raw comment text.

HUMAN_ERROR_TYPE: Whether the software defect described is a slip, lapse, or mistake.

THESE_V4_ID: Manually assigned T.H.E.S.E. category with labels corresponding to Version 4 of T.H.E.S.E.

THESE_NAME: Name corresponding to manually assigned T.H.E.S.E. category.


Annotation Details

Human error types span slips, lapses, and mistakes from James Reason's Generic Error Modelling System (GEMS):



Slips: Failures of attention.

Lapses: Failures of memory.

Mistakes: Failures of planning.


T.H.E.S.E. categories are summarized below:



S01: Typos & Misspellings

S02: Syntax Errors

S03: Overlooking documented Information

S04: Multitasking Errors

S05: Hardware Interaction Errors

S06: Overlooking Proposed Code Changes

S07: Overlooking Existing Functionality

S08: General Attentional Failure

L01: Forgetting to Finish a Development Task

L02: Forgetting to Fix a Defect

L03: Forgetting to Remove Development Artifacts

L04: Working with Outdated Source Code

L05: Forgetting an Import Statement

L06: Forgetting to Save Work

L07: Forgetting Previous Development Discussion

L08: General Memory Failure

M01: Code Logic Errors

M02: Incomplete Domain Knowledge

M03: Wrong Assumption Errors

M04: Internal Communication Errors

M05: External Communication Errors

M06: Solution Choice Errors

M07: Time Management Errors

M08: Inadequate Testing

M09: Incorrect/Insufficient Configuration

M10: Code Complexity Errors

M11: Internationalization/String Encoding Errors

M12: Inadequate Experience Errors

M13: Insufficient Tooling Access Errors

M14: Workflow Order Errors

M15: General Planning Failure


Contact

Please contact Benjamin S. Meyers (email) with questions about this data and its collection.

Acknowledgments

Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).
DOI: 10.5281/zenodo.10080449
title: 200 Annotated Developer Human Errors from GitHub

description: Software Engineers' Human Errors

This dataset contains 200 GitHub comments with manual human error annotations, released as part of the following publication:



Benjamin S. Meyers. Human Error Assessment in Software Engineering. Rochester Institute of Technology. 2023.


Included Files

The "developer_human_errors.csv" file contains the full dataset of 200 software defect descriptions annotated with human error types (slips, lapses, mistakes) and T.H.E.S.E. categories.

CSV Fields



ID: Unique identifier for the comment.

SOURCE: Whether this comment originates from a commit, issue, or pull request.

COMMENT_URL: The URL linking to the comment.

COMMENT_TEXT: The raw comment text.

HUMAN_ERROR_TYPE: Whether the software defect described is a slip, lapse, or mistake.

THESE_V4_ID: Manually assigned T.H.E.S.E. category with labels corresponding to Version 4 of T.H.E.S.E.

THESE_NAME: Name corresponding to manually assigned T.H.E.S.E. category.


Annotation Details

Human error types span slips, lapses, and mistakes from James Reason's Generic Error Modelling System (GEMS):



Slips: Failures of attention.

Lapses: Failures of memory.

Mistakes: Failures of planning.


T.H.E.S.E. categories are summarized below:



S01: Typos & Misspellings

S02: Syntax Errors

S03: Overlooking documented Information

S04: Multitasking Errors

S05: Hardware Interaction Errors

S06: Overlooking Proposed Code Changes

S07: Overlooking Existing Functionality

S08: General Attentional Failure

L01: Forgetting to Finish a Development Task

L02: Forgetting to Fix a Defect

L03: Forgetting to Remove Development Artifacts

L04: Working with Outdated Source Code

L05: Forgetting an Import Statement

L06: Forgetting to Save Work

L07: Forgetting Previous Development Discussion

L08: General Memory Failure

M01: Code Logic Errors

M02: Incomplete Domain Knowledge

M03: Wrong Assumption Errors

M04: Internal Communication Errors

M05: External Communication Errors

M06: Solution Choice Errors

M07: Time Management Errors

M08: Inadequate Testing

M09: Incorrect/Insufficient Configuration

M10: Code Complexity Errors

M11: Internationalization/String Encoding Errors

M12: Inadequate Experience Errors

M13: Insufficient Tooling Access Errors

M14: Workflow Order Errors

M15: General Planning Failure


Contact

Please contact Benjamin S. Meyers (email) with questions about this data and its collection.

Acknowledgments

Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).
DOI: 10.6084/m9.figshare.24943233
title: COMSOL Fringing Electric Fields in Barium Titanate

description: A dataset extracted from COMSOL Multiphysics containing fringing electric fields inside a Barium Titanate slab waveguide. These fields were used to calculate intricate patterns capable of coupling light out of the waveguide. Both 2D and 3D data is included for various crystal orientations.
DOI: 10.5281/zenodo.10459146
title: The coral Sr/Ca, δ18O and ΔSSH data for Article "Influences of East Asian winter monsoon and El Niño-Southern Oscillation variability on the Kuroshio intrusion to the South China Sea over the past 60 years"
DOI: 10.6084/m9.figshare.24942900
title: Data for COBALT Casco Bay GIS in R Workshop

description: Initial Landsat, DEP, and DMR data for COBALT Casco Bay workshop.
DOI: 10.5281/zenodo.10459168
title: Cronograma de doutorado

description: O cronograma para o desenvolvimento das atividades previstas no presente projeto de pesquisa de doutorado foi elaborado, apresentando um planejamento dividido por anos e trimestres. Nos anos indicados nas colunas, o número 1 representa o primeiro semestre, enquanto o número 2 representa o segundo semestre.
DOI: 10.5281/zenodo.10459169
title: Cronograma de doutorado

description: O cronograma para o desenvolvimento das atividades previstas no presente projeto de pesquisa de doutorado foi elaborado, apresentando um planejamento dividido por anos e trimestres. Nos anos indicados nas colunas, o número 1 representa o primeiro semestre, enquanto o número 2 representa o segundo semestre.
DOI: 10.5281/zenodo.10459180
title: THE IMPORTANCE OF TRANSLITERATION IN LANGUAGE DEVELOPMENT
DOI: 10.5281/zenodo.10459181
title: THE IMPORTANCE OF TRANSLITERATION IN LANGUAGE DEVELOPMENT
DOI: 10.5281/zenodo.10338988
title: Using big sequencing data to identify chronic SARS-Coronavirus-2 infections

description: This dataset supports the "Using big sequencing data to identify chronic SARS-Coronavirus-2 infections" publication in Nature Communications. 
It contains the following files and folders: 

Readme.txt - detailed information on all folders and files.
Supplementary Dataset 1
Supplementary Dataset 2
Supplementary Dataset 3
Supplementary Dataset 4
Supplementary Dataset 5
Supplementary Dataset 6
Supplementary Dataset 7
DOI: 10.5281/zenodo.10430415
title: Using big sequencing data to identify chronic SARS-Coronavirus-2 infections

description: This dataset supports the "Using big sequencing data to identify chronic SARS-Coronavirus-2 infections" publication in Nature Communications. 
It contains the following files and folders: 

Readme.txt - detailed information on all folders and files.
Supplementary Dataset 1
Supplementary Dataset 2
Supplementary Dataset 3
Supplementary Dataset 4
Supplementary Dataset 5
Supplementary Dataset 6
Supplementary Dataset 7
DOI: 10.5281/zenodo.10338987
title: Using big sequencing data to identify chronic SARS-Coronavirus-2 infections

description: This dataset supports the "Using big sequencing data to identify chronic SARS-Coronavirus-2 infections" publication in Nature Communications. 
It contains the following files and folders: 

Readme.txt - detailed information on all folders and files.
Supplementary Dataset 1
Supplementary Dataset 2
Supplementary Dataset 3
Supplementary Dataset 4
Supplementary Dataset 5
Supplementary Dataset 6
Supplementary Dataset 7
DOI: 10.5281/zenodo.10459171
title: Using big sequencing data to identify chronic SARS-Coronavirus-2 infections

description: This dataset supports the "Using big sequencing data to identify chronic SARS-Coronavirus-2 infections" publication in Nature Communications. 
It contains the following files and folders: 

Readme.txt - detailed information on all folders and files.
Supplementary Dataset 1
Supplementary Dataset 2
Supplementary Dataset 3
Supplementary Dataset 4
Supplementary Dataset 5
Supplementary Dataset 6
Supplementary Dataset 7
DOI: 10.5281/zenodo.7595729
title: Thermal Hall effect in a an van der Waals triangular magnet FeCl2

description: Thermal transport is a pivotal probe for studying low-energy, charge-neutral quasi-particles in insulating magnets. In this Letter, we report an observation of large magneto-thermal conductivity and thermal Hall effect (THE) in a van der Waals antiferromagnet FeCl2. The magneto-thermal conductivity reaches over ~700%, indicating strong magnon-phonon coupling. Furthermore, we find an appreciable thermal Hall signal which changes sign concurrently with the spin-flip transition from the antiferromagnetic state to the polarized ferromagnetic state. Our theoretical calculations suggest that, in addition to the Berry curvature induced at the anticrossing points of the hybridized magnon and acoustic phonon modes of FeCl2, other mechanisms are needed to account for the magnitude of the observed THE.


The data of figures presented in this paper is available at this link.