cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11583/dtu.21155521.v1
title: 2H-TMDC CO and H adsorption databases

description: ASE databases with most favorable adsorption sites for H , CO and COOH at four different 2H-TMDC edges calculated by DFT. Initial TMDC structures are selected from the Computational 2D database (C2DB) based on thermodynamic and dynamic stability criteria. The results are presented in the following article: <br> Pedersen, P. D. et al., Trends in CO2 reduction on Transition Metal Dichalcogenide Edges, ACS Catalysis (2023)
DOI: 10.57775/raq1-3f33
title: Data set: Service delivery and institutional mortality indicators from Haiti

description: This dataset contains a series of service delivery and institutional mortality indicators from the Haiti dhis2 for the period of January 2019 to December 2020. This monthly dataset includes 15 months pre-COVID and 9 months during the pandemic.

The unit of analysis is the health facility. The dataset covers all health facilities in Haiti over 24 months.

Data cleaning included: imputing 0s for missing mortality indicator values if the service that it relates to is non-missing (e.g., facility deliveries and maternal deaths, neonatal deaths and stillbirths, inpatient admissions and inpatient deaths). For any given indicator, removing any unit that reports less than 15 out of 24 months. Set to missing any value greater than 3.5 standard deviation from the mean over 24 months.

Permissions to use this dataset must be obtained from the Ministry of Public Health and Population of Haiti.
DOI: 10.57775/hde7-e669
title: Data set: Service delivery and institutional mortality indicators from Ghana

description: This dataset contains a series of service delivery and institutional mortality indicators from the Ghana dhis2 for the period of January 2019 to December 2020. This monthly dataset includes 15 months pre-COVID and 9 months during the pandemic.

The unit of analysis is the region (N=16). The dataset covers all regions over 24 months.

Permissions to use this dataset must be obtained from the Policy, Planning, Monitoring and evaluation department of Ghana Health Services.
DOI: 10.57775/vpqa-v968
title: Data set: Service delivery and institutional mortality indicators from Ethiopia

description: This dataset contains a series of service delivery and institutional mortality indicators from the Ethiopian dhis2 for the period of January 2019 to December 2020. This monthly dataset includes 15 months pre-COVID and 9 months during the pandemic. 

The unit of analysis is the health facility, woreda, or primary health care unit (PHCU). The dataset covers all regions over 24 months, for the exception of Tigray which stopped reporting in October 2020 due to the ongoing conflict in the region.

Data cleaning included: imputing 0s for missing mortality indicator values if the service that it relates to is non-missing (e.g., facility deliveries and maternal deaths, neonatal deaths and stillbirths, inpatient admissions and inpatient deaths). For any given indicator, removing any unit that reports less than 15 out of 24 months. Set to missing any value greater than 3.5 standard deviation from the mean over 24 months.

Permissions to use this dataset must be obtained from the Ministry of Health of Ethiopia.
DOI: 10.57775/c1fc-t014
title: Data set: Service delivery and institutional mortality indicators from KwaZulu-Natal, South Africa

description: This dataset contains a series of service delivery and institutional mortality indicators from the South African dhis2 for the period of January 2019 to December 2020. This monthly dataset includes 15 months pre-COVID and 9 months during the pandemic.

The unit of analysis is the health facility. The dataset covers all health facilities in the KwaZulu-Natal Province over 24 months.

Data cleaning included: imputing 0s for missing mortality indicator values if the service that it relates to is non-missing (e.g., facility deliveries and maternal deaths, neonatal deaths and stillbirths, inpatient admissions and inpatient deaths). For any given indicator, removing any unit that reports less than 15 out of 24 months. Set to missing any value greater than 3.5 standard deviation from the mean over 24 months.

Permissions to use this dataset must be obtained from the South Africa National Department of Health
DOI: 10.5281/zenodo.7670492
title: Audit Opinion Score Dataset

description: Please read the Code Book. Author: Tomohiro Hosoi 1. About the Dataset This dataset contains information on the Auditor General South Africa’s audit opinions based on the Public Finance Management Act (PFMA) and Municipal Finance Management Act (MFMA). The dataset aims to understand the trend of audit opinion. 2. Procedure The Auditor General gives five audit opinions. Namely, (i) unqualified with no findings; (ii) unqualified with findings; (iii) qualified; (iv) adverse; (v) disclaimed; (vi) outstanding. Sheets named “PFMA_Original” and “MFMA_Original” are records of these audit opinions. The coder gives each opinion score based on the following rule; (i) unqualified with no findings is 5 points; (ii) unqualified with findings is 4 points; (iii) qualified is 3 points; (iv) adverse is 2 points; (v) disclaimed is 1 point; (vi) outstanding is 0 point. Sheets named “PFMA_Score” and “MFMA_Score” were records of these points.
DOI: 10.5281/zenodo.8130708
title: Audit Opinion Score Dataset

description: Please read the Code Book. Author: Tomohiro Hosoi 1. About the Dataset This dataset contains information on the Auditor General South Africa’s audit opinions based on the Public Finance Management Act (PFMA) and Municipal Finance Management Act (MFMA). The dataset aims to understand the trend of audit opinion. 2. Procedure The Auditor General gives five audit opinions. Namely, (i) unqualified with no findings; (ii) unqualified with findings; (iii) qualified; (iv) adverse; (v) disclaimed; (vi) outstanding. Sheets named “PFMA_Original” and “MFMA_Original” are records of these audit opinions. The coder gives each opinion score based on the following rule; (i) unqualified with no findings is 5 points; (ii) unqualified with findings is 4 points; (iii) qualified is 3 points; (iv) adverse is 2 points; (v) disclaimed is 1 point; (vi) outstanding is 0 point. Sheets named “PFMA_Score” and “MFMA_Score” were records of these points.
DOI: 10.5281/zenodo.7670491
title: Audit Opinion Score Dataset

description: Please read the Code Book. Author: Tomohiro Hosoi 1. About the Dataset This dataset contains information on the Auditor General South Africa’s audit opinions based on the Public Finance Management Act (PFMA) and Municipal Finance Management Act (MFMA). The dataset aims to understand the trend of audit opinion. 2. Procedure The Auditor General gives five audit opinions. Namely, (i) unqualified with no findings; (ii) unqualified with findings; (iii) qualified; (iv) adverse; (v) disclaimed; (vi) outstanding. Sheets named “PFMA_Original” and “MFMA_Original” are records of these audit opinions. The coder gives each opinion score based on the following rule; (i) unqualified with no findings is 5 points; (ii) unqualified with findings is 4 points; (iii) qualified is 3 points; (iv) adverse is 2 points; (v) disclaimed is 1 point; (vi) outstanding is 0 point. Sheets named “PFMA_Score” and “MFMA_Score” were records of these points.
DOI: 10.11583/dtu.7438607
title: Models for the prediction of the thermophysical properties of halogenated substances

description: This entry contains, first, the Matlab functions code for the calculation of the critical temperature, critical pressure, acentric factor, normal boiling temperature, and ideal gas heat capacity by using a classical group contribution approach (GCM functions.p) or a neural-network-based approach (ANN functions.p). The input for their use is anarray containing the number of groups for the definition of the fluid, except for the case of the ideal gas capacity, where the first element of the array will be the temperature in K. An example of use can be found in the related publications.<br>Second, function for the estimation of the uncertainty of the predicted values are also included:uncertainty model.m: obtains the estimated uncertainty for a specified model and property. The input for its use is the array containing the number of groups for the fluid, exceptfor the case of the ideal gas capacity, where the first element of the array will be the temperature in Kelvin. An example of use of this function can be found in the related publication.<br>Tc model.mat: is a structure that contains the array of experimental values of Tc (Tcexp), the fluid groups (groups), the models' parameters (param GCM, param ANN), the predictions of the GCM and the ANN (Tc GCM, Tc ANN), their uncertainties (u GCM, u ANN), and the PDF of both model parameters (pdf GCM, pdf ANN). Pc model.mat, w model.mat, and cp0 model.mat are analogous to Tc model.mat.Tc model.csv, Pc model.csv, w model.csv, and cp0 model.csv: contain the data of the .mat files in csv format.
DOI: 10.11583/dtu.7438607.v1
title: Models for the prediction of the thermophysical properties of halogenated substances

description: This entry contains, first, the Matlab functions code for the calculation of the critical temperature, critical pressure, acentric factor, normal boiling temperature, and ideal gas heat capacity by using a classical group contribution approach (GCM functions.p) or a neural-network-based approach (ANN functions.p). The input for their use is anarray containing the number of groups for the definition of the fluid, except for the case of the ideal gas capacity, where the first element of the array will be the temperature in K. An example of use can be found in the related publications.<br>Second, function for the estimation of the uncertainty of the predicted values are also included:uncertainty model.m: obtains the estimated uncertainty for a specified model and property. The input for its use is the array containing the number of groups for the fluid, exceptfor the case of the ideal gas capacity, where the first element of the array will be the temperature in Kelvin. An example of use of this function can be found in the related publication.<br>Tc model.mat: is a structure that contains the array of experimental values of Tc (Tcexp), the fluid groups (groups), the models' parameters (param GCM, param ANN), the predictions of the GCM and the ANN (Tc GCM, Tc ANN), their uncertainties (u GCM, u ANN), and the PDF of both model parameters (pdf GCM, pdf ANN). Pc model.mat, w model.mat, and cp0 model.mat are analogous to Tc model.mat.Tc model.csv, Pc model.csv, w model.csv, and cp0 model.csv: contain the data of the .mat files in csv format.
DOI: 10.17028/rd.lboro.23652792
title: Supplementary information files for Temperature dependency of diagnostic methods in lithium-ion batteries

description: Supplementary files for article Temperature dependency of diagnostic methods in lithium-ion batteries Accurate estimation of battery performance and capacity is essential for assessing the capabilities of different batteries and determining state of health (SOH). Typically, diagnostic methods such as constant current charge/discharge, current interrupt, and electrochemical impedance spectroscopy (EIS) are demonstrated in laboratory tests with controlled environmental conditions. In real-world applications, such as electric vehicles, batteries are subjected to wide temperature ranges and temperature control for performance and SOH estimation is often not possible. This study investigates how battery diagnostic techniques are influenced by small temperature variations in order to determine suitable temperature windows over which different diagnostic methods can be performed without negatively influencing results. A lithium-ion 18650 cell is subjected to constant current charge, pulse discharge and EIS between 0 °C and 40 °C in 2 °C increments. The influence of variations in operating temperature on the diagnostic abilities of incremental capacity analysis (ICA), differential voltage analysis (DVA) and equivalent circuit fitting is explored. The findings of this study indicate that small temperature variations between diagnostic tests can lead to significant errors in the ability to diagnose degradation mechanisms, particularly at low temperatures (&lt;10 °C). If temperature variations of ±4 °C or greater are expected between characterisation tests, then temperature regulation will be required to ensure accurate diagnostics. EIS based diagnostics was shown to be less temperature sensitive than voltage-based analysis techniques such as ICA.
DOI: 10.17028/rd.lboro.23652792.v1
title: Supplementary information files for Temperature dependency of diagnostic methods in lithium-ion batteries

description: Supplementary files for article Temperature dependency of diagnostic methods in lithium-ion batteries Accurate estimation of battery performance and capacity is essential for assessing the capabilities of different batteries and determining state of health (SOH). Typically, diagnostic methods such as constant current charge/discharge, current interrupt, and electrochemical impedance spectroscopy (EIS) are demonstrated in laboratory tests with controlled environmental conditions. In real-world applications, such as electric vehicles, batteries are subjected to wide temperature ranges and temperature control for performance and SOH estimation is often not possible. This study investigates how battery diagnostic techniques are influenced by small temperature variations in order to determine suitable temperature windows over which different diagnostic methods can be performed without negatively influencing results. A lithium-ion 18650 cell is subjected to constant current charge, pulse discharge and EIS between 0 °C and 40 °C in 2 °C increments. The influence of variations in operating temperature on the diagnostic abilities of incremental capacity analysis (ICA), differential voltage analysis (DVA) and equivalent circuit fitting is explored. The findings of this study indicate that small temperature variations between diagnostic tests can lead to significant errors in the ability to diagnose degradation mechanisms, particularly at low temperatures (&lt;10 °C). If temperature variations of ±4 °C or greater are expected between characterisation tests, then temperature regulation will be required to ensure accurate diagnostics. EIS based diagnostics was shown to be less temperature sensitive than voltage-based analysis techniques such as ICA.
DOI: 10.5281/zenodo.8130567
title: Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS"

description: Training Data for "Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS""
DOI: 10.5281/zenodo.8130566
title: Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS"

description: Training Data for "Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS""
DOI: 10.25405/data.ncl.23642955
title: Data for the manuscript Berberine isolation from Coscinium fenestratum: optical, electrochemical, and computational studies

description: READ ME <br> ======================== General information ======================== <br> Author: B R Horrocks Contact: ben.horrocks@ncl.ac.uk DOI: 10.1039/D3RA01769A License: http://creativecommons.org/licenses/by/4.0 Last updated: 7 July 2023 Related article: Rajapakse et al. RSC Adv., 2023,13, 17062-17073 <br> ======================== Introductory information ======================== Files included in the data deposit (figure numbers refer to the article above): <br> 1) UV-Vis. Data for Fig. 6 in text format. 2) Calculations. Log files from Gaussian for the reported calculations. Filenaming conventions in files notes.txt 3) FTIR. Data for Fig. S2 of SI file in text format. 4) Fluorescence. Data for Fig. 9 in text format. 5) CV. Data for Figs. 11 and 12 in text format. 6) UME. Data for Fig. 13 in text format. <br> Zip file organised into folders 1-6 above. In each folder, file notes.txt gives details of the file format. <br> ========================== Methodological information ========================== The methodology used to collect the data is given in the experimental methods section of the article: <br> Rajapakse et al. RSC Adv., 2023,13, 17062-17073 <br> <br> ========================= Data-specific information ========================= All data is given as text files. Leftmost column is wavelength / nm for optical spectra, wavenumber / cm-1 for FTIR and poitential / V for electrochemistry. Files notes.txt give format details. <br> <br>
DOI: 10.25405/data.ncl.23642955.v1
title: Data for the manuscript Berberine isolation from Coscinium fenestratum: optical, electrochemical, and computational studies

description: READ ME <br> ======================== General information ======================== <br> Author: B R Horrocks Contact: ben.horrocks@ncl.ac.uk DOI: 10.1039/D3RA01769A License: http://creativecommons.org/licenses/by/4.0 Last updated: 7 July 2023 Related article: Rajapakse et al. RSC Adv., 2023,13, 17062-17073 <br> ======================== Introductory information ======================== Files included in the data deposit (figure numbers refer to the article above): <br> 1) UV-Vis. Data for Fig. 6 in text format. 2) Calculations. Log files from Gaussian for the reported calculations. Filenaming conventions in files notes.txt 3) FTIR. Data for Fig. S2 of SI file in text format. 4) Fluorescence. Data for Fig. 9 in text format. 5) CV. Data for Figs. 11 and 12 in text format. 6) UME. Data for Fig. 13 in text format. <br> Zip file organised into folders 1-6 above. In each folder, file notes.txt gives details of the file format. <br> ========================== Methodological information ========================== The methodology used to collect the data is given in the experimental methods section of the article: <br> Rajapakse et al. RSC Adv., 2023,13, 17062-17073 <br> <br> ========================= Data-specific information ========================= All data is given as text files. Leftmost column is wavelength / nm for optical spectra, wavenumber / cm-1 for FTIR and poitential / V for electrochemistry. Files notes.txt give format details. <br> <br>
DOI: 10.11583/dtu.6825443
title: Numerical models for the design and analysis of heat pumps with zeotropic mixtures

description: The work presents numerical models for the design and analysis of heat pump cycles with pure and mixed working fluids. The models are suitable for working fluid screenings for pure and mixed working fluids and offer possibilities for a detailed analysis of the heat pump cycle.
DOI: 10.25835/2lg7peic
title: MM_Claims Dataset

description: This dataset is introduced by the paper "MM-Claims: A Dataset for Multimodal Claim Detection in Social Media" If you use this dataset in your work, please cite: @inproceedings{cheema-etal-2022-mm, title = "{MM}-Claims: A Dataset for Multimodal Claim Detection in Social Media", author = {Cheema, Gullal Singh and Hakimov, Sherzod and Sittar, Abdul and M{\"u}ller-Budack, Eric and Otto, Christian and Ewerth, Ralph}, booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-naacl.72", pages = "962--979" } Information about columns in the files: 1. claim_binary: {0: 'Not a claim', 1: 'claim'} 2. claim_three: {0: 'Not a claim', '1': 'claim but not check-worthy', 2: 'check-worthy claim'} 3. claim_vis: {0: 'Not a claim', '1': 'visually-irrelevant claim', 2: 'visually-relevant claim'} Official code repository: https://github.com/TIBHannover/MM_Claims **All files were updated on 5th May 2023, with some images removed because of obscene images that were not automatically detected in the first phase.** **If you are interested in the binary task on check-worthiness estimation in multimodal claims, you can find the refined dataset with new test data released as part of the CLEF Checkthat! 2023 challenge: https://gitlab.com/checkthat_lab/clef2023-checkthat-lab/-/tree/main**
DOI: 10.6084/m9.figshare.23638992
title: Supplementary Table 2

description: WGS of isolates in this study are deposited in PubMLST with their IDs listed in Supplementary Table 2. The dataset consists of 15,532 isolates from 66 countries, spanning the time from 1928 to 2022. <em>Neisseria gonorrhoeae</em> core genome cluster (Ng_cgc_400) and plasmid carriage of individual isolates is indicated.
DOI: 10.6084/m9.figshare.23638992.v1
title: Supplementary Table 2

description: WGS of isolates in this study are deposited in PubMLST with their IDs listed in Supplementary Table 2. The dataset consists of 15,532 isolates from 66 countries, spanning the time from 1928 to 2022. <em>Neisseria gonorrhoeae</em> core genome cluster (Ng_cgc_400) and plasmid carriage of individual isolates is indicated.
DOI: 10.11583/dtu.14394254
title: Data for "Ultra-coherent nanomechanical resonators based on inverse design"

description: Figures and data used in the manuscript titled "Ultra-coherent nanomechanical resonators based on inverse design" by Dennis Høj, Fengwen Wang, Wenjun Gao, Ulrich Busk Hoff, Ole Sigmund and Ulrik Lund Andersen. Accepted for publication in Nature Communications.
DOI: 10.11583/dtu.14394254.v1
title: Data for "Ultra-coherent nanomechanical resonators based on inverse design"

description: Figures and data used in the manuscript titled "Ultra-coherent nanomechanical resonators based on inverse design" by Dennis Høj, Fengwen Wang, Wenjun Gao, Ulrich Busk Hoff, Ole Sigmund and Ulrik Lund Andersen. Accepted for publication in Nature Communications.
DOI: 10.5519/qd.i8znnh47
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.11583/dtu.19137326
title: Datasets for journal article "Pore solution alkalinity of cement paste as determined by Cold Water Extraction"

description: Complete datasets for the journal article "Pore solution alkalinity of cement paste as determined by Cold Water Extraction". Contains three files, related to: - Parametric study on Cold Water Extraction (Parametric_study_CWE.xlsx) - Comparison between Cold Water Extraction and Pore Water Extraction (Comparison_CWE-PWE.xlsx) - Determination of the water content of paste samples by mass loss methods (Mass_loss_methods.xlsx)
DOI: 10.11583/dtu.19137326.v1
title: Datasets for journal article "Pore solution alkalinity of cement paste as determined by Cold Water Extraction"

description: Complete datasets for the journal article "Pore solution alkalinity of cement paste as determined by Cold Water Extraction". Contains three files, related to: - Parametric study on Cold Water Extraction (Parametric_study_CWE.xlsx) - Comparison between Cold Water Extraction and Pore Water Extraction (Comparison_CWE-PWE.xlsx) - Determination of the water content of paste samples by mass loss methods (Mass_loss_methods.xlsx)