cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/b25smy28cj.1
title: HiggsTools: BSM scalar phenomenology with new versions of HiggsBounds and HiggsSignals

description: The codes HiggsBounds and HiggsSignals compare model predictions of BSM models with extended scalar sectors to searches for additional scalars and to measurements of the detected Higgs boson at 125 GeV. We present a unification and extension of the functionalities provided by both codes into the new HiggsTools framework. The codes have been re-written in modern C++ with native Python and Mathematica interfaces for easy interactive use. We discuss the user interface for providing model predictions, now part of the new sub-library HiggsPredictions, which also provides access to many cross sections and branching ratios for reference models such as the SM. HiggsBounds now implements experimental limits purely through json data files, can better handle clusters of BSM particles of similar masses (even for complicated search topologies), and features an improved handling of mass uncertainties. Moreover, it now contains an extended list of Higgs-boson pair production searches and doubly-charged Higgs boson searches. In HiggsSignals, the treatment of different types of measurements has been unified, both in the χ^2 computation and in the data file format used to implement experimental results.
DOI: 10.17632/4dgzkpxtwb.1
title: Supplementary Material for "Extracting Chronic Kidney Disease Comorbidities from Abstracts using Advanced Machine Learning Techniques: A Comparative Analysis"

description: Background: Chronic Kidney Disease (CKD) is a global health concern and is frequently underdiagnosed due to its subtle initial symptoms, contributing to increasing morbidity and mortality.  A comprehensive understanding of CKD comorbidities could help with identifying risk-groups, providing more efficient therapies, and increasing patient outcomes. Our study has dual aims: to establish a robust machine learning (ML) process for identifying comorbidities from abstracts, and to compile an extensive list of conditions that influence CKD's onset and progression.

Methods: We analysed 39,680 abstracts with CKD in the title downloaded from the Embase library. Seven machine-learning classifiers were compared in identifying abstracts with information of a disease affecting CKD development and/or progression. The top-performing classifier was then further trained with active learning. The relevant disease names were extracted from the chosen abstracts using a novel entity relation extraction technique. The corresponding abstract of each disease was manually reviewed and a final comorbidity list was established.

Findings: The SVM classifier proved to be the most effective and was selected for further active learning training. Our machine learning (ML) pipeline helped to identify 71 comorbidities across 15 ICD-10 disease groups that play a role in the onset or progression of CKD. A review of the selected abstracts revealed that certain diseases have a direct causative impact on CKD, while others, such as schizophrenia, have an indirect effect.

Interpretation: These insights could steer future research into CKD by promoting the consideration of a wider range of comorbidities in CKD prognostic models. Our study ultimately boosts understanding of prognostic comorbidities and aids in clinical practice by improving patient tracking, preventative strategies, and early detection for individuals at elevated risk of CKD onset or progression.

Funding: This research is a part of the first author's PhD project. No additional funding was received for this study.
DOI: 10.17632/7cfpg4prhk.1
title: Assessing Asian Countries' Competitiveness: Two-Stage DEA Analysis of Global Innovation Index and Logistics Performance Index Integration

description: This dataset is associated with the article titled "Assessing Asian Countries' Competitiveness: Two-Stage DEA Analysis of Global Innovation Index and Logistics Performance Index Integration" explores the competitiveness of Asian countries through a two-stage DEA (Data Envelopment Analysis) analysis. 
The study combines the Global Innovation Index (GII) and the Logistics Performance Index (LPI) to provide a comprehensive assessment.
The researchers aim to evaluate the innovation capabilities and logistics performance of Asian countries to understand their competitiveness in the global market. The GII measures countries' innovation performance based on various indicators, including institutions, human capital, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, and creative outputs. On the other hand, the LPI assesses the efficiency and effectiveness of logistics performance, considering aspects such as infrastructure quality, customs efficiency, logistics competence, tracking and tracing, and timeliness.
To conduct the analysis, the researchers employ a two-stage DEA approach. In the first stage, they evaluate each country's efficiency scores separately for innovation and logistics using DEA models. In the second stage, the efficiency scores from both stages are integrated to obtain an overall competitiveness score for each country. This integration allows for a holistic assessment of a country's competitiveness, considering both innovation and logistics aspects.
The study focuses specifically on Asian countries and provides a detailed analysis of their competitiveness. The findings highlight the variations in innovation capabilities and logistics performance across the region. By identifying the strengths and weaknesses of each country, policymakers and stakeholders can make informed decisions to enhance competitiveness and economic growth.
DOI: 10.17632/735jgv3xpw.1
title: Data for figures

description: This research data repository comprises the original data of Figure3, 6-12  of manuscript titiled "Fluid inclusion calibration of clumped-isotope solid-state reordering in dolomite: Implication for thermal history reconstruction in deeply buried reservoirs"
DOI: 10.17632/twvpw6d7nx.1
title: Tzanck smear sans blade: A friendlier method

description: Supplementary figure 1. Tzanck smear made with A) traditional blade method, compared to that made with B)
cotton swab. Black arrows show acantholytic cells, seen in both smears.
DOI: 10.17632/fm6y2szjhv.1
title: Data for: COVID-19 impact on copper futures volatility

description: The daily return is defined as R_t=ln⁡(P_t/P_(t-1) ), where P_t and P_(t-1) are the closing prices at day t and t-1, respectively. According to (WHO 2020), March 11, 2020 is the day which the global COVID-19 outbreak is considered as a pandemic. 
We define the dummy variable D = 0 before this date and D = 1 after that. 
The daily closing price for COMEX’s copper from January 02, 2018, to December 30, 2022, which was extracted from Nasdaq.
DOI: 10.17632/fm6y2szjhv.2
title: Data for: The impact of COVID-19 on the Volatility of Copper Futures 

description: The daily return is defined as R_t=ln⁡(P_t/P_(t-1) ), where P_t and P_(t-1) are the closing prices at day t and t-1, respectively. According to (WHO 2020), March 11, 2020 is the day which the global COVID-19 outbreak is considered as a pandemic. 
We define the dummy variable D = 0 before this date and D = 1 after that. 
The daily closing price for COMEX’s copper from January 02, 2018, to December 30, 2022, which was extracted from Nasdaq.
DOI: 10.17632/4kstmv3dbc.1
title: Maturity_Assement_Survey

description: The following survey, represents the survey distributed to the IT team, relative to the change management process, in order to evaluate the company maturity level according to the PMF framework.
DOI: 10.17632/58drsrsf8n.1
title: Fraction of fcc phase in Cr-Fe-Mn-Ni alloys

description: The dataset for fraction of fcc phase with errors, as well as difference between free energies of formation between fcc and bcc phases in the Cr-Fe-Mn-Ni alloys as a function of composition and temperature, obtained from DFT-based Monte Carlo simulations
DOI: 10.17632/58drsrsf8n.2
title: Fcc phase percentage in Cr-Fe-Mn-Ni alloys

description: The dataset for fcc phase percentage, as well as difference between Gibbs free energies of formation between fcc and bcc phases in the Cr-Fe-Mn-Ni alloys as a function of composition and temperature, obtained from DFT-based Monte Carlo simulations with subsequent fitting of the thermodynamic databases and utilizing them in the OpenCalphad calculations.
This data is analyzed in the following article:
Mark Fedorov, Jan S. Wróbel, Witold Chromiński, Grzegorz Cieślak, Magdalena Płocińska, Krzysztof J. Kurzydłowski, Duc Nguyen-Manh, "Composition stability of single fcc phase in Cr-Fe-Mn-Ni alloys: First-principles prediction and experimental validation", Acta Materialia (2023) 119047, available at https://doi.org/10.1016/j.actamat.2023.119047.
DOI: 10.17632/27xmdrt2cz.1
title: Investigating the factors that contribute to stock market bubbles and subsequent crashes.

description: The research paper aims to investigate the factors that contribute to stock market bubbles and subsequent crashes. The study will analyze the macroeconomic factors, market micro-structure factors, and behavioral factors that affect the pattern of stock price fluctuations. The study will differentiate between the rational and irrational components of behavioral factors to provide more insight into financial crises. The research will also categorize crashes into different patterns based on three dimensions: cumulative decline, speed of decline, and duration of the crash. The study will use innovative measures and comprehensive analyses to identify the most influential factors explaining the magnitude and duration of the crash. The study will also review and synthesize empirical research on the antecedents of stock price crash risk to ascertain the macro-, meso-, and micro-level determinants contributing to stock price crashes. The research will identify priority areas for future research and provide a multilevel framework to categorize the determinants of stock price crashes into micro-, meso-, and macro-level factors. The study will provide strong and robust evidence that conservatism in financial reporting reliably predicts stock price crash risk. The research will also investigate the effect of financial risks on the stock market crash occurrence. The study will use the E-GARCH approach to explore the influence of investor sentiment on the return rate of the stock market. The research will assist market participants in making more rational investment decisions based on market laws and help regulators in their roles of supervision and policy making.
DOI: 10.17632/x7v3gd2wm3.2
title: Pooled Longitudinal Dataset on the Assessment of an Apprenticeship-based Entrepreneurship Intervention in Nigeria

description: This dataset presents longitudinal data collected through four surveys – baseline, midline1, midline2 and endline – of fresh graduates who underwent the National Youth Service Corps (NYSC) programme across ten states in Nigeria. The data were collected from 21,940 unique young men and women who underwent compulsory one-year national service after graduating from universities and polytechnics across Nigeria.  

The surveys were designed for a longitudinal study to assess the impact of the Skills Acquisition and Enterpreneurship Development (SAED) programme of the NYSC, which is the largest apprenticeship-based entrepreneurship intervention for young people in Nigeria, and arguably in Africa. The surveys sought to fully profile the entrepreneurship and apprenticeship characteristics of the graduates, their experience and assessment of the SAED programme, as well as assess the impacts of the SAED programme.  The surveys were collected at roughly 6-month interval across the ten selected states, including a baseline survey in November 2020 when the respondents were newly mobilized for their service and were having the Orientation programme on the camps, two follow-up surveys respectively in April and October 2021, and an endline survey in April 2022 after the respondents had exited the NYSC scheme for about six months.

The usefulness of the dataset is many folds. One, the dataset can be used to assess the impacts of the over-a-decade-old introduction of compulsory entrepreneurship training into the Nigerian university system that produced most of the respondents. Two, it can also be used to quantify the potential pool of future entrepreneurs among highly educated Nigerian youth.  Three, the design and impacts of the SAED programme of the NYSC can be gauged using the dataset.  The full entrepreneurship and apprenticeship profile of a representative sample of educated young people in the largest and most populous nation in Africa is useful for researchers, donors and policymakers in characterizing and designing appropriate interventions to boost self-employment as a way of reaping Africa’s demographic dividend.
DOI: 10.17632/t75s72xxf9.1
title: Social Support and the Learning Attitudes of Junior High School Students toward Home-Based Education amidst the Covid-19 Pandemic

description: This dataset was used to assess the social support received by Junior High School Students toward Home-Based Education amidst the Covid-19 pandemic in Zamboanga City, Philippines. This dataset includes the questionnaires that were used to gather data to include the Social Support Survey Questionnaire developed by by Malecki, Demaray and Elliot (2000) as cited by Pappas (2014) and the Learning Attitude Survey Scale adopted from Kara (2009); the supplementary materials which include the approved permission letter, ethics clearance, informed consent, and students' assent.
DOI: 10.17632/t75s72xxf9.2
title: Social Support and the Learning Attitudes of Junior High School Students toward Home-Based Education amidst the Covid-19 Pandemic

description: This dataset was used to assess the social support received by Junior High School Students toward Home-Based Education amidst the Covid-19 pandemic in Zamboanga City, Philippines. This dataset includes the questionnaires that were used to gather data to include the Social Support Survey Questionnaire developed by by Malecki, Demaray and Elliot (2000) as cited by Pappas (2014) and the Learning Attitude Survey Scale adopted from Kara (2009); the supplementary materials which include the approved permission letter, ethics clearance, informed consent, and students' assent.
DOI: 10.17632/9sm7m2mhsk.1
title: Raw Data for Social Support and Learning Attitudes of JHS Students in Zamboanga City Division amidst the Covid-19 Pandemic

description: This dataset contains raw data to assess the social support and learning attitudes of JHS students in Zamboanga City Division amidst the Covi-19 Pandemic
DOI: 10.17632/9sm7m2mhsk.2
title: Raw Data for Social Support and Learning Attitudes of JHS Students in Zamboanga City Division amidst the Covid-19 Pandemic

description: This dataset contains raw data to assess the social support and learning attitudes of JHS students in Zamboanga City Division amidst the Covi-19 Pandemic
DOI: 10.22003/xfel.eu-data-005056-00
title: MHz Holography
DOI: 10.22003/xfel.eu-data-004916-00
title: Rise in temperature of a beryllium CRL
DOI: 10.22003/xfel.eu-data-005418-00
title: MHz-Tmoscopy, characterisation of crystal splitters
DOI: 10.5281/zenodo.8030164
title: Dataset for 'Weld map tomography for determining local grain orientations from ultrasound'

description: This dataset contains data files and Jupyter notebooks used to produce figures in the manuscript 'Weld map tomography for determining local grain orientations from ultrasound'.
DOI: 10.5281/zenodo.8030163
title: Dataset for 'Weld map tomography for determining local grain orientations from ultrasound'

description: This dataset contains data files and Jupyter notebooks used to produce figures in the manuscript 'Weld map tomography for determining local grain orientations from ultrasound'.
DOI: 10.5281/zenodo.7506076
title: Smart Meter Water Consumption Measurements

description: <strong>Data</strong><br> Between September 2021 and September 2022, we collected the cumulative water consumption data of 17 households in Germany using the commercially available smart meters <em>Hydrus 1.3, DN 20,00</em> from <em>Diehl Metering</em>.<br> Further information on data collection and description of the dataset can be found in section 3 of the original article, which is available at the following DOI: https://doi.org/10.3390/jsan12030046 <strong>Structure</strong><br> The root folder of the <em>Zenodo</em> archive contains the documents <em>readme.md</em>, <em>info.txt</em>, and <em>info.json</em>, as well as the 17 folders for the individual households. The <em>readme.md</em> gives some basic information on the data set and its use. The documents <em>main.txt</em> and <em>main.json</em> contains the metadata for the measurements of all households in both human and machine-readable format.<br> For each test household, there is a separate folder with the documents <em>info.txt </em>and <em>info.json</em>. These contain household-specific metadata. The total water consumption measurements are stored in <em>smartmeter.csv</em>.
description: Gathering this data set was funded by the Bavarian State Ministry of Family Affairs, Labor, and Social Affairs.
DOI: 10.5281/zenodo.7506075
title: Smart Meter Water Consumption Measurements

description: <strong>Data</strong><br> Between September 2021 and September 2022, we collected the cumulative water consumption data of 17 households in Germany using the commercially available smart meters <em>Hydrus 1.3, DN 20,00</em> from <em>Diehl Metering</em>.<br> Further information on data collection and description of the dataset can be found in section 3 of the original article, which is available at the following DOI: https://doi.org/10.3390/jsan12030046 <strong>Structure</strong><br> The root folder of the <em>Zenodo</em> archive contains the documents <em>readme.md</em>, <em>info.txt</em>, and <em>info.json</em>, as well as the 17 folders for the individual households. The <em>readme.md</em> gives some basic information on the data set and its use. The documents <em>main.txt</em> and <em>main.json</em> contains the metadata for the measurements of all households in both human and machine-readable format.<br> For each test household, there is a separate folder with the documents <em>info.txt </em>and <em>info.json</em>. These contain household-specific metadata. The total water consumption measurements are stored in <em>smartmeter.csv</em>.
description: Gathering this data set was funded by the Bavarian State Ministry of Family Affairs, Labor, and Social Affairs.
DOI: 10.5281/zenodo.8007955
title: Dataset for "Light Scalar Meson and Decay Constant in SU(3) Gauge Theory with Eight Dynamical Flavors"

description: <strong>Decoding File Names</strong>: Consider the file name f8l24t48b48m00889_S0.csv. We will break down the meaning of the various pieces of the filename "f8" means 8 Dirac flavors. "l24t48" means 24<sup>3</sup>×48 lattice. "b48" means beta=4.8, related to the inverse bare gauge coupling. "m00889" means fermion mass m=0.00889. "S" means flavor-singlet scalar meson. Other options are "P" for flavor non-singlet pseudoscalar meson and "C" for flavor non-singlet scalar meson. "0" an integer from 0 to 4 proportional to the squared length of the spatial momentum vector of the correlation function. <strong>Columns of the CSV files</strong>: Each line of the CSV file should contain 41 entries, separated by commas. Refer to the Eq. (8) which defines model A in the accompanying paper to understand the physical interpretation of these parameters. Model number: 1 is model A, 2 is model B, 3 is model C. n<sub>max</sub>: the number of non-oscillating states in the fit. j<sub>max</sub>: the number of oscillating states in the fit. t<sub>min</sub>: the minimum t value used in the fit. t<sub>max</sub>: the maximum t value used in the fit. 𝜒<sup>2</sup> of the fit. \(\log\ p\left(\left.M\right|D\right)\): log of model probability used in Bayesian model averaging. fit value for c<sub>0</sub> (model A) or \(\overline{c}_0\) (model B). fit error for c<sub>0</sub> (model A) or \(\overline{c}_0\) (model B). fit value for c<sub>1</sub>. fit error for c<sub>1</sub>. fit value for c<sub>2</sub>. fit error for c<sub>2</sub>. fit value for c<sub>3</sub>. fit error for c<sub>3</sub>. fit value for c<sub>4</sub>. fit error for c<sub>4</sub>. fit value for \(c_1^\prime\). fit error for \(c_1^\prime\) fit value for \(c_2^\prime\). fit error for \(c_2^\prime\). fit value for \(c_3^\prime\). fit error for \(c_3^\prime\). fit value for \(c_4^\prime\). fit error for \(c_4^\prime\). fit value for \(\log(E_2 - E_1)\). fit error for \(\log(E_2-E_1)\). fit value for \(\log(E_3-E_2)\). fit error for \(\log(E_3-E_2)\). fit value for \(\log(E_4-E_3)\). fit error for \(\log(E_4-E_3)\). fit value for \(\log(E_2^\prime - E_1^\prime)\). fit error for \(\log(E_2^\prime - E_1^\prime)\). fit value for \(\log(E_3^\prime - E_2^\prime)\). fit error for \(\log(E_3^\prime - E_2^\prime)\). fit value for \(\log(E_4^\prime - E_3^\prime)\). fit error for \(\log(E_4^\prime - E_3^\prime)\). fit value for \(\log(E_1)\). fit error for \(\log(E_1)\). fit value for \(\log(E_1^\prime)\). fit error for \(\log(E_1^\prime)\).
DOI: 10.5281/zenodo.8007954
title: Dataset for "Light Scalar Meson and Decay Constant in SU(3) Gauge Theory with Eight Dynamical Flavors"

description: <strong>Decoding File Names</strong>: Consider the file name f8l24t48b48m00889_S0.csv. We will break down the meaning of the various pieces of the filename "f8" means 8 Dirac flavors. "l24t48" means 24<sup>3</sup>×48 lattice. "b48" means beta=4.8, related to the inverse bare gauge coupling. "m00889" means fermion mass m=0.00889. "S" means flavor-singlet scalar meson. Other options are "P" for flavor non-singlet pseudoscalar meson and "C" for flavor non-singlet scalar meson. "0" an integer from 0 to 4 proportional to the squared length of the spatial momentum vector of the correlation function. <strong>Columns of the CSV files</strong>: Each line of the CSV file should contain 41 entries, separated by commas. Refer to the Eq. (8) which defines model A in the accompanying paper to understand the physical interpretation of these parameters. Model number: 1 is model A, 2 is model B, 3 is model C. n<sub>max</sub>: the number of non-oscillating states in the fit. j<sub>max</sub>: the number of oscillating states in the fit. t<sub>min</sub>: the minimum t value used in the fit. t<sub>max</sub>: the maximum t value used in the fit. 𝜒<sup>2</sup> of the fit. \(\log\ p\left(\left.M\right|D\right)\): log of model probability used in Bayesian model averaging. fit value for c<sub>0</sub> (model A) or \(\overline{c}_0\) (model B). fit error for c<sub>0</sub> (model A) or \(\overline{c}_0\) (model B). fit value for c<sub>1</sub>. fit error for c<sub>1</sub>. fit value for c<sub>2</sub>. fit error for c<sub>2</sub>. fit value for c<sub>3</sub>. fit error for c<sub>3</sub>. fit value for c<sub>4</sub>. fit error for c<sub>4</sub>. fit value for \(c_1^\prime\). fit error for \(c_1^\prime\) fit value for \(c_2^\prime\). fit error for \(c_2^\prime\). fit value for \(c_3^\prime\). fit error for \(c_3^\prime\). fit value for \(c_4^\prime\). fit error for \(c_4^\prime\). fit value for \(\log(E_2 - E_1)\). fit error for \(\log(E_2-E_1)\). fit value for \(\log(E_3-E_2)\). fit error for \(\log(E_3-E_2)\). fit value for \(\log(E_4-E_3)\). fit error for \(\log(E_4-E_3)\). fit value for \(\log(E_2^\prime - E_1^\prime)\). fit error for \(\log(E_2^\prime - E_1^\prime)\). fit value for \(\log(E_3^\prime - E_2^\prime)\). fit error for \(\log(E_3^\prime - E_2^\prime)\). fit value for \(\log(E_4^\prime - E_3^\prime)\). fit error for \(\log(E_4^\prime - E_3^\prime)\). fit value for \(\log(E_1)\). fit error for \(\log(E_1)\). fit value for \(\log(E_1^\prime)\). fit error for \(\log(E_1^\prime)\).
DOI: 10.7281/t1/nt8fqn/8ogne8
title: README.txt

description: README file for the dataset
DOI: 10.7281/t1/nt8fqn/ggphxm
title: TP-DMBI paper.zip

description: Raw data files behind publication