cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/9mz86zvrcc.1
title: Fish production, export and domestic consumption in Bangladesh, Brazil, China, Egypt, India, Indonesia, Myanmar, Philippines, Thailand, Vietnam

description: This data is derived primarily from the FAO FishStat J database on aquaculture and capture fisheries production and trade (http://www.fao.org/fishery/statistics/software/fishstatj/en). It is used to generate two alternative estimates of the share of aquaculture and capture fisheries production consumed in domestic markets in the 10 most important aquaculture producing developing economies: Bangladesh, Brazil, China, Egypt, India, Indonesia, Philippines, Thailand and Vietnam. All data is for the year 2011, which at the time of writing was the most recent year for which both production and trade data were available from FishStat J. The live weight equivalent of exported products is estimated using published conversion factors referenced in the dataset. All data is downloadable from http://www.fao.org/fishery/statistics/software/fishstatj/en. All data used is for the year 2011. All calculations used to derive both estimates of the shares of aquaculture and capture fishery production remaining in domestic markets are embedded in the Excel file. Worksheets in the Excel file are interlinked. All published Live Weight Equivalent conversion factors and their sources are reported in the Excel file.
DOI: 10.17632/jpj2syvmc4.1
title: Data for: Cataracts and statins. A disproportionality analysis using data from VigiBase

description: The file include the number of Individual case safety reports for ICSRs contained in the World Health Organization (WHO) global database of individual case safety reports (VigiBase) in which the following MedDRA preferred terms (PTs): “Cataract”, “Cataract cortical”, “Cataract nuclear” and “Cataract subcapsular” were reported for HMG-CoA reductase inhibitors according to the anatomical therapeutic chemical classification (ATC: C10AA) between inception on Nov 14, 1967, and Jan 15, 2019. Fixed-dose combinations of statins with other drugs were not considered. We also searched for ICSRs for the PTs mentioned above and prednisolone as a positive control – a drug previously known to cause cataract- and paracetamol/acetaminophen which served as a negative control -a drug not likely to be related with the occurrence of cataracts. Furthermore, we searched ICSRs of cataracts related to other lipid-lowering drugs classes different from statins: fibrates (ATC: C10AB), ezetimibe (ATC: C10AX09) and proprotein convertase subtilisin/kexin type 9 (PCSK9) antibodies (ATC C10AX13, C10AX14). Disproportional reporting was investigated through the calculation of the Reporting Odds Ratio with their 95% Confidence Interval using Woolf’s method. Subgroup analysis of the ROR of statins as a drug class were performed by sex and by age groups (more or less than 65 years old).
DOI: 10.17632/22rgdy6cbv.1
title: Translation and Cross Cultural Adaptation, Reliability and Validity of Hindi version of Lower Extremity Functional Scale

description: The database shows the characteristics for the reliability and validity testing
DOI: 10.17632/ywsxzx5v88.3
title: Banana Project Dataset
DOI: 10.17632/jp4ncmtxbc.1
title: GDP Spatialization in Zhengzhou City Based on NPP/VIIRS Nighttime Light and Socioeconomic Statistical Data Using Machine Learning

description: GIS DATASETS：
RESEARCH_AREA_NLD_DATA.zip: the vector maps data of the study area.
2012_NLD_of_Zhengzhou_City, 
2013_NLD_of_Zhengzhou_City, 
2014_NLD_of_Zhengzhou_City,
2015_NLD_of_Zhengzhou_City, 
2016_NLD_of_Zhengzhou_City, 
2017_NLD_of_Zhengzhou_City,
PROGRAMS DATASETS:
FCN8.py, 
Unet.py, 
predict.py, 
训练数据.py
FIGUREs DATASETS:
Figure 1. Location map of the study area (Zhengzhou City, Henan Province).
Figure 2. Night Light Data (NPP) of Zhengzhou City, 2017.
Figure 3. Road Map of the research study.
Figure 4. Flow chart of classification process of SVM algorithm.
Figure 5. Flow chart of the semantic segmentation classification process.
Figure 6. Structure diagram of Full Convolution Neural Network (FCN) Model.
Figure 7. Structure diagram of U-Net Neural Network Model.
Figure 8. (a) NPP training results of SVM algorithm (2012-2017)
Figure 8. (b) NPP training results of FCN model (2012-2017)
Figure 8. (c) NPP training results of U-Net model (2012-2017).
Figure 9. Comparison of Calculated data.
Figure 10. ScatterPlot (Response vs. Predictor).
Figure 11. Accuracy with Given Data by Municipal of Bureau.
Figure 12. (a) GDP 2012 Model.
Figure 12. (b) GDP 2013 Model.
Figure 12. (c) GDP 2014 Model.
Figure 12. (d) GDP 2015 Model.
Figure 12. (e) GDP 2016 Model.
Figure 12. (f) GDP 2017 Model.
DOI: 10.17632/njjzjrj6wt.1
title: Postmenopausal women treated for breast cancer with insulin resistance: Clinical, Analytical, Cross-sectional

description: In spite of the growing body of evidence, the connection between heat shock proteins (HSPs), particularly HSP60 and HSP70, with insulin resistance (IR) and the prognosis of cardiovascular disease (CVD) risk among female breast cancer (BC) survivors remains a matter of contention. While intracellular HSP70 plays an anti-inflammatory role, extracellular HSP70 can trigger pro-inflammatory pathways and has been linked to IR. However, the exact relationship between HSPs and IR in BC survivors remains poorly understood. Through an investigation into the link between HSPs and IR, this study aims to illuminate this intricate relationship and potentially open doors for the development of innovative treatments or interventions aimed at addressing insulin resistance in postmenopausal BC survivors. Consequently, the study's objective is to explore the potential correlation between serum levels of HSP60 and HSP70 and insulin resistance in postmenopausal women.
DOI: 10.17632/w4x4gwcyvh.1
title: Data for: Hotel image and reputation on building customer loyalty: An empirical study in Macau
DOI: 10.17632/xw5xc7xrmp.2
title: HVDROPDB Datasets for Research in Retinopathy of Prematurity, Ranjana Agrawal

description: HVDROPDB_RetCam_Neo_Segmentation is the first dataset to be published for the retinal structure segmentation to identify the Retinopathy of Prematurity (ROP). It is prepared by screening the preterm infants visiting PBMA's H.V. Desai Eye Hospital, Pune with two diverse imaging systems RetCam and Neo. The sub-datasets are provided for the segmentation of optic disc, blood vessels, and demarcation line/ridge from the fundus images of preterm infants, and a group of ROP experts annotates them. Each sub-dataset contains retinal fundus images of premature infants with the ground truths prepared manually to assist researchers in developing an explainable automated ROP screening system.
DOI: 10.17632/r5bztdd766.2
title: Script for: Zhou et al. (2024). A Simulation Study of the Performance of Statistical Models for Count Outcomes with Excessive Zeros

description: This repository contains the R code for the data generation and analysis for the paper: 

Zhou, Z., Li, D., Huh, D., Xie, M., & Mun, E. Y. (2024). A Simulation Study of the Performance of Statistical Models for Count Outcomes with Excessive Zeros. Statistics in Medicine. https://doi.org/10.1002/sim.10198


Abstract 

Background: Outcome measures that are count variables with excessive zeros are common in health behaviors research. Examples include the number of standard drinks consumed or alcohol-related problems experienced over time. There is a lack of empirical data about the relative performance of prevailing statistical models for assessing the efficacy of interventions when outcomes are zero-inflated, particularly compared with recently developed marginalized count regression approaches for such data.
Methods: The current simulation study examined five commonly used approaches for analyzing count outcomes, including two linear models (with outcomes on raw and log-transformed scales, respectively) and three prevailing count distribution-based models (i.e., Poisson, negative binomial, and zero-inflated Poisson (ZIP) models). We also considered the marginalized zero-inflated Poisson (MZIP) model, a novel alternative that estimates the overall effects on the population mean while adjusting for zero-inflation. Motivated by alcohol misuse prevention trials, extensive simulations were conducted to evaluate and compare the statistical power and Type I error rate of candidate statistical models and approaches across data conditions that varied in sample size (N = 100 to 500), zero rate (0.2 to 0.8), and intervention effect sizes conditions.
Results: Under zero-inflation, the Poisson model failed to control the Type I error rate, resulting in higher than expected false positive results. When the intervention effects on the zero (vs. non-zero) and count parts were in the same direction, the MZIP model had the highest statistical power, followed by the linear model with outcomes on the raw scale, negative binomial model, and ZIP model. The performance of linear model with a log-transformed outcome variable was unsatisfactory. When only one of the effects on the zero (vs. non-zero) part and the count part existed, the ZIP model had the highest statistical power.
Conclusions: The MZIP model demonstrated better statistical properties in detecting true intervention effects and controlling false positive results for zero-inflated count outcomes. This MZIP model may serve as an appealing analytical approach to evaluating overall intervention effects in studies with count outcomes marked by excessive zeros.
DOI: 10.17632/6bhr52dc5v.2
title: Test Anxiety, Wellbeing, Buoyancy, Adaptability, and Mental Health Risk

description: Data were collected from 1198 participants aged 16-19 years from 3 sixth form colleges (academic track upper secondary education), located in England, in a two-wave design. Each wave of measurement was separated by seven months.

Multidimensional Test Anxiety Scale (Putwain et al., 2020) labelled MTAS in the dataset.
School-Related Wellbeing Scale (Loderer et al., 2016) labelled W in the dataset.
Social, Academic, and Emotional Behavior Risk Screener (von der Embse et al., 2016) labelled S in the dataset.
Academic Buoyancy Scale (Martin & Marsh, 2008) labeled B in the dataset.
Adaptability scale (Martin et al., 2012) labeled A in the dataset.

In addition socio-demographic data was collected for gender (male or female), age, and ethnic heritage (Asian, Black, White, Other/ mixed heritage). Missing data were coded as 999.

Data were saved as an SPSS .sav file (that includes all items for the above scales, response ranges and anchors, and the categorical coding for the aforementioned socio-demographic data.

The principal research question was as follows: Is test anxiety reciprocally related to wellbeing, buoyancy, adaptability, and mental health risk?
We hypothesised that test anxiety would be positively related to mental health risk, and negatively related to wellbeing, buoyancy and adaptability; all relations were expected to be directional.

References

Chorpita, B. F., Moffitt, C., & Gray, J. (2005). Psychometric properties of the Revised Child Anxiety and Depression Scale in a clinical sample. Behaviour Research and Therapy, 43, 309-322. https://doi.org/10.1016/j.brat.2004.02.004
Loderer, K., Vogl, E., & Pekrun, R. (2016, August 24-27). Students’ well-being at school revisited: Development and initial validation of a unidimensional self-report scale [Conference session]. International Conference on Motivation (ICM), Thessaloniki, Greece. http://www.psy.auth.gr/el/conference/programme.
Martin, A.J. & Marsh, H.W. (2008) Academic buoyancy: towards an understanding of students’ everyday academic resilience, Journal of School Psychology, 46, 53–83. https://doi.org/10.1016/j.jsp.2007.01.002
Martin, A. J., Nejad, H., Colmar, S., & Liem, G. A. D. (2012). Adaptability: Conceptual and empirical perspectives on responses to change, novelty and uncertainty. Australian Journal of Guidance and Counselling,22, 58–81. https://doi.org/10.1017/jgc.2012.8
Putwain, D.W., von der Embse, N.P., Rainbird, E.C., & West, G. (20209). The development and validation of a new Multidimensional Test Anxiety Scale (MTAS). European Journal of Psychological Assessment. Advance online pulication, https://doi.org/10.1027/1015-5759/a000604
von der Embse, N. P., Pendergast, L., Kilgus, S. P., & Eklund, K. R. (2016). Evaluating the applied use of a mental health screener: Structural validity of the Social, Academic, and Emotional Behavior Risk Screener. Psychological Assessment, 28, 1265–1275.doi:10.1037/pas0000253
DOI: 10.17632/8mbtvgtr6g.1
title: Partisan Residential Sorting on Climate Change Risk

description: Code used in the paper "Partisan Residential Sorting on Climate Change Risk".
DOI: 10.17632/yt3tgpt48d.1
title: Genome-Wide Local Ancestry and Evidence for Mitonuclear Coadaptation in African Hybrid Cattle Populations ( Bos Taurus/Indicus). Ward et al. 2022

description: High density SNP array data for African cattle.
DOI: 10.17632/xvpjzhbnyn.1
title: IOBF SEARCH DATABASE

description: Clinical relevance: Tomographic analysis of the maxillary sinus and associated anatomical structures, such as the nasal cavity, is the most reliable resource for planning and carrying out implant-supported rehabilitation of the maxilla posterior region.

That was a cross-sectional tomographic study of 58 totally edentulous adult patients, totaling 116 maxillary sinuses. The RadiAnt DICOM Viewer™ 2020.2 (64-bit) software was used to measure basal/alveolar widths and maxillary length, in addition to linear distances from the maxillary sinus and nasal cavity to calculate volumes. Results: There was no significant correlation between the maxillary sinus nasal cavity volumes (ρ=0.048 / p=0.609). The volume of the nasal cavity showed a significant difference between genders (p=0.007), bigger in males. Greater alveolar and basal maxillary widths correlated with greater maxillary sinus volume. Maxilla length was significantly correlated with nasal cavity volume (ρ=0.425 / p&lt;0.001). Sinus membrane thickness was not significantly associated with maxillary sinus volume. There was no significant correlation between sinus thickness lateral wall and sinus membrane thickness.
DOI: 10.17632/f9gny773br.1
title: Does C-score-based world ranking give an accurate account of the impact of research papers?

description: John Ioannidis et. al [1] created a publicly available database of top-cited scientists in the world (popularly known as the world's top 2% Scientist list of Stanford University). This database has generated a lot of interest among the scientific community, institutions, and media attention. Many institutions gave wide publicity to this ranking and prominently displayed the names of their colleagues in various media. Some institutions even rewarded those who made the list in different ways. At the same time, many people look at this list with skepticism, citing the approach and shortfalls in the formula used in the evaluation of the c-score. This database is based on a unique composite indicator (c-score), where standardized details such as citations, h-index, co-authorship adjusted hm-index, and citations to papers in different authorship positions are used[1]. The original database has been updated yearly since its creation in 2019[2-3]. Two separate databases are created based on career-long and, single recent year impact. Career-long data is based on the citations from 1996 to 2022, but the single-year database is based on citations received during the calendar year 2022. This database is created using Scopus data from Elsevier. The latest database is performed using all Scopus author profiles as of October 1, 2023[3]. The Scientists included in this database are classified into 22 scientific fields and 174 sub-fields. To address the concerns originating from the research community on the authenticity of this database, and to assess the strengths and weaknesses of the c-score-based evaluation, I have done a detailed analysis of the matrix parameters of the last 10-year Nobel laureates of Physics, chemistry, and medicine. In this analysis, the latest career-long database is used. In the case of Physics Nobel laureates, analysis is done with the details of Nobel laureates of the last 25 years. The details of the analysis are presented in this article. Though the article says the selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field, the actual career-based ranking list has 204644 names. In the career-based rank list, the person with the lowest h index in the list has an h22 of 2 (with an np6022 of 11 and total citations nc9622 of 835). There are 1155 authors with single-digit h22 index too. In the same list, the person with the lowest number of papers has an nc6022 of 2 (with nc9622 of 12128 and first and last publications in 1929 and 1966, respectively). The author with a minimum number of citations in the list has an nc9622 of 41 (h22 of 3). The c-score-based lowest rank holder in the list has a rank of 4809825 (nc9622 of 41, and h22 of 3). These results indicate aberrations in the ranking.
DOI: 10.17632/t7hx4gz5bw.1
title: District-wise Cropping Pattern of Bihar

description: The dataset contains the district-wise cropping statistics of 7 major crops (Rice, Wheat, Maize, Pulses, Sugarcane, Oilseeds and Jute) in Bihar at five cross-section points i.e., 2001-02, 2006-07, 2011-12, 2016-17, 2020-21.
DOI: 10.17632/5xth87zwb5.4
title: Sanadset 650K: Data on Hadith Narrators

description: Sanadset is a full hadith dataset that contains over 650,986 records collected from 926 historical Arabic books of hadith. This dataset can be used for further investigation and classification of hadiths (Strong/Weak), and narrators (trustworthy/not) using AI techniques, and also it can be used as a linguistic resource tool for Arabic Natural Language Processing.
DOI: 10.17632/rfdz6wfzn4.1
title: RBCdataset 

description: The RBCdataset is a single erythrocyte dataset collected from thalassemia's peripheral blood smear. This dataset will be a source of data for computer application researchers related to RBC classification in thalassemia cases.
DOI: 10.17632/ttvm4cr25s.1
title: MATLAB Code to Estimate Logit-Mixed Logit Model (Preference space, fixed and random parameters)

description: This is a sample MATLAB code to estimate Logit-Mixed Logit Model in preference space. The example is provided for a model with 2 fixed parameters, 2 random parameters, and 3 alternatives. This code is an extension of the original code by Kenneth Train which considers all utility parameters to be random and the model is estimated in willingness-to-pay space. This code uses a simuated data. data_generation.m generate this data and give test.csv and test_save.mat as outputs. In the attached folder, main_test.m is the main file which takes test.csv and test_save.mat as inputs. The default setting is "no bootstrapping" because it would take some time to run. You can change WantBoot=1 to get the bootstrapped standard errors. You can increase the number of repetitions (NReps) to 50 to get stable standard error estimates. To get the histogram of random coefficient 1, use bar(MidEst(1,:),FreqEst(1,:)). Please cite the following papers if you use this code in any form: Bansal, P., Daziano, R. A., & Achtnicht, M. (2018). Extending the logit-mixed logit model for a combination of random and fixed parameters. Journal of choice modelling, 27, 88-96. Bansal, P., Daziano, R. A., & Achtnicht, M. (2018). Comparison of parametric and semiparametric representations of unobserved preference heterogeneity in logit models. Journal of choice modelling, 27, 97-113.
DOI: 10.17632/kc5znhxwxv.1
title: Fama and French three and five factors

description: Our dataset contains daily and monthly data for the Fama-French Three-factor model, and the Fama-French Five-factor model in the Vietnam stock market from July 2010 to June 2021. The folders contain the data files in an Excel file (.xlsx).
DOI: 10.17632/z8t3s8btxv.1
title: Ambient particulate air pollution and daily stock market returns and volatility in 47 cities worldwide

description: Major stock index data collected from Investing.com. After identifying the coordinate points for all the corresponding stock exchanges, air pollution and meteorological data was collected from as near as possible. All meteorological data was downloaded from the NOAA GSOD database, and air pollution data was collected from various sources that are described more accurately in the metadata file. Sufficient daily data for fine particulate matter concentrations were found for 47 of the locations. The meteorological variables contained a rather large amount of randomly missing observations which were imputed using CART (Classification and Regression Training) method from MICE package for R. Variables in masterdata: log_ret - logarithmic daily returns pm25 - daily PM2.5 concentration in ug/m3 lag1_ret - 1 day lagged returns lag2_ret - 2 day lagged returns temp_bins - temperature as 2.5C wide dummy bins dewp_bins - dew point as 2.5C wide dummy bins RH - relative humidity DEWP - dew point TEMP - temperature (C) SLP - sea level pressure VISIB - visibility WDSP - wind speed PRCP - precipitation woy - week of year dow - day of week vixopen - daily VIX index value date - date city - name of the city
DOI: 10.17632/85bp79ct68.1
title: Trade-off between Semantic Radicals’ Characterhood and Host Phonograms’ Transparency in Semantic Radicals’ Semantic Activation

description: ACC and Mean RT of each item for each participant
DOI: 10.17632/zzhh6fvkrv.1
title: The role of gender inequality in the obesity epidemic: a case study from India using IHDS panel data (2005-2011/12)

description: Recent empirical evidence emphasizes the higher prevalence of overweight and obesity for women, especially in developing countries. However, the potential link between gender inequality and obesity has rarely been investigated. Using longitudinal data from India (IHDS 2005-11), we implement Hausman-Taylor and fixed-effect models to estimate the effect of different dimensions of gender inequalities on female overweight. This study demonstrates that the form of gender inequality or women’s mistreatment differently affects female bodyweight. Indeed, we show that some forms of women’s mistreatments (such as perceived community violence and age difference with husband) increase the risk of female overweight, whereas more severe forms of abuse such as child marriage increase the risk of underweight. Moreover, we also find that higher decision-making power and autonomy about outings are risk factors of weight gain and obesity, especially in urban settings, perhaps indicating a higher exposure to urban obesogenic lifestyles. To conclude, our results suggest that, although improving women’s status in society may be a key action to address the epidemic of obesity, policies must also target hazardous habits that emancipation may imply in urban (obesogenic) environments. These meta-data include: (i) the merged database from the two waves of IHDS we used in the study (.dta in Stata format); (ii) the codes used for data treatment and analysis (.do in Stata format). Original IHDS data are freely available on: https://ihds.umd.edu. Further details about our methods and results will be published soon in a scientific journal and will reference these meta-data. 

Keywords: India; Gender inequality; Obesity; Hausman-Taylor estimations; Fixed effects estimations.
JEL codes: I14 I15 J16
DOI: 10.17632/z7gkhwg243.1
title: SEM and TEM data of nuclear graphite and glassy carbon microstructures

description: This data was taken from multiple nuclear graphite grades to illustrate the phases of these materials.
DOI: 10.17632/hh8yctdy8f.1
title: Data for: eDNA Metabarcoding on Zooplankton Improves the Ecological Status Assessment of Freshwater Ecosystem

description: eDNA Metabarcoding on Zooplankton community in a large freshwater ecosystem.