John Ioannidis and co-authors [1] created a publicly available database of top-cited scientists in the world. This database, intended to address the misuse of citation metrics, has generated a lot of interest among the scientific community, institutions, and media. Many institutions used this as a yardstick to assess the quality of researchers. At the same time, some people look at this list with skepticism citing problems with the methodology used. Two separate databases are created based on career-long and, single recent year impact. This database is created using Scopus data from Elsevier[1-3]. The Scientists included in this database are classified into 22 scientific fields and 174 sub-fields. The parameters considered for this analysis are total citations from 1996 to 2022 (nc9622), h index in 2022 (h22), c-score, and world rank based on c-score (Rank ns). Citations without self-cites are considered in all cases (indicated as ns). In the case of a single-year case, citations during 2022 (nc2222) instead of Nc9622 are considered.

To evaluate the robustness of c-score-based ranking, I have done a detailed analysis of the matrix parameters of the last 25 years (1998-2022) of Nobel laureates of Physics, chemistry, and medicine, and compared them with the top 100 rank holders in the list. The latest career-long and single-year-based databases (2022) were used for this analysis. The details of the analysis are presented below: 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[1]. The single-year database contains 210199 names. So, the list published contains ~ the top 4% of scientists. In the career-based rank list, for the person with the lowest rank of 4809825, the nc9622, h22, and c-score were 41, 3, and 1.3632, respectively. Whereas for the person with the No.1 rank in the list, the nc9622, h22, and c-score were 345061, 264, and 5.5927, respectively. Three people on the list had less than 100 citations during 96-2022, 1155 people had an h22 less than 10, and 6 people had a C-score less than 2.
In the single year-based rank list, for the person with the lowest rank (6547764), the nc2222, h22, and c-score were 1, 1, and 0. 6, respectively. Whereas for the person with the No.1 rank, the nc9622, h22, and c-score were 34582, 68, and 5.3368, respectively. 4463 people on the list had less than 100 citations in 2022, 71512 people had an h22 less than 10, and 313 people had a C-score less than 2. The entry of many authors having single digit H index and a very meager total number of citations indicates serious shortcomings of the c-score-based ranking methodology. These results indicate shortcomings in the ranking methodology.

The dataset of "Research data_regression" is used for almost empirical estimation in this paper except the long-term impact. It includes water pollution index, CPE reform variables, natural factors, economic factors, social factors, etc. Another dataset of "Research data_year2004t2017" is used for long-term impact analysis in this paper. More details are shown in the manuscript.

This article is investigating possibilities of using social qualimetry as a new tool in assessing the quality of public services to improve services quality and their accessibility in the Kyrgyz Republic; as well as the need to develop common indicators for assessing the quality. Figure1. The system of factors that have a negative impact on the quality and comfort (accessibility) of public services.

Research Hypothesis:

The hypothesis is that service quality and trust significantly influence customer satisfaction with Telkomsel’s Veronika chatbot. Key dimensions include reliability, responsiveness, and empathy in service quality, and trust based on the chatbot's ability, benevolence, and integrity.

Data and Data Collection:

Data for this study were collected from Generation Z users who have experience using Telkomsel’s Veronika chatbot. A structured questionnaire was administered to 240 respondents, 52.9% of whom were female and 47.1% male, with ages ranging from 18 to 22 years. The data collection occurred between May and June 2024, and the questionnaire was distributed via social media platforms such as Instagram, Line, and WhatsApp. Non-probability sampling methods, specifically purposive and quota sampling, were used to ensure that only those familiar with the chatbot were surveyed.

The questionnaire comprised 31 questions designed to assess three key variables: service quality, trust, and customer satisfaction. A five-point Likert scale, ranging from "Strongly Disagree" to "Strongly Agree," was employed for all questions. Service quality was evaluated using the SERVQUAL model, while trust was measured through dimensions of ability, benevolence, and integrity. Customer satisfaction was assessed using items adapted from the Customer Satisfaction Index (CSI).

Key Findings:

1.Service Quality: A significant positive impact on customer satisfaction was found (β = 0.496, p < 0.001), with reliability and responsiveness being key factors. The highest loading (0.837) was on Veronika’s ability to provide alternative solutions.

2.Trust: Trust was also a significant predictor (β = 0.337, p < 0.001), with confidentiality being the most important trust factor (outer loading = 0.835).

3.Customer Satisfaction: Satisfaction was strongly influenced by both service quality and trust, with outer loadings from 0.908 to 0.918, particularly in terms of the chatbot's clarity and communication effectiveness.

Data Interpretation:

Both service quality and trust are essential to customer satisfaction, with service quality being a stronger predictor. Users value reliability and responsiveness more than trust, though both are necessary for high satisfaction. The reliability of the questionnaire was confirmed with high Cronbach’s alpha values, such as 0.938 for service quality.

Conclusion and Implications:

Improving service quality, especially reliability and responsiveness, will enhance user satisfaction. Strengthening trust, particularly in data security, is also crucial. Future research should explore broader demographics and long-term effects, while qualitative studies could offer more insights into user experiences.

This dataset consists of 985 rows (responses) and 16 columns (features), focusing on the relationship between insomnia and its impact on educational outcomes. It includes self-reported data on sleep patterns, quality, fatigue, stress levels, academic performance, and lifestyle habits. The survey was conducted using Google Forms, ensuring broad accessibility and ease of participation.

Data Collection: The data was collected through an online survey administered via Google Forms in Oct-Nov 2024. Respondents were asked to provide insights into their sleep behaviors and the effects on their academic and daily activities.

Key Features: 1. Demographics: Year of study and gender. 2. Sleep Patterns: Frequency of difficulty falling asleep, hours of sleep, night awakenings, and overall sleep quality. 3. Cognitive and Academic Effects: Impact on concentration, fatigue, class attendance, assignment completion, and overall academic performance. 4. Lifestyle Factors: Electronic device usage before sleep, caffeine consumption, and physical activity frequency. 5. Stress Levels: Self-reported stress related to academic workload.

This dataset can be used for: 1. Machine learning analysis to model and predict academic performance based on sleep and lifestyle factors. 2. Statistical studies investigating the connection between sleep disturbances and educational outcomes. 3. Developing behavioral and educational interventions to improve student well-being and performance.

Format: The dataset consists of 16 columns in categorical or ordinal formats. It contains 785 rows with no missing data, making it ready for analytics and machine learning applications.

The data about the share of English Language documents in previous two years and value of Impact Factor in 3th year from 2006 to 2016 years. The data was prepared for article: Non-English language publications in Citation Indexes – quantity and quality / Olga Moskaleva and Mark Akoev // 17th International Conference on Scientometrics and Informetrics ISSI 2019 and 24rd International Conference on Science, Technology and Innovation STI 2019

Supplemental Material. Figure S1. PLS-DA biplot based on carcass and meat quality traits (1 day of ageing time) of offspring Nellore born to cows submitted to high and low herbage during mid-late pregnancy: A - VIP (variable importance in the projection) score; B – Contribution on component 1; C – Contribution on component 2. Figure S2. PLS-DA biplot based on meat quality traits from 1 day of ageing time of offspring Nellore born to cows submitted to high and low herbage during mid-late pregnancy: A, B and C- PLS-DA biplot. D - VIP (variable importance in the projection) score; E – Contribution on component 1, 2 and 3. Figure S3. PLS-DA biplot based on meat quality traits from 7 days of ageing time of offspring Nellore born to cows submitted to high and low herbage during mid-late pregnancy: A, B and C- PLS-DA biplot. D - VIP (variable importance in the projection) score; E – Contribution on component 1, 2 and 3. Figure S4. PLS-DA biplot based on meat quality traits from 14 days of ageing time of offspring Nellore born to cows submitted to high and low herbage during mid-late pregnancy: A- PLS-DA biplot. B - VIP (variable importance in the projection) score; C – Contribution on component 1 and 2.

Nurse caring behaviors play a significant role in clinical practice. The extent to which patients’ feeling cared for derives from their perceptions of nurse caring behaviors. Various factors are reported to be associated with nurses’ perceptions of caring behaviors. Recognizing these factors that impact how nurses perceive care is essential for improving care quality. This study aimed to examine the factors associated with medical-surgical nurses’ perceptions of caring behaviors using a multicenter sequential explanatory mixed-methods study. Data are presented in Stata DTA File and Microsoft Excel 97-2003 Worksheet formats.