This dataset contains data from a pilot study that quantitatively measured the developmental characteristics of deictic verb use in Japanese-speaking children.The dataset includes two SPSS files:240603Correlations among three variables.sav: Results of the correlation analysis among linguistic responses, body movements based on actual directions, and the amount of body movement.240603Multiple comparisons of number of correct responses.sav: Results of the multiple comparison analysis of the number of correct responses to four types of question sentences.This dataset is useful for researching language acquisition, cognitive development, and the relationship between language and body movements in Japanese-speaking children.

Intraclass Correlation Coefficient calculations for resting and end-exercise measures in SPSS using single-rating, absolute-agreement, 2-way random effects model (n = 11).

The ranks and mean scores of health information sources related to adolescents’ high-risk behaviors.

Mean scores of the criteria for the quality of health information related to high-risk behaviors from adolescents’ perspective.

If this Data Set is useful, and upvote is appreciated. This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd-period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

This dataset accompanies the research titled "Leadership Models and the Promotion of Innovation: Case Study in the Banking and Fintech Sector in Kosovo". It includes responses collected via a structured questionnaire aimed at examining the relationship between leadership styles and the promotion of innovation within Kosovo's banking and fintech sectors.

The dataset captures responses using a 5-point Likert scale, covering variables such as transformational, transactional, and laissez-faire leadership, and measures of process, product, and organizational innovation.

The dataset is in SPSS (.sav) format and is suitable for analysis using statistical techniques including descriptive statistics, correlation, and regression.

This data is shared to promote reproducibility, transparency, and further research in leadership and innovation within emerging economies.

Ranks and mean scores of difficulties and barriers to adolescents for accessing health information related to high-risk behaviors.

The purpose of the present study was to examine how information source (control—no source, USDA, fictitious hospital, or fictitious social media) impacts perceptions of diet information. Participants included 943 American adults who were aged 18-74 years (M = 37.51, SD = 9.50) and were recruited from across the United States through Amazon Mechanical Turk (MTurk). As a manipulation check we assessed whether participants accurately completed the manipulation by ensuring their response to the question of who made the flyer. Participants who answered the question incorrectly were excluded from the analysis. In total, 537 answered correctly and were included in the analyses (Control = 113, Hospital = 144, Social Media = 121, USDA = 159). The majority of our eligible sample identified as men (N = 350), while the remainder identified as women (N = 185), nonbinary (N = 1), or “other” (N = 1).Participants completed an online survey in which they viewed one flyer containing dietary information and guidance on consuming pulses. The purported source of the flyer information was manipulated to create the 4 conditions. Participants rated the flyer in terms of perceived accuracy, trustworthiness, reliability, desirability for learning more from the source, and likelihood of following the advice. Attitudes, perceived control and norms, and past behavior were used to measure components of the Theory of Planned Behavior (TPB). ANOVA results indicated that the USDA and hospital sources were perceived as more accurate, trustworthy, reliable, and more desirable to learn more from relative to control and social media. There were no differences in likelihood of following guidance depending on source. Multiple regression showed that measures of the TPB were predictors of likelihood of following advice. Participants also ranked their top 3 most trusted sources for health information from a list of 29 sources. Doctors, scientists, nurses, and family and friends were among the most frequently trusted sources. Overall, these findings suggest that trust in the source of information does not influence perceived likelihood of following dietary recommendations for pulses. Resources in this dataset:Resource Title: Effect of Source on Trust of Pulse Nutrition Information and Perceived Likelihood of Following Dietary Guidance. File Name: EffectofSource_Data.xlsxResource Description: One-way analyses of variance (ANOVA) were used to assess between-condition differences for ratings of each of the 5 primary dependent variables (i.e., perceptions of the flyer; variables named Flyer_InfoAccuracy, Flyer_TrustInSource, Flyer_SourceReliability, Flyer_LearnMore, Flyer_FollowAdvice). Tukey tests were used to examine all pairwise comparisons for each of the significant ANOVA effects. A bivariate Pearson correlation was used to examine the relationship between trust in source and likelihood of following advice (variables Flyer_TrustInSource and Flyer_FollowAdvice). Multiple regression/correlation (MRC) was used to assess whether components of the TPB (TPB_Attitudes1, TPB_Attitudes2, TPB_PerceivedNorms1, TPB_PerceivedNorms2, TPB_PerceivedControl1, TPB_PerceivedControl2, TPB_PastBehavior) were predictive of likelihood of following advice (Flyer_FollowAdvice). Finally, frequency data was used to assess percentage with which participants selected sources as being in their top 3 most trusted (Trust_Ald_2_0_GROUP1-Trust_Ald_2_0_29_RANK). Sources that were selected are noted as either 1, 2, or 3 depending on rank, and the sources participants did not select are listed as #NULL!. Data was analyzed using SPSS statistical software, version 28. Resource Software Recommended: SPSS,url: https://www.ibm.com/products/spss-statistics?utm_content=SRCWW&p1=Search&p4=43700050715561164&p5=e&gclid=EAIaIQobChMI2fnV4I6e-AIVErfICh00pwcfEAAYASAAEgIkHfD_BwE&gclsrc=aw.ds

Data from: Doctoral dissertation; Preprint article entitled: Managers' and physicians’ perception of palm vein technology adoption in the healthcare industry. Formats of the files associated with dataset: CSV; SAV. SPSS setup files can be used to generate native SPSS file formats such as SPSS system files and SPSS portable files. SPSS setup files generally include the following SPSS sections: DATA LIST: Assigns the name, type, decimal specification (if any), and specifies the beginning and ending column locations for each variable in the data file. Users must replace the "physical-filename" with host computer-specific input file specifications. For example, users on Windows platforms should replace "physical-filename" with "C:\06512-0001-Data.txt" for the data file named "06512-0001-Data.txt" located on the root directory "C:". VARIABLE LABELS: Assigns descriptive labels to all variables. Variable labels and variable names may be identical for some variables. VALUE LABELS: Assigns descriptive labels to codes in the data file. Not all variables necessarily have assigned value labels. MISSING VALUES: Declares user-defined missing values. Not all variables in the data file necessarily have user-defined missing values. These values can be treated specially in data transformations, statistical calculations, and case selection. MISSING VALUE RECODE: Sets user-defined numeric missing values to missing as interpreted by the SPSS system. Only variables with user-defined missing values are included in the statements. ABSTRACT: The purpose of the article is to examine the factors that influence the adoption of palm vein technology by considering the healthcare managers’ and physicians’ perception, using the Unified Theory of Acceptance and Use of Technology theoretical foundation. A quantitative approach was used for this study through which an exploratory research design was utilized. A cross-sectional questionnaire was distributed to responders who were managers and physicians in the healthcare industry and who had previous experience with palm vein technology. The perceived factors tested for correlation with adoption were perceived usefulness, complexity, security, peer influence, and relative advantage. A Pearson product-moment correlation coefficient was used to test the correlation between the perceived factors and palm vein technology. The results showed that perceived usefulness, security, and peer influence are important factors for adoption. Study limitations included purposive sampling from a single industry (healthcare) and limited literature was available with regard to managers’ and physicians’ perception of palm vein technology adoption in the healthcare industry. Researchers could focus on an examination of the impact of mediating variables on palm vein technology adoption in future studies. The study offers managers insight into the important factors that need to be considered in adopting palm vein technology. With biometric technology becoming pervasive, the study seeks to provide managers with the insight in managing the adoption of palm vein technology. KEYWORDS: biometrics, human identification, image recognition, palm vein authentication, technology adoption, user acceptance, palm vein technology

Dataset Title: Impact of Social Media Algorithms on Consumer Behavior in Rondon do Pará, Brazil (2025)

General Description:

This dataset provides quantitative information for analyzing the influence of social media algorithms on consumer behavior in the municipality of Rondon do Pará, Brazil. The data were compiled from public sources and complemented by empirical online responses, encompassing variables related to social media usage, exposure to personalized advertisements, and online purchasing decisions. The dataset aims to support research in the fields of digital marketing, consumer behavior, and regional economic development.

Methodology:

The research adopts a quantitative, descriptive, and applied approach, based on the analysis of secondary dataobtained from public databases such as IBGE, SEBRAE, Statista, Ebit/Nielsen, and Meta Business Suite, as well as locally collected online data. Variables are grouped into thematic blocks as follows: 1. Sociodemographic Profile – age, average income, occupation, and internet usage frequency. 2. Use of Social Media – average daily usage time, most accessed platforms, and advertisement exposure frequency. 3. Algorithmic Influence and Personalization – engagement rates, retention time, and targeted content. 4. Role of Digital Influencers – audience reach, credibility, and purchase decision impact. 5. Online Consumer Behavior – purchase frequency, motivations, and comparison between online and physical shopping. 6. Impact on Local Commerce – perception of e-commerce substitution effects and influence on local economic activity.

Data analysis was conducted using Microsoft Excel and IBM SPSS Statistics, applying descriptive statistics, Pearson’s correlation, and regional comparative analysis.

Data Format and Structure:

• File type: .csv • Number of observations: 102 valid records • Number of variables: 21 columns corresponding to the thematic categories above • Encoding: UTF-8 • Delimiter: Comma (,)

Main Variables:

• age (numeric) • gender (categorical) • monthly_income (numeric, in BRL) • daily_social_media_use (numeric, hours/day) • most_used_social_media (categorical) • ad_exposure_frequency (Likert scale 1–6) • ad_influence_level (Likert scale 1–6) • trust_in_influencers (Likert scale 1–6) • online_purchase_preference (binary: 0 = physical store, 1 = online) • impact_on_local_commerce (Likert scale 1–6)

Temporal Coverage: January – October 2025

Geographical Coverage: Rondon do Pará, State of Pará, Brazil.

Categories

Business to Business Marketing

Acknowledgement

Carlos Cesar Santos

BackgroundThe conduction and report of network meta-analysis (NMA), including the presentation of the network-plot, should be transparent. We aimed to propose metrics adapted from graph theory and social network-analysis literature to numerically describe NMA geometry.MethodsA previous systematic review of NMAs of pharmacological interventions was performed. Data on the graph’s presentation were collected. Network-plots were reproduced using Gephi 0.9.1. Eleven geometric metrics were tested. The Spearman test for non-parametric correlation analyses and the Bland-Altman and Lin’s Concordance tests were performed (IBM SPSS Statistics 24.0).ResultsFrom the 477 identified NMAs only 167 graphs could be reproduced because they provided enough information on the plot characteristics. The median nodes and edges were 8 (IQR 6–11) and 10 (IQR 6–16), respectively, with 22 included studies (IQR 13–35). Metrics such as density (median 0.39, ranged 0.07–1.00), median thickness (2.0, IQR 1.0–3.0), percentages of common comparators (median 68%), and strong edges (median 53%) were found to contribute to the description of NMA geometry. Mean thickness, average weighted degree and average path length produced similar results than other metrics, but they can lead to misleading conclusions.ConclusionsWe suggest the incorporation of seven simple metrics to report NMA geometry. Editors and peer-reviews should ensure that guidelines for NMA report are strictly followed before publication.

This study selected the relevant literature related to the adverse drug reactions of metformin from 1991 to 2020 as the data source, divided the time segment with a period of 3 years, and obtained the title information (see the title collection of the literature included in the study. zip), and then extracted the subject words through the bicomb2021 software to construct the co-occurrence matrix, and a total of 10 co-occurrence matrices were obtained (see the subject word co-occurrence matrix collection included in the study. zip). Import the 10 co-occurrence matrices into the self-designed python code and r code (see opportunity code. zip; trust code. zip; open triangle and closed triangle code. zip) to obtain the opportunity, trust value and the number of edge triangles of each node pair in the 10 networks. Use GePhi0.9.7 software to calculate the motivation value of the node pair, use Excel to calculate the global clustering coefficient of each network, and the edge clustering coefficient of each node pair, The number of edge triangles of each node pair is built by using excel software to construct the scatter diagram of node pair opportunity, trust, motivation value and node pair edge clustering coefficient, and the correlation between node pair opportunity value and edge clustering coefficient is calculated by using spss software, as well as the correlation between node pair trust, motivation value and edge clustering coefficient, and the number of closed triangles of node pair (see code operation and software calculation result set. zip).Select the literature bibliography data from 2000 to 2009 to build the panel data (see the literature bibliography collection included in the study. zip), and also use the self-designed python code and r code (see opportunity code. zip; trust code. zip; open triangle and closed triangle code. zip) to get the opportunity, trust value and the number of edge triangles of each node pair in 10 networks, and use GePhi0.9.7 software to calculate the motivation value of node pairs Proximity centrality, intermediary centrality, feature vector centrality and average path length of node pairs are imported into Stata/MP 17.0 software to obtain the correlation between node attributes and network characteristics (see code operation and software calculation result set. zip).The data contained in each data name is described in detail:1. Collection of bibliographies included in the studyThe data collection contains two folders, named the literature collection from 1991 to 2020 and the literature collection from 2000 to 2009. The literature collection from 1991 to 2020 stores the bibliographic data of 10 time periods from 1991 to 2020, and the literature collection from 2000 to 2009 stores the bibliographic data of 10 overlapping windows from 2000 to 2009.2. Co-occurrence matrix set of subject words included in the studyThe data set contains two folders, named the 1991-2020 subject word co-occurrence matrix set and the 2000-2009 subject word co-occurrence matrix set. The subject word co-occurrence matrix of 1991-2020 contains the subject word co-occurrence matrix of 10 time segments from 1991-2020. The first row and first column of each co-occurrence matrix are subject words, and the number represents the number of co-occurrence times of the subject word pair. The subject word co-occurrence matrix set in 2000-2009 stores the subject word co-occurrence matrix of 10 time windows in 2000-2009.3. Opportunity Code.zipThis code is used to calculate the opportunity value of node pair. The input data is co-occurrence matrix, and the input format is. csv format.4. Trust Code.zipThis code is used to calculate the opportunity value of node pair. The input data is co-occurrence matrix, and the input format is. csv format.5. Code of open triangle and closed triangle.zipThis code is used to calculate the number of closed triangles and open triangles on the side of the node pair. The input data is the co-occurrence matrix, and the input format is. csv format.6. Code run and software calculation result set.zipThe data set contains two folders, named 1991-2020 calculation results and 2000-2009 calculation results. The 1991-2020 calculation results store the calculation results and scatter diagrams of 10 time segments in 1991-2020. Take 1991-1993 as an example, the first row of each table is marked with the opportunity, comprehensive trust, motivation, edge clustering coefficient, and the number of closed triangles. At the end of each table, the mean value of opportunity, trust, motivation and Pearson correlation coefficient with edge clustering coefficient and the number of closed triangles are calculated.The 2000-2009 folder stores the panel data and the opportunity, trust, motivation of the stata software calculation, and the correlation between the node attributes and the network characteristics of the node.

The psychological and physiological health of undergraduates was correlated with the sleep quality, which can be improved through increasing physical activity. However, the correlations between physical activity and sleep quality are subject to various factors. In this study, we investigated the effects of self-control and mobile phone addiction on the correlations between physical activity on undergraduates’ sleep quality at the psychological and behavioral levels. Data was collected through a survey with a convenient sample of 2,274 students in China. The study utilized scales of physical activity, sleep quality, self-control, and mobile phone addiction to quantitatively evaluate the impact of physical activity on the sleep quality of undergraduates. The correlations were analyzed using SPSS 26.0, including descriptive statistics, confidence tests, common method bias tests, correlation analysis, and hypothesis tests. Pearson correlation analysis shows that physical activity was significantly correlated with sleep quality (r = -0.541, p < 0.001), and that physical activity and sleep quality were significantly correlated with self-control and mobile phone addiction. Regression analysis shows that physical activity had a significant positive regression effect on self-control (standardized regression coefficient β = 0.234, p < 0.001), a significant negative regression effect on mobile phone addiction (β = –0.286, p < 0.001), and a significant negative regression effect on sleep quality (β = –0.351, p < 0.001). Further, a chain mediation model of physical activity → self-control → mobile phone addiction → sleep quality was proposed. The findings provide basic data for college students to promote physical activity and improve sleep quality.

The data files submitted here are related to the research, in which we compared psychological and biological indicators of life history strategies of criminals (N=84) and control group - men without criminal record (N=117), working as soldiers (N =32, the last 32 items in the dataset) and firefighters (N =85, the first 85 items in the dataset).

We hypothesized that there would be differences in life history strategies employed by these two groups of subjects and we also expected that biological and psychological life history indicators used in the study would correlate with each other as, according to life history theory, they are reflections of one consistent life history strategy.

We used two questionnaires: the Mini-K (Figueredo et al., 2006) used to assess psychological aspects of life history strategy and the questionnaire we created to measure biological life history variables such as age of the subjects’ parents at the appearance of their first child, father presence, number of biological siblings and step-siblings, twins in family, intervals between subsequent mother’s births, age at sexual onset, having children, age of becoming a father, number of offspring, number of women with whom the subjects have children and life expectancy. The research on criminals took place in medium-security correctional institution. Firefighters and soldiers participated in the study in their workplaces. All subjects were completing questionnaires in a paper-and-pencil version.The participation was voluntary.

The results showed that criminals tended to employ faster life history strategies than men who have not been incarcerated, but this regularity only emerged in relation to biological variables. There were no intergroup differences in the context of psychological indicators of LH strategy measured by the Mini-K. Moreover, the overall correlation between the biological and psychological LH indicators used in this study was weak. Thus, in our study biological indicators proved to reliably reflect life history strategies of the subjects, in contrast to psychological variables.

All statistical analysis was performed using SPSS and Statistica. Raw data as well as encoded data in SPSS format are attached.

Figueredo, A.J., Vásquez, G., Brumbach, B.H., Schneider, S.M.R., Sefcek, J.A., Tal, I.R., Hill, D., Wenner, C.J., & Jacobs, W.J. (2006). Consilience and life history theory: From genes to brain to reproductive strategy. Developmental Review, 26, 243-275.

Introduction

We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file Short_Description_of_Data_Analysis.pdf and Statistical_formulas.pdf

The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.

The dataset was expanded on 23rd September 2024 to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.

Short Description of Data Analysis and Attached Files (datasets):

Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.

Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: https://www.ksh.hu/stadat_files/nep/hu/nep0035.html, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the Data_of_demography.xlsx file.

In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.

The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): https://www.nnk.gov.hu/index.php/efi (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: https://semmelweis.hu/egeszsegfejlesztes/en/ (n=1). (accessed 05 Dec. 2023.)

Geocoordinates were determined using Google Maps (N=108): https://www.google.com/maps. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file.

The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:

https://qgis.org/en/site/forusers/download.html. (accessed 04 Jan. 2024.)

The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the

Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file,

imported via .csv file.

The OpenStreetMap tileset is directly accessible from www.openstreetmap.org in QGIS. (accessed 04 Jan. 2024.)

The Hungarian county administrative boundaries were downloaded from the following website: https://data2.openstreetmap.hu/hatarok/index.php?admin=6 (accessed 04 Jan. 2024.)

HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.

Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).

A brief description of the statistical formulas applied is included in the Statistical_formulas.pdf.

Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.

• Aggregated number of HDOs by county: Number_of_HDOs.xlsx
• Standardised data (Number of HDOs per 100,000 residents): Standardized_data.xlsx
• Calculation of the Lorenz curve: Lorenz_curve.xlsx
• Calculation of the Gini index: Gini_Index.xlsx
• Calculation of the LQ index: LQ_Index.xlsx
• Calculation of the Herfindahl-Hirschman Index: Herfindahl_Hirschman_Index.xlsx
• Calculation of the Entropy index: Entropy_Index.xlsx
• Regression and correlation analysis calculation: Regression_correlation.xlsx

Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:

• Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.
• Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.
• Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv
• Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv
• Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv
• Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv
• Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv

For easier readability, the files have been provided in both SPV and PDF formats.

The translation of these supplementary files into English was completed on 23rd Sept. 2024.

If you have any further questions regarding the dataset, please contact the corresponding author: domjan.peter@phd.semmelweis.hu

The data were preprocessed by using IBM SPSS 19.0 software to conduct descriptive statistical and correlation analyses on 540 participants. The community dataset was complete and without missing values. Network model estimation, establishment, and centrality index calculation were then performed. The network was estimated using the EBICglasso function in the qgraph software package (Version 1.9.3; Epskamp et al., 2012) in R (Version 4.1.3, RCore Team, 2022). The Glasso network was used to calculate a partial correlation network, in which the relationship between symptoms can explain all other relationships in the model; each item is represented as a node, and the association between items is referred to as the edge.

The Altai Mountains, located in northern Xinjiang, China, extend in a northwest-southeast direction and stand as a significant mountain range in the region. Spruce is an important tree species in Xinjiang's mountainous forests, with Siberian spruce being the primary species distributed in the Altai Mountains. The Estimation of spruce biomass is crucial for assessing the forest ecological structure and carbon balance in Xinjiang. This dataset includes field survey data, topographic data, meteorological data, remote sensing data, and spruce biomass data, comprising 28 auxiliary variables. Field survey data were obtained through on-site surveys, and topographic data were derived from a Digital Elevation Model (DEM). Meteorological data were extracted using masking techniques in ArcGIS, while remote sensing data underwent preprocessing steps such as radiometric correction, atmospheric correction, and image mosaicking, followed by the calculation of vegetation indices RVI and NDVI. Spruce biomass was estimated using diameter at breast height (DBH) measurements and spruce biomass regression equations to calculate sample plot biomass and subsequently unit plot biomass. Correlation analysis using SPSS software was conducted to identify factors highly correlated with unit plot biomass, with NDVI showing the highest correlation and being selected as an auxiliary variable. Based on co-kriging, a spatial map of spruce biomass co-kriging interpolation was generated. To ensure data reliability, biomass interpolation as well as meteorological and DEM data processing were conducted in ArcGIS 10.2 and Excel 2010 using the co-kriging method, with cross-validation yielding an accuracy of RMSE = 12.42 t/hm².This dataset provides essential data support for estimating spruce biomass in the Altai Mountains and offers scientific backing for future ecological research in the region.

This dataset originates from the development and validation of the Daoist Flexibility Belief Scale (DFB), which aims to empirically examine the psychological structure underlying Daoist concepts such as “Suppleness-over-Rigidity” and “Inner Vitality” described in the Daodejing. Data were collected between March and September 2024 via the Credamo online platform, covering 26 provinces, autonomous regions, and municipalities across mainland China. All participants were adults aged 18 years and above who completed an online questionnaire and passed attention-check items. Data were recorded automatically through the platform and exported in CSV format from participants’ personal devices (PC or mobile).The dataset comprises three independent samples, totaling 838 valid cases:Sample 1 (Exploratory Factor Analysis): 349 cases, used to explore the latent factor structure of the scale.Sample 2 (Confirmatory Factor Analysis): 248 cases, used to verify the model structure and test its goodness of fit.Sample 3 (Criterion-related Validity Analysis): 241 cases, used to examine correlations with relevant psychological variables (nonattachment, junzi personality, life satisfaction, and psychological resilience).Each dataset file includes multiple questionnaire items and demographic variables. Rows represent individual participants (one row per participant), and columns represent variable names. Questionnaire items are labeled Q1–Q34, rated on 5- or 7-point Likert scales (1 = “strongly disagree,” 7 = “strongly agree,” unitless). Demographic variables include gender, age, and education level. A small proportion of missing values (less than 2%) are coded as “NA” and retained to preserve data authenticity.Data were processed using SPSS 26.0 and R 4.3.1 (packages psych and lavaan). The main steps included reverse scoring, item mean computation, exploratory and confirmatory factor analyses, internal consistency reliability estimation, and criterion-related correlation analysis. Results indicated that the data distributions and factor structures met psychometric standards, with reliability coefficients (Cronbach’s α) ranging from 0.60 to 0.91.The data are provided in standard CSV format and can be opened or analyzed with any statistical software (e.g., SPSS, R, or Python). File names and contents are as follows:DFB_EFA.csv: Exploratory factor analysis sample (N = 349)DFB_CFA.csv: Confirmatory factor analysis sample (N = 248)DFB_Criterion.csv: Criterion validity sample (N = 241)

• SPSS raw data - SPSS coded data - Questionnaire - Correlation matrix

BackgroundsThe characteristics and conditions of growth and development have made adolescence one of the most vital and influential ages for prevention and health promotion, especially in the area of high-risk behaviors. Accordingly, the aim of this study was to determine adolescent health information seeking behavior related to high-risk behaviors in a selected educational district in Isfahan (Iran).MethodologyThe present study was of an applied type, which was conducted using the survey research method. The statistical population consisted of adolescent students at public schools in Isfahan (6519 subjects), and the sample size was determined to be 364 based on Cochran's formula. The sampling method was of a cluster sampling type, and the data collection tool was a researcher-made questionnaire. The validity of the questionnaire was approved by medical librarians, and using the Cronbach's alpha method, the reliability was obtained to be 0.85. SPSS 16 software was used for data analysis at two statistical levels: descriptive and inferential (independent t-test, one-sample t-test, chi-square, Pearson correlation coefficient and Mann-Whitney).Findings"Lack of mobility" was the most important health information need related to adolescent high-risk behaviors. The most important sources to obtain health information related to high-risk behaviors were "the Internet" with a mean score of 3.69 and "virtual social media" with a mean score of 3.49 out of 5. Adolescents had a positive attitude towards health information. The most important barriers to seeking health information were mentioned as follows: "difficulty in determining the quality of information found", "absence of appropriate information", and "concerns about the disclosure of their problems or illness to others". From the perspective of adolescents, the most important criterion for the evaluation of information quality was "the trueness and correctness of the information" and the need for health information related to high-risk behaviors was higher in girls than in boys.Conclusions/SignificanceConsidering adolescents’ positive attitude towards use of health information, it is necessary to put valid information at their disposal through different information resources, taking into account their level of information literacy. Accordingly, medical librarians’ abilities are suggested to be used for the production, evaluation, and introduction of health-related reading materials in the field of high-risk behaviors in easy language and suitable for adolescents.