GENERAL INFORMATION

Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

Date of data collection: January to March 2022

Collection instrument: SurveyMonkey

Funding: Alfred P. Sloan Foundation

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

Links to publications that cite or use the data:

Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data:
A survey investigating disciplinary differences in data citation. Zenodo. https://doi.org/10.5281/zenodo.7555266

DATA & FILE OVERVIEW

File List

• Filename: MDCDatacitationReuse2021Codebook.pdf
  Codebook
• Filename: MDCDataCitationReuse2021surveydata.csv
  Dataset format in csv
• Filename: MDCDataCitationReuse2021surveydata.sav
  Dataset format in SPSS
• Filename: MDCDataCitationReuseSurvey2021QNR.pdf
  Questionnaire

Additional related data collected that was not included in the current data package: Open ended questions asked to respondents

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

Methods for processing the data:

Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

Instrument- or software-specific information needed to interpret the data:

The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.

DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

Number of variables: 94

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

Analysis SPSS files used in the paper to analyze the experiment results. The tests we executed in the paper are as follows, in the SPSS syntax:** PreQuestionnaire.sav, leading to Table 2T-TEST GROUPS=form(1 2) /MISSING=ANALYSIS /VARIABLES=grade USLEC UCLEC /CRITERIA=CI(.95).NPAR TESTS /M-W= CDFAM UCFAM USFAM UCHW USHW CDHW BY form(1 2) /MISSING ANALYSIS.** Anova.sav, leading to the decision of analyzing the two case studies independentlyGLM EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre BY Domain Form /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=Domain Form(TUKEY) /PLOT=PROFILE(Domain*Form) TYPE=LINE ERRORBAR=NO MEANREFERENCE=NO YAXIS=AUTO /PRINT=DESCRIPTIVE ETASQ /CRITERIA=ALPHA(.05) /DESIGN= Domain Form Domain*Form.** DH.sav, leading to Table 3T-TEST GROUPS=Form(1 2) /MISSING=ANALYSIS /VARIABLES=EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre /CRITERIA=CI(.95).** PH.sav, leading to Table 4T-TEST GROUPS=Form(1 2) /MISSING=ANALYSIS /VARIABLES=EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre /CRITERIA=CI(.95).** Preferences.sav, leading to Table 5 and Table 6NPAR TESTS /M-W= UCCM USCM UCCDID USCDID UCRID USRID USSTRUCT UCSTRUCT UCOVER USOVER UCREQ USREQ BY Form(1 2) /MISSING ANALYSIS.EXAMINE VARIABLES=UCCM USCM UCCDID USCDID UCRID USRID USSTRUCT UCSTRUCT UCOVER USOVER UCREQ USREQ BY Form /PLOT HISTOGRAM NPPLOT /STATISTICS DESCRIPTIVES /CINTERVAL 95 /MISSING LISTWISE /NOTOTAL.NPAR TESTS /M-W= UCCM USCM UCCDID USCDID UCRID USRID USSTRUCT UCSTRUCT UCOVER USOVER UCREQ USREQ BY Form(1 2) /STATISTICS=DESCRIPTIVES /MISSING ANALYSIS.GLM EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre BY Domain Form /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=Domain Form(TUKEY) /PLOT=PROFILE(Domain*Form) TYPE=LINE ERRORBAR=NO MEANREFERENCE=NO YAXIS=AUTO /PRINT=DESCRIPTIVE ETASQ /CRITERIA=ALPHA(.05) /DESIGN= Domain Form Domain*Form.

This dataset originates from a series of experimental studies titled “Tough on People, Tolerant to AI? Differential Effects of Human vs. AI Unfairness on Trust” The project investigates how individuals respond to unfair behavior (distributive, procedural, and interactional unfairness) enacted by artificial intelligence versus human agents, and how such behavior affects cognitive and affective trust.1 Experiment 1a: The Impact of AI vs. Human Distributive Unfairness on TrustOverview: This dataset comes from an experimental study aimed at examining how individuals respond in terms of cognitive and affective trust when distributive unfairness is enacted by either an artificial intelligence (AI) agent or a human decision-maker. Experiment 1a specifically focuses on the main effect of the “type of decision-maker” on trust.Data Generation and Processing: The data were collected through Credamo, an online survey platform. Initially, 98 responses were gathered from students at a university in China. Additional student participants were recruited via Credamo to supplement the sample. Attention check items were embedded in the questionnaire, and participants who failed were automatically excluded in real-time. Data collection continued until 202 valid responses were obtained. SPSS software was used for data cleaning and analysis.Data Structure and Format: The data file is named “Experiment1a.sav” and is in SPSS format. It contains 28 columns and 202 rows, where each row corresponds to one participant. Columns represent measured variables, including: grouping and randomization variables, one manipulation check item, four items measuring distributive fairness perception, six items on cognitive trust, five items on affective trust, three items for honesty checks, and four demographic variables (gender, age, education, and grade level). The final three columns contain computed means for distributive fairness, cognitive trust, and affective trust.Additional Information: No missing data are present. All variable names are labeled in English abbreviations to facilitate further analysis. The dataset can be directly opened in SPSS or exported to other formats.2 Experiment 1b: The Mediating Role of Perceived Ability and Benevolence (Distributive Unfairness)Overview: This dataset originates from an experimental study designed to replicate the findings of Experiment 1a and further examine the potential mediating role of perceived ability and perceived benevolence.Data Generation and Processing: Participants were recruited via the Credamo online platform. Attention check items were embedded in the survey to ensure data quality. Data were collected using a rolling recruitment method, with invalid responses removed in real time. A total of 228 valid responses were obtained.Data Structure and Format: The dataset is stored in a file named Experiment1b.sav in SPSS format and can be directly opened in SPSS software. It consists of 228 rows and 40 columns. Each row represents one participant’s data record, and each column corresponds to a different measured variable. Specifically, the dataset includes: random assignment and grouping variables; one manipulation check item; four items measuring perceived distributive fairness; six items on perceived ability; five items on perceived benevolence; six items on cognitive trust; five items on affective trust; three items for attention check; and three demographic variables (gender, age, and education). The last five columns contain the computed mean scores for perceived distributive fairness, ability, benevolence, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variables are labeled using standardized English abbreviations to facilitate reuse and secondary analysis. The file can be analyzed directly in SPSS or exported to other formats as needed.3 Experiment 2a: Differential Effects of AI vs. Human Procedural Unfairness on TrustOverview: This dataset originates from an experimental study aimed at examining whether individuals respond differently in terms of cognitive and affective trust when procedural unfairness is enacted by artificial intelligence versus human decision-makers. Experiment 2a focuses on the main effect of the decision agent on trust outcomes.Data Generation and Processing: Participants were recruited via the Credamo online survey platform from two universities located in different regions of China. A total of 227 responses were collected. After excluding those who failed the attention check items, 204 valid responses were retained for analysis. Data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in a file named Experiment2a.sav in SPSS format and can be directly opened in SPSS software. It contains 204 rows and 30 columns. Each row represents one participant’s response record, while each column corresponds to a specific variable. Variables include: random assignment and grouping; one manipulation check item; seven items measuring perceived procedural fairness; six items on cognitive trust; five items on affective trust; three attention check items; and three demographic variables (gender, age, and education). The final three columns contain computed average scores for procedural fairness, cognitive trust, and affective trust.Additional Notes: The dataset contains no missing values. All variables are labeled using standardized English abbreviations to facilitate reuse and secondary analysis. The file can be directly analyzed in SPSS or exported to other formats as needed.4 Experiment 2b: Mediating Role of Perceived Ability and Benevolence (Procedural Unfairness)Overview: This dataset comes from an experimental study designed to replicate the findings of Experiment 2a and to further examine the potential mediating roles of perceived ability and perceived benevolence in shaping trust responses under procedural unfairness.Data Generation and Processing: Participants were working adults recruited through the Credamo online platform. A rolling data collection strategy was used, where responses failing attention checks were excluded in real time. The final dataset includes 235 valid responses. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in a file named Experiment2b.sav, which is in SPSS format and can be directly opened using SPSS software. It contains 235 rows and 43 columns. Each row corresponds to a single participant, and each column represents a specific measured variable. These include: random assignment and group labels; one manipulation check item; seven items measuring procedural fairness; six items for perceived ability; five items for perceived benevolence; six items for cognitive trust; five items for affective trust; three attention check items; and three demographic variables (gender, age, education). The final five columns contain the computed average scores for procedural fairness, perceived ability, perceived benevolence, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variables are labeled using standardized English abbreviations to support future reuse and secondary analysis. The dataset can be directly analyzed in SPSS and easily converted into other formats if needed.5 Experiment 3a: Effects of AI vs. Human Interactional Unfairness on TrustOverview: This dataset comes from an experimental study that investigates how interactional unfairness, when enacted by either artificial intelligence or human decision-makers, influences individuals’ cognitive and affective trust. Experiment 3a focuses on the main effect of the “decision-maker type” under interactional unfairness conditions.Data Generation and Processing: Participants were college students recruited from two universities in different regions of China through the Credamo survey platform. After excluding responses that failed attention checks, a total of 203 valid cases were retained from an initial pool of 223 responses. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in the file named Experiment3a.sav, in SPSS format and compatible with SPSS software. It contains 203 rows and 27 columns. Each row represents a single participant, while each column corresponds to a specific measured variable. These include: random assignment and condition labels; one manipulation check item; four items measuring interactional fairness perception; six items for cognitive trust; five items for affective trust; three attention check items; and three demographic variables (gender, age, education). The final three columns contain computed average scores for interactional fairness, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variable names are provided using standardized English abbreviations to facilitate secondary analysis. The data can be directly analyzed using SPSS and exported to other formats as needed.6 Experiment 3b: The Mediating Role of Perceived Ability and Benevolence (Interactional Unfairness)Overview: This dataset comes from an experimental study designed to replicate the findings of Experiment 3a and further examine the potential mediating roles of perceived ability and perceived benevolence under conditions of interactional unfairness.Data Generation and Processing: Participants were working adults recruited via the Credamo platform. Attention check questions were embedded in the survey, and responses that failed these checks were excluded in real time. Data collection proceeded in a rolling manner until a total of 227 valid responses were obtained. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in the file named Experiment3b.sav, in SPSS format and compatible with SPSS software. It includes 227 rows and

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

As the UK went into the first lockdown of the COVID-19 pandemic, the team behind the biggest social survey in the UK, Understanding Society (UKHLS), developed a way to capture these experiences. From April 2020, participants from this Study were asked to take part in the Understanding Society COVID-19 survey, henceforth referred to as the COVID-19 survey or the COVID-19 study.

The COVID-19 survey regularly asked people about their situation and experiences. The resulting data gives a unique insight into the impact of the pandemic on individuals, families, and communities. The COVID-19 Teaching Dataset contains data from the main COVID-19 survey in a simplified form. It covers topics such as

• Socio-demographics
• Whether working at home and home-schooling
• COVID symptoms
• Health and well-being
• Social contact and neighbourhood cohesion
• Volunteering

The resource contains two data files:

• Cross-sectional: contains data collected in Wave 4 in July 2020 (with some additional variables from other waves);
• Longitudinal: Contains mainly data from Waves 1, 4 and 9 with key variables measured at three time points.

Key features of the dataset

• Missing values: in the web survey, participants clicking "Next" but not answering a question were given further options such as "Don't know" and "Prefer not to say". Missing observations like these are recorded using negative values such as -1 for "Don't know". In many instances, users of the data will need to set these values as missing. The User Guide includes Stata and SPSS code for setting negative missing values to system missing.

• The Longitudinal file is a balanced panel and is in wide format. A balanced panel means it only includes participants that took part in every wave. In wide format, each participant has one row of information, and each measurement of the same variable is a different variable.

• Weights: both the cross-sectional and longitudinal files include survey weights that adjust the sample to represent the UK adult population. The cross-sectional weight (betaindin_xw) adjusts for unequal selection probabilities in the sample design and for non-response. The longitudinal weight (ci_betaindin_lw) adjusts for the sample design and also for the fact that not all those invited to participate in the survey, do participate in all waves.

• Both the cross-sectional and longitudinal datasets include the survey design variables (psu and strata).

A full list of variables in both files can be found in the User Guide appendix.

Who is in the sample?

All adults (16 years old and over as of April 2020), in households who had participated in at least one of the last two waves of the main study Understanding Society, were invited to participate in this survey. From the September 2020 (Wave 5) survey onwards, only sample members who had completed at least one partial interview in any of the first four web surveys were invited to participate. From the November 2020 (Wave 6) survey onwards, those who had only completed the initial survey in April 2020 and none since, were no longer invited to participate

The User guide accompanying the data adds to the information here and includes a full variable list with details of measurement levels and links to the relevant questionnaire.

This database represents data from 480 respondents with the purpose to measure their intervention choice in community care with the instrument AICN (Assessment of Intervention choice in Community Nursing). Data collection took place at the Faculty of Health of the Amsterdam University of Applied Sciences, the Netherlands. The respondents are all baccalaureate nursing students in the fourth year of study close to graduation. Data were collected at three timepoints: around May 2016 (group 1215), May 2017 (group 1316) and May 2018 (group 1417). The student cohorts 1215 and 1316 form a historical control group, 1417 is the intervention group. The intervention group underwent a new four-year more ‘community-oriented’ curriculum, with five new curriculum themes related to caregiving in peoples own homes: (1) fostering patient self-management, (2) shared decision-making, (3) collaboration with the patients’ social system, (4) using healthcare technology, and (5) allocation of care.The aim of this study is to investigate the effect of this redesigned baccalaureate nursing curriculum on students’ intervention choice in community care. AICN is a measuring instrument containing three vignettes in which a situation in caregiving in the patients’ home is described. Each vignette incorporates all five new curriculum themes. The interventions with regard to each theme are a realistic option, while more ‘traditional’ intervention choices are also possible. To avoid students responding in a way they think to be correct, they are not aware of the instrument’s underlying purpose (i.e., determining the five themes). After reading each vignette, the respondents briefly formulate five, in their opinion, most suitable interventions for nursing caregiving. The fifteen interventions yield qualitative information. To allow for quantitative data analysis, the AICN includes a codebook describing the criteria used to recode each of the qualitative intervention descriptions into a quantitative value. As the manuscript describing AICN and codebook is yet under review, a link to the instrument will be added after publication. Filesets:1: SPSS file – 3 cohorts AICN without student numbers2: SPSS syntax file Variables in SPSS file (used in analysis):1: Cohort type2: Curriculum type (old vs. new)3-20: Dummy variables of demographics21-35: CSINV refers to case/intervention; CS1INV2 means case 1, intervention 236-50: Dummy variables of 21-35, representing the main outcome old vs. new intervention type51: Sum of dummy variables (range 1-15) representing the primary outcome AICN52: Sum of dummys like 51, but with respondents with missing variables included, used in the regression analysis53-58: Count the number of chosen interventions per curriculum theme59-60: Count missings (old curriculum = 59, new = 60)61-62: Count no intervention theme (old curriculum = 61, new = 62)ContactBecause of the sensitive nature of the data, the fileset is confidential and will be shared only under strict conditions. For more information contact opensciencesupport@hva.nl

The Structural Equation Modeling (SEM) software market is experiencing robust growth, driven by increasing adoption across diverse sectors like education, healthcare, and the social sciences. The market's expansion is fueled by the need for sophisticated statistical analysis to understand complex relationships between variables. Researchers and analysts increasingly rely on SEM to test theoretical models, assess causal relationships, and gain deeper insights from intricate datasets. While the specific market size for 2025 isn't provided, a reasonable estimate, considering the growth in data analytics and the increasing complexity of research questions, places the market value at approximately $500 million. A Compound Annual Growth Rate (CAGR) of 8% seems plausible, reflecting steady but not explosive growth within a niche but essential software market. This CAGR anticipates continued demand from academia, government agencies, and market research firms. The market is segmented by software type (commercial and open-source) and application (education, medical, psychological, economic, and other fields). Commercial software dominates the market currently, due to its advanced features and professional support, however the open-source segment shows strong potential for growth, particularly within academic settings and amongst researchers with limited budgets. The competitive landscape is relatively concentrated with established players like LISREL, IBM SPSS Amos, and Mplus offering comprehensive solutions. However, the emergence of Python-based packages like semopy and lavaan demonstrates an ongoing shift towards flexible and programmable SEM software, potentially increasing market competition and innovation in the years to come. Geographic distribution shows North America and Europe currently holding the largest market share, with Asia-Pacific emerging as a key growth region due to increasing research funding and investment in data science capabilities. The sustained growth of the SEM software market is expected to continue throughout the forecast period (2025-2033), largely driven by the rising adoption of advanced analytical techniques within research and businesses. Factors limiting market growth include the high cost of commercial software, the steep learning curve associated with SEM techniques, and the availability of alternative statistical methods. However, increased user-friendliness of software interfaces, alongside the growing availability of online training and resources, are expected to mitigate these restraints and expand the market's reach to a broader audience. Continued innovation in SEM software, focusing on improved usability and incorporation of advanced features such as handling of missing data and multilevel modeling, will contribute significantly to the market's future trajectory. The development of cloud-based solutions and seamless integration with other analytical tools will also drive future market growth.

Background and purposeHydrocephalus is a frequent complication following subarachnoid hemorrhage. Few studies investigated the association between laboratory parameters and shunt-dependent hydrocephalus. This study aimed to investigate the variations of laboratory parameters after subarachnoid hemorrhage. We also attempted to identify predictive laboratory parameters for shunt-dependent hydrocephalus.MethodsMultiple imputation was performed to fill the missing laboratory data using Bayesian methods in SPSS. We used univariate and multivariate Cox regression analyses to calculate hazard ratios for shunt-dependent hydrocephalus based on clinical and laboratory factors. The area under the receiver operating characteristic curve was used to determine the laboratory risk values predicting shunt-dependent hydrocephalus.ResultsWe included 181 participants with a mean age of 54.4 years. Higher sodium (hazard ratio, 1.53; 95% confidence interval, 1.13–2.07; p = 0.005), lower potassium, and higher glucose levels were associated with higher shunt-dependent hydrocephalus. The receiver operating characteristic curve analysis showed that the areas under the curve of sodium, potassium, and glucose were 0.649 (cutoff value, 142.75 mEq/L), 0.609 (cutoff value, 3.04 mmol/L), and 0.664 (cutoff value, 140.51 mg/dL), respectively.ConclusionsDespite the exploratory nature of this study, we found that higher sodium, lower potassium, and higher glucose levels were predictive values for shunt-dependent hydrocephalus from postoperative day (POD) 1 to POD 12–16 after subarachnoid hemorrhage. Strict correction of electrolyte imbalance seems necessary to reduce shunt-dependent hydrocephalus. Further large studies are warranted to confirm our findings.

Hypothesis 1: Generic job demands are positively related to a) emotional exhaustion, and b) depersonalization. Hypothesis 2: GP-specific job demands are positively related to a) emotional exhaustion and b) depersonalization. Hypothesis 3: Generic job resources are negatively related to a) emotional exhaustion and b) depersonalization. Hypothesis 4: GP-specific resources are negatively related to a) emotional exhaustion and b) depersonalization. Hypothesis 5: Time-based negative WHI partially mediates the relationship between generic job demands and a) emotional exhaustion, b) depersonalization. Hypothesis 6: Time-based negative WHI partially mediates the relationship between GP specific job demands and a) emotional exhaustion and b) depersonalization. Hypothesis 7: Strain-based negative WHI partially mediates the relationship between generic job demands and a) emotional exhaustion and b) depersonalization. Hypothesis 8: Strain-based negative WHI partially mediates the relationship between GP specific job demands and a) emotional exhaustion and b) depersonalization. The dataset includes raw data obtained from questionnaires, before single imputation with EM algorithm in SPSS to deal with missing values; Description of variables: WPQ (work pace and quantity; generic job demand, q0001 – q0006) MENT (mental load, generic job demand, q0007 – q0010) AUTO (autonomy, generic job resource, q0011 – q0013), not included in the current study OPPOR (opportunity for development, generic job resource, q0014-q0016) FEEDB (feedback, generic job resource, q0017-q0019) COLL (collaboration, generic job resource, q0020-q0022) SELF (self-efficacy, generic personal resource, q0023-q0026, not included in the current study) OPTIM (optimism, generic personal resources, q0027-q0030, not included in the current study) STRAIN (strain-based negative work-home interference, q0031, q0032, q0038, q0041) TIME (time-based negative work-home interference, q0034, q0037, q0039, q0042) EE (emotional exhaustion, q0044, q0045, q0046, q0049, q0051, q0055, q0056, q0059) DP (depersonalization, q0048, q0053, q0054, q0061) PA (personal accomplishment, q0047, q0050, q0052, q0057, q0058, q0060) JDGP (occupation-specific job demands, q0062-q0074) JRGP (occupation-specific job resources, q0075-q0084) PRGP (occupation-specific personal resources, q0085-q0087, not included in the current study) gender q0088 year of birth q0089 marital status q0090 year of start in present practice q0091 number of employees q0092 partner with job q0093 partner works overtime q0094 flexible childcare arrangements q0095 non-flexible childcare arrangements q0096 practice type q0097 care hours q0098 work hours q0099

Abstract (en): This data collection contains information gathered in 1975 on attorneys in Chicago, Illinois. The purpose of this data collection was to describe and analyze the social organization of the legal profession in Chicago. Several major aspects of the legal profession were investigated: the organization of lawyers' work, the social stratification within the Chicago Bar Association, prestige within the profession, lawyers' personal values, career patterns and mobility, networks of association, and the "elites" within the profession. Specific questions elicited information on areas of law in which the respondents spent most of their time practicing, and the ethnicities, educational background, religion, political affiliation, bar association memberships, and sex of respondents' friends and colleagues. Other variables probe respondents' backgrounds, such as father's occupation, home town, law school from which the respondent graduated, religious and political affiliations, ethnicity, sex, and income. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.. A total of 13,823 attorneys in Chicago, Illinois, had law offices, were not retired, had graduated from law school more than one year previous to the study, and were listed in SULLIVAN'S LAW DICTIONARY FOR THE STATE OF ILLINOIS, 1974-1975, and/or the MARTINDALE-HUBBELL LAW DICTIONARY. A stratified probability sample with simple random selection of elements within strata resulted in 1,024 attorneys. 2006-01-06 ICPSR created SAS, SPSS, and Stata setup files, a SAS transport file, an SPSS portable file, and a Stata system file containing variable locations, variable labels, and missing value specifications. The data were transformed from card image to LRECL format and the cases were ordered by sequence number. Variables 343 (Law School Attended), 349 (Religious Preference), 352 (Respondents Nationality), and 353 (Spouses Nationality) were recoded due to confidentiality concerns. Previously unreleased hardcopy documentation has been scanned and included with the codebook. Funding insitution(s): National Science Foundation (SOC-77-24699). American Bar Foundation. Russell Sage Foundation. personal interview(1) Variables 343 (Law School Attended), 349 (Religious Preference), 352 (Respondents Nationality), and 353 (Spouses Nationality) were recoded due to confidentiality concerns. Values with counts less than 5 were collapsed into a "Recoded Other" (100) value. (2) ICPSR has assigned missing value designations according to the available documentation for this study. However, some continuous variables have high and low values that may fall out of a valid range. Users of the data should be aware of the possibility that these values may not be valid.

Background and Objectives: Pharmacogenomics (PGx) leverages genomic information to tailor drug therapies, enhancing precision medicine. Despite global advancements, its implementation in Lebanon, Qatar, and Saudi Arabia faces unique challenges in clinical integration. This study aimed to investigate PGx attitudes, knowledge implementation, associated challenges, forecast future educational needs, and compare findings across the three countries. Methods: This cross-sectional study utilized an anonymous, self-administered online survey distributed to healthcare professionals, academics, and clinicians in Lebanon, Qatar, and Saudi Arabia. The survey comprised 18 questions to assess participants' familiarity with PGx, current implementation practices, perceived obstacles, potential integration strategies, and future educational needs. Results: The survey yielded 337 responses from healthcare professionals across the three countries. Data revealed significant variations in PGx familiarity an..., Ethical statement and informed consent Ethical approval for this study was obtained from the institutional review boards of the participating universities: Beirut Arab University (2023-H-0153-HS-R-0545), Qatar University (QU-IRB 1995-E/23), and Alfaisal University (IRB-20270). Informed consent was obtained from all participants online, ensuring their confidentiality and the right to withdraw from the study without any consequences. Participants were informed that all collected data would be anonymous and confidential, with only the principal investigator having access to the data. Completing and submitting the survey was considered an agreement to participate. Study design This study utilized a quantitative cross-sectional research design, involving healthcare professionals (pharmacists, nurses, medical laboratory technologists), university academics, and clinicians from Lebanon, Qatar, and Saudi Arabia. Data was collected through a voluntary, anonymous, private survey to gather PGx per..., , # Integrating pharmacogenomics in three Middle Eastern countries’ healthcare (Lebanon, Qatar, and Saudi Arabia)

Description of the data and file structure

• This is a data sheet exported as .CSV file from the original SPSS database.
• Methods of data processing and analysis: data were generated by SPSS software version 20 (SPSS, Inc., IL, USA). Details are included in the manuscript.
• No patient identifiers are included in these data sets.
• Specialized software used: these data need to be entered in SPSS.

• Description of the data set: o 1 dataset is included; PGx_database : it includes the raw data of our paper. o In the data set, each row represent one participant. o All the variables can contain empty cells. When participants didn't answer, empty cells were added to show the missing data. o The number in each cell has a specific value depending on the variable.

• Listed variables:

1. ID
   • Description: Record identifier for each participant.
   • Values: Nu...

The dataset was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538 http://www.catcheyou.eu/. The data set consists of: 1 data file saved in .sav format “CATCH-EyoU Processes in Youth’s Construction of Active EU Citizenship Cross-national Wave 1 Questionnaires Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia - EXTRACT.sav” 1 README file The file was generated through IBM SPSS software. Discrete missing values: 88, 99. The .sav file (SPSS) can be processed using “R” (library “foreign”): https://cran.r-project.org This dataset relates to following paper: Ekaterina Enchikova, Tiago Neves, Sam Mejias, Veronika Kalmus, Elvira Cicognani, Pedro Ferreira (2019) Civic and Political Participation of European Youth: fair measurement in different cultural and social contexts. Frontiers in Education. Data Set Contact Person: Ekaterina Enchikova [UP-CIIE]; mail: enchicova@gmail.com Data Set License: this data set is distributed under a Creative Commons Attribution (CC-BY) http://creativecommons.org/licenses

Data from 160 patients suffering from schizophrenia and 26 from bipolar disorder. See data_info.xlsx for detailed information on the variables contained in the database (db2demographics.xlsx and BD_DISC_1.sav). data_info.xlsx: information of the variables contained in the database.db2demographics: database used for the analyses in excel format. Filtered for moment = 1 and containing only complete cases (in the variables used for the analyses)BD_DISC_1.sav: complete database, in SPSS format. Only moment = 1 was used for the analyses. Contains missing values.

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.

This collection contains the 17 anonymised datasets from the RAAAP-2 international survey of research management and administration professional undertaken in 2019. To preserve anonymity the data are presented in 17 datasets linked only by AnalysisRegionofEmployment, as many of the textual responses, even though redacted to remove institutional affiliation could be used to identify some individuals if linked to the other data. Each dataset is presented in the original SPSS format, suitable for further analyses, as well as an Excel equivalent for ease of viewing. There are additional files in this collection showing the the questionnaire and the mappings to the datasets together with the SPSS scripts used to produce the datasets. These data follow on from, but re not directly linked to the first RAAAP survey undertaken in 2016, data from which can also be found in FigShare Errata (16/5/23) an error in v13 of the main Data Cleansing syntax file (now updated to v14) meant that two variables were missing their value labels (the underlying codes were correct) - a new version (SPSS & Excel) of the Main Dataset has been updated

Abstract (en): This study is part of a time-series collection of national surveys fielded continuously since 1952. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. In addition to core items, new content includes questions on values, political knowledge, and attitudes on racial policy, as well as more general attitudes conceptualized as antecedent to these opinions on racial issues. The Main Data File also contains vote validation data that were expanded to include information from the appropriate election office and were attached to the records of each of the respondents in the post-election survey. The expanded data consist of the respondent's post case ID, vote validation ID, and two variables to clarify the distinction between the office of registration and the office associated with the respondent's sample address. The second data file, Bias Nonresponse Data File, contains respondent-level field administration variables. Of 3,833 lines of sample that were originally issued for the 1990 Study, 2,176 resulted in completed interviews, others were nonsample, and others were noninterviews for a variety of reasons. For each line of sample, the Bias Nonresponse Data File includes sampling data, result codes, control variables, and interviewer variables. Detailed geocode data are blanked but available under conditions of confidential access (contact the American National Election Studies at the Center for Political Studies, University of Michigan, for further details). This is a specialized file, of particular interest to those who are interested in survey nonresponse. Demographic variables include age, party affiliation, marital status, education, employment status, occupation, religious preference, and ethnicity. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Response Rates: The response rate for this study is 67.7 percent. The study was in the field until January 31, although 67 percent of the interviews were taken by November 25, 80 percent by December 7, and 93 percent by December 31. All United States households in the 50 states. National multistage area probability sample. 2015-11-10 The study metadata was updated.2009-01-09 YYYY-MM-DD Part 1, the Main Data File, incorporates errata that were posted separately under the Fourth ICPSR Edition. Part 2, the Bias Nonresponse Data File, has been added to the data collection, along with corresponding SAS, SPSS, and Stata setup files and documentation. The codebook has been updated by adding a technical memorandum on the sampling design of the study previously missing from the codebook. The nonresponse file contains respondent-level field administration variables for those interested in survey nonresponse. The collection now includes files in ASCII, SPSS portable, SAS transport (CPORT), and Stata system formats.2000-02-21 The data for this study are now available in SAS transport and SPSS export formats in addition to the ASCII data file. Variables in the dataset have been renumbered to the following format: 2-digit (or 2-character) year prefix + 4 digits + [optional] 1-character suffix. Dataset ID and version variables have also been added. Additionally, the Voter Validation Office Administration Interview File (Expanded Version) has been merged with the main data file, and the codebook and SPSS setup files have been replaced. Also, SAS setup files have been added to the collection, and the data collection instrument is now provided as a PDF file. Two files are no longer being released with this collection: the Voter Validation Office Administration Interview File (Unexpanded Version) and the Results of First Contact With Respondent file. Funding insitution(s): National Science Foundation (SOC77-08885 and SES-8341310). face-to-face interviewThere was significantly more content in this post-election survey than ...

Biofilm-Associated Multidrug-Resistant and Methicillin-Resistant Staphylococcus aureus Infections in Children

https://doi.org/10.5061/dryad.hx3ffbgmm

Description of the data and file structure

The dataset is of paediatric patients with Staphylococcus auresus infection. Dataset comprises single sheet. The sheet details for demographic information, such as age and gender of the patients; clinical information, including clinical samples; microbiological findings comprising antimicrobial resistance patterns of S. aureus, multi-drug-resistant or non-multi-drug-resistant, biofilm formers or non-formers. Exact age is removed and is categorized as age group in order to anonymize the data.

S. auresus strains from patients with infections were coded with alphanumeric with initial Isolate. (Isolate 1, Isolate23, etc.).

Units of the study variables were standard and as follows:

(a) Age group Years

(b) Optical densities (ODs) nanometer (nm)

Disc diffusion or antimicrobial su...

This database represents data from 281 respondents with the purpose to measure their perceptions of community care with the measuring instrument SCOPE (Scale on Community Care Perceptions/ see references). Data collection took place at the Faculty of Health Of the Amsterdam University of Applied Sciences, the Netherlands. The respondents are all part of one single cohort of baccalaureate nursing students who started their education in September 2014. Data were collected at four timepoints in the same respondents once in each study year (1,2,3 and 4): around September/October 2014 (group is named 1414), February 2016 (group 1415), February 2017 (group 1416) and May 2018 (group 1417). This student cohort group underwent a new four-year curriculum containing more elements of community nursing. SCOPE is a 35-item scale containing: background variables, 11 measuring the affective component, 5 measuring community care perception as a placement, 17 as a future profession, 6 on students' preferences of areas in healthcare for a placement, and 2 on the reasons underlying student preference. The items in the three subscales have a range of 1-10. The placement and profession scale also have the option 11 (I don't know).Filesets: 1 t/m 6: SPSS files (4 source files, 2 working files, a wide and a long version)7: SPSS syntax file8: PDF-file with a description of the variables9: PDF-file with the questionnaire SCOPEVariables in merged file/ wide version (used in analysis):1-241 correspond with items in SCOPE (the instrument is published by open access). The cohort name is added to each item242-245 correspond with sums of the variables in three subscales and total scale (name: @perceptotal)246-261 represents calculated means of each subscales and scale per cohort262 and 263 are calculations of the times a respondent mentioned a preference for rehabilitation (262) and the hospital (263)264 are calculations of the times a respondent did not participate, due to missing or drop-out.The long version of the SPSS file is created to conduct a Mixed Method analysis, and it contains the same variables.ContactBecause of the sensitive nature of the data, the fileset is confidential and will be shared only under strict conditions. For more information contact opensciencesupport@hva.nl

Sociodemographic and reproductive factors associated with good quality antenatal care.

Abstract (en): This survey is the first broad-based, systematic examination of the nature of civil litigation in state general jurisdiction trial courts. Data collection was carried out by the National Center for State Courts with assistance from the National Association of Criminal Justice Planners and the United States Bureau of the Census. The data collection produced two datasets. Part 1, Tort, Contract, and Real Property Rights Data, is a merged sample of approximately 30,000 tort, contract, and real property rights cases disposed during the 12-month period ending June 30, 1992. Part 2, Civil Jury Cases Data, is a sample of about 6,500 jury trial cases disposed over the same time period. Data collected include information about litigants, case type, disposition type, processing time, case outcome, and award amounts for civil jury cases. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Forty-five jurisdictions chosen to represent the 75 most populous counties in the nation. The sample for this study was designed and selected by the United States Bureau of the Census. It was a two-stage stratified sample with 45 of the 75 most populous counties selected at the first stage. The top 75 counties account for about 37 percent of the United States population and about half of all civil filings. The 75 counties were divided into four strata based on aggregate civil disposition data for 1990 obtained through telephone interviews with court staffs in the general jurisdiction trial courts. The sample consisted of tort, contract, and real property rights cases disposed between July 1, 1991, and June 30, 1992. 2011-11-02 All parts are being moved to restricted access and will be available only using the restricted access procedures.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions.2004-06-01 The data have been updated by the principal investigator to include replicate weights and a few other variables. The codebook and SAS and SPSS data definition statements have been revised to reflect these changes.2001-03-26 The data have been updated by the principal investigator to include replicate weights. The codebook and SAS and SPSS data definition statements have been revised to reflect these changes.2001-03-26 The data had been updated by the principal investigator to include replicate weights. The codebook and SAS and SPSS data definition statements had been revised to reflect these changes.1997-07-29 The codebook had been revised to correct errors documenting both data files. Column location (and width) of variable WGHT "TOTAL WEIGHT" was incorrectly shown as 10.4 for Part 1, Tort, Contract, and Real Property Data. It was accurately shown in the data definition statements as 9.4. Variables listed after WGHT were inaccurately reported one column off in the codebook. Similarly, column location (and width) of variable WGHT "TOTAL WEIGHT" was incorrectly shown as 10.2 for Part 2, Civil Jury Data. It was accurately shown in the data definition statements as 9.2. Variables listed after WGHT were inaccurately reported one column off in the codebook. Fundi...