The survey dataset for identifying Shiraz old silo’s new use which includes four components: 1. The survey instrument used to collect the data “SurveyInstrument_table.pdf”. The survey instrument contains 18 main closed-ended questions in a table format. Two of these, concern information on Silo’s decision-makers and proposed new use followed up after a short introduction of the questionnaire, and others 16 (each can identify 3 variables) are related to the level of appropriate opinions for ideal intervention in Façade, Openings, Materials and Floor heights of the building in four values: Feasibility, Reversibility, Compatibility and Social Benefits. 2. The raw survey data “SurveyData.rar”. This file contains an Excel.xlsx and a SPSS.sav file. The survey data file contains 50 variables (12 for each of the four values separated by colour) and data from each of the 632 respondents. Answering each question in the survey was mandatory, therefor there are no blanks or non-responses in the dataset. In the .sav file, all variables were assigned with numeric type and nominal measurement level. More details about each variable can be found in the Variable View tab of this file. Additional variables were created by grouping or consolidating categories within each survey question for simpler analysis. These variables are listed in the last columns of the .xlsx file. 3. The analysed survey data “AnalysedData.rar”. This file contains 6 “SPSS Statistics Output Documents” which demonstrate statistical tests and analysis such as mean, correlation, automatic linear regression, reliability, frequencies, and descriptives. 4. The codebook “Codebook.rar”. The detailed SPSS “Codebook.pdf” alongside the simplified codebook as “VariableInformation_table.pdf” provides a comprehensive guide to all 50 variables in the survey data, including numerical codes for survey questions and response options. They serve as valuable resources for understanding the dataset, presenting dictionary information, and providing descriptive statistics, such as counts and percentages for categorical variables.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

The main SPSS dataset of over 700 variables covers 11 sections on 12 developed capitalist electoral democracies. (see Outline file included here for an overview of both sections and countries included, with the names in order of every variable and label.) The second SPSS file is of 29 variables of Covid-related daily data from OWID website that covers the same 12 countries from the start of Covid-19 in January 2020 to Aug 2, 2023.

Study setting We conducted this study in the paediatrics departments of the Ola During Children's Hospital (ODCH), Rokupa Government Hospital (RGH), and the King Harman Maternity and Children Hospital (KHMCH) located in Freetown, the Capital city of Sierra Leone. Ola During Children's hospital is a tertiary teaching hospital and the leading paediatric referral hospital in Sierra Leone. Rokupa Government and KHMCH are secondary hospitals that provide comprehensive emergency obstetric and newborn care inpatient and outpatient paediatric and maternity services.Study design and duration This study was conducted between April 2021 to July 2021 and had two phases. Phase 1 was a descriptive cross-sectional retrospective study of paediatrics prescriptions from the respective pharmacy departments from May 1 to May 31, 2021. In phase 2, we conducted a point prevalence descriptive inpatient chart review that lasted for one week to assess MEs and pDDIs among the paediatric patient population.Study populationThis data set is the SPSS file with both variable and data view. It contains the variables that were analysed for the two phases of the study namely: For phase 1 of the study, the population included paediatric prescriptions that came to the respective pharmacy departments in May 2021. Phase 2 included inpatients <16 years irrespective of their working diagnosis and gender and whose parents or guardians consented to participate in the study.Data collection procedure and tool For phase 1 of the study, the data collection tool was adapted from the Sierra Leone Pharmacy and Drugs Act 2001, the World Health Organization (WHO) guidelines for prescription writing, and a previous study [12, 32, 33]. Seventeen essential elements were selected for this study and compiled into a single data collection tool. We manually extracted all data through a review of prescriptions accessed from the pharmacies. The data collection tool for phase 2 was adapted from the WHO guide on reporting and learning systems for medication errors, American Society of Health System Pharmacists (ASHP) guidelines for preventing medication errors in hospitals, and previous studies [3, 4, 18, 34]. Data collection tools were piloted, and feedback was used to develop the final versions used in the study. The treatment charts were reviewed, and the following were extracted and entered into the data collection tool: wrong patient, wrong dose, wrong route, wrong medicine, wrong dosage form, wrong time of administration, contraindication including allergy, wrong duration, dose omitted or delay, wrong frequency, wrong indication, unnecessary medicine, and therapeutic duplication. In addition, nurses were accompanied during the medicine administration rounds and patients and caretakers were interviewed to gather information when necessary. Ethical consideration Clearance to conduct the study was obtained from the Research, Innovation and Publication Review Committee of the Faculty of Pharmaceutical Sciences, College of Medicine and Allied Health Sciences, University of Sierra Leone. The management of the hospitals permitted the study to be done in their facilities. Written informed consent was obtained from parents/caregivers after explaining the purpose and procedures of the study. Parents gave consent before data was collected, and they were not coerced to participate in the study. Patient information was coded and kept confidential. Data analyses The researchers evaluated the completion of the essential elements for each prescription, such as the use of the generic names, recommended abbreviations, and prescription legibility. We determined the accuracy score out of 34 total points. Each element was assessed, scoring 0, 1 or 2 for 'not completed', 'partially completed', or 'fully completed', respectively. Legibility was scored subjectively according to the prescription quality index (PQI) as 0, 1, or 2 for 'illegible', 'barely legible' or 'legible', respectively, by two or more persons [35]. The global accuracy score (GAS) for each prescription was determined by calculating the total percentage achieved out of 34 possible points for the 17 prescription elements considered. The GAS was then classified into one of four scores: 100%, 80% – 99%, 40% – 79%, and less than 40%. The desired prescription-writing accuracy score, or gold standard, is 100%. The definition and severity categorisation of the National Coordinating Council of Medication Error Reporting and Prevention (NCCMERP) was used [5]. Potential drug-drug interactions (pDDIs) were assessed by the Drug.com interaction checker and classified into no interaction, minor, moderate, and major [36]. The data obtained was cleaned and coded and then entered into Statistical Package for Social Sciences (SPSS) version 20 (IBM Statistics, Armonk, NY, USA) for analysis. Descriptive statistics were applied, and results were presented as frequency, percentages, mean, and standard deviation. Inferential statistics, including the Kruskal Wallis, Mann-Whitney U and Pearson correlation, were employed, and a p-value of < 0.05 was considered statistically significant.

Misophonia is a condition characterized by negative affect, intolerance, and functional impairment in response to particular repetitive sounds usually made by others (e.g., chewing, sniffing, pen tapping) and associated stimuli. To date, researchers have largely studied misophonia using self-report measures. As the field is quickly expanding, assessment approaches need to advance to include more objective measures capable of differentiating those with and without misophonia. Although several studies have used sounds as experimental stimuli, few have used standardized stimuli sets with demonstrated reliability or validity. To conduct rigorous research to better understand misophonia, it is important to have an easily accessible, standardized set of acoustic stimuli for use across studies. Accordingly, in the present study, the International Affective Digitized Sounds (IADS-2), developed by Bradley and Lang [1], were used to determine whether participants with misophonia responded to cert..., Group differences in sound ratings were examined using a two-way, mixed analysis of covariance (2 groups x 3 sound types, where “group†corresponds to participants with misophonia or controls, and “sound type†refers to positive, negative, or neutral sounds) on four dependent variables (ratings of valence, arousal, similarity, and avoidance). When statistically significant interactions were observed for sound type, pairwise comparisons were used to determine group differences on each dependent variable, as well as mean differences between sound type on each dependent variable. All analyses were conducted using IBM SPSS27 statistical software. The first step in the data analytic plan included cleaning and screening the dataset by (a) inspecting all variables for data entry errors (none were observed), and (b) examining the normality of distributions across study variables. Next, bivariate correlations were explored to examine the relationships among variables and determine whether it wou..., , # Using a standardized sound set to help characterize misophonia: The international affective digitized sounds

https://doi.org/10.5061/dryad.kh18932fd

Description of the data and file structure

MQincluded is the group variable. MQincluded=1 describes all participants who meet misophonia criteria and were included in the dataset. MQincluded=0 describes healthy controls. All variable names for the measures have descriptors in the "label" column of SPSS. Average ratings for the dependent variables are found at the end of the variable view in SPSS, as well as PANAS positive and negative scores and the AIM total score.

Sharing/Access information

Data was derived from the following sources:

• Amazon Mechanical Turk

Code/Software

SPSS syntax is included with the data upload.Â

The Cohort Hip & Cohort Knee (CHECK) is a population-based observational multicenter cohort study of 1002 individuals with early symptomatic osteoarthritis (OA) of knee and/or hip in the Netherlands. The participants were followed for 10 years. The study evaluated clinical, radiographic and biochemical variables in order to establish the course, prognosis and underlying mechanisms of early symptomatic osteoarthritis. The Dutch Artritis Foundation initiated and funded this inception cohort.

This dataset covers the data collection of baseline (T0) without the variable 'Subject identification number'. Included is a Kellgren-Lawrence radiographic classification covering T0,T2,T5, T8 and T10. Also X-rays of hips and knees of baseline are available. More information on the variables can be found in the documentation. In the description file you can find an overview of the data belonging to this dataset and more information about the format and kind of view of the X rays.

The complete data are available via three separate datasets, each containing again the baseline T0 data of this current dataset. All SPSS data files of these three datasets include the variable 'Subject identification number'.

The X-ray data are not included in the dataset, they are stored outside of EASY. If you wish to use this data, please contact DANS via info@dans.knaw.nl. Or consult the X-ray_data_request.pdf document for more information.

If you wish to make use of the complete CHECK data, please see the see relations for the other CHECK datasets and for the overview 'Thematic collection: CHECK (Cohort Hip & Cohort Knee)'.

The Comparative Political Economy Database (CPEDB) began at the Centre for Learning, Social Economy and Work (CLSEW) at the Ontario Institute for Studies in Education at the University of Toronto (OISE/UT) as part of the Changing Workplaces in a Knowledge Economy (CWKE) project. This data base was initially conceived and developed by Dr. Wally Seccombe (independent scholar) and Dr. D.W. Livingstone (Professor Emeritus at the University of Toronto). Seccombe has conducted internationally recognized historical research on evolving family structures of the labouring classes (A Millennium of Family Change: Feudalism to Capitalism in Northwestern Europe and Weathering the Storm: Working Class Families from the Industrial Revolution to the Fertility Decline). Livingstone has conducted decades of empirical research on class and labour relations. A major part of this research has used the Canadian Class Structure survey done at the Institute of Political Economy (IPE) at Carleton University in 1982 as a template for Canadian national surveys in 1998, 2004, 2010 and 2016, culminating in Tipping Point for Advanced Capitalism: Class, Class Consciousness and Activism in the ‘Knowledge Economy’ (https://fernwoodpublishing.ca/book/tipping-point-for-advanced-capitalism) and a publicly accessible data base including all five of these Canadian surveys (https://borealisdata.ca/dataverse/CanadaWorkLearningSurveys1998-2016). Seccombe and Livingstone have collaborated on a number of research studies that recognize the need to take account of expanded modes of production and reproduction. Both Seccombe and Livingstone are Research Associates of CLSEW at OISE/UT. The CPEDB Main File (an SPSS data file) covers the following areas (in order): demography, family/household, class/labour, government, electoral democracy, inequality (economic, political & gender), health, environment, internet, macro-economic and financial variables. In its present form, it contains annual data on 725 variables from 12 countries (alphabetically listed): Canada, Denmark, France, Germany, Greece, Italy, Japan, Norway, Spain, Sweden, United Kingdom and United States. A few of the variables date back to 1928, and the majority date from 1960 to 1990. Where these years are not covered in the source, a minority of variables begin with more recent years. All the variables end at the most recent available year (1999 to 2022). In the next version developed in 2025, the most recent years (2023 and 2024) will be added whenever they are present in the sources’ datasets. For researchers who are not using SPSS, refer to the Chart files for overviews, summaries and information on the dataset. For a current list of the variable names and their labels in the CPEDB data base, see the excel file: Outline of SPSS file Main CPEDB, Nov 6, 2023. At the end of each variable label in this file and the SPSS datafile, you will find the source of that variable in a bracket. If I have combined two variables from a given source, the bracket will begin with WS and then register the variables combined. In the 14 variables David created at the beginning of the Class Labour section, you will find DWL in these brackets with his description as to how it was derived. The CPEDB’s variables have been derived from many databases; the main ones are OECD (their Statistics and Family Databases), World Bank, ILO, IMF, WHO, WIID (World Income Inequality Database), OWID (Our World in Data), Parlgov (Parliaments and Governments Database), and V-Dem (Varieties of Democracy). The Institute for Political Economy at Carleton University is currently the main site for continuing refinement of the CPEDB. IPE Director Justin Paulson and other members are involved along with Seccombe and Livingstone in further development and safe storage of this updated database both at the IPE at Carleton and the UT dataverse. All those who explore the CPEDB are invited to share their perceptions of the entire database, or any of its sections, with Seccombe generally (wseccombe@sympatico.ca) and Livingstone for class/labour issues (davidlivingstone@utoronto.ca). They welcome any suggestions for additional variables together with their data sources. A new version CPEDB will be created in the spring of 2025 and installed as soon as the revision is completed. This revised version is intended to be a valuable resource for researchers in all of the included countries as well as Canada.

The 2019 version of the transatlantic slave trade database contains 36,002 voyages compared to 34,940 in 2008 (and 27,233 in the 1999 version of the database that appeared on CD-ROM). Since 2008, several thousand corrections have been made and additional information added. Thus 284 of the 2008 voyages have been deleted either because we found they had been entered twice, or because we discovered that a voyage was not involved in the transatlantic slave trade. For example voyage id 16772, the Pye, Captain Adam, turned out to have carried slaves from Jamaica to the Chesapeake, but obtained its captives in Jamaica, not Africa. Offsetting the deletions are 1,345 voyages added on the basis of new information. Further, many voyages that are common to both 2008 and 2019 versions of the database now contain information that was not available in 2008 (see table 1 of “Understanding the Database” for the current summary). The 2019 version has 274 variables, compared with 98 in the Voyages Database available online. Users interested in working with this larger data set can download it in a file formatted for use with SPSS software. Because some users may find it useful to view data as it existed in earlier versions, the database as it was in 1999, 2008 and 2010 can also be selected for download. A codebook describing all variable names, variable labels, and values of the expanded dataset is available as a pdf document. With only a few exceptions, it retains variable names in the original 1999 CD-ROM version.

This SPSS dataset is from a 2019 survey conducted via Prolific. There are 323 participants in the file, 306 with complete data for the key measures. Measures include the Big Five Inventory, the Interest/Deprivation Curiosity Scale, the Need for Cognition Scale, the Need to Belong Scale, the Basic Psychological Need Satisfaction Scale, the General Belongingness Scale, the Meaning in Life Questionnaire, the Mindful Attention Awareness Scale, the Smartphone Addiction Scale, and some questions about listening to podcasts.

In relation to podcasts, participants were first asked if they had ever listened to a podcast. Those who said yes (N = 240) were asked questions related to amount of listening, categories and format of podcasts, setting of listening, device used, social engagement around podcasts, and parasocial relationships with their favourite podcast host. Participants also indicated their age, gender, and country of residence.

The datafile contains item ratings and scale scores for all measures. Item wording and response labels are provided in the variable view tab of the downloaded file. Other files available on the OSF site include a syntax file related to the analyses reported in a published paper and a copy of the survey.

This dataset contains almost all of the variables from the 2019 "Survey of academic and research librarians in Germany and the USA" (http://doi.org/10.17606/7vas-4p59). Several open-ended questions in the original data were coded into new variables in this dataset by the P.I., and the open-ended question variables removed. The ZIP file made available here contains: 1.) the dataset in SPSS data file (.sav) format; 2.) the dataset in comma-separated values (.csv) format; 3.) the dataset codebook in SPSS output file (.spv) format; 4.) the dataset codebook in portable document format (.PDF). A subset of this dataset is also available - the replication dataset (http://doi.org/10.3886/E111106V1) for the first published article that resulted from the survey: Kramer, S. and Horstmann, W., 2020. Perceptions and beliefs of academic librarians in Germany and the USA: a comparative study. LIBER Quarterly, 29(1), pp.1–18. DOI: http://doi.org/10.18352/lq.10314

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

The Sicily and Calabria Extortion Database was extracted from police and court documents by the Palermo team of the GLODERS — Global Dynamics of Extortion Racket Systems — project which has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 315874 (http://www.gloders.eu, “Global dynamics of extortion racket systems”). The data are provided as an SPSS file with variable names, variable labels, value labels where appropriate, missing value definitions where appropriate. Variable and value labels are given in English translation, string texts are quoted from the Italian originals as we thought that a translation could bias the information and that users of the data for secondary analysis will usually be able to read Italian. The rows of the SPSS file describe one extortion case each. The columns start with some technical information (unique case number, reference to the original source, region, case number within the regions (Sicily and Calabria). These are followed by information about when the cases happened, the pseudonym of the extorter, his role in the organisation and the name and territory of the mafia family or mandamento he belongs to. Information about the victims, their affiliations and the type of enterprise they represent follows; the type of enterprise is coded according to the official Italian coding scheme (AtEco, which can be downloaded from http://www.istat.it/it/archivio/17888). The next group of variables describes the place where the extortion happened. The value labels for the numerical pseudonyms of extorters and victims (both persons and firms) are not contained in this file, hence the pseudonyms can only be used to analyse how often the same person or firm was involved in extortion. After this more or less technical information about the extortion cases the cases are described materially. Most variables come in two forms, both the original textual description of what happened and how it happened and a recoded variable which lends itself better for quantitative analyses. The features described in these variables encompass • whether the extortion was only attempted (and unsuccessful from the point of view of the extorter) or completed, i.e. the victim actually paid, • whether the request was for a periodic or a one-off payment or both and what the amount was (the amounts of periodic and one-off amounts are not always comparable as some were only defined in terms of percentages of victim income or in terms of obligations the victim accepted to employ a relative of the extorter etc.), • whether there was an intimidation and whether it was directed to a person or to property, • whether the extortion request was brought forward by direct personal contact or by some indirect communication, • whether there was some negotiation between extorter and victim, and if so, what it was like, and whether a mediator interfered, • how the victim reacted: acquiescent, conniving or refusing, • how the law enforcement agencies got to know about the case (own observation, denunciation, etc.), • whether the extorter was caught, brought to investigation custody or finally sentenced (these variables contain a high percentage of missing data, partly due to the fact that some cases are still under prosecution or before court or as a consequence of incomplete documents. Kompilation Transkription Compilation Transcription Extortion cases in Sicily and Calabria Reasoned sampling, trying to represent the proportional distribution of the cases between East and West Sicily. For Calabria the focus was on the province capital.

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.
The study reported in this paper employed the mixed methods approach comprising a quantitative and qualitative analysis. The quantitative and econometric analysis of the dependent variable, namely, the final marks for the research report and the independent variables that explain it. The results show significance in terms of the assignments and existing knowledge marks in terms of their bachelor’s average mark. We extended the analysis to a qualitative and quantitative survey, which indicated that the mean statistical feedback was above average and therefore strongly agreed/agreed except for library use by the student. Students, therefore, need more guidance in terms of library use and the open questions showed a need for a research methods course in the future. Furthermore, supervision tends to be a significant determinant in all cases. It is also here where supervisors can use social media instruments such as WhatsApp and Facebook to inform students further. This study contributes as the first to investigate the preparation and research skills of students for master's and doctoral studies during the COVID-19 pandemic in an online environment.

The General Household Survey (GHS) is a continuous national survey of people living in private households conducted on an annual basis, by the Social Survey Division of the Office for National Statistics (ONS). The main aim of the survey is to collect data on a range of core topics, covering household, family and individual information. This information is used by government departments and other organisations for planning, policy and monitoring purposes, and to present a picture of house holds, family and people in Great Britain. From 2008, the General Household Survey became a module of the Integrated Household Survey (IHS). In recognition, the survey was renamed the General Lifestyle Survey (GLF/GLS). The GHS started in 1971 and has been carried out continuously since then, except for breaks in 1997-1998 when the survey was reviewed, and 1999-2000 when the survey was redeveloped. Following the 1997 review, the survey was relaunched from April 2000 with a different design. The relevant development work and the changes made are fully described in the Living in Britain report for the 2000-2001 survey. Following its review, the GHS was changed to comprise two elements: the continuous survey and extra modules, or 'trailers'. The continuous survey remained unchanged from 2000 to 2004, apart from essential adjustments to take account of, for example, changes in benefits and pensions. The GHS retained its modular structure and this allowed a number of different trailers to be included for each of those years, to a plan agreed by sponsoring government departments. Further changes to the GHS methodology from 2005: From April 1994 to 2005, the GHS was conducted on a financial year basis, with fieldwork spread evenly from April of one year to March the following year. However, in 2005 the survey period reverted to a calendar year and the whole of the annual sample was surveyed in the nine months from April to December 2005. Future surveys will run from January to December each year, hence the title date change to single year from 2005 onwards. Since the 2005 GHS (held under SN 5640) does not cover the January-March quarter, this affects annual estimates for topics which are subject to seasonal variation. To rectify this, where the questions were the same in 2005 as in 2004-2005, the final quarter of the latter survey was added (weighted in the correct proportion) to the nine months of the 2005 survey. Furthermore, in 2005, the European Union (EU) made a legal obligation (EU-SILC) for member states to collect additional statistics on income and living conditions. In addition to this the EU-SILC data cover poverty and social exclusion. These statistics are used to help plan and monitor European social policy by comparing poverty indicators and changes over time across the EU. The EU-SILC requirement has been integrated into the GHS, leading to large-scale changes in the 2005 survey questionnaire. The trailers on 'Views of your Local Area' and 'Dental Health' have been removed. Other changes have been made to many of the standard questionnaire sections, details of which may be found in the GHS 2005 documentation. Further changes to the GLF/GHS methodology from 2008 As noted above, the General Household Survey (GHS) was renamed the General Lifestyle Survey (GLF/GLS) in 2008. The sample design of the GLF/GLS is the same as the GHS before, and the questionnaire remains largely the same. The main change is that the GLF now includes the IHS core questions, which are common to all of the separate modules that together comprise the IHS. Some of these core questions are simpl y questions that were previously asked in the same or a similar format on all of the IHS component surveys (including the GLF/GLS). The core questions cover employment, smoking prevalence, general health, ethnicity, citizenship and national identity. These questions are asked by proxy if an interview is not possible with the selected respondent (that is a member of the household can answer on behalf of other respondents in the household). This is a departure from the GHS which did not ask smoking prevalence and general health questions by proxy, whereas the GLF/GLS does from 2008. For details on other changes to the GLF/GLS questionnaire, please see the GLF/GLS 2008: Special Licence Access documentation held with SN 6414. Currently, the UK Data Archive holds only the SL (and not the EUL) version of the GLF/GLS for 2008. Changes to the drinking section There have been a number of revisions to the methodology that is used to produce the alcohol consumption estimates. In 2006, the average number of units assigned to the different drink types and the assumption around the average size of a wine glass was updated, resulting in significantly increased consumption estimates. In addition to the revised method, a new question about wine glass size was included in the survey in 2008. Respondents were asked whether they have consumed small (125 ml), standard (175 ml) or large (250 ml) glasses of wine. The data from this question are used when calculating the number of units of alcohol consumed by the respondent. It is assumed that a small glass contains 1.5 units, a standard glass contains 2 units and a large glass contains 3 units. (In 2006 and 2007 it was assumed that all respondents drank from a standard 175 ml glass containing 2 units.) The datasets contain the original set of variables based on the original methodology, as well as those based on the revised and (for 2008 onwards) updated methodologies. Further details on these changes are provided in the Guidelines documents held in SN 5804 - GHS 2006; and SN 6414 - GLF/GLS 2008: Special Licence Access. Special Licence GHS/GLF/GLS Special Licence (SL) versions of the GHS/GLF/GLS are available from 1998-1999 onwards. The SL versions include all variables held in the standard 'End User Licence' (EUL) version, plus extra variables covering cigarette codes and descriptions, and some birthdate information for respondents and household members. Prospective SL users will need to complete an extra application form and demonstrate to the data owners exactly why they need access to t he extra variables, in order to get permission to use the SL version. Therefore, most users should order the EUL version of the data. In order to help users choose the correct dataset, 'Special Licence Access' has been added to the dataset titles for the SL versions of the data. A list of all GHS/GLF/GLS studies available from the UK Data Archive may be found on the GHS/GLF/GLS major studies web page. See below for details of SL datasets for the corresponding GHS/GLF/GLS year (1998-1999 onwards only). UK Data Archive data holdings and formats The UK Data Archive GHS/GLF/GLS holdings begin with the 1971 study for EUL data, and from 1998-1999 for SL versions (see above). Users should note that data for the 1971 study are currently only available as ASCII files without accompanying SPSS set-up files. SPSS files for the 1972 study were created by John Simister, and redeposited at the Archive in 2000. Currently, the UK Data Archive holds only the SL versions of the GHS/GLF/GLS for 2007 and 2008. Reformatted Data 1973 to 1982 - Surrey SPSS Files SPSS files have been created by the University of Surrey for all study years from 1973 to 1982 inclusive. These early files were restructured and the case changed from the household to the individual with all of the household information duplicated for each individual. The Surrey SPSS files contain all the original variabl es as well as some extra derived variables (a few variables were omitted from the data files for 1973-76). In 1973 only, the section on leisure was not included in the Surrey SPSS files. This has subsequently been made available, however, and is now held in a separate study, General Household Survey, 1973: Leisure Questions (held under SN 3982). Records for the original GHS 1973-1982 ASCII files have been removed from the UK Data Archive catalogue, but the data are still preserved and available upon request. Users should note that GHS/GLF/GLS data are also available in formats other than SPSS.

1) The content of this dataset includes demographic information (such as age, gender, education level, marital status, etc.), social health (such as social networks, social participation, etc.), and emotional health (such as depression, loneliness, etc.). 2) This dataset is sourced from a questionnaire survey of senior university students in Beijing, Xinxiang, and Ningbo from June to July 2021. This survey was conducted by trained participants with the assistance of local senior university staff to conduct face-to-face interviews with senior university students, collecting a series of information on emotional health, social health, and other aspects. Before participating in the survey, the research group obtained verbal informed consent from the respondents. 3) A total of 713 questionnaires were collected during the survey. The data is placed in the SPSS data file (U3A. sav), and the variable view interface annotates the labels and value labels of each variable. 4) This data provides data support for understanding the participation of middle-aged and elderly people in formal classroom learning in China.

The files are in reference to this paper:

Peira, Nathalie, Armita Golkar, Arne Öhman, Silke Anders, and Stefan Wiens. Emotional Responses in Spider Fear Are Closely Related to Picture Awareness. Cognition & Emotion 26, no. 2 (February 2012): 252–60. https://doi.org/10.1080/02699931.2011.579087.

Note that the actual number of subjects was 37 (20 fearful), not 36 (as stated in the paper).

The "Peira2012Rawdata.xls" contains the raw data (as Excelfile; https://microsoft-excel.en.softonic.com/). The same data are also saved as separate tab-separated textfiles. Thus, the Excelfile is not necessary but provided for convenience.

The main tabs/files are: General: Id FearGr 0=no spider fear, 1=spider fear fear=([1,3,4,7,8,9,10,12,13,20,21,22,24,25,28,30,31,32,34,36]); nofear=([2,5,6,11,14,15,16,17,18,19,23,26,27,29,33,35,37]); spweb modified SAS questionnaire before participation SPQ spider fear questionnaire STAI trait anxiety DS disgust sensitivity

Detect: mean recognition ratings Rate: mean emotion ratings SCLmean: mean skin conductance levels for the three tasks (view, detect, rate) HRmean: mean heart rates for the three tasks (view, detect, rate)

The variable names are: f20 f40 f70 f200 s20 s40 s70 s200 Note that f refers to flower and s to spider. 20,40,70, and 200 refer to different SOAs (stimulus onset asynchronies).

The files in the Peira2012SPSS.zip are not necessary. They just illustrate how we conducted some of the analyses in SPSS (https://www.ibm.com/products/spss-statistics).

The dataset includes information on the use of e-mail reply suggestions from a socio-cultural perspective of some language-related variables. It also aimed to correlate how users perceive the use of e-mail reply suggestions to some variables: job, specialization, age, and gender. In this descriptive-inferential survey design, a self-developed questionnaire was distributed online to collect the data from a convenient sample of e-mail users. A questionnaire was used to collect data after checking the issues of validity and reliability indications. The collected data were analyzed using SPSS software.

This dataset consists of a single .sav SPSS data file presenting the anonymised survey results of the Ural Eye and Medical Study into the frequency and associated factors of bone fractures in Russians.Data can be accessed via the IBM software program SPSS: Statistical Package for Social Science, or via open-source .sav viewers.The data present answers to survey questions into bone fracture types suffered, diet, behaviour (such as smoking and alcohol consumption), daily physical activity, depression and suicidal ideas, and medical history including known diagnosis and therapy of major diseases such as arterial hypertension, diabetes mellitus, and cardiovascular diseases. Alongside survey questions, anthropometry measures such as arterial and systolic blood pressure, blood sample measures (e.g. concentrations of glucose, blood lipids, C-reactive protein, erythrocyte sedimentation rate, hemoglobin), blood glucose concentration and hearing loss data were obtained through direct testing. For more detailed explanation of the parameters and their response labels please see the Variables view in the data file.Standardised interviews were performed by trained social workers, including 5,397 eligible participants from the urban region of Kirovskii of the city of Ufa in Russia, and in villages of the rural region of the Karmaskalinsky District within a distance of 65 km from Ufa. According to the Declaration of Helsinki, the Ethics Committee of the Academic Council of the Ufa Eye Research Institute approved the study and all participants gave informed written consent. Inclusion criterion for the participation in the study was living in the study region (population ~1.1 million) and having an age of 40+ years. There were no exclusion criteria. The related study seeks to address the gap in recorded frequency of bone fractures in Russia, and the related burden of disease in terms of age-standardised DALY (disability-adjusted life years) rates.Note that parameters of this datafile that could lead to identification of research participants have been removed (for example gender, age, marital status, religion); thus the number of variables is fewer than reported in the related publication.

This dataset is derived from Project Two, "Development of Assessment Tools and Systems for the Psychological Health of the Elderly." It was initiated to conduct a questionnaire survey aimed at developing assessment tools for the social health of the elderly. The data collection included: 1) basic socio-demographic information of the respondents, their mobility experiences, and social integration experiences. A total of 233 individuals participated in the survey. 2) In April 2023, our research team conducted data collection in Beijing, Guangzhou, and other areas to design a measurement tool suitable for assessing the social integration of mobile populations of all ages, including older adults. 3) All data is stored in an SPSS data file (social_integration_230606.sav), and each variable is labeled with variable and value labels in the variable view interface. 4) This dataset can be used to explore the social integration experiences of China's elderly population in resettlement and migration, providing reference for policies aimed at social adaptation and social health of such populations.