These datasets contain cleaned data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. These specific iterations of data reflect cleaning and standardization so that data can be analyzed using Python. Ultimately, the three files reflect the removal of survey begin/end times, other data auto-recorded by Qualtrics, blank rows, blank responses after question four (the first section of the survey), and non-United States responses. Note that State names for "What state is your library located in?" (Q36) were also standardized beginning in Impact_of_COVID_on_Tech_Services_Clean_3.csv to aid in data analysis. In this step, state abbreviations were spelled out and spelling errors were corrected.

Empty cells in the data file represent data not available in the study due to failure to report by the study participant.

Objectives: Shiftworkers routinely obtain inadequate sleep, which has major health consequences. Sleep hygiene describes a range of behaviours, lifestyle and environmental factors that can improve sleep. To date, limited research has examined sleep hygiene in shiftworkers. This study aimed to assess the sociodemographic and behavioural correlates of sleep hygiene knowledge and engagement with sleep hygiene practices in Australian shiftworkers.

Study Design: An online, cross-sectional survey.

Setting and Participants: Australian adults from across multiple industries (n = 588) who work shift work.

Measures: The online survey included questions regarding sleep hygiene knowledge, and questions from modified versions of the Pittsburgh Sleep Quality Index and Sleep Hygiene Index.

Results: Of the 588 participants, 52.9% reported having heard of ‘sleep hygiene’. Of these participants, 77.5% reported understanding the term moderately, extremely or very well. Engagement with each sleep hygie...

Background: Existing research comparing expressive suppression levels across cultural groups have largely been conducted in countries where there is a very strong dominant majority culture. There is also a lack of empirical research on the differences in levels of expressive suppression between different Asian cultures. Investigating levels of expressive suppression across cultural groups is important, as different groups have been found to display different expressive suppression levels, which has different effects on wellbeing for different cultural groups. The data collected allows for comparison of expressive suppression levels between Chinese and Malay individuals (2 Asian cultures) in Singapore (a multicultural country where maintenance of all cultural groups' heritage are equally promoted).

This data record contains:

• Raw Qualtrics data: Responses downloaded from Qualtrics, with identifying / irrelevant information removed
• Processed data: Responses with incomplete responses removed, negatively keyed questions reverse coded
• Questionnaire: Questions included in online survey and debriefing message included at the end of the survey

Data collection:

• SONA & personal contacts, snowball method
• Participants completed online Qualtrics questionnaire
• Participant inclusion criteria: Singaporean citizen / PR, identify as either Chinese or Malay, aged 17 or 18 and above for university and non-university students respectively

Data analysis:

• Data cleaning: incomplete responses removed, negatively keyed questions reverse coded
• Data analysis software: SPSS (ANOVA, reliability analysis, descriptive statistics)

The RAAAP project surveyed Research Managers and Administrators from across the world, asking questions about why people became RMAs, why they stayed as RMAs, what skills they need for their jobs (soft and hard), what level of seniority they are, demographic information, and so on - overall up to 222 data points were collected from each respondent. This is the output from the SPSS syntax file (DOI: 10.6084/m9.figshare.6269090) developed to process the raw qualtrics data, including data cleansing and anonymising. The process is described in detail in the "RAAAP Data Cleansing Process" DOI:10.6084/m9.figshare.5948461