This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

This dataset pertains to the first preliminary results of a longitudinal study relating to the impact of a new integrative therapeutic model for addictive disorders and psychological correlates. This first small study had the goal of understanding if the programme has a significant impact on certain psychological complaints that often appear in comorbidity with an addictive disorder, more specifically depressive symptomatology, suicide ideation and anxiety (state and trait as measured by STAI). MoCA was also used to screen for cognitive function at the beginning of treatment and was repeated at the end to evaluate possible changes. Preliminary results indicate a positive impact of the model in study regarding all the relevant variables. The sample is still small and further research needs to be made to confirm the results. Available are: the raw data, the statistical analyses output, information pertaining to the therapeutic model in study.

Several datasets with alignments between knowledge base triples and free text have been built, for several independent tasks, such as Relation Extraction, Knowledge base population, Relation Discovery…But where the others datasets have a small number of documents (TAC-KBP), only have the relations without the original documents (FB15K-237), have a little amount of relations (Google-RE), we present a dataset containing large-scale high-quality alignments between DBpedia abstracts and Wikidata triples, T-REx.

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit 

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema 

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys 

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{
  _id: 

Data for a Brief Report/Short Communication published in Body Image (2021). Details of the study are included below via the abstract from the manuscript. The dataset includes online experimental data from 167 women who were recruited via social media and institutional participant pools. The experiment was completed in Qualtrics.Women viewed either neutral travel images (control), body positivity posts with an average-sized model (e.g., ~ UK size 14), or body positivity posts with a larger model (e.g., UK size 18+); which images women viewed is show in the ‘condition’ variable in the data.The data includes the age range, height, weight, calculated BMI, and Instagram use of participants. After viewing the images, women responded to the Positive and Negative Affect Schedule (PANAS), a state version of the Body Satisfaction Scale (BSS), and reported their immediate social comparison with the images (SAC items). Women then selected a lunch for themselves from a hypothetical menu; these selections are detailed in the data, as are the total calories calculated from this and the proportion of their picks which were (provided as a percentage, and as a categorical variable [as used in the paper analyses]). Women also reported whether they were on a special diet (e.g., vegan or vegetarian), had food intolerances, when they last ate, and how hungry they were.

Women also completed trait measures of Body Appreciation (BAS-2) and social comparison (PACS-R). Women also were asked to comment on what they thought the experiment was about. Items and computed scales are included within the dataset.This item includes the dataset collected for the manuscript (in SPSS and CSV formats), the variable list for the CSV file (for users working with the CSV datafile; the variable list and details are contained within the .sav file for the SPSS version), and the SPSS syntax for our analyses (.sps). Also included are the information and consent form (collected via Qualtrics) and the questions as completed by participants (both in pdf format).Please note that the survey order in the PDF is not the same as in the datafiles; users should utilise the variable list (either in CSV or SPSS formats) to identify the items in the data.The SPSS syntax can be used to replicate the analyses reported in the Results section of the paper. Annotations within the syntax file guide the user through these.

A copy of SPSS Statistics is needed to open the .sav and .sps files.

Manuscript abstract:

Body Positivity (or ‘BoPo’) social media content may be beneficial for women’s mood and body image, but concerns have been raised that it may reduce motivation for healthy behaviours. This study examines differences in women’s mood, body satisfaction, and hypothetical food choices after viewing BoPo posts (featuring average or larger women) or a neutral travel control. Women (N = 167, 81.8% aged 18-29) were randomly assigned in an online experiment to one of three conditions (BoPo-average, BoPo-larger, or Travel/Control) and viewed three Instagram posts for two minutes, before reporting their mood and body satisfaction, and selecting a meal from a hypothetical menu. Women who viewed the BoPo posts featuring average-size women reported more positive mood than the control group; women who viewed posts featuring larger women did not. There were no effects of condition on negative mood or body satisfaction. Women did not make less healthy food choices than the control in either BoPo condition; women who viewed the BoPo images of larger women showed a stronger association between hunger and calories selected. These findings suggest that concerns over BoPo promoting unhealthy behaviours may be misplaced, but further research is needed regarding women’s responses to different body sizes.

This dataset presents 4,000 short stories that have been classified in terms of their emotional content and semantic structure. Emotional content was calculated using the valence, arousal and dominance norms in Warriner et al. (2014). Semantic structure was derived using the doc2vec algorithm, which classifies each text as a 300-place vector. The authors created this dataset as part of a study of the impact of narrative literature on mental health.

This database is comprised of 603 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 208 males (34%) and 395 females (66%). Their ages ranged from 12 to 15 years. Their age in years at baseline is provided. The majority were born in Australia. Data were drawn from students at two Australian independent secondary schools. The data contains total responses for the following scales: The Intolerance of Uncertainty Scale (IUS-12; Short form; Carleton et al, 2007) is a 12-item scale measuring two dimensions of Prospective and Inhibitory intolerance of uncertainty. Two subscales of the Children’s Automatic Thoughts Scale (CATS; Schniering & Rapee, 2002) were administered. The Peronalising and Social Threat were each composed of 10 items. UPPS Impulsive Behaviour Scale (Whiteside & Lynam, 2001) which is comprised of 12 items. Dispositional Envy Scale (DES; Smith et al, 1999) which is comprised of 8 items. Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. Three subscales totals included were the GAD subscale (labelled SCAS_GAD), the OCD subscale (labelled SCAS_OCD) and the Social Anxiety subscale (labelled SCAS_SA). Each subscale was comprised of 6 items. Avoidance and Fusion Questionnaire for Youth (AFQ-Y; Greco et al., 2008) which is comprised of 17 items. Distress Disclosure Index (DDI; Kahn & Hessling, 2001) which is comprised of 12 items. Repetitive Thinking Questionnaire-10 (RTQ-10; McEvoy et al., 2014) which is comprised of 10 items. The Brief Fear of Negative Evaluation Scale, Straightforward Items (BFNE-S; Rodebaugh et al., 2004) which is comprised of 8 items. Short Mood and Feelings Questionnaire (SMFQ; Angold et al., 1995) which is comprised by 13 items. The Self-Compassion Scale Short Form (SCS-SF; Raes et al., 2011) which is comprised by 12 items. The subscales include Self Kindness, Self Judgment, Social Media subscales - These subscale scores were based on social media questions composed for this project and also drawn from three separate scales as indicated in the table below. The original scales assessed whether participants experience discomfort and a fear of missing out when disconnected from social media (taken from the Australian Psychological Society Stress and Wellbeing Survey; Australian Psychological Society, 2015a), style of social media use (Tandoc et al., 2015b) and Fear of Missing Out (Przybylski et al., 2013c). The items in each subscale are listed below. Pub_Share Public Sharing When I have a good time it is important for me to share the details onlinec On social media how often do you write a status updateb On social media how often do you post photosb Surveillance_SM On social media how often do you read the newsfeed On social media how often do you read a friend’s status updateb On social media how often do you view a friend’s photob On social media how often do you browse a friend’s timelineb Upset Share On social media how often do you go online to share things that have upset you? Text private On social media how often do you Text friends privately to share things that have upset you? Insight_SM Social Media Reduction I use social media less now because it often made me feel inadequate FOMO I am afraid that I will miss out on something if I don’t stay connected to my online social networksa. I feel worried and uncomfortable when I can’t access my social media accountsa. Neg Eff of SM I find it difficult to relax or sleep after spending time on social networking sitesa. I feel my brain ‘burnout’ with the constant connectivity of social mediaa. I notice I feel envy when I use social media.
I can easily detach from the envy that appears following the use of social media (reverse scored) DES_SM Envy Mean acts online Feeling envious about another person has led me to post a comment online about another person to make them laugh Feeling envious has led me to post a photo online without someone’s permission to make them angry or to make fun of them Feeling envious has prompted me to keep another student out of things on purpose, excluding her from my group of friends or ignoring them. Substance Use: Two items measuring peer influence on alcohol consumption were adapted from the SHAHRP “Patterns of Alcohol Use” measure (McBride, Farringdon & Midford, 2000). These items were “When I am with friends I am quite likely to drink too much alcohol” and “Substances (alcohol, drugs, medication) are the immediate way I respond to my thoughts about a situation when I feel distressed or upset. Angold, A., Costello, E. J., Messer, S. C., & Pickles, A. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5(4), 237–249. Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Greco, L.A., Lambert, W. & Baer., R.A. (2008) Psychological inflexibility in childhood and adolescence: Development and evaluation of the Avoidance and Fusion Questionnaire for Youth. Psychological Assessment, 20, 93-102. https://doi.org/10.1037/1040-3590.20.2.9 Kahn, J. H., & Hessling, R. M. (2001). Measuring the tendency to conceal versus disclose psychological distress. Journal of Social and Clinical Psychology, 20(1), 41–65. https://doi.org/10.1521/jscp.20.1.41.22254 McBride, N., Farringdon, F. & Midford, R. (2000) What harms do young Australians experience in alcohol use situations. Australian and New Zealand Journal of Public Health, 24, 54–60 https://doi.org/10.1111/j.1467-842x.2000.tb00723.x McEvoy, P.M., Thibodeau, M.A., Asmundson, G.J.G. (2014) Trait Repetitive Negative Thinking: A brief transdiagnostic assessment. Journal of Experimental Psychopathology, 5, 1-17. Doi. 10.5127/jep.037813 Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in human behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Rodebaugh, T. L., Woods, C. M., Thissen, D. M., Heimberg, R. G., Chambless, D. L., & Rapee, R. M. (2004). More information from fewer questions: the factor structure and item properties of the original and brief fear of negative evaluation scale. Psychological assessment, 16(2), 169. https://doi.org/10.1037/10403590.16.2.169 Schniering, C. A., & Rapee, R. M. (2002). Development and validation of a measure of children’s automatic thoughts: the children’s automatic thoughts scale. Behaviour Research and Therapy, 40(9), 1091-1109. . https://doi.org/10.1016/S0005-7967(02)00022-0 Smith, R. H., Parrott, W. G., Diener, E. F., Hoyle, R. H., & Kim, S. H. (1999). Dispositional envy. Personality and Social Psychology Bulletin, 25(8), 1007-1020. https://doi.org/10.1177/01461672992511008 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5 Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 Whiteside, S.P. & Lynam, D.R. (2001) The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences 30,669-689. https://doi.org/10.1016/S0191-8869(00)00064-7 The data was collected by Dr Danielle A Einstein, Dr Madeleine Fraser, Dr Anne McMaugh, Prof Peter McEvoy, Prof Ron Rapee, Assoc/Prof Maree Abbott, Prof Warren Mansell and Dr Eyal Karin as part of the Insights Project. The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

The distribution of effect sizes may offer insights about the research done and reported in a scientific field. We have evaluated 12,412 manually collected correlation effect sizes (Sample 1) and 31,157 computer-extracted correlation effect sizes (Sample 2) published in journals focused on social or developmental psychology. Sample 1 consisted of 243 studies from 6 journals published in 2010 and 2019. Sample 2 consisted of 5,012 papers published in 10 journals between 2010–2019. The 25th, 50th and 75th effect size percentiles were 0.08, 0.17 and 0.33, and 0.17, 0.31, and 0.52 in Samples 1 and 2, respectively. Sample 2 percentiles were probably larger because Sample 2 only included effect sizes from the text but not from tables. In text, authors may have emphasized larger correlations. Large sample sizes were associated with smaller reported correlations. In Sample 1 about 70% of studies specified a directional hypothesis. In 2010, no papers had power calculations while in 2019, 14% of p...

Supporting data and code from 'The time-course of real-world spatial and semantic processing'

---------------------- General Info ----------------------

If there are any bugs/issues, contact Matt Anderson: Matt.Anderson@soton.ac.uk

doi: //doi.org/10.5258/SOTON/D2036

ORCID ID (Matt Anderson): 0000-0002-7498-2719

Research funded by a University of Southampton Jubilee Scholarship, EPSRC grant EP/K005952/1, EPSRC grant EP/S016368/1, and a York University VISTA Visiting Trainee Award

---------------------- File Info ----------------------

The files AggData/SEM_categorization.txt and AggData/STR_categorization.txt contain trial-by-trial response data from the semantic and spatial structure tasks respectively. The columns should be intuitively named, but here is a brief description of each:

• Ppt_No: unique participant identifier
• Scene: scene identifier in the SYNS database (see https://syns.soton.ac.uk/)
• View: view identifier in the SYNS database
• cat_agreement: proportion of participants who selected the ground-truth category, using unlimited viewing durations (see Anderson et al., 2021)
• GT_Category: Ground-truth category. In the semantic file, the numbers 1->6 correspond to Nature, Road, Residence, Farm, Beach, and Car Park / Commercial respectively. In the spatial structure file, numbers 1->4 correspond to Cluttered, Closed Off, Flat, and Tunnel respectively.
• TrialNumber: order in which the image was presented for a given participant
• imageID: unique identifier for each image
• distance_*: The next 6 variables compute various statistics of the image using coregistered ground-truth LiDAR data. These are the (i) mean across the image, standard deviation, and range. These statistics are also computed for a small patch around the fixation location.
• distance_bin: median split by the column distance_mean
• Colour_GrayScale: 1 = Grayscale, 2 = Colour
• Stereo_Cond: 1 = Mono, 2 = Stereo, 3 = Stereo-Reversed
• Pres_Time: number of frames, as a multiple of 13.33 msecs (75 Hz refresh rate)
• SceneCat: participant-selected category
• DepthCat: participant-selected depth
• ResponsePeriod: Response time (dunno why I called it period)
• Elapsed: empirical measure of computer flip interval (psychtoolbox)
• Cat_correct: 1 = correct, 0 = incorrect
• Depth_Correct: 1 = correct, 0 = incorrect

VCAP Dataset is the product of "Short-term cognitive change in adults from 18 to 80", an NIH-funded project directed by Dr. Timothy Salthouse between 2001 and 2019. The dataset contains longitudinal assessments from about 6000 individuals aged 19-99 years old. The availability of the dataset is curated by project transition team after the retirement of Dr. Salthouse. We request that the guestbook to be signed before downloading the datasets. It is also expected that users are committed (1) to using the data only for research purposes and not to identify any individual participant; (2) to securing the data using appropriate computer technology; and (3) to citing the VCAP appropriately if the use of data leads to new publications or other products.

Dataset and syntax for "Are Civility Norms Morality Norms’ Little Sister? The Truth Value That Lay Thinking Associates With Civility and Morality Social Norms".

Dataset Card for "heliosbrahma/mental_health_chatbot_dataset"

  Dataset Description





  Dataset Summary

This dataset contains conversational pair of questions and answers in a single text related to Mental Health. Dataset was curated from popular healthcare blogs like WebMD, Mayo Clinic and HeatlhLine, online FAQs etc. All questions and answers have been anonymized to remove any PII data and pre-processed to remove any unwanted characters.

  Languages

The… See the full description on the dataset page: https://huggingface.co/datasets/heliosbrahma/mental_health_chatbot_dataset.

This is a data set collected with the paper and pencil method from a non-volunteer student sample during an instructional demonstration. The data was collected with the aim to determine the psychometric properties of a translated Hungarian short version happiness scale based on the Subjective Happiness Scale (Lyubomirsky and Lepper, Social Indicators Research, 46, 137–155, 1999).

Most psychologists are likely to have at least some clients bring a dream into therapy. In the few studies looking at the use of dreams in therapy, therapists report that they do not feel confident or competent to adequately respond to their clients' introduction of dream material in therapy. The possible consequences of this include a negative impact on the therapeutic alliance and misinterpretation of the therapist's rejection of a dream narrative as a disinterest in the client's inner life. This research project seeks to identify psychologists' and psychology clients' understanding of their experiences of the use of dream material in therapy and their understanding of the role of dreams in contemporary psychological practice. While there have been some surveys about the use of dreams in therapy, relatively little is known about this topic, so a phenomenological, qualitative approach will be used. This research will be broken into two studies. The first study will use semi-structured interviews to interview psychologists and the second study will use semi-structured interviews to interview psychology clients. A hermeneutic phenomenological analysis of the interview transcripts will be completed with the aid of Dedoose software.

The main results file are saved separately:- ASSR2.html: R output of the main analyses (N = 33)- ASSR2_subset.html: R output of the main analyses for the smaller sample (N = 25)FIGSHARE METADATACategories- Biological psychology- Neuroscience and physiological psychology- Sensory processes, perception, and performanceKeywords- crossmodal attention- electroencephalography (EEG)- early-filter theory- task difficulty- envelope following responseReferences- https://doi.org/10.17605/OSF.IO/6FHR8- https://github.com/stamnosslin/mn- https://doi.org/10.17045/sthlmuni.4981154.v3- https://biosemi.com/- https://www.python.org/- https://mne.tools/stable/index.html#- https://www.r-project.org/- https://rstudio.com/products/rstudio/GENERAL INFORMATION1. Title of Dataset:Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones2. Author Information A. Principal Investigator Contact Information Name: Stefan Wiens Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.su.se/profiles/swiens-1.184142 Email: sws@psychology.su.se B. Associate or Co-investigator Contact Information Name: Malina Szychowska Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.researchgate.net/profile/Malina_Szychowska Email: malina.szychowska@psychology.su.se3. Date of data collection: Subjects (N = 33) were tested between 2019-11-15 and 2020-03-12.4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden5. Information about funding sources that supported the collection of the data:Swedish Research Council (Vetenskapsrådet) 2015-01181SHARING/ACCESS INFORMATION1. Licenses/restrictions placed on the data: CC BY 4.02. Links to publications that cite or use the data: Szychowska M., & Wiens S. (2020). Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Submitted manuscript.The study was preregistered:https://doi.org/10.17605/OSF.IO/6FHR83. Links to other publicly accessible locations of the data: N/A4. Links/relationships to ancillary data sets: N/A5. Was data derived from another source? No 6. Recommended citation for this dataset: Wiens, S., & Szychowska M. (2020). Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.12582002DATA & FILE OVERVIEWFile List:The files contain the raw data, scripts, and results of main and supplementary analyses of an electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information.ASSR2_experiment_scripts.zip: contains the Python files to run the experiment. ASSR2_rawdata.zip: contains raw datafiles for each subject- data_EEG: EEG data in bdf format (generated by Biosemi)- data_log: logfiles of the EEG session (generated by Python)ASSR2_EEG_scripts.zip: Python-MNE scripts to process the EEG dataASSR2_EEG_preprocessed_data.zip: EEG data in fif format after preprocessing with Python-MNE scriptsASSR2_R_scripts.zip: R scripts to analyze the data together with the main datafiles. The main files in the folder are: - ASSR2.html: R output of the main analyses- ASSR2_subset.html: R output of the main analyses but after excluding eight subjects who were recorded as pilots before preregistering the studyASSR2_results.zip: contains all figures and tables that are created by Python-MNE and R.METHODOLOGICAL INFORMATION1. Description of methods used for collection/generation of data:The auditory stimuli were amplitude-modulated tones with a carrier frequency (fc) of 500 Hz and modulation frequencies (fm) of 20.48 Hz, 40.96 Hz, or 81.92 Hz. The experiment was programmed in python: https://www.python.org/ and used extra functions from here: https://github.com/stamnosslin/mnThe EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.com) and saved in .bdf format.For more information, see linked publication.2. Methods for processing the data:We conducted frequency analyses and computed event-related potentials. See linked publication3. Instrument- or software-specific information needed to interpret the data:MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html#Rstudio used with R (R Core Team, 2020): https://rstudio.com/products/rstudio/Wiens, S. (2017). Aladins Bayes Factor in R (Version 3). https://www.doi.org/10.17045/sthlmuni.4981154.v34. Standards and calibration information, if appropriate:For information, see linked publication.5. Environmental/experimental conditions:For information, see linked publication.6. Describe any quality-assurance procedures performed on the data:For information, see linked publication.7. People involved with sample collection, processing, analysis and/or submission:- Data collection: Malina Szychowska with assistance from Jenny Arctaedius.- Data processing, analysis, and submission: Malina Szychowska and Stefan WiensDATA-SPECIFIC INFORMATION:All relevant information can be found in the MNE-Python and R scripts (in EEG_scripts and analysis_scripts folders) that process the raw data. For example, we added notes to explain what different variables mean.

This dataset comprises an extensive compilation of quotes extracted from the Malazan Book of the Fallen series, authored by Steven Erikson. It has been sourced from Goodreads and each quote is tagged and assigned a popularity score based on likes, facilitating sorting based on various categories. Additionally, the Book Column, which represents categorical data, enables sorting based on books as well.

One exercise that can be performed on this dataset is to identify and eliminate duplicate quotes or to exclude quotes that form a small part of a larger quote. This will also come in handy for NLP and recognizing fantasy elements, character instances and general sentiment analysis for the tone of Malazan Books throughout the series.

Recent research not only confirms the existence of substantial psychological variation around the globe but also highlights the peculiarity of many Western populations. We propose that part of this 15 variation can be traced back to the action and diffusion of the Western Church, the branch of Christianity that evolved into the Roman Catholic Church. Specifically, we propose that the Church’s transformation of European kinship, by promoting small, nuclear households, weak family ties and residential mobility, fostered greater individualism, less conformity and more impersonal prosociality. By combining data on 24 psychological outcomes with historical measures of both Church exposure and kinship, we find support for these ideas in a comprehensive array of analyses across countries, among European regions and between individuals from different cultural backgrounds.

This data set allows the replication of the analyses reported in the main text and the supplementary material of the manu...

This dataset's answers referes to 281 employees (fixed employees and public servants), both genders. We have information about marital status, level of education, income and also questions to better understand how populous the teleworking was, considering the second pandemic pick. It means that data was collected between May and July/2021. About the constructs, we publish in this database the follow constructs. - Work as Meaning Inventory WAMI: is a 10-item measure assessing search for Greater Good Motivation (3 items; e.g. My work helps me make sense of the world around me), positive meaning (4 items; e.g. I have found a meaningful career”) and contribution to meaning-making (3 items; e.g. I view my work as contributing to my personal growth”). The items of the inventory are rated from 1 (absolutely untrue) to 5 (absolutely true); - Emotions: Scale composed by positive emotions (N= 4 items) and reverse score of negative emotions (5 items) (item of positive emotion example "over the past six months my work made me feel happy"; item of negative emotion example "over the past six months my work made me feel upset". The response scale is a 5-point agreement scale. - Performance. Short Version of General Self-Assessment Scale of Job Performance has 10 items to measure Task and Context performance (e.g. I take initiatives to improve my results at work). Items are rated from 1 (absolutely false) to 5 (absolutely true). - I- deal. The HR flexibility and development i-deal is a measure with 6 items (HR i-deal e.g. I try to negotiate my job conditions with the company). We use the scale is a single factor just considering flexibility aspect. Items rated from 1 (Never) to 5 (Always). - Recovery Experience Questionnaire is composed of four types of recovery experience (psychological detachment, relaxation, mastery and control). We reduce 1 item of each scale based on the factor loading. Participants indicated on a 5-point scale (1 = never to 5 = always) the extent to which each recovery experience was de-scribing the psychological detachment (3-item), relaxation (3-item), mastery (3-item), and control (3-item).. We keep in this database all the factors about the constructs measured and also the clusters we found considering Belasos Model. All these information was collected in a first moment (T1 386_COVID-19 BR.csv)

The first part of this data set (PaperNET = 1) was collected with the paper and pencil method from a non-volunteer student sample during an instructional demonstration (PaperNET = 1) . The second part (PaperNET = 2) was collectd online from adult volunteers using the Qualtrics research platform. The data was collected with the aim to determine the psychometric properties of a translated Hungarian short version happiness scale based on the English version of the Subjective Happiness Scale (Lyubomirsky and Lepper, Social Indicators Research, 46, 137–155, 1999).

Missing data is a frequent occurrence in both small and large datasets. Among other things, missingness may be a result of coding or computer error, participant absences, or it may be intentional, as in a planned missing design. Whatever the cause, the problem of how to approach a dataset with holes is of much relevance in scientific research. First, missingness is approached as a theoretical construct, and its impacts on data analysis are encountered. I discuss missingness as it relates to structural equation modeling and model fit indices, specifically its interaction with the Root Mean Square Error of Approximation (RMSEA). Data simulation is used to show that RMSEA has a downward bias with missing data, yielding skewed fit indices. Two alternative formulas for RMSEA calculation are proposed: one correcting degrees of freedom and one using Kullback-Leibler divergence to result in an RMSEA calculation which is relatively independent of missingness. Simulations are conducted in Java, with results indicating that the Kullback-Leibler divergence provides a better correction for RMSEA calculation. Next, I approach missingness in an applied manner with an existing large dataset examining ideology measures. The researchers assessed ideology using a planned missingness design, resulting in high proportions of missing data. Factor analysis was performed to gauge uniqueness of ideology measures.