This comprehensive dataset is a meticulously curated collection of mental health statuses tagged from various statements. The dataset amalgamates raw data from multiple sources, cleaned and compiled to create a robust resource for developing chatbots and performing sentiment analysis.

Data Source:

The dataset integrates information from the following Kaggle datasets:

• 3k Conversations Dataset for Chatbot
• Depression Reddit Cleaned
• Human Stress Prediction
• Predicting Anxiety in Mental Health Data
• Mental Health Dataset Bipolar
• Reddit Mental Health Data
• Students Anxiety and Depression Dataset
• Suicidal Mental Health Dataset
• Suicidal Tweet Detection Dataset

Data Overview:

The dataset consists of statements tagged with one of the following seven mental health statuses: - Normal - Depression - Suicidal - Anxiety - Stress - Bi-Polar - Personality Disorder

Data Collection:

The data is sourced from diverse platforms including social media posts, Reddit posts, Twitter posts, and more. Each entry is tagged with a specific mental health status, making it an invaluable asset for:

• Developing intelligent mental health chatbots.
• Performing in-depth sentiment analysis.
• Research and studies related to mental health trends.

Features:

• unique_id: A unique identifier for each entry.
• Statement: The textual data or post.
• Mental Health Status: The tagged mental health status of the statement.

Usage:

This dataset is ideal for training machine learning models aimed at understanding and predicting mental health conditions based on textual data. It can be used in various applications such as:

• Chatbot development for mental health support.
• Sentiment analysis to gauge mental health trends.
• Academic research on mental health patterns.

Acknowledgments:

This dataset was created by aggregating and cleaning data from various publicly available datasets on Kaggle. Special thanks to the original dataset creators for their contributions.

This dataset is made available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). See LICENSE.pdf for details.

Dataset description

Parquet file, with:

• 35694 rows
• 154 columns

The file is indexed on [participant]_[month], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.

Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.

File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (https://clinicaltrials.gov/ct2/show/NCT03421223). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.

The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.

The data subset used in this work comprises the following:

• Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study
• Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities
• Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month
• Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity

From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).

The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.

Dataset

This dataset was created by Raoof Naushad

Contents

This dataset contains a collection of posts from Reddit. The posts have been collected from 3 subreddits: r/teenagers, r/SuicideWatch, and r/depression. There are 140,000 labeled posts for training and 60,000 labeled posts for testing. Both training and testing datasets have an equal split of labels. This dataset is not mine. The original dataset is on Kaggle: https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch/versions/13

This is a source dataset for a Let's Get Healthy California indicator at "https://letsgethealthy.ca.gov/." This table displays the proportion of adults who were ever told they had a depressive disorder in California. It contains data for California only. The data are from the California Behavioral Risk Factor Surveillance Survey (BRFSS). The California BRFSS is an annual cross-sectional health-related telephone survey that collects data about California residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. This indicator is based on the question: "“Has a doctor, nurse or other health professional EVER told you that you have a depressive disorder (including depression, major depression, dysthymia, or minor depression)?” NOTE: Denominator data and weighting was taken from the California Department of Finance, not U.S. Census. Values may therefore differ from what has been published in the national BRFSS data tables by the Centers for Disease Control and Prevention (CDC) or other federal agencies.

Dataset Card for "emotion"

  Dataset Summary

Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.

  Supported Tasks and Leaderboards

More Information Needed

  Languages

More Information Needed

  Dataset Structure





  Data Instances

An example looks as follows. { "text": "im feeling quite sad and sorry for myself but… See the full description on the dataset page: https://huggingface.co/datasets/dair-ai/emotion.

The Canadian Biomarker Integration Network in Depression (CAN-BIND) is a national program of research and learning. From 2013 to 2017, data were collected from 211 participants with major depressive disorder and 112 healthy individuals. The objective of this data-set is to integrate detailed clinical, imaging, and molecular data to predict outcome for patients experiencing a Major Depressive Episode (MDE) and receiving pharmacotherapy reflective of standard practice. The clinical characterization consists of symptom assessment, behavioural dimensions, and environmental factors. The neuroimaging data consist of structural, resting and task-based functional, and diffusion-weighted MRI images, as well as scalp-recorded EEG data. The molecular data currently consist of DNA methylation, inflammatory markers and urine metabolites. Baseline and Phase 1 (Weeks 2-8) data are now available for request.

Dataset

This dataset was created by Prasad Meesala

Released under MIT

Contents

Student Depression Dataset: Analyzing Mental Health Trends and Predictors Among Students

Overview
This dataset compiles a wide range of information aimed at understanding, analyzing, and predicting depression levels among students. It is designed for research in psychology, data science, and education, providing insights into factors that contribute to student mental health challenges and aiding in the design of early intervention strategies.

Data Description
- Format: CSV (each row represents an individual student)
- Features:
- ID: Unique identifier for each student
- Demographics: Age, Gender, City
- Academic Indicators: CGPA, Academic Pressure, Study Satisfaction
- Lifestyle & Wellbeing: Sleep Duration, Dietary Habits, Work Pressure, Job Satisfaction, Work/Study Hours
- Additional Factors: Profession, Degree, Financial Stress, Family History of Mental Illness, and whether the student has ever had suicidal thoughts
- Target Variable:
- Depression_Status: A binary indicator (0/1 or Yes/No) that denotes whether a student is experiencing depression

Key Highlights
- Multifaceted Data: Integrates demographic, academic, and lifestyle factors to offer a comprehensive view of student wellbeing.
- Ethical Considerations: Data collection adhered to strict ethical standards with an emphasis on privacy, informed consent, and anonymization.
- Research & Practical Applications: Ideal for developing predictive models, conducting statistical analyses, and informing mental health intervention strategies in educational environments.

Usage & Potential Applications
- Academic Research: Explore correlations between academic pressures and mental health trends.
- Data Science Projects: Build predictive models to identify at-risk students based on various indicators.
- Policy Making: Inform the development of targeted mental health support programs within academic institutions.

Ethical Note
Due to the sensitive nature of the data, please ensure that any analysis or published results respect privacy and ethical guidelines. Users of this dataset should be mindful of the ethical implications when interpreting and sharing insights.

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). Adults included in this indicator are those who reported ever being diagnosed with depression AND either currently being treated for depression or currently having symptoms of depression.There is growing recognition that mental health is as essential to overall wellbeing as physical health. Individuals who are exposed to chronic stress from financial worry, work and family demands, job insecurity, unsafe living environments, social isolation, or discrimination are at a greater risk for developing mental health conditions, such as depression, anxiety, or post-traumatic stress disorder. Cities and communities can take an active role in fostering mental health by ensuring community safety, promoting equitable employment opportunities and economic security, expanding affordable housing, creating varied opportunities for residents to engage in community issues, reducing the stigma associated with mental health, and providing support services, particularly for seniors and other vulnerable community members.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This table contains 94080 series, with data for years 2003 - 2003 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (70 items: Newfoundland and Labrador; Health and Community Services Eastern Region; Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador ...) Age group (14 items: Total; 12 years and over; 12 to 14 years; 12 to 19 years; 15 to 19 years ...) Sex (3 items: Both sexes; Females; Males ...) Probability of depression (4 items: Total population for the variable probability of depression; Probability of depression; 0.9 or greater; Probability of depression; less than 0.9 ...) Characteristics (8 items: Number of persons; High 95% confidence interval; number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons ...).

In this project, we aimed to increase what is known about the negative effects of maternal depression and anxiety disorders (MDAD) on the mental health outcomes of children. Mental health is a topical area of research that is receiving increasing attention in the media and is one of five ESRC strategic priorities for investment. The main aim of the project was to help develop an understanding of how mental depression and anxiety disorders are transmitted from one generation to the next and ultimately help to design interventions better able to reduce the consequences of maternal mental health for children. We have used data from QResearch, a large consolidated database derived from anonymized health records from general practices in England matched with hospital administrative data, the Hospital Episode Statistics (HES). Further information is available under Related Resources.

Problems relating to Maternal Depression and Anxiety Disorders (MDAD) are common and are known to affect child health and development. In the UK, the cost of perinatal mental health problems has been estimated at £8.1 billion for each birth cohort of children, and 72 percent of this cost is related to the direct impact on the children.

The overarching aim of our proposed research is to examine the effect of MDAD on child health outcomes, with a special focus on the role that MDAD plays in the development of child depression and anxiety disorders (CDAD) in adolescence. In particular, this research will provide robust empirical evidence to understand how depression and anxiety disorders are transmitted from one generation to the next and to help design interventions aimed at reducing the negative consequences of poor maternal mental health for children.

To achieve this aim, we will address the following research questions:

1) Are the negative effects of MDAD on children exclusively explained by genetic transmission and family background characteristics? Or are these negative effects also explained by changes in the child's home environment? If the transmission of mental and anxiety disorders is explained exclusively by genetic traits and family background characteristics, then interventions targeted at reducing the negative effect of MDAD on maternal behaviour, e.g. through cognitive behavioural therapy, would be ineffective. On the contrary, evidence on significant effects of MDAD after controlling for genetic and family background characteristics would suggest that MDAD can lead to changes in the child home environment, e.g. changes in maternal behaviour, harsher parenting style and lower time investments in the child, with negative consequences on children.

2) Do school policies and health practices have a role in attenuating the negative effect of maternal depression on children? We will answer this research question by focusing on whether starting school earlier harms or protects children who are exposed to MDAD, and on whether an early diagnosis of maternal depression can attenuate the negative effects suffered by children.

We will develop and use state-of-the-art estimation methods in combination with a novel administrative dataset covering general practices and hospitals created by merging two population-based health databases from England - namely QResearch and Hospital Episode Statistics. Using this merged database, we will create a longitudinal household dataset that will allow us to study the mental health of mothers and their children at different stages of the children's lives up to adolescence.

We are a multi-disciplinary team from the Universities of Oxford and York, consisting of experts in applied econometric methods, child and maternal mental health, psychology, general practice, and on the data that we plan to utilise.

We will translate our research findings into advice for policy-makers to help them design new interventions aimed at achieving better outcomes for patients suffering from maternal mental health issues and their children. Our research will also have an impact on health practitioners, psychologists, academics and charities working with mothers and children. We will produce papers aimed at academics as well as non-technical outputs to engage with policy-makers and a non-academic audience. Furthermore, by sharing and explaining our data and estimation methods to academics, we will build capacity for further research based on large health datasets.

The final central element of the project will be to build the capacity of early career researchers to undertake and lead large interdisciplinary projects.

EmoKey Moments Muse EEG Dataset (EKM-ED): A Comprehensive Collection of Muse S EEG Data and Key Emotional Moments

Dataset Description:

The EmoKey Moments EEG Dataset (EKM-ED) is an intricately curated dataset amassed from 47 participants, detailing EEG responses as they engage with emotion-eliciting video clips. Covering a spectrum of emotions, this dataset holds immense value for those diving deep into human cognitive responses, psychological research, and emotion-based analyses.

Dataset Highlights:

Precise Timestamps: Capturing the exact millisecond of EEG data acquisition, ensuring unparalleled granularity.

Brainwave Metrics: Illuminating the variety of cognitive states through the prism of Delta, Theta, Alpha, Beta, and Gamma waves.

Motion Data: Encompassing the device's movement in three dimensions for enhanced contextuality.

Auxiliary Indicators: Key elements like the device's positioning, battery metrics, and user-specific actions are meticulously logged.

Consent and Ethics: The dataset respects and upholds privacy and ethical standards. Every participant provided informed consent. This endeavor has received the green light from the Ethics Committee at the University of Granada, documented under the reference: 2100/CEIH/2021.

A pivotal component of this dataset is its focus on "key moments" within the selected video clips, honing in on periods anticipated to evoke heightened emotional responses.

Curated Video Clips within Dataset:

    Film
    Emotion
    Duration (seconds)




    The Lover
    Baseline
    43


    American History X
    Anger
    106


    Cry Freedom
    Sadness
    166


    Alive
    Happiness
    310


    Scream
    Fear
    395

The cornerstone of EKM-ED is its innovative emphasis on these key moments, bringing to light the correlation between distinct cinematic events and specific EEG responses.

Key Emotional Moments in Dataset:

    Film
    Emotion
    Key moment timestamps (seconds)




    American History X
    Anger
    36, 57, 68


    Cry Freedom
    Sadness
    112, 132, 154


    Alive
    Happiness
    227, 270, 289


    Scream
    Fear
    23, 42, 79, 226, 279, 299, 334

Citation: Gilman, T. L., et al. (2017). A film set for the elicitation of emotion in research. Behavior Research Methods, 49(6). Link to the study

With its unparalleled depth and focus, the EmoKey Moments EEG Dataset aims to advance research in fields such as neuroscience, psychology, and affective computing, providing a comprehensive platform for understanding and analyzing human emotions through EEG data.

——————————————————————————————————— FOLDER STRUCTURE DESCRIPTION ———————————————————————————————————

• questionnaires: all there response questionnaires (Spanish); raw and preprocessed Including SAM | ——preprocessed: Ficha_Evaluacion_Participante_SAM_Refactored.csv: the SAM responses for every film clip

• key_moments: the key moment timestamps for every emotion’s clip

• muse_wearable_data: XXXX | |—raw |——1: ID = 1 of subject |————muse: EEG data of Muse device |—————————ANGER_XXX.csv : leg data of the anger elicitation |—————————FEAR_XXX.csv : leg data of the fear elicitation |—————————HAPPINESS_XXX.csv : leg data of the happiness elicitation |—————————SADNESS_XXX.csv : leg data of the sadness elicitation |————order: film elicitation order of play: For example: HAPPINESS,SADNESS,ANGER,FEAR … | |—preprocessed |——unclean-signals: without removing EEG artifacts, noise, etc. |————muse: EEG data of Muse device |—————————0.0078125: data downsampled to 128 Hz from 256Hz recorded |——clean-signals: removed EEG artifacts, noise, etc. |————muse: EEG data of Muse device |—————————0.0078125: data downsampled to 128 Hz from 256Hz recorded

The ethical consent for this dataset was provided by La Comisión de Ética en Investigación de la Universidad de Granada, as documented in the approval titled: 'DETECCIÓN AUTOMÁTICA DE LAS EMOCIONES BÁSICAS Y SU INFLUENCIA EN LA TOMA DE DECISIONES MEDIANTE WEARABLES Y MACHINE LEARNING' registered under 2100/CEIH/2021.

Dataset Card for Depression: Reddit Dataset (Cleaned)

  Dataset Summary

The raw data is collected through web scrapping Subreddits and is cleaned using multiple NLP techniques. The data is only in English language. It mainly targets mental health classification.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/reddit-depression-cleaned.

Data used in the analysis presented in Myers, S. and Johns, S.E. (2019). Male infants and birth complications are associated with increased incidence of postnatal depression. Social Science & Medicine 220: 56-64. The data reflects the complete reproductive histories of post-menopausal women collected by retrospective survey. Respondents reported details about every birth they had experienced and were assessed on a number of demographic and psychological measures. Valid responses from 306 women were received. Most women did the majority of their childrearing in the UK (74.7%), followed by the United States (12.6%), and the rest of the World (12.7%) - this data is omitted to ensure participant anonymity. The spreadsheet contains a guide to the variable coding and data on the following variables: parity, birth type, infant sex, postnatal depression, infant death, infant adoption, maternal depression-anxiety-stress, birth complications, current depression, socioeconomic status during childbearing years, postnatal social support, year of mother's birth. For more details regarding data collection and the variables measured see Myers, S. and Johns, S.E. (2019).

This dataset was used to investigate the brain mechanism underlying rumination state (Chen et al., 2020, NeuroImage). The data was shared through the R-fMRI Maps Project (RMP) and Psychological Science Data Bank.Investigators and AffiliationsXiao Chen, Ph. D. 1, 2, 3, 4, Chao-Gan Yan, Ph. D. 1, 2, 3, 41. CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China;2. International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China;3. Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China;4. Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China. AcknowledgmentsWe would like to thank the National Center for Protein Sciences at Peking University in Beijing, China, for assistance with data acquisition at PKU and Dr. Men Weiwei for his technical support during data collection. FundingNational Key R&D Program of China (2017YFC1309902);National Natural Science Foundation of China (81671774 and 81630031);13th Five-year Informatization Plan of Chinese Academy of Sciences (XXH13505);Key Research Program of the Chinese Academy of Sciences (ZDBS-SSW-JSC006);Beijing Nova Program of Science and Technology (Z191100001119104);Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (Y9CX422005);China Postdoctoral Science Foundation (2019M660847). Publication Related to This DatasetThe following publication include the data shared in this data collection:Chen, X., Chen, N.X., Shen, Y.Q., Li, H.X., Li, L., Lu, B., Zhu, Z.C., Fan, Z., Yan, C.G. (2020). The subsystem mechanism of default mode network underlying rumination: A reproducible neuroimaging study. Neuroimage, 221, 117185, doi:10.1016/j.neuroimage.2020.117185. Sample SizeTotal: 41 (22 females; mean age = 22.7 ± 4.1 years).Exclusion criteria: Any MRI contraindications, current psychiatric or neurological disorders, clinical diagnosis of neurologic trauma, use of psychotropic medication and any history of substance or alcohol abuse. Scan procedures and ParametersMRI scanningSeveral days prior to scanning, participants were interviewed and briefed on the purpose of the study and the mental states to be induced in the scanner. Subjects also generated key words of 4 individual negative autobiographical events as the stimuli for the sad memory phase. We measured participants’ rumination tendency with the Ruminative Response Scale (RRS) (Nolen-Hoeksema and Morrow, 1991), which can be further divided into a more unconstructive subtype, brooding and a more adaptive subtype, reflection (Treynor, 2003). All participants completed identical fMRI tasks on 3 different MRI scanners (order was counter-balanced across participants). Time elapsed between 2 sequential visits were 22.0 ± 14.6 days. The fMRI session included 4 runs: resting state, sad memory, rumination state and distraction state. An 8-minute resting state came first as a baseline. Participants were prompted to look at a fixation cross on the screen, not to think anything in particular and stay awake. Then participants would recall negative autobiographical events prompted by individualized keywords from the prior interview. Participants were asked to recall as vividly as they could and imagine they were re-experiencing those negative events. In the rumination state, questions such as “Think: Analyze your personality to understand why you feel so depressed in the events you just remembered” were presented to help participants think about themselves, while in the distraction state, prompts like “Think: The layout of a typical classroom” were presented to help participants focus on an objective and concrete scene. All mental states (sad memory, rumination and distraction) except for the resting state contained four randomly sequentially presented stimuli (keywords or prompts). Each stimulus lasted for 2 minutes, and then was switched to the next without any inter-stimuli intervals (ISI), forming an 8-minute continuous mental state. The resting state and negative autobiographical events recall were sequenced first and second while the order of rumination and distraction states was counter-balanced across participants. Before the resting state and after each mental state, we assessed participants’ subjective affect with a scale (item score ranged from 1 = very unhappy to 9 = very happy). Thinking contents and the phenomenology during each mental state were assessed with a series of items which were derived from a factor analysis (Gorgolewski et al., 2014) regarding self-generated thoughts (item scores ranged from 1 = not at all to 9 = almost all). Image AcquisitionImages were acquired on 3 Tesla GE MR750 scanners at the Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences (henceforth IPCAS) and Peking University (henceforth PKUGE) with 8-channel head-coils. Another 3 Tesla SIEMENS PRISMA scanner (henceforth PKUSIEMENS) with an 8-channel head-coil in Peking University was also used. Before functional image acquisitions, all participants underwent a 3D T1-weighted scan first (IPCAS/PKUGE: 192 sagittal slices, TR = 6.7 ms, TE = 2.90 ms, slice thickness/gap = 1/0mm, in-plane resolution = 256 × 256, inversion time (IT) = 450ms, FOV = 256 × 256 mm, flip angle = 7º, average = 1; PKUSIEMENS: 192 sagittal slices, TR = 2530 ms, TE = 2.98 ms, slice thickness/gap = 1/0 mm, in-plane resolution = 256 × 224, inversion time (TI) = 1100 ms, FOV = 256 × 224 mm, flip angle = 7º, average=1). After T1 image acquisition, functional images were obtained for the resting state and all three mental states (sad memory, rumination and distraction) (IPCAS/PKUGE: 33 axial slices, TR = 2000 ms, TE = 30 ms, FA = 90º, thickness/gap = 3.5/0.6 mm, FOV = 220 × 220 mm, matrix = 64 × 64; PKUSIEMENS: 62 axial slices, TR = 2000 ms, TE = 30 ms, FA = 90º, thickness = 2 mm, multiband factor = 2, FOV = 224 × 224 mm). Code availabilityAnalysis codes and other behavioral data are openly shared at https://github.com/Chaogan-Yan/PaperScripts/tree/master/Chen_2020_NeuroImage. ReferencesGorgolewski, K.J., Lurie, D., Urchs, S., Kipping, J.A., Craddock, R.C., Milham, M.P., Margulies, D.S., Smallwood, J., 2014. A correspondence between individual differences in the brain's intrinsic functional architecture and the content and form of self-generated thoughts. PLoS One 9, e97176-e97176.Nolen-Hoeksema, S., Morrow, J., 1991. A Prospective Study of Depression and Posttraumatic Stress Symptoms After a Natural Disaster: The 1989 Loma Prieta Earthquake.Treynor, W., 2003. Rumination Reconsidered: A Psychometric Analysis.(Note: Part of the content of this post was adapted from the original NeuroImage paper)

Dataset

This dataset was created by Ishan Tewari

Released under CC0: Public Domain

Contents

This dataset tracks the updates made on the dataset "Indicators of Anxiety or Depression Based on Reported Frequency of Symptoms During Last 7 Days" as a repository for previous versions of the data and metadata.

Research investigating whether depression is an adaptation or a disorder has been hindered by the lack of an experimental paradigm that can test causal relationships. Moreover, studies attempting to induce the syndrome often fail to capture the suite of feelings, thoughts, and behaviours that characterize depression. An experimental paradigm for triggering depressive symptoms can improve our etiological understanding of the syndrome. The present study attempts to induce core symptoms of depression, particularly those related to rumination, in a healthy, non-clinical sample through a controlled social experiment. These symptoms are sad or depressed mood, anhedonia, feelings of worthlessness or guilt, and difficulty concentrating. 134 undergraduate students were randomly assigned to either an Exclusion (EX) or Inclusion (IN) group. Participants in the Exclusion group were exposed to a modified Cyberball paradigm, designed to make them feel socially excluded, followed by a dual-interference task to assess whether their exclusion interfered with their working memory. Excluded participants: (1) self-reported a significant increase in sadness and decrease in happiness, but not anxiety or calmness; (2) scored significantly higher in four of five variables related to depressive rumination; and (3) performed significantly worse on a dual-interference task, suggesting an impaired ability to concentrate.

This dataset comprises mental health data from 1977 Bangladeshi university students across 15 top universities, collected from November to December 2023 using Google Forms. It includes assessments of academic anxiety, stress, and depression using widely used psychometric scales. The structured questionnaire covers sociodemographic variables and their associations, facilitating comprehensive analysis. Statistical analysis yielded satisfactory internal consistency (Cronbach’s alpha: 0.79), with anonymized participant data valuable for policymakers.