Mental Health Queries and Personality Dataset

  Overview

This dataset encompasses a collection of mental health queries paired with personality scores and responses generated by a Large Language Model (LLM). It aims to provide insights into the interplay between personality traits and mental health inquiries, facilitating research in personalized conversational agents and mental health support systems.

  Dataset Description

Each record in the dataset contains:

A… See the full description on the dataset page: https://huggingface.co/datasets/TVRRaviteja/Mental-Health-Data.

Includes programs that are licensed by the Office of Mental Health (OMH) and programs that are funded by OMH but do not need a license to operate.

Mental Health reports the prevalence of the mental illness in the past year by age range.

The National Database for Clinical Trials Related to Mental Illness (NDCT) is an extensible informatics platform for relevant data at all levels of biological and behavioral organization (molecules, genes, neural tissue, behavioral, social and environmental interactions) and for all data types (text, numeric, image, time series, etc.) related to clinical trials funded by the National Institute of Mental Health. Sharing data, associated tools, methodologies and results, rather than just summaries or interpretations, accelerates research progress. Community-wide sharing requires common data definitions and standards, as well as comprehensive and coherent informatics approaches for the sharing of de-identified human subject research data. Built on the National Database for Autism Research (NDAR) informatics platform, NDCT provides a comprehensive data sharing platform for NIMH grantees supporting clinical trials.

The following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

Database of the nation''s substance abuse and mental health research data providing public use data files, file documentation, and access to restricted-use data files to support a better understanding of this critical area of public health. The goal is to increase the use of the data to most accurately understand and assess substance abuse and mental health problems and the impact of related treatment systems. The data include the U.S. general and special populations, annual series, and designs that produce nationally representative estimates. Some of the data acquired and archived have never before been publicly distributed. Each collection includes survey instruments (when provided), a bibliography of related literature, and related Web site links. All data may be downloaded free of charge in SPSS, SAS, STATA, and ASCII formats and most studies are available for use with the online data analysis system. This system allows users to conduct analyses ranging from cross-tabulation to regression without downloading data or relying on other software. Another feature, Quick Tables, provides the ability to select variables from drop down menus to produce cross-tabulations and graphs that may be customized and cut and pasted into documents. Documentation files, such as codebooks and questionnaires, can be downloaded and viewed online.

The Katie A. Settlement Agreement requires the Department of Health Care Services (DHCS) to collect and post data used to evaluate utilization of services and timely access to appropriate care. These county datasets show services used by children and youth (under the age of 21) identified as Katie A. Subclass members and/or utilizing Katie A. specialty mental health services (Intensive Care Coordination, Intensive Home Based Services, and Therapeutic Foster Care). This data assists in evaluating each county’s progress with implementing.

bhandari-safal/Mental-Health-Database dataset hosted on Hugging Face and contributed by the HF Datasets community

A dataset containing basic conversations, mental health FAQ, classical therapy conversations, and general advice provided to people suffering from anxiety and depression.

This dataset can be used to train a model for a chatbot that can behave like a therapist in order to provide emotional support to people with anxiety & depression.

The dataset contains intents. An “intent” is the intention behind a user's message. For instance, If I were to say “I am sad” to the chatbot, the intent, in this case, would be “sad”. Depending upon the intent, there is a set of Patterns and Responses appropriate for the intent. Patterns are some examples of a user’s message which aligns with the intent while Responses are the replies that the chatbot provides in accordance with the intent. Various intents are defined and their patterns and responses are used as the model’s training data to identify a particular intent.

New York State is moving Medicaid behavioral health services from a fee-for-service system into Managed Care. OMH Medicaid Behavioral Health Measures were developed to help monitor the transition of mental health services from a fee-for-service to behavioral managed care. The MH Engagement in Care dataset displays percentages of Medicaid discharges for members 6-64 years of age who were hospitalized in an inpatient setting with a primary diagnosis of mental illness that were followed by two or more outpatient visits, intensive outpatient encounters or partial hospitalizations for mental health treatment within 30 days of discharge.

The following datasets are based on the adult (age 21 and over) beneficiary population and consist of aggregate MHS data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

This data package contains data on substance abuse and mental health and additional information including spending estimates for substance abuse and mental health and health facilities for mental health treatment.

Database of the nation''s substance abuse and mental health research data providing public use data files, file documentation, and access to restricted-use data files to support a better understanding of this critical area of public health. The goal is to increase the use of the data to most accurately understand and assess substance abuse and mental health problems and the impact of related treatment systems. The data include the U.S. general and special populations, annual series, and designs that produce nationally representative estimates. Some of the data acquired and archived have never before been publicly distributed. Each collection includes survey instruments (when provided), a bibliography of related literature, and related Web site links. All data may be downloaded free of charge in SPSS, SAS, STATA, and ASCII formats and most studies are available for use with the online data analysis system. This system allows users to conduct analyses ranging from cross-tabulation to regression without downloading data or relying on other software. Another feature, Quick Tables, provides the ability to select variables from drop down menus to produce cross-tabulations and graphs that may be customized and cut and pasted into documents. Documentation files, such as codebooks and questionnaires, can be downloaded and viewed online.

This dataset was collected as part of a large-scale mental health screening initiative conducted between 2019 and 2024 among postsecondary students in China. The sample includes 34,689 university and vocational college students , who completed the Symptom Checklist-90-Revised (SCL-90-R). The dataset includes demographic information (e.g., gender, age, education type, intake year) and symptom scores across nine SCL-90-R domains: depression, anxiety, somatization, obsessive-compulsive symptoms, phobic anxiety, interpersonal sensitivity, hostility, paranoid ideation, and psychoticism.The data were used to identify latent internalizing mental health profiles using Gaussian Mixture Modeling (GMM) and to build a Random Forest classifier for predicting profile membership. The dataset supports research on transdiagnostic symptom patterns, person-centered profiling, and machine learning applications in non-clinical educational contexts.All personally identifying information was removed. The dataset is anonymized and suitable for secondary analysis. Documentation and replication code (Python scripts) are provided to facilitate reproducibility.

Comprehensive dataset of 89,338 Mental health services in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness.

The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, gender, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

Comprehensive dataset of 9,469 Mental health services in California, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset contains responses from an online survey of 2187 participants primarily located in the UK. All participants stated that they had used the UK National Health Service (NHS) at some time in their lives. The data were collected between December 2018 and August 2019. Participants' views on data sharing - this dataset contains information about people's willingness to share mental and physical health data for research purposes. It also includes information on willingness to share other types of data, such as financial information. The dataset includes participants' responses to questions relating to mental health data sharing, including the trustworthiness of organisations which use such data, how much the presence of different governance measures (such as deidentification, opt-out, etc.) would alter their views, and whether they would be less likely to access NHS mental health services if they knew their data might be shared with researchers. Participants' satisfaction and interaction with UK mental and physical health services - the dataset includes information regarding participants' views on and interaction with NHS services. This includes ratings of satisfaction at first contact and in the previous 12 months, frequency of use, and type of treatment received. Information about participants - the dataset includes information about participants' mental and physical health, including whether or not they have experience with specific mental health conditions, and how they would rate their mental and physical health at the time of the survey. There is also basic demographic information about the participants (e.g. age, gender, location etc.).

Background: Mental health research is commonly affected by difficulties in recruiting and retaining participants, resulting in findings which are based on a sub-sample of those actually living with mental illness. Increasing the use of Big Data for mental health research, especially routinely-collected data, could improve this situation. However, steps to facilitate this must be enacted in collaboration with those who would provide the data - people with mental health conditions.Methods: We used the Delphi method to create a best practice checklist for mental health data science. Twenty participants with both expertise in data science and personal experience of mental illness worked together over three phases. In Phase 1, participants rated a list of 63 statements and added any statements or topics that were missing. Statements receiving a mean score of 5 or more (out of 7) were retained. These were then combined with the results of a rapid thematic analysis of participants' comments to produce a 14-item draft checklist, with each item split into two components: best practice now and best practice in the future. In Phase 2, participants indicated whether or not each item should remain in the checklist, and items that scored more than 50% endorsement were retained. In Phase 3 participants rated their satisfaction with the final checklist.Results: The final checklist was made up of 14 “best practice” items, with each item covering best practice now and best practice in the future. At the end of the three phases, 85% of participants were (very) satisfied with the two best practice checklists, with no participants expressing dissatisfaction.Conclusions: Increased stakeholder involvement is essential at every stage of mental health data science. The checklist produced through this work represents the views of people with experience of mental illness, and it is hoped that it will be used to facilitate trustworthy and innovative research which is inclusive of a wider range of individuals.

Comprehensive dataset of 5,684 Mental health services in Texas, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.