This data was used to examine how thought patterns in the real world relate to the contexts in which they naturally emerge. We determined the prevalence of thought patterns (identified using Principal Component Analysis (PCA)) in a real-world experience sampling cohort. Participants completed multidimensional experience sampling (MDES) surveys eight times daily for five consecutive days. PCA was applied to these data to identify common "patterns of thought". Linear mixed modelling compared the prevalence of each thought pattern across different social, activity, location, and time contexts. We found that participants reported patterns of thought with episodic and social features when they were interacting with people in either a physical or virtual manner, replicating previous results. Furthermore, we discovered associations between four ongoing thought patterns captured by MDES and the everyday activities people were engaged in. Additionally, location predicted detailed task focus thought, especially when inside a workplace. Lastly, time of day was associated with both detailed task focus and episodic social cognition thought patterns. Overall, our study replicated the influence of socializing on patterns of ongoing thought and mapped patterns of thought across real-world contexts, such as social environment, activity, location, and time, as people went about their daily lives.

For full details of how this data was collected, see Mulholland et al. (2023), Consciousness and Cognition, Patterns of ongoing thought in the real world.

Psychology

This dataset is associated with the paper Reasoning to Learn from Latent Thoughts. It contains data used for pretraining language models with a focus on improving data efficiency by modeling and inferring latent thoughts underlying the text generation process, such as on reasoning-intensive math corpus. An expectation-maximization algorithm is developed for models to self-improve their self-generated thoughts and data efficiency.

logical reasoning

Presentation Date: Friday, March 23, 2018. Location: Steward Observatory, Arizona. Abstract: A synopsis of how and why astronomers should and (easily) can adopt a more high-dimensional view of their data, followed by live demos of glue (http://glueviz.org) and WorldWide Telescope (http://worldwidetelescope.org).

[!NOTE] We have released a paper for OpenThoughts! See our paper here.

  Open-Thoughts-114k

Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles! Inspect the content with rich formatting with Curator Viewer.

  Available Subsets

default subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models: ds = load_dataset("open-thoughts/OpenThoughts-114k", split="train")… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k.

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According to a survey carried out in January 2020, ** percent of young healthcare professionals reported that the potential of AI use in healthcare was important because it would reduce inefficiencies in administrative work. A further ** percent advised that integrating big data into patient records would allow conditions to be predicted and therefore help with diagnoses.

Abstract (en): A standard response of both policy-makers and private citizens to hardships—from natural disasters to mass shootings—is to offer “thoughts and prayers.” Critics argue that such gestures are meaningless and may obstruct structural reforms intended to mitigate catastrophes. In this study, we elicit the value of receiving thoughts and prayers from strangers following adversity. We find that Christians value thoughts and prayers from religious strangers and priests, while atheists and agnostics are “prayer averse”—willing to pay to avoid receiving prayers. Further, while indifferent to receiving thoughts from other secular people, they negatively value thoughts from Christians.

This dataset contains all data and analysis scripts pertaining to the research conducted for the PLOSOne paper: “Natural language processing for cognitive therapy: extracting schemas from thought records.” The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. To date, the schemas underlying such thought records have been largely manually identified. Using recent advances in natural language processing, we take this one step further by automatically extracting schemas from thought records. We used the Amazon Mechanical Turk crowd sourcing platform to collect a set of 1600 thought records. In total, these thought records contain 5747 thoughts of various depth levels, with the automatic thought constituting the most shallow level and the core belief the deepest level. We here deliver:
1. a natural language dataset: the thoughts delineated by participants in the scenario-based and open thought records2. reliability analyses: all thoughts were labeled with respect to the degree to which they reflect a set of 9 possible schemas by the first author. An independent second coder also labeled a sample of the thoughts.3. analyses to determine whether automatic identification of thoughts is possible.4. additional materials (scenarios, instruction videos, qualtrics survey, osf preregistration form) that could assist in the replication of the study.

This paper is a collection of thoughts from multiple discussions about the importance of appreciating and embracing statistical thinking in public health research and education. We think that statistical simulations can play an important role in fostering statistical reasoning in public health and that they can be a great didactic tool for students to generate and learn from data. Two main points are of relevance here. First, simulations can foster critical thinking and improve our reasoning about public health problems by going from theoretical thoughts to practical implementation of designing a computer experiment. Second, simulations can support researchers and their students to better understand statistical concepts used when describing and analysing population health in terms of distributions. Overall, we advocate for the use of more simulations in public health research and education to strengthen statistical reasoning when studying the health of populations.

Here you find the research materials, the analysis scripts, and the de-identified raw data of our project on how counterfactuals distinguish benign and malicious envy. The project is organized by the two lines of the research as described in the paper. The two main folders contain the meta-analyses of the two research lines, their subfolders contain the materials and data of the individual studies.

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Mental health characteristics and suicidal thoughts, by age group and sex, Canada (excluding territories) and provinces.

This Excel spreadsheet contains all the data for the 120 participants reported in the analysis of the "Improving Mental Health by Training the Suppression of Unwanted Thoughts" manuscript.

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Globally, women remain underrepresented in STEM. Our lab-in-the-field study delves into parental influence on adolescents' perceptions of scientific versus humanistic aptitude. We find that thinking about parental recommendation affects students' beliefs on their comparative advantage in a gender-stereotypical way. Girls are 23% less likely to choose math when they think about the mothers’ recommendation before selecting their field. The paper underscores the critical role parents play in shaping gender-specific beliefs about academic strengths, highlighting potential avenues for fostering diversity in STEM.

Data for manuscript 'Do Pandemics Trigger Death Thoughts?' Study 1

Methodology for data collection and analysis is reported in: Hammond Wagner, C.R., White, A., Darby, H., Ewing, P., Faulkner, J., Fisher, B., Galford, G., Horner, C., Jones, W.D., Neher, D., Ritzenthaler, C., von Wettberg, E.B. & Zeraatpisheh, M. (2025). Holistic systems thinking underpins Vermont soil health practitioners’ preferences and beliefs. Soil Security, 19, 100186. https://doi.org/10.1016/j.soisec.2025.100186Data archival consists of data, R scripts, and R projects for the analysis of two surveys from Vermont, USA:Study 1: Vermont Soil Health Metrics Preferences SurveyData was collected in 2020n = 62Sample is a convenience sample of soil health practitioners, including farmers, researchers, government service providers, extension agents, technical service providers, and othersDataset consists of quantitative closed ended ordinal and binary questions and qualitative open response questionsQuestions cover soil health definitions, assessment methods, and preferred metrics for different decision contexts using the online Qualtrics survey platform.Study 2: Vermont Farmer and Conservation and Payment for Ecosystem Services SurveyData was collected in 2022n = 179Sample is a convenience sample of Vermont farmersDataset consists of quantitative closed ended ordinal and binary questionsQuestions cover farmers’ soil health beliefs, stewardship motivations, farm demographics, and experience with soil testing

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