Share your research data

Salivary microbiome data of four population groups ( Black African, Coloured, Indian/Asian, whites) from South Africa. The data is derived by 16S RNA gene amplicon sequencing on Pac Bio platform

The data contains ultrasonic data, taken from 1MHz transducers and 150 kHz transducers, and processed by a Verasonics Vantage Research system. The data is saved in a .segy format, to be compatible with conventional seismic data. There are four main datasets, each corresponds to a full scan of a 3m x 1m tank, containing a plastic model and submerged in water. Each dataset contains roughly 150 .segy files, which are recordings from multiple transducers at one location within the tank. The x-position of the transducer grid is defined in the title as x_pos: XX (in m). The last dataset (Stationary zreo-offset data), is a dataset acquired by performing continuous recordings from the transducers, while keeping the grid stationary and slowly injecting air into the model.

This dataset and accompanying code provide the materials necessary to replicate the empirical analysis in the paper “Antipollution Willingness and Urban Population Distribution: Evidence from Chinese Cities.” The replication package contains: • Cleaned and raw data on city-level population distribution, air pollution measures, and related socioeconomic indicators used in the study. • Code files (Stata/R/MATLAB) that reproduce all tables, figures, and main quantitative results in the paper. • A README file with step-by-step instructions on how to run the code, including software requirements, data preparation steps, and directions for generating each table and figure.

This is the first-version open-source dataset for the manuscript "Contrasting coprecipitation and recrystallization mechanisms for Ra immobilization via (Ba,Ra)SO4 solid solution formation in fractured crystalline rocks: Insights from 3D reactive transport modeling" submitted to GCA. The dataset of this version contains: 1) stoichiometric solubility products (Kst) for (Ba,Ra)SO4 solid solutions calculated for non-ideal and ideal mixing used in our reactive transport modeling (RTM); 2) the Python source code used to calculate the Kst values; 3) Kinetic rate constants for discrete (Ba,Ra)SO4 solid solutions for RTM; 4) the Python source code used to calculate stoichiometric supersaturation index (SSI).

This study investigates metaphorical perceptions of online learning communities within a large-scale open education system, focusing on their experiences with online examinations. The data were collected between January 1 and February 14, 2024, using a structured questionnaire created on Google Forms from 392 participants, yielding 330 valid metaphors for analysis. The questionnaire had two sections: the first gathered demographic information, and the second focused on participants’ perceptions of online student communities within the Open Education System. Participants were asked to complete the sentence: Student communities are similar to …; because …, and the valid metaphors derived from their responses formed the main data source of the study. The metaphors revealed a dichotomy: 71.8% were positive, emphasizing convenience, flexibility, and reduced stress, with terms like "map," "mirror," and "vitamin" reflecting exams as tools for self-assessment and growth. Conversely, 28.2% were negative, dominated by concerns about cheating and technical difficulties, with metaphors like "ready meal" and "theft of labour" highlighting doubts about fairness. Notably, one linguistically engaged community produced the most metaphors, indicating varied engagement across groups. The findings depict a student body that values online exams for accessibility and personal development but is anxious about their fairness and reliability.

This dataset contains the research data supporting the findings of the associated paper, which introduces a novel framework for conducting infodemiological research using freely available Large Language Models (LLMs). The data exemplifies the application of the framework's five-phase methodology (Research Design, Data Collection, LLM Analysis, Validation, and Visualization) to two key use cases from the COVID-19 pandemic. The dataset is structured into the following primary components: 1. Vaccine Hesitancy Rhetoric Analysis Data: This subset includes: ● Anonymized Twitter Post IDs and Metadata: A list of Tweet IDs and corresponding dates collected via the Twitter API v2 for both pro-vaccine and vaccine-hesitant discourse during the initial vaccine rollout period (Dec 2020 - June 2021). ● Structured LLM Prompts: The exact prompt templates used for the iterative LLM-facilitated rhetorical analysis. ● LLM Analysis Outputs: Coded data from the LLM, identifying rhetorical frames (e.g., "Appeal to Personal Sovereignty," "Distrust of Pharmaceutical Motives"), representative quotes, and classified emotions. 2. Mental Health Discourse Evolution Data: This subset includes: ● Anonymized Reddit Post IDs and Metadata: A list of Post IDs from the r/COVID19_support subreddit for three key pandemic phases (Q2 2020, Q2 2021, Q2 2022). ● Structured LLM Prompts: The prompt templates used for simultaneous sentiment and thematic analysis of mental health discussions. ● LLM Analysis Outputs: Coded data from the LLM, including sentiment classifications (Positive, Negative, Neutral) and identified primary mental health concerns (e.g., "Social Isolation," "Pandemic Fatigue," "Grief") for each time period. 3. Validation Data: This includes the researcher-coded samples used for the Inter-Rater Reliability (IRR) checks, allowing for the verification of the LLM's analytical consistency. This dataset provides a practical, real-world benchmark for researchers aiming to apply the proposed LLM-facilitated framework to public health discourse. It demonstrates the entire pipeline from raw data collection to validated, analyzed results, ensuring the reproducibility and transparency of the research.

README: Replication Package Paper Title Does Public Data Openness Facilitate Corporate ESG Performance? Evidence from China Authors: Hongyan Wang (School of Economics, Nankai University) Guodong Huang (School of Economics and Trade, Hunan University) Prof. Hui Wang (School of Economics and Trade, Hunan University) Date: October 1, 2025 1. Overview This replication package provides all the publicly available data, code, and instructions required to reproduce the main results of the above-mentioned research paper. The package is designed to facilitate transparncy and enable independent verification of all statistical findings reported in the study.

All data generated or analyzed during“Metabolic Rewiring by HIF1α/c-Jun-ENO2 Axis Undermines DC Function in Transarterial Embolization-Resistant HCC”

Dataset for systematic literature review on computational thinking in the African context.

All data generated or analyzed during“Metabolic Rewiring by HIF1α/c-Jun-ENO2 Axis Undermines DC Function in Transarterial Embolization-Resistant HCC”