Share your research data

Research Data Quick Commerce Indonesia

The dataset in this repository serves as supplementary resource data for the article titled "Low-burden and precursor-free cell-cell communication in mammalian cells enabled by de novo design of super-sensitive intercellular signals" (Sun et al.), published in Cell Systems. This repository contains two datasets: 1. Gene differential expression analysis results and 2. GO biological pathway enrichment results. The raw RNA-Seq transcriptome data is not included in this repository. Please contact the corresponding author of the article if needed. The transcriptome analysis protocol is detailed in the STAR Methods section of the article. The relevant R code has been uploaded to GitHub. Further information and requests should be directed to and will be fulfilled by the corresponding author of the article.

This data set contains the data extracted from simple shear simulations using discrete element method (DEM).

Understanding Listening Difficulties in School-Aged Children: A Representative Sample from the UK This dataset includes all z-scores used to construct the structural equation model (SEM), along with the SEM code, to investigate the contributions of auditory processing, language, and cognitive abilities to speech-in-noise performance. The data provide a comprehensive resource for examining the interactions among these domains in a representative sample of 221 school-aged children in the Great Manchester.

Tetrahydrocannabinol (THC) vaping has become increasingly prevalent, yet available empirical data on puffing behavior during THC vape use remain limited. This article presents a dataset of puff durations extracted from publicly available YouTube videos showing people using tetrahydrocannabinol (THC) vaping devices. Videos published between 2018 and 2024 were identified using predefined search terms and screened based on clear visibility of puffing events. Puff durations were measured using a frame-by-frame video analysis tool, with puff initiation and termination defined by facial muscle contraction and relaxation. The dataset also includes information on device type and reported liquid composition. A total of 456 puffs were recorded from 100 analyzed videos. This data can be found in the THC_Puff Duration_Raw.xlsx file. In addition, the file THC_Puff Duration_Reanalyzed.xlsx file contains data from 25 videos randomly selected from the original dataset. These videos were independently reanalyzed by a second rater to assess measurement reliability.

Replication code for Environmental Regulation and Corporate Innovation: The Roles of Regulatory Exposure, Risk, and Sentiment.

This is the supplementary materials for the article entitled "Accessory minerals constrain anatexis and melt extraction in deep crust and implications for granite genesis". It includes two files. Supplementary Material 1: Tables S1-S8; Supplementary Material 2: Standard values. In Supplementary Material 1, zircon U–Pb–O isotopic data obtained by SIMS and SHRIMP II are provided in Tables S1 to S3, while zircon Lu–Hf isotopic data analyzed via LA–MC–ICPMS are presented in Table S4. Titanite U–Pb isotopic data and Sm–Nd isotopic compositions are given in Tables S5 and S6, respectively. Apatite Sm–Nd isotopic compositions are listed in Table S7, and whole-rock Sr–Nd–Hf isotopic compositions of the Ta'erhe migmatites are shown in Table S8. Supplementary Material 2 contains analyzed values for the standard samples, compiled in Tables MS1–MS7.

This dataset contains chlorophyll alpha concentration measurements taken from 10 stations (S1 to S10) located in the Satarkhan Reservoir, East Azerbaijan, Iran. Sampling was conducted seasonally on the following dates: Fall (10 December 2023), Winter (08 January 2024), Spring (05 April 2024), and Summer (23 June 2024). For each station and season, three replicate samples were collected to ensure measurement accuracy. Data includes the latitude and longitude coordinates of each station, individual replicate measurements, and calculated average concentrations in micrograms per liter (μg/L).

This dataset, organized under the folder “Prawn Circular Economy Project,” supports reproducible research on AI-based modeling of the prawn circular economy. It includes synthetic datasets (prawn_circular_economy_main.csv, prawn_waste_synthetic_data.csv), configuration files (morf_config.json), and the reproducibility script (prawn_morf_reproducibility.py). The dataset facilitates the analysis of multi-parameter aquaculture systems using a Multi-Output Random Forest framework to predict profit and harvest yield under diverse operational and environmental conditions.

This database contains two core asset types: Data Files and Model Files. 1. Data Files The dataset is provided in two separate .xlsx files: Raw-nir-spectra-data: This file contains the raw near-infrared spectral dataset. It records the spectral information for all 347 tobacco samples and includes metadata such as each sample's unique ID, cultivation year, and country of origin. 13-Chemical-Components-data: This file contains the reference dataset for the chemical constituents. It includes the quantitative analysis results for the 13 key chemical components for all 347 samples, corresponding one-to-one with the spectral data. 2. Model Files The database provides 99 pre-trained prediction and classification models in .joblib format. All models were built in a Python 3.9 environment and can be loaded and called directly. To facilitate easy identification and use, the model files adhere to the following naming conventions: A. Quantitative Models (Chemical Prediction) This naming format is used for the quantitative prediction models of the 13 chemical constituents. Format: [Chemical_Component]_[Preprocessing_Method]_[Modeling_Method].joblib Example: TotalSugars_MSC_PLS.joblib represents a PLS model for predicting Total Sugars using MSC preprocessing. B. Classification Models (Origin Prediction) This naming format is used for classification models built with different types of input data. Format (based on spectral data): [Preprocessing_Method]_[Modeling_Method].joblib Example: SecondDerivative_RF.joblib represents a Random Forest (RF) classification model built using second-derivative spectral data. Special Note: The file Thirteen_chemical_components-RF.joblib is a special classification model. It does not use spectral data; instead, it is built using the quantitative results of the 13 chemical components directly as its input features.