Share your research data

Abstract: Natural soil microbial methane (CH4) uptake is well documented, but uncertainty remains about uptake rates in subtropical forest and the extent to which tree stem emissions may offset the soil CH4 sink. We compared seasonal CH4 fluxes from trees and soils in a subtropical Australian rainforest. We conducted four campaigns to measure in situ CH4 fluxes from tree stems and soils across three plots along a moisture gradient (valley floor, lower slope, and upper slope plots). Tree stem and soil CH4 fluxes showed a significant positive correlation with soil moisture. Valley floor soil CH4 fluxes transitioned between a CH4 sink (end dry season: -151 µmol soil m-2 d-1) to a source (end wet season: 830 µmol soil m-2 d-1). Drier soils of the slope plots acted as a net CH4 sink year-round, with average soil CH4 fluxes of -135 µmol soil m-2 d-1 (lower slope) and -156 µmol soil m-2 d-1 (upper slope). In the sloped plots, tree stems emitted negligible CH4, except for two “high-emitting” trees, featuring CH4 fluxes 200 and 300-fold higher than the adjacent trees. In the valley floor at the end of the wet season, tree stem emission contributed an extra 27 % to the soil CH4 source, while in the slope plots, tree CH4 emissions offset < 1 % of soil CH4 uptake. This study adds to the limited knowledge of high temporal and spatial heterogeneity of upland tree and soil CH4 fluxes, and it highlights the importance of rainforest soils as important global CH4 sink.

This dataset contains the curated data and reproducible Python code supporting the study “Target-dependent cleaner production screening of recycling pathways for end-of-life electric-vehicle lithium-ion batteries using an interpretable machine-learning framework”. Version 2 retains the Version 1 analysis paths and adds physical-configuration-grouped evaluation for all six screening targets on cell and pack bases, route-held-out transfer stress tests, target-specific feature exclusions, dependence-aware uncertainty analyses, active-cathode spend-proxy sensitivity outputs, and machine-readable validation records. The package includes the 1,008-row designed scenario grid; deterministic cell- and pack-basis screening targets; the prespecified 168-row machine-learning input set representing 84 paired physical configurations; seven-model comparison results; common-rank Monte Carlo and paired physical-configuration cluster-bootstrap outputs; fold-aligned permutation-importance results; compact result-source tables; documentation; pinned dependencies; and SHA-256 integrity metadata. The included scripts reproduce the packaged numerical workflow from the supplied design matrix and curated evidence tables. The outputs are conditional screening indicators within the documented partial boundary: the operational carbon burden covers grid electricity and direct diesel/natural-gas combustion, material credits are spend-based proxies, and pack-basis values are cross-model mass-intensity sensitivities. They are not complete plant-level life-cycle assessment or techno-economic assessment results.

This replication package contains all code necessary to replicate the tables and figures in "Climate Regulatory Risks and Corporate Bonds" published in the Journal of Financial Economics. The analysis examines how climate regulatory risks affect corporate bond markets through: - Credit ratings analysis - Bond spreads and pricing - Institutional ownership patterns - Structural estimation of default probabilities CITATION: Seltzer, Lee, Laura Starks, and Qifei Zhu. "Climate Regulatory Risks and Corporate Bonds." Journal of Financial Economics Forthcoming. DATA AVAILABILITY: This package includes pseudodata for all proprietary datasets. Researchers with access to the underlying proprietary data sources can substitute real data to replicate published results. See Section 2 of readme file for complete details.

This repository contains the supplementary dataset, sparse scribble annotations, data splits, and pre-trained model weights associated with the paper "GraphScrib: Integrating Graph Neural Networks with CNN-Transformer for Medical Image Segmentation". The research introduces a novel weakly-supervised semantic segmentation (WSSS) framework that unifies Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Neural Networks (GNNs) to address the partial activation anomaly in medical imaging. Contents of this repository: Scribble Annotations: The generated sparse scribble masks used as weak supervision signals. Data Splits: The exact Train/Validation/Test patient splits used for the ACDC and MSCMRseg datasets to ensure fair comparison and reproducibility. Pre-trained Weights: The final model weights for GraphScrib achieving the reported Dice scores. Note: The raw MRI images belong to the original ACDC and MSCMRseg challenges and should be obtained from their official platforms.

This contribution compiles the problems, solutions and results of the Cuban National Chemistry Contest (CNQ) for high school students celebrated nationwide this year. This chemistry olympiad is an annual competition that encourages high school students to study the chemical sciences during their bachelor's degree. It is sponsored by the Cuban Ministry of Education, the Cuban Society of Chemistry, the University of Havana and other institutions. Tenth-, eleventh-, and twelfth-graders (15-18 years old, on average) are welcome to participate. It takes place in every province of the country at the Vocational High School for Exact Sciences (IPVCE). It is organized as a two-day competition in which students solve two sets of theoretical or theoretical-practical problems created by previous contestants and mentors. The problem sets have both common questions for two or three grades and individual questions. The highest scores are awarded with gold, silver and bronze medals. Top winners are part of the national pre-selection and are invited to Havana for a short training period. After a final selection round, the national team for the International Chemistry Olympiads is formed. The exam qualifications were conducted in Havana by a committee composed of selected students and mentors from the University of Havana. Herein, the problem sets of the Preparatory Problems booklet, the national exams and their solutions, and the results of the Cuban students in the national and international competitions of the previous year are provided in the official Spanish version and English translation.

This dataset contains de-identified participant-level data from a case–control study comparing serum interleukin-10 (IL-10) concentrations between pregnant women with preeclampsia and normotensive pregnant women, and between the two largest ethnic groups in Medan, North Sumatra, Indonesia (Batak and Javanese). Sixty-eight pregnant women beyond 20 weeks of gestation were recruited by consecutive sampling at Prof. Chairuddin P. Lubis Hospital, Universitas Sumatera Utara: 30 women with preeclampsia and 38 normotensive controls, with ethnicity balanced within each group (34 Batak, 34 Javanese in total). Serum IL-10 was measured by enzyme-linked immunosorbent assay at the Integrated Laboratory, Universitas Sumatera Utara. Recorded variables are study group, ethnicity, maternal age, gestational age at sampling, parity, highest level of education, body mass index category, urine dipstick proteinuria, and IL-10 concentration in pg/mL. All direct identifiers were removed before deposit: participant names were deleted and replaced with sequential anonymous identifiers, and no dates of birth, admission dates, medical record numbers or addresses are included. The dataset is complete, with no missing values. It is provided so that the analyses reported in the associated article — group comparisons, ethnicity-stratified comparisons, multivariable logistic regression, and receiver operating characteristic analysis — can be reproduced and re-examined.

This code and data accompanies the research article "FoodMetric: A semi-automated framework to enable individuals and organisations to reduce their food-related environmental impacts to meet environmental targets"

This dataset contains quantitative survey data from 151 office workers in Greater Jakarta who were exposed to AlloFresh advertisements displayed on digital screens inside office elevators. The data were collected through an online questionnaire using Google Forms from April 20 to May 8, 2026. The dataset includes measures of Advertising Exposure Frequency, Attentional Engagement, Brand Relevance, Brand Recall, and Purchase Intention, along with respondents' demographic and work-related characteristics. The data were analyzed using PLS-SEM with SmartPLS to examine the effectiveness of elevator-based Digital Out-of-Home (DOOH) advertising as a captive and unavoidable advertising medium.

This dataset contains multi-source environmental, meteorological, air quality, and population data used to investigate PM₂.₅ exposure and dust pollution in Central Iran during 2015–2025. The dataset includes ground-based PM₂.₅ monitoring measurements, meteorological observations, population data, satellite-derived aerosol products (AOD, AAI, and AOT), and satellite-derived Normalized Difference Water Index (NDWI) data. The dataset integrates observations from monitoring stations, satellite remote sensing products, and atmospheric analyses to support studies on air quality, dust transport, environmental change, and population exposure.

Detailed insights into MR procurement volumes, market share, pricing trends, and industry performance in China during the first half of 2026, highlighting high-end MR demand and centralized procurement policies.