Share your research data

This study investigated the relationship between spatial visualisation ability, gender, and students’ achievement in Physics within simulation-based instructional environments. Grounded in Spatial Cognition Theory and Cognitive Load Theory, the study examined how cognitive abilities and learner characteristics interact with technology-enhanced instructional strategies to influence academic performance in Physics. A pretest-posttest quasi-experimental design was adopted using 204 pre-degree Physics students from six private universities in Southwestern Nigeria. Participants were grouped into Simulated Video-Based Inverted Classroom (SVBIC) (n = 83), Virtual Laboratory-Based Inverted Classroom (VLBIC) (n = 51), and Conventional Classroom (n = 70) groups. Data were collected using the Students’ Achievement Test in Physics (SATP) and Spatial Ability Test (SAT) over a thirteen-week intervention period. The study tested three hypotheses: whether spatial visualisation ability significantly affects students’ achievement in Physics, whether gender significantly affects achievement, and whether there is a significant interaction effect between spatial ability and gender on achievement. Analysis of Covariance (ANCOVA) was used to analyse the data while controlling for pretest differences. Findings revealed that spatial visualisation ability alone had no statistically significant main effect on students’ achievement in Physics, F(2,187) = 0.946, p = .390, partial η² = .010. Similarly, gender alone did not significantly influence achievement, F(1,187) = 3.212, p = .075, partial η² = .017. However, a statistically significant interaction effect was found between spatial ability and gender on students’ achievement in Physics, F(2,187) = 3.847, p = .023, partial η² = .040. Estimated Marginal Mean analysis showed that female students with low and medium spatial ability achieved higher posttest scores than their male counterparts, while male students with high spatial ability performed slightly better than females with high spatial ability. These findings suggest that simulation-based instructional environments provided supportive cognitive scaffolding that reduced traditional gender disparities associated with spatial tasks in STEM learning. The data indicate that spatial visualisation ability does not independently determine achievement in Physics; rather, its influence depends on interactions with gender and instructional context. The study demonstrates that simulation-based instructional strategies can support diverse learners by externalising complex spatial processes, thereby improving conceptual understanding and reducing barriers to learning. The dataset may be useful for researchers, educators, and curriculum developers interested in STEM education, spatial cognition, gender studies, and technology-enhanced learning interventions.

The ACTN3 polymorphism has been associated with muscle damage, joint injury, and strength and power phenotypes. However, few studies have examined associations between genetic polymorphisms and injury incidence in physically active populations. We investigated whether the ACTN3 rs1815739 polymorphism is associated with musculoskeletal injury risk (MSIs) in military PES

Tlemcen-NeuroOncMRI is a clinically annotated multisequence brain MRI dataset collected retrospectively at the Anti-Cancer Center of Tlemcen, Algeria. The dataset includes anonymized MRI examinations from 45 patients with primary and secondary brain tumors, including 28 primary brain tumor cases and 17 secondary brain metastasis cases. For each patient, the dataset provides multisequence MRI data, including T1-weighted, T2-weighted, FLAIR, and post-contrast T1-weighted images. The imaging data are organized at the patient level and are accompanied by a structured patient-level metadata file in CSV format. The metadata include anonymized demographic information, tumor origin, general tumor type, histological information when available, localization, laterality, metastatic source for secondary tumors, contrast enhancement pattern, necrosis, edema, multiplicity, and border characteristics. The dataset contains 19,009 MRI slices and is intended for research on primary versus secondary brain tumor classification, transfer learning, external validation, multimodal image-tabular modeling, radiomics, and weakly supervised learning. Since the dataset contains multiple slices and sequences per patient, all machine learning experiments should use patient-level splitting to avoid data leakage. Slice-level random splitting is not recommended. The current release does not include voxel-level segmentation masks. Therefore, the dataset is primarily designed for classification-oriented studies, patient-level modeling, multimodal learning, and external validation rather than fully supervised tumor segmentation. All data were anonymized prior to release. Direct identifiers were removed, and patient identifiers were replaced by study-specific anonymized codes. Users must not attempt to re-identify patients and should cite the dataset appropriately in any resulting publication.

In RNA-Seq dataset, we provided Quads', Liver's, eWAT's and islets' RNA-Seq for exercise intervention mice (Chow diet for 4.5M, HFD for 4.5M, HFD-exercise : High-fat feeding for 3 months followed by 1.5 months of exercise intervention. ). In proteomics dataset, we provided proteomic analysis on plasma from mice subjected to 1.5M exercise intervention. In ScRNA-seq dataset, we provided islets' ScRNA-seq for exercise intervention mice (Chow diet for 5M, HFD for 5M, HFD-exercise : High-fat feeding for 3 months followed by 2 months of exercise intervention. )

Microdata from the Great Integrated Household Survey (GEIH) in Colombia

These are the results of the survey conducted in the department of Antioquia during 2024-2025.

This dataset was generated from a psychometric study evaluating the paternal Breastfeeding Self-Efficacy Scale–Short Form (paternal BSES-SF) among Brazilian fathers during the immediate postpartum period. The study was conducted within a prospective cohort of mother–father dyads recruited in a hospital setting in Brazil. Fathers were assessed between 24 and 36 hours after childbirth, a period characterized by early transition to parenthood and limited direct experience with breastfeeding support. The primary research hypothesis was that the paternal BSES-SF would demonstrate adequate psychometric properties during the immediate postpartum period and that higher paternal breastfeeding self-efficacy would be associated with maternal breastfeeding self-efficacy and improved breastfeeding outcomes. The study also explored whether paternal cohabitation status influenced the psychometric performance of the instrument. The dataset includes anonymized sociodemographic variables, paternal cohabitation status, paternal and maternal breastfeeding self-efficacy scores, item-level responses to the paternal BSES-SF, and prospective breastfeeding outcomes, including exclusive breastfeeding at 1 month and duration of exclusive breastfeeding. Data collection occurred between November 2024 and September 2025 through in-person baseline assessments and monthly follow-up via WhatsApp messaging. The data demonstrated that paternal self-efficacy scores presented limited variability and ceiling effects during the immediate postpartum period. Internal consistency was slightly below commonly recommended thresholds when estimated using Cronbach’s alpha, although omega coefficients suggested moderate reliability. Confirmatory factor analysis supported a unidimensional structure with partial model fit. Paternal self-efficacy was moderately associated with maternal self-efficacy but was not significantly associated with exclusive breastfeeding outcomes. Additional exploratory analyses suggested differences in internal consistency according to paternal cohabitation status, with higher reliability observed among cohabiting fathers. These findings suggest that timing of assessment and contextual factors may substantially influence how paternal breastfeeding self-efficacy is expressed and measured during the early postpartum period. The dataset may be useful for researchers interested in breastfeeding self-efficacy, paternal involvement in breastfeeding, psychometric validation studies, postpartum family dynamics, and contextual influences on health behavior measures. The data may also support secondary analyses, methodological comparisons, and cross-cultural psychometric research.

This dataset includes observational data and the code of the GOTM-ERGOM model for the manuscript "An Observation-Constrained 1-D GOTM–ERGOM Framework for Quantifying Resuspension-Driven Oxygen Depletion in Shallow Coastal Seas"

To further understand the fate of organic carbon (OC) in the Arctic marine system, we examined the influence of deposition settings on OC degradation on the East Siberian Arctic shelf (ESAS). The sedimentary factors such as water depth, sedimentation rate (SR), and the physical chemistry of sedimentary debris and water mass that may affect OC degradation were considered in this study. This dataset includes 3 Excel spreadsheets. Sheet 1 shows the collected 210Pb-based sedimentation rates (SR) on the ESAS. Sheet 2 displays the information of 75 sediment samples investigated in this study, including the chemical environment of bottom water, and the analysis results of grain size, specific surface area (SSA), total organic carbon (TOC), carbon isotope, pyrolysis of organic matter, source apportionment, and degradation state. Sheet 3 shows the collected TOC, SSA, HMW n-alkanes, HMW n-alkanoic acids, Lignin, and Cutin acids of OM in the surface sediment of the ESAS.

Indonesia is a rich cultural diversity country, its seen through their traditional regional foods. However, pempek Palembang is currently experiencing challenges in sustaining its relevance as user preferences are continuing to shift under the influence of globalization. Earlier studies mostly depended on questionnaire-based methods; which often restrained the users from sharing their genuine experiences. This study identifies drivers and barriers of pempek Palembang consumption by analyzing organic comments using the Latent Dirichlet Allocation (LDA) algorithm. 778 comments were collected from 4 review videos on Tiktok, after preprocessing, 566 clean comments were filtered. The analysis identified 6 optimal topics with a coherence score of 0.6153. The main drivers that were found are authentic taste, product preferences, ingredient quality, and user recommendations, while the main barriers consist of price-quality mismatch, limited accessibility, and inconsistent delivery experience. These findings also provide a practical insight for pempek Small and Medium Enterprises (SMEs).