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This dataset contains anonymized clinical and laboratory data from a retrospective observational cohort of 340 adult women who underwent laparoscopic sleeve gastrectomy between 2020 and 2025 in southern Brazil. The data were collected from routine clinical follow-up and were used to investigate interval-based biochemical trajectories across the first postoperative year. Biochemical measurements were organized into five predefined clinical intervals: a preoperative baseline (0 days) and postoperative assessments at approximately 40, 120, 180, and 365 days after surgery. Due to heterogeneity in real-world follow-up schedules, the dataset was structured using an interval-based analytical framework, in which each participant contributes data to one or more postoperative intervals, without requiring complete longitudinal follow-up at the individual level. The dataset includes a comprehensive multisystem biochemical panel covering hematologic parameters, iron metabolism, protein markers, vitamins and minerals, metabolic markers, hepatic enzymes, inflammatory markers, and renal and endocrine indicators. Variables include, but are not limited to, hemoglobin, hematocrit, red blood cell indices, serum iron, ferritin, albumin, total protein, globulins, folate, vitamin B12, vitamin D, zinc, calcium, fasting glucose, lipid profile, liver enzymes (AST, ALT, GGT), C-reactive protein, creatinine, thyroid-stimulating hormone, and free thyroxine. All laboratory analyses were performed in accredited clinical laboratories as part of standard postoperative care, using validated analytical methods. No protocol-driven sampling or experimental intervention was conducted. Data were fully de-identified prior to analysis, and no personal identifiers are included in the dataset. This dataset supports the analyses presented in the associated manuscript entitled “Integrated Metabolic and Micronutrient Dynamics Across the First Postoperative Year: Interval-Based Profiling in 340 Brazilian Women Undergoing Sleeve Gastrectomy” . It is intended to facilitate reproducibility, secondary analyses, and comparative research on metabolic and nutritional adaptations following bariatric surgery in real-world clinical settings.

We conducted a cross-sectional study to analyze the distribution and accuracy of Conflict of Interest disclosure among Spanish authors of relevant Clinical Practice Guidelines, comparing their declaration with the transfers of value records published annually by the main pharmaceutical company.

Data set for"γ-Oryzanol restores MCI in obese aged mice"

The samples were extracted using a 400 µL methanol: acetonitrile (1:1, v/v) solution. The mixture then sonicated at 40 kHz for 30 min at 5°C. The samples were placed at -20°C for 30min to precipitate proteins. After centrifugation at 13000g at 4°C for 15min, the supernatant was carefully transferred to new microtubes and evaporated to dryness under a gentle stream of nitrogen. For UHPLC-MS/MS analysis, the samples were reconstituted in 100 µL loading solution of acetonitrile: water (1:1, v/v) by brief sonication in a 5°C water bath. Extracted metabolites were spun for 15 min at 13000g at 4°C on a bench-top centrifuge and cleared supernatant were transferred to sample vials for LC-MS/MS analysis. The instrument platform for LC-MS analysis is UHPLC-Q-Exactive system of Thermo Fisher Scientific. The selection of significantly different metabolites was determined based on the Variable importance in the projection (VIP) obtained by the OPLS-DA model and the p-value of student’s t test, and the metabolites with VIP>1, p<0.05. The analysis was performed using the free online platform of majorbio cloud platform (cloud.majorbio.com).

Reproducing the Spatiotemporal Distribution of fCO₂ in the Northwest Pacific Using the Random Forest Algorithm

The data and R codes for replicating the figure and tables in "DK (don't know) responses in surveys on inflation expectations: Are they ignorable?" The data sets are - MSC.csv: micro data from the Michigan Survey of Consumers (MSC) at https://www.sca.isr.umich.edu - FRED.csv: macro data from Federal Reserve Economic Data (FRED) at https://fred.stlouisfed.org - ALFRED.xls: vintage macro data from Archival FRED (ALFRED) at https://alfred.stlouisfed.org - MCH_Panel_202008.csv: replication data for Sheen and Wang (2023, European Economic Review) at https://doi.org/10.1016/j.euroecorev.2022.104345 The R codes are - MSC.R: FIgure 1 - correction.R: Table 2 - stats.R: Tables 3 and 4 - noexclusion.R: Tables 5 and 6 - exclusion.R: Table 7 - classical.R: Table 8 - robust.R: Table 9

Data Measuring the Causal Impact of Generative AI Adoption on SME Productivity: A Double Machine Learning Approach

SAS code for comparability and stability analysis simulations. The comparability simulation file reports the power and Type I error rates for different variations of the Mixed (and paired) model described by "Planning Split‑Apheresis Designs for Demonstrating Comparability of Cellular and Gene Therapy Products". The stability simulation generates and fits several stability datasets under different settings for amount of within-lot correlation. The stability single-run file fits a particular (simulated) data set to illustrate differences in lot-specific intervals arising due to choice of DDFM option. This is the same dataset as stability_example_data.csv, which may be imported into JMP or other software. SAS code was constructed with assistance from GPT 5.2 Pro.

Unraveling the Mechanisms and Spatiotemporal Patterns of Net Primary Productivity in Response to Urban Expansion in Xi’an. Research data include includes the annual average NPP, land use data , monthly precipitation, temperature, and radiation from meteorological stations.

This dataset contains soil physical, chemical, and biological measurements from three Pinus massoniana forest types in subtropical China: pure plantation (MPF), conifer-conifer mixed (MCLMF), and conifer-broadleaf mixed (MLMF). It includes aggregate mass distribution (four size classes), concentrations of soil organic carbon (SOC) fractions (e.g., POC, MBC, LOC, NC333), and key soil properties (texture, nitrogen, pH, etc.). Notable findings show that micro-aggregates hold higher carbon concentrations than macro-aggregates. Mixed afforestation had divergent effects: MCLMF increased SOC and stable carbon (NC333) storage, while MLMF decreased it. Both mixtures enhanced carbon stability by increasing the NC333 proportion. The shift in aggregate mass ratio and associated microbial activity (MBC) were key drivers of post-afforestation carbon dynamics. Data was gathered via soil sampling, wet-sieving for aggregates, and standard lab protocols for carbon fractionation. This dataset is valuable for verifying forest management impacts on soil carbon, supporting meta-analyses, calibrating models, and investigating specific mechanisms like nitrogen-aggregate-carbon interactions.