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We provide data and code to replicate the results presented in "ESG Performance and Stock Market Responses to Geopolitical Turmoil: evidence from the Russia-Ukraine War" (Boccaletti, Maranzano, Morelli & Ossola, 2025). The subfolders allow replicating the following: 1. Folder "Event Study - Synthetic" replicates the event study from Section 4 2. Folder "Regressions replication - Table 5 and Table 6" replicates the regression analysis from Section 5. For each subfolder a README file is provided. It contains information about the reproduction steps.

Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)

Drawing from sensemaking theory, this study investigates how socially responsible human resource management (SRHRM) influences employee proactive work behavior (EPWB) through underlying psychological processes and contextual factors. While prior research suggests SRHRM enhances positive employee outcomes, limited attention has been paid to the mechanisms through which employees interpret and internalize SRHRM practices, and how these interpretations drive proactive behaviors. Using a time-lagged survey of 756 employees in multinational companies in Southeast Vietnam, the study tests a serial mediation model where work meaningfulness and organizational pride sequentially transmit the effects of SRHRM to EPWB. Structural equation modeling results reveal that SRHRM does not directly predict EPWB but operates entirely through this psychological chain. Furthermore, multi-group analysis shows leadership gender moderates the indirect pathway: female leaders amplify the positive effects of SRHRM on employees’ sensemaking and subsequent behaviors, while male leaders weaken them. These findings contribute to HRM and organizational behavior literature by advancing sensemaking theory in explaining how socially responsible practices translate into discretionary employee actions and identifying leadership gender as a critical contextual variable. Practical implications suggest organizations should design SRHRM interventions that foster meaningful work experiences and strategically leverage leadership characteristics to enhance employee proactivity.

This replication package accompanies the paper titled “The Power of Title: Unintended Consequences of a Place-Based Innovation Policy in China.” The original dataset is confidential and cannot be shared publicly. To meet reproducibility requirements, this package includes: (1) A synthetic firm-year panel dataset (19,312 observations), (2) Computational code that reproduces all tables and figures in the paper, (3) A custom Stata program (regcurve.do) used to generate Figure 3, (4) A folder (Map/) containing synthetic mapping files for generating Figure 1, (5) And a README file with documentation. The synthetic dataset mimics the structure and statistical properties of the original confidential data and enables an independent observer to replicate the full computational workflow. Interested readers may contact the authors for information on accessing the original data under appropriate conditions.

This repository contains the code and data used for the replication of results presented in the article: **"Stealthy Shorts: Informed Liquidity Supply"** by Amit Goyal, Adam V. Reed, Esad Smajlbegovic, Amar Soebhag

supporting data and files for manuscript MOLECULAR-CELL-D-24-01420R2

NMR data: recorded NMR spectra used for the characterization of intermediates and products. Computational data: Cartesian coordinates of the studied molecules, optimized in S0, S1, and T1 states.

This dataset includes multiple alignments of nuclear (nu) SSU rDNA, nu LSU rDNA, plastid (pt) LSU rDNA, pt rbcL, and pt psaA sequences, as well as the final concatenated alignment used to infer the Bayesian phylogeny.

Supporting figures and tables for G-Cubed article "Structural architecture of the Cenozoic Kallianos (post-?)orogenic vein-hosted Au-Ag-Te deposit and its relationship to the North Cycladic Detachment System, Greece" by L. Hamel and co-authors.

This dataset accompanies the study "Optimizing Real-Time Phenotyping in Critical Care Using Machine Learning on Electronic Health Records," which hypothesizes that a patient's latent disease state can be continuously and accurately estimated from real-time biomedical signals without requiring full ICU trajectories. It supports replication and evaluation of our predictive framework, which dynamically models phenotype probabilities as data accumulates. All elements are reported in line with the TRIPOD statement to ensure transparency and reproducibility. The training and test data are derived from the MIMIC-IV database and consist of vectorized representations of multivariate, irregularly sampled biomedical time series and associated phenotype labels. These were generated through a structured pipeline that includes cohort selection, event aggregation using fixed-length time bins, and feature engineering to represent both value trends and missingness. Supplementary Tables S.1 to S.6 describe the variables used in this transformation, their sources within the EHR, aggregation methods, and descriptive statistics for both static (e.g., demographics, admission data) and dynamic (e.g., vital signs, lab results, ventilator settings) features across the train and test sets. Table S.7 summarizes the model’s real-time phenotyping performance using multiple evaluation perspectives. The results reveal strong generalization and early predictive value: in the (ls) setting, the model achieved good diagnostic performance (AUROC ≥ 0.8) for 69% of phenotypes and excellent performance (AUROC ≥ 0.9) for 30%. In the real-time (fs) setting—using only the earliest recorded physiological data—the model still achieved good performance for 40% of phenotypes and excellent performance for 5%, demonstrating the feasibility of early, actionable phenotyping. The intermediate (td) evaluation shows that predictive quality improves consistently as more data becomes available, supporting the framework’s ability to track dynamic disease progression in real time. To interpret and use the data: - Each patient stay is represented as a multivariate time series with associated phenotype labels. - Time series are aligned in fixed time intervals (e.g., 2 hours), where each variable is aggregated using statistical functions (e.g., mean, last, sum). - The phenotype labels correspond to ICD-9-CM diagnostic categories assigned at discharge but are used here as latent variables to be estimated continuously. This dataset enables reproducibility of the results and further research in developing machine learning models for early, interpretable, and actionable phenotyping in critical care.