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This dataset and code archive supports a multispecies field-burial experiment examining how seasonal soil exposure changes seed germination responses in a temperate desert-steppe flora in Inner Mongolia, China. The study tested whether germination changes progressively with burial duration or instead varies among seasonal retrievals; whether thermal and water-potential responses shift together; whether burial alters germination timing independently of final germination; and whether seed mass, life history, and growth type explain interspecific variation after accounting for phylogenetic relatedness. We predicted that winter exposure would most strongly increase germination under cool temperatures and that daily germination trajectories would reveal burial effects not captured by final germination percentages. This archive contains the curated data and R code supporting analyses of seed germination responses during burial and retrieval across 50 plant species. It includes dish-level final germination records, daily germination observations under temperature and water-potential gradients, species trait and dormancy information, phylogenetic data, soil temperature and moisture records, derived environmental-exposure metrics, analysis panels, and source data for all main and supplementary figures. The R scripts in R_pipeline reproduce endpoint and niche-metric calculations, generalized linear mixed models, phylogenetic generalized least-squares models, discrete-time hazard analyses, model diagnostics, publication-quality figures, and ten supplementary tables. Scripts should be run sequentially from 00_config.R to 06_tables_supplement.R. Outputs are organized into curated data, intermediate tables, fitted models, figures, and manuscript tables within the df directory. The input directory contains the original dormancy audit and soil-monitoring workbook used to prepare the curated inputs.

Hospital wastewater surveillance dataset comprising Aeromonas spp. isolates characterized for antimicrobial susceptibility, MDR/XDR profiles, clinically relevant β-lactam resistance genes, and class 1 integrons, providing insight into hospital wastewater as a reservoir of antimicrobial resistance.

This replication package accompanies the manuscript “Political Institutions and the Fiscal Transmission of Resource Windfalls.” The package contains the processed municipal panel, political-institutional indicators, Stata code, and supporting documentation required to reproduce the empirical results reported in the manuscript and its online appendix. The analysis covers 320 Chilean municipalities over the period 2009–2023. The replication files reproduce the descriptive evidence, baseline dynamic panel system-GMM estimates, political heterogeneity analyses, spatial econometric models based on alternative connectivity matrices, robustness checks, tables, and figures. The political indicators were constructed and harmonized from official municipal election records, while the fiscal and socioeconomic variables were assembled from publicly available administrative sources. The package is organized around a master.do file and modular Stata scripts covering data preparation, variable construction, baseline estimation, spatial models, robustness exercises, and the production of tables and figures. The README file and the documentation folder describe the directory structure, variable definitions, original data sources, software requirements, and execution sequence. The repository-level license is Creative Commons Attribution 4.0 International (CC BY 4.0). The Stata code is additionally released under the MIT License, as specified in the included LICENSE.txt file. Authors: Mauricio Oyarzo and Dusan Paredes.

REPLICATION PACKAGE “When Culture Meets Policy: How Individualism Shapes Government Financial Assistance in Times of Crisis” Authors: Ioana Georgiana Farcaș, Zbigniew Korzeb, Simona Nistor This replication package contains the data and Stata code required to reproduce the empirical figures, tables, and appendix results reported in the paper. It includes both the analysis-ready datasets used in the estimations and the raw source-data files used to construct them. The package uses two main analysis datasets: 1. Panel data (4).dta — quarterly country-level panel dataset. 2. Cross-sectional data (4).dta — country-level cross-sectional dataset. The raw data are organized in two folders: 1. raw_data/panel_quarterly/ — raw files used to construct the quarterly panel dataset. 2. raw_data/cross_sectional/ — raw files used to construct the annual/cross-sectional dataset. Each raw-data folder includes a dedicated Stata do-file that merges the raw source files, harmonizes country-year or country-quarter identifiers, constructs the required variables, and produces the corresponding analysis-ready dataset. The folder code_data_management/ contains the central scripts used to rebuild both datasets from the raw files. Government financial assistance commitments are based on the IMF Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic, following Kirti et al. (2022). Cultural variables are based primarily on Hofstede’s individualism index, with alternatives from the GLOBE Project and Minkov and Kaasa (2022). Macroeconomic, institutional, political, and COVID-19 controls are drawn from the World Bank, Trading Economics, Oxford COVID-19 Government Response Tracker, Economist Intelligence Unit, V-Dem, Romelli (2022), CPDS, Fincher et al. (2008), Davis and Abdurazokzoda (2016), and other sources documented in the README and variable-definition files. The Stata code is organized into three components: 1. Data management: rebuilds the panel and cross-sectional datasets from raw source files. 2. Figures and descriptive statistics: reproduces Figures 1–3 and Tables 1–4. 3. Regression results: reproduces Tables 5–17 and Appendix 1. Main scripts include: 00_run_data_management.do 00_run_figures_tables_1_4.do 00_run_regression_tables_5_17.do 01_panel_statistics_figures_tables_1_4.do 02_cross_section_statistics_figures_tables_1_4.do 03_panel_regressions_tables_5_17_appendix1A.do 04_cross_section_regressions_tables_5_17_appendix1B_updated.do Requirements: Stata with outreg2, estout, ivreg2, and ranktest. To run the package, set the Stata working directory to the package root and execute the relevant master scripts. Outputs are saved to output/tables, output/figures, and output/logs.

Original data for analysis of construction workers' suicide rate from 2001-2021, and prediction of 2022-2025

Approximately 150 axial CT slices

Datas obtained from characterisations

This dataset summarizes the results of 386 vegetation surveys and relevant land management actions conducted over 11 growing seasons from 2015 through 2025. Surveys were conducted at 74 long-term vegetation monitoring plots in terrestrial habitats within the Central Platte River Valley of Nebraska, which are owned and/or managed by the Crane Trust. Survey areas consisted of upland and lowland herbaceous and woody habitats including wet meadows, prairies, woodlands, shrublands, and savannas. Additionally, monitoring sites have a range of land use histories. “Relict” sites have never been tilled and largely retain their historic character, with vegetation dominated by native species. “Rehabilitated” sites were also never tilled but were historically degraded through neglect or overuse, losing many native species and/or shifting to an alternative stable state (e.g., meadow to woodland). These sites have since been actively managed to recover their historic character, such as by reversing woody encroachment or controlling invasive species. “Reconstructed” sites were once tilled for row crops but have been reseeded with native prairie species believed to have historically occurred in the region and have also often been recontoured to reflect historic topography before industrial agriculture. Survey sites were visited between 1 and 9 times each and 5.22 times over 11 growing seasons on average (SD = 2.03, SE = 0.2). Woodland sites were only surveyed during our initial inventory efforts unless later management or restoration activities (e.g., prescribed fire) occurred, as woodland habitats generally laid outside of the Crane Trust’s rotationally managed pastures under routine monitoring. Monitoring of all other terrestrial habitats (i.e., meadows, prairies, etc.) occurred on an every-other-year (i.e., biennial) cycle. However, when necessary, survey plots were visited more frequently to assess the initial response to significant woody or invasive species control efforts. Overall, woodland sites were surveyed an average of 2.0 times (range = 1-3) while all other habitats were surveyed 5.6 times (range = 1-9) over 11 growing seasons. Finally, monitoring plots were progressively added along with the conservation of additional properties; therefore, some sites were surveyed less simply because they were established more recently. The dataset provides a great deal of information for each monitoring plot including plant species richness, dominance, and cover metrics per site visit disaggregated plant growth habit as well as several standard indices for site assessment including the floristic quality index, the mean wetland indicator status, and the Simpson diversity index. Finally, the dataset also provides key management data such grazing and controlled burning metrics. Version 2 has been updated to fix formatting errors, remove excess data rows, and add data missing from Version 1.

QCA Energy Dissipation dataset based on SCDD and cell polarization (Version 1)

This dataset contains the main data supporting the conclusions in the paper Who Needs RIA: The Strategic Use of Regulatory Impact Analysis. It consists of the data gathered in archival research (RIAs and contacts) and data generated in two surveys. Tentative abstract for the paper follows: Regulatory Impact Analysis (RIA) is commonly understood as a procedural instrument through which elected officials constrain bureaucratic discretion by requiring regulatory decisions to be supported by technical evidence. This article advances a different interpretation. We argue that once procedural requirements become embedded within organizations populated by specialized career bureaucrats, they become institutional resources through which bureaucrats construct epistemic authority and strategically influence policy outcomes. Rather than eliminating bureaucratic politics, procedural reforms may relocate political conflict from formal decision-making arenas to the production and communication of technical expertise. We examine this argument through a mixed-methods study of Brazil's eleven federal regulatory agencies, combining an original database of 552 Regulatory Impact Analyses, two surveys of bureaucrats responsible for preparing these analyses, and a preregistered survey experiment. The evidence shows that bureaucrats perceive RIAs as increasing the political costs of disregarding technical recommendations and become significantly more willing to employ procedural strategies when political appointees threaten the agency's institutional mission. Rather than openly confronting political authorities, however, bureaucrats overwhelmingly rely on procedural actions embedded within ordinary administrative routines. These findings demonstrate that procedural institutions simultaneously constrain and empower bureaucracy by transforming expertise into a politically consequential institutional resource. Procedural institutions simultaneously constrain and empower bureaucracy by transforming expertise into epistemic authority.