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This dataset contains a series of micrographs, both raw and processed, as well as a large scale stitched micrograph covering the span of an electron beam weld in PM-HIP SA508 Grade 3 RPV steel. The dataset also contains a series of plain-text CSV files containing load-displacement data for a nanoindentation profile covering the same weld in the same direction.

This dataset contains the soil water content (SWC, %), soil bulk density (BD, g cm-3), soil particle size composition, soil organic carbon content (SOC, g kg-3), total nitrogen content (TN, g kg-3), total phosphorus content (TP, g kg-3), Olsen phosphorus content (OP, mg kg-3), available potassium content (AK, mg kg-3), ammonia nitrogen content (NH4+-N, mg kg-3), nitrate nitrogen content (NO3--N, mg wdkg-3) and soil pH corresponding to five vegetation restoration types (Pinus sylvestris var. mongholica Litv. (PS), Amygdalus pedunculata Pall. (AP), Salix psammophila (SP), Amorpha fruticosa L. (AF), Artemisia desertorum Spreng. (AD)) and bare sandy land (BS) in the Mu Us Sandy Land.

Dataset and python based ArcGIS Pro tools for creation of LSM of Kodagu

This replication package accompanies the article “In the Weeds of Traffic Fatalities: Revisiting the Effect of Medical Marijuana Laws.” The research re-evaluates the widely cited finding that medical marijuana laws (MMLs) significantly reduce traffic fatalities. The central hypothesis is that previous estimates of MML effects may be biased due to unaccounted-for pre-treatment trends and hard to interpret because of heterogeneity across states. The dataset is a panel of U.S. states from 1990 to 2010, constructed to closely replicate Anderson et al. (2013). It includes annual, state-level traffic fatality rates (log-transformed per 100,000 population), a binary indicator for MML adoption, and a rich set of covariates covering demographics, driving laws, traffic enforcement measures, and substance-related policies. The key finding is that states legalizing medical marijuana were already experiencing declining traffic fatalities before legalization. When accounting for these pre-trends using the Imputation Procedure (Borusyak et al., 2024), the estimated effect of MMLs shifts from negative to either zero or positive—depending on included covariates. The data also reveal large heterogeneity across states, with California disproportionately influencing population-weighted estimates.

Apatite and zircon (U-Th)/He data from the Eastern Pyrenees, covering the main tectonic units of the Axial Zone east from the Tec fault (Aspres, Canigó, Roc de Frausa, Albera). This data was collected in the framework of the PhD thesis by Sabí Peris Cabré, at Universitat Autònoma de Barcelona. The analyses were conducted in the laboratory facilities at University of Texas at Austin, in the Geochron lab.

IQ motif-containing protein; Leishmania infantum (strain JPCM5)

Data related to analysis of indirect carbon reduction effects by green space in Kyungpook National University. The analysis was conducted using ENVI-met(v5.0.2), a microclimate analysis model. 1. Meteorology input data 2. ENVI-met space construction results(ENVI-met space file) 2. ENVI-met analysis result(Kyungpook National University_Average air temperature, 2pm to 5pm)

Computer code and data to replicate delineations described in "A Better Delineation of U.S. Metropolitan Areas'", Federal Reserve Bank of Kansas City Research Working Paper 25-01, May 2025. The paper is conditionally accepted for publication in the Journal of Urban Economics.

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

Supplementary data for the publication "Comparing generative process synthesis approaches with superstructure optimization for the conception of supercritical CO2 Brayton cycles" (by Antonio Rocha Azevedo, Tahar Nabil, Valentin Loubière, Romain Privat, Thibaut Neveux and Jean-Marc Commenge), currently under peer review. The pre-print of the paper may be found at: https://www.ssrn.com/abstract=5200900 This includes: - A dataset of > 400.000 randomly generated power cycles (Random_SFILES_all.csv), in both the SFILES 2.0 and modified SFILES notations discussed in the article; - Results of the generative process synthesis approaches (Evolutionary Programming and Machine Learning). It contains data for all generated cycles which have a positive cycle efficiency and an LCOE under 200$/MWh (= 3.62 in the normalized values presented in the publication)). Datasets are named according to experiment name and objective function optimized. All datasets include, for each process: • Efficiency and LCOE values (attention to the objective function being optimized); • The total number of simulations run during optimization; • Real time elapsed during optimization (hours); • CPU time elapsed during optimization (hours); • [Machine Learning only] Modified SFILES generated by the model; • SFILES 2.0 and modified SFILES representations for the optimized process; • A "parameter SFILES 2.0" representation of the process, which embeds unit operation parameters into the SFILES 2.0 string (modified SFILES representations are not available as we may not guarantee that its syntax is completely respected). For more information and examples, check the README.txt file; • SFILES 2.0 and modified SFILES representation of the post-treated process (after bypassed units and branches are removed, according to the procedure described in the manuscript's supplementary material); • [Evolutionary Programming only] The generation in which the process is generated; • [Machine Learning only] The iteration in which the process is generated. - UnitsData.json, which includes the unit operation parameters involved in simulation and optimization, as well as their default values and bounds; - A README.txt with examples on how to read UnitsData.json and parameter SFILES. Notes: - Post-treated SFILES may eventually contain errors/not accurately represent the underlying process, as the post-treatment procedure is done automatically. - When conversion to modified SFILES was not possible, the associated entry from the SFILES 2.0 column is used. - In case of errors with the generation of the SFILES representation (e.g., for evolutionary approach results or during post-treatment), the "(null)(out)" SFILES placeholder isused (represents an empty process).