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"en": "Automated sorption balances are widely used for characterizing the interaction of water vapor with hygroscopic materials. This data publication is part of an interlaboratory study investigating the stability and performance of automated sorption balances. For this portion of the study, we investigated the mass stability of western larch (Larix occidentalis Nutt.) wood samples held at constant relative humidity (RH) for seven to ten days after a step change had been included. The reason for the long hold times was to collect data to “operational equilibrium” where the change in mass is on the order of the inherent operational stability of the instrument. A total of 80 datasets were acquired from 21 laboratories around the world, collected between February 2022 and October 2024, covering absorption with final RH levels ranging from 10% to 95%. Measurements within each dataset include the time, mass, relative humidity, and temperature data for each sorption step of interest.\r\n\r\nThese data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:\r\n\r\n Zelinka, Samuel L.; Glass, Samuel V.; Farkas, Natalia; Thybring, Emil E.; Altgen, Michael; Rautkari, Lauri; Curling, Simon; Cao, Jinzhen; Wang, Yujiao; Künniger, Tina; Nyström, Gustav; Dreimol, Christopher Hubert; Burgert, Ingo; Roper, Mark G.; Broom, Darren P.; Schwarzkopf, Matthew; Yudhanto, Arief; Subah, Mohammad; Lubineau, Gilles; Fredriksson, Maria; Olek, Wiesław; Majka, Jerzy; Pedersen, Nanna Bjerregaard; Burnett, Daniel J.; Garcia, Armando R.; Dreisbach, Frieder; Waguespack, Louis; Schott, Jennifer; Esteban, Luis G.; García-Iruela, Alberto; Colinart, Thibaut; Rémond, Romain; Mazian, Brahim; Perré, Patrick; Emmerich, Lukas. 2025. Water vapor absorption data for western larch (Larix occidentalis Nutt.): Results of a worldwide interlaboratory study. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2025-0010"
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"en": "This dataset accompanies/reflects the simulations in: “Avalanches in CuZrAl metallic glasses”Phys. Rev. E 111, 014107 – Published 3 January, 2025DOI: https://doi.org/10.1103/PhysRevE.111.014107\r\n\r\nSee the paper for full methods, parameters, and analysis choices.\r\n\r\nWhat this dataset contains--------------------------Molecular‑dynamics (LAMMPS) samples of Cu–Zr(–Al) metallic glasses, prepared by quenching from 2000 K to 300 K ata specified cooling rate, then deformed via athermal quasistatic shear (AQS) at T = 300 K while recording stress–strainresponses and stress‑drop statistics.\r\n\r\nTop‑level layout----------------Cu30Zr70_10_12/Cu37Zr37Al26_10_12/Cu40Zr40Al20_10_12/Cu45Zr45Al10_10_12/Cu47Zr47Al06_10_12/Cu50Zr50_10_11/Cu50Zr50_10_12/Cu50Zr50_10_13/Cu50Zr50_10_14/Cu50Zr50_10_15/Cu70Zr30_10_12/figure/\r\n\r\nNaming convention-----------------Cu{CuPct}Zr{ZrPct}[Al{AlPct}]_10_{NN}\r\n\r\n• Composition is given in atomic percent (e.g., Cu47Zr47Al06 → Cu47% Zr47% Al6%).• The suffix “10_{NN}” encodes the cooling rate Ṫ = 10^{NN} K/s (e.g., “10_12” → 10^12 K/s; “10_14” → 10^14 K/s). – Multiple rates exist for Cu50Zr50 (10_11 … 10_15). Other compositions are at 10_12 K/s.\r\n\r\nInside each composition/rate folder-----------------------------------1/ 2/ … 100/ ← independent replicate runs (distinct initial configurations/seeds).\r\n\r\nTypical per‑run contents (filenames may vary by workflow)---------------------------------------------------------• stress–strain data (e.g., stress_strain.*)• identified stress drops / intervals (e.g., drops.*)• LAMMPS logs and metadata (e.g., log.lammps, metadata.json if present)• optional atomistic dumps/snapshots (e.g., dump.*)\r\n\r\nCoverage summary----------------At Ṫ = 10^12 K/s: Cu30Zr70, Cu70Zr30, Cu47Zr47Al06, Cu45Zr45Al10, Cu40Zr40Al20, Cu37Zr37Al26.Cu50Zr50 at multiple Ṫ: 10^11, 10^12, 10^13, 10^14, 10^15 K/s.\r\n\r\nMethodological notes (see paper for details)--------------------------------------------• Engine: LAMMPS with EAM potential for Cu–Zr(–Al). Hybrid MD+MC (VC‑SGC) used to target compositions.• Quench: 2000 K → 300 K at the rate encoded in the folder name; zero external pressure.• AQS: incremental shear with δγ = 1e‑4; stress–strain recorded per increment.• Stress‑drop detection: consecutive decreasing‑stress segments; drops < 1e‑4 GPa discarded.• System sizes used in the paper include N ≈ 6k (main), plus size‑effects at 3k/12k/24k atoms.• Figure folders contain data used for figures\r\n\r\nProvenance & citation---------------------If you use these data, please cite the paper above. When reporting results, include composition, cooling rate, and run ID.\r\n\r\nThis dataset is licensed under the Creative Commons Attribution 4.0 International license (CC BY 4.0).You are free to share and adapt the material for any purpose, provided you give appropriate credit.\r\n\r\nLink: https://creativecommons.org/licenses/by/4.0\r\n\r\n-----------\r\n\r\nSB acknowledges support from the National Science Center in Poland through the SONATA BIS grant DEC-2023/50/E/ST3/00569 (PI: SB). \r\n\r\nSB acknowledges support from the Foundation for Polish Science in Poland through the FIRST TEAM FENG.02.02-IP.05-0177/23 project (PI: SB)."
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"en": "This dataset contains measurement data from the following publication:\r\n\r\nBelt T, Awais M, Nousianen P, Rautkari L, Mäkelä M (2025) Cell wall polymer degradation during Rhodonia placenta brown rot decay of thermally modified and unmodified wood. ACS Sustainable Chemistry & Engineering, https://doi.org/10.1021/acssuschemeng.5c08352.\r\n\r\nAbstract\r\n\r\nThermal modification produces decay-resistant wood suitable for sustainable applications. Although thermally modified wood remains degradable by fungi, its degradation mechanisms are poorly understood, impacting long-term eco-friendly use. This study investigated thermally modified wood degradation by elucidating chemical changes to wood cell wall polymers during Rhodonia placenta brown rot decay. Modified and unmodified Scots pine samples were exposed to R. placenta in stacked-sample decay tests, generating decay stage progressions. Decayed samples were analysed by near infrared spectroscopy with multivariate analysis to identify key chemical changes. Milled wood lignin was isolated and analysed by two-dimensional nuclear magnetic resonance spectroscopy for further lignin chemistry insight. Results showed that R. placenta degraded thermally modified wood to high mass losses. Chemical changes were characterised by carbohydrate degradation and oxidative lignin modification, typical for brown rot. While most degradative changes were similar between modified and unmodified wood, differences in lignin modification patterns were observed. Interestingly, spectroscopic data revealed different chemical changes in early and late decay stages in modified and unmodified wood. These findings highlight the time-dependent nature of R. placenta degradation and show that thermally modified and unmodified wood are degraded by similar yet different mechanisms, providing new insight into brown rot wood degradation.\r\n\r\nData description\r\n\r\nMethodological details can be found in the associated publication.\r\n\r\nThe \"IdentifiersAndMassData.csv\" –file gives the sample identifiers and the modification and decay test mass data for all samples. The identifiers “sample type”, “replicate”, and “sample position” are the wood sample type (reference, 200 °C, or 230 °C), the replicate number (1-7), and the position of the sample in the stacked-sample decay test (1-6 from top to bottom). Masses m0, m1, m2, and m3 are the initial unmodified dry mass, the modified dry mass, the decaying wet mass, and the decayed dry mass, respectively. \r\n\r\nThe ”NIR_images_raw.mat” –file contains NIR imaging data organised as a structured cell array. Each row represents a sample with six columns: NIR hyperspectral image cube (1245×384×288 double - digital signals), sample ID, white reference correction image (25×384×288 double), sample ID, dark current correction image (25×384×288 double), and sample ID. The dataset contains sample measurements and their corresponding calibration (white reference and dark current), facilitating radiometric correction workflows and multivariate analysis.\r\n\r\nThe ”NIR_spectra_unprocessed.csv” –file contains unprocessed average NIR spectra extracted from regions of interest (ROI) within the hyperspectral images. The spectral data processing workflow involved: (1) conversion from raw digital signals to reflectance values using white reference and dark current corrections, (2) extraction of average reflectance spectra from predefined ROI, and (3) conversion from reflectance to absorbance units.\r\n\r\nThe ”NIR_spectra_preprocessed.csv” –file contains preprocessed average NIR spectra extracted from the images. The unprocessed spectra were first converted to second derivative and then mean-centered by subtracting the average spectrum of the whole dataset from each individual spectrum."
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"en": "A subset of railroad environment data collected in Finland in December 2019 with a mobile laser scanner placed on a service locomotive driven on the track. Parts of the full data are used in [1]: see the publication for more information about the data acquisition process.\r\n\r\nThe dataset covers a continuous region of length 2km and contains 42.9M points. The region contains between 2–4 parallel tracks with multiple intersections.\r\n\r\nData includes x-, y-, and z-coordinates stored in the x, y, and z fields and single-wavelength intensity values stored in the intensity field. The original measurement times have been mapped to uniformly distributed values within the interval [0,1] and stored within the gps_time field. All other fields are unused.\r\n\r\nBoth compressed (.laz) and uncompressed files (.las) are provided.\r\n\r\n[1] A. Kononen, H. Kaartinen, A. Kukko, M. Lehtomäki, J. Taher, and J. Hyyppä, “Fully automated extraction of railtop centerline from mobile laser scanning data,” Automation in Construction, vol. 168, article number 105812, 2024, doi:10.1016/j.autcon.2024.105812."
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"en": "This MATLAB script include an implementation of a first order correction model for level ice resistance of a wind farm described in Polojärvi et al. (2025). The model assumes a wind farm arranged as an n×n array of wind turbines on a Cartesian grid with a unit square side length L. The entire farm is initially assumed to be enveloped by ice, and the total initial ice resistance is given by R=(n×n)Ri, where Ri is the resistance of wind turbine i (defined by using DEM simulations). The influence of turbine wakes is incorporated by considering the drift angle α; depending on this angle, wakes may interact with other turbines and lead to a reduction in total ice resistance. The magnitude and probability of this resistance reduction are determined using a function φ∗ described in Polojärvi et al. (2025). Modifying the script to include more complex wind farm geometries should be straightforward.\r\n\r\nWe acknowledge the funding from the European Union – NextGenerationEU instrument through Academy of Finland under grant number (348586) WindySea - Modelling engine to design, assess environmental impacts, and operate wind farms for ice-covered waters. Also we thank the supported by the NOCOS DT project, funded by the Nordic Council of Ministers. Model was partly created under WINDRIFT project, awarded by the Finnish Transport Infrastructure Agency and funded by the European Union through the CEF Infrastructure Project WINMOS III (101122620)."
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