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Raw Western blot data and transcriptomic profiles of GP-transfected and trVLP-EBOV-infected HEK293T cells.

This dataset contains the complete input data used in the computational experiments for the manuscript “Green fuels in liner feeder shipping: integrated network design, speed optimization, and bunker management.” The files include network, vessel, cargo demand and supply, fuel, bunkering, cost, time, and emission-related parameters for the six feeder-network instances and five fuel types considered in the study.

MATLAB model for the techno-economic assessment of waste heat recovery at megawatt charging hubs for battery-electric heavy trucks. The liquid-cooled charging points reject heat that can be sold to a heat network. The model compares two ways of connecting a hub: • TSN – feed-in to a low-temperature source network (5GDHC) through a plate heat exchanger • DH – feed-in to a conventional district heating network through a heat pump For both paths it calculates the levelised cost of heat (LCOH), the net present value (NPV) and the discounted payback period. It also shows how these respond to connection distance, hub size, energy prices, subsidy rate and every cost assumption. The package contains: • an interactive explorer app (WhrExplorerApp) with a guided Live Script tour • the full calculation chain as plain MATLAB functions • run_all.m, which reproduces all tables and figures of the accompanying study • a test suite (107 tests) and a complete model reference (docs/model_reference.md) Requirements: base MATLAB, no toolboxes (tested with R2026a; the Live Script tour needs R2025a or newer). Quick start: run setup_path, then WhrExplorerApp. See README.md for details. Accompanies: Müller et al. (2026), Economic feasibility of waste heat integration from high-power charging stations for electric trucks in district heating networks (submitted).

The dataset include EPMA data for olivine, majoritic garnet, glass, and ahrensite, STEM-EDS data for olivine and Al-rich clinopyroxene in a shock melt vein from lunar meteorite NWA 14526, and EELS data for olivine, clinoenstatite, magnesiowustite, majoritic garnet, and glass, and Raman data for maskelynite, majoritic garnet, tissintite, stishovite, and seifertite.

This dataset supports the reconstruction of spatiotemporal seismotectonic deformation in the Crimean–Black Sea region during 1970–2012. It combines a working earthquake catalogue, parameters of 158 averaged focal mechanisms, and code for analysing mechanism-associated hypocentral groups and energy-weighted centres in four structural-depth intervals: sediments, consolidated crust, the Moho interval, and the mantle interval. The single event table contains 1587 earthquakes with dates, times, coordinates, depths, and final structural-depth interval, block, and regime assignments. Energy-class K values are available for 1583 events; four events without K contribute only to arithmetic-centre calculations. According to L.A. Shumlianska, who participated in preparing and analysing the data, the working catalogue derives from the relocation variant using the Black Sea mantle P-wave velocity model presented by Bugaenko, Shumlyanskaya, Zaets and Tsvetkova (2008), developed within V.S. Geyko’s seismic-tomography approach. The deposited file is the working selection used in this study, rather than a transcription of a published event table. Table 1 of Burmin and Shumlianska (2017) reports relocated depths attributed to their 2015 study and differs from the working dataset; it should not be treated as an identical catalogue version. The package includes averaged-mechanism Tables S1–S4, the event table (S5), calculation scripts, methodological documentation, and calculated centre and transition tables (S6–S7). The code supports reproduction of centres, within-block transitions, sectoral cycles, azimuthal tests, and energy analyses. Original printed first-motion bulletins and the complete 1988-event source catalogue are not included. Further provenance details, file descriptions, and reproduction limits are provided in README.txt.

Berna R7 is a 1.06B-parameter multilingual language model trained from scratch on English, Mathematics, and Code (2.66B tokens). It is the first implementation of the DNA-Kernel Plexus architecture proposed in Berna R5 (DOI: 10.5281/zenodo.23015308). This dataset (v0.1.0) contains: - Architecture documentation (ARCHITECTURE.md, DNA.md) - Cell lifecycle implementation: saturation (6D knowledge vector), DNA Kernel (100 chromosomes x 4 genes), splitting mechanism (S >= S*), dynamic plexus graph - Training pipeline: train.py, dataset.py, eval.py - Experiment scripts for H1 (saturation-triggered splitting) and H2 (bounded forgetting) - Baselines: vanilla transformer, EWC, PackNet - SQL registry with schema for model lineage and connection tokens - Paper outline - Bundled documentation PDF Status: Model weights are still training; they will be released as v0.2.0. Key facts: - Parameters: 1,056,265,728 (1.06B) - Data: 2.66B tokens - Hardware: 1x RTX 5090 (32 GB) - License: Berna Research License v1.0 Related works: - Berna R5: DOI 10.5281/zenodo.23015308 - Zenodo mirror: DOI 10.5281/zenodo.23037065 - GitHub: https://github.com/Berna-Labs/berna-r7 - HuggingFace: https://huggingface.co/BernaLabs/berna-r7-mother

Type frequency list of Chinese Quadrisyllabic Idioms, obtained from THUCNews corpus.

This dataset provides abridged life tables and mortality indicators for Uzbekistan at the national level, covering historical reference years (2000, 2005, 2010, 2015, 2019) and annual forecasts to 2050 (recommended Lee–Carter 2024–2050; experimental hybrid 2020–2050). Seven life-table functions are reported — central death rate (nMx), probability of dying (nqx), survivors of a radix of 100,000 (lx), deaths (ndx), person-years lived (nLx), total person-years remaining (Tx) and life expectancy (ex) — across 19 abridged age groups (<1, 1–4, 5–9, …, 80–84, 85+). Historical indicators are given for both sexes, males and females; forecasts are given for males and females. Historical rates are drawn from the WHO Global Health Observatory. The 2008 all-population empirical life table (single year of age) is from the State Committee of the Republic of Uzbekistan on Statistics (www.stat.uz). The recommended forecast product is an independent, out-of-sample-validated Lee–Carter model fitted to annual UN WPP 2024 abridged life tables for Uzbekistan (2000–2023), with the mortality index k_t extrapolated by a random walk with drift (the standard Lee–Carter time-series choice). The recommended file, forecast_lee_carter_2024_2050_long.csv, provides central estimates with 95% prediction intervals (columns value_central, value_lo95, value_hi95) for all seven life-table functions by sex. It closely matches the UN WPP 2024 medium variant for both sexes. An experimental hybrid Lee–Carter–Gompertz forecast, in which the log central death rate is expressed as ln m_x(t) = (ln A + B·x) + a_x + b_x·k_t + ε, is retained separately under data/experimental/ for methodological comparison. It does not outperform Lee–Carter out of sample (e0 mean absolute error 2.58/2.92 years vs 0.68/0.31 years for males/females); it is close to the benchmarks for females but optimistic for males, and should be treated as an optimistic scenario. Data are provided in tidy (long) CSV format keyed by year × sex × age group × indicator, together with a multi-sheet Excel workbook, two single-year 2008 reference life tables (empirical and Gompertz), model-comparison tables (R²/RMSE and AIC/BIC by model and sex), an out-of-sample validation table, a data dictionary, and reproducible Python scripts. Values in the historical and experimental-hybrid files carry a provenance flag: historical rows are historical_estimate (WHO GHO estimates); hybrid-forecast rows are forecast_model (nMx, nqx, lx, ex) or forecast_derived (ndx, nLx, Tx reconstructed from the model's lx by the standard abridged life-table algorithm). The recommended Lee–Carter file instead reports central values with 95% prediction intervals. All life-table identities were validated programmatically (0 ≤ nqx ≤ 1; monotone lx; ndx = lx(x) − lx(x+n); ex = Tx/lx); a validation report is included.

1. Synthetic network 1: graphs built with 200 nodes and 20 snapshots. In the initialization, nodes are divided into two groups, with 100 nodes each. Then, at a randomly selected time period, 40 % of the nodes are chosen to migrate to a new community in Dataset 1, and 80 % in Dataset 2. The membership of each node is chosen according to a stochastic block model (SBM), where nodes within the same community are connected with probability 0.3, and edges between communities are drawn with a probability of 0.1. For the generation of the attributes, we propose 4 cases using a univariate or multivariate normal distribution with a standard deviation of 0.1. In Case 1, a three-variate normal distribution is used to generate three attributes, where only the first attribute contributes to identifying the groups. In Case 2, we propose to use only one meaningful attribute. In Case 3, there are three irrelevant random attributes. In Case 4, there is one irrelevant random attribute. This corresponds to Data17 (change of 40 %), with R0M0 (one relevant attribute), R0M1 (three attributtes with one relevant), R1M0 (one irrelevant attribute), R1M1 (three irrelevant attributes). Data18 (change of 80 %) as a similar interpretation for attributes. 2. Synthetic network 2: we generated 10 timestamps with communities that grow and shrink. We designed 3 datasets with 3 groups of 80, 90 and 100 nodes. The network structure is generated by a degree-corrected SBM. Three types of attributed networks were proposed, with different original link probabilities, to assess strongly assortative structures, weakly assortative structures, and disassortative structures. This corresponds to Data107PV0.21, Data108PV0.1, and Data109PV0.19. 3. Synthetic network 3: we design four datasets with different evolution of links over time, consisting of attributed networks of 100 nodes and 60 time points were generated. Intra- and inter-cluster edges are selected from a truncated Gaussian distribution in the range [0; 1]. Changes in the structure of the networks are generated between time t = 20 to t = 21 and t = 40 to t = 41. Attributes were generated similarly to Synthetic network 1. This corresponds to Data19, Data20, Data21, and Data22, with similar interpretation for attributes as in Synthetic network 1. 4. Synthetic network 4: We use the synthetic benchmark DANCer to create 12 attributed graphs with undirected edges that can change over time, where nodes are grouped into densely connected sets, relatively homogeneous according to the attributes. The number of nodes start at 1000 nodes, the number of communities at 10, and the number of edges at 5000, which increase over time, ending with a maximum value of 4814 nodes, 15 communities and 21908 edges among the built networks. This corresponds to Data122 to Data125, Data128 to Data131, and Data135 to Data138. For all datasets, 10 seeds where used. All data files were generated using Matlab.

Pet Owner Survey: Antibiotic Use and Antimicrobial Resistance