Our Work

This Zenodo deposit accompanies the JGR: Solid Earth manuscript above and contains the analysis pipeline (Python code) and the processed data products (NumPy .npz arrays) needed to reproduce all main …

This Zenodo deposit accompanies the JGR: Solid Earth manuscript above and contains the analysis pipeline (Python code) and the processed data products (NumPy .npz arrays) needed to reproduce all main and supporting figures and tables of the paper. Raw magnetometer time series are not re-distributed here — they remain available at their original public archives (see Section 6, Data Sources).

A one-dimensional model of an NMC111 half cell (lithium foil, separator, porous cathode) with two particle models: uniform-concentration particles, or porous agglomerates of crystals coupled to the el…

A one-dimensional model of an NMC111 half cell (lithium foil, separator, porous cathode) with two particle models: uniform-concentration particles, or porous agglomerates of crystals coupled to the electrode through their surfaces (a double-porosity model solved with a fully coupled, condensed Newton step). Dilute-solution electrolyte transport, electronic conduction and Butler-Volmer insertion kinetics are solved by finite volumes, backward Euler and Newton's method with the BAND block-tridiagonal solver. It runs constant-current, constant-voltage and rest protocols. Fortran, C++ and Python implementations read the same input file.

This dataset contains the environmental and benthic macroinvertebrate data analysed in Nakamura et al., "Seasonal and spatial dynamics of in-channel water bodies on a river sandbar and associated macr…

This dataset contains the environmental and benthic macroinvertebrate data analysed in Nakamura et al., "Seasonal and spatial dynamics of in-channel water bodies on a river sandbar and associated macroinvertebrate communities" (PLOS ONE).   Study site and sampling In-channel water bodies (ICWBs) formed around traditional wooden river-training structures ("Seigyu") on a sandbar in the lowland reach of the Kizu River, Kyoto, Japan (34°48'27.1" N, 135°48'10.9" E), and one adjacent main-channel reference site were sampled on 3 June, 11 August and 21 November 2020, and 3 March and 1 June 2021. Each row in both files is one site-by-season sample (61 samples). The two files share the same sample_id and row order.   Habitat codes (column "habitat"): UB = upstream backwater; DB = downstream backwater; Pond = riverine pond (RP in the article); River = main-channel reference. sample_id = <month>_#<point number>_<site code> (WU = UB, T = Pond, WD = DB, R = River; "Jun" = June 2020, "JUN" = June 2021).   1. kidzu_sandbar_water_quality.csv Columns 1–3 are sample_id, sampling_month and habitat   - lateral_distance_m, longitudinal_distance_m: distance from the river edge and from the upstream edge of the sandbar (m), measured from drone imagery - EC: electrical conductivity (mS/m) - water_temperature_C: water temperature (°C) - pH - depth_cm: water depth at the deepest point (cm) - chl_a_mg/m3: chlorophyll a (mg/m3) - FPOM: fine particulate organic matter (ash-free dry mass of particles 100 µm–1 mm collected with plankton nets) - P_uM: soluble reactive phosphorus (PO4-P, µM) - N_uM: nitrate nitrogen (NO3-N, µM) - habitat_area_m2: area of the water body containing the sampling point (m2); NA = not available or not applicable (main channel)   2. kidzu_sandbar_macroinvertebrates.csv Columns 1–3 are sample_id, sampling_month and habitat; the remaining 194 columns are taxa.   Values are numbers of individuals (≥1 mm) collected with a D-frame net (mesh 500 µm, opening 30 × 30 cm); two 30-s samples were pooled per sample. Taxa were identified to the lowest possible level (usually genus). Where a taxon was recorded separately by life stage, the stage is given in parentheses (e.g. "Micronecta sp. (nymph)"). "Diptera fam. gen. sp. A" and "B" are two unidentified larval types.

This record contains the data and R code underlying the manuscript "Convergence as the exception: divergence and asymmetric reconfiguration in international AI research collaboration" by Minseung Chan…

This record contains the data and R code underlying the manuscript "Convergence as the exception: divergence and asymmetric reconfiguration in international AI research collaboration" by Minseung Chang and Keuntae Cho (Sungkyunkwan University). Bibliographic data were retrieved from OpenAlex (https://openalex.org) through its API between 19 and 30 May 2026 and are openly available under a CC0 license. Titles and abstracts are not included; they can be retrieved from OpenAlex with the identifiers provided in the corpus list. Contents of Data_and_Code.zip data/ — corpus_715134.csv (one row per paper: OpenAlex ID, DOI, publication year, work type, language, author countries, number of countries, collaboration type, search keyword); theta_K30.csv.gz (document–topic proportions from the structural topic model with K = 30; the manuscript topic number is 31 minus the column index); topics_K30.csv (topic labels, highest-FREX and highest-probability terms, mean prevalence, semantic coherence, exclusivity). results/ — country_pairs_622.csv (the 622 country pairs with at least 50 co-authored papers: paper counts, Jensen–Shannon divergences before and after the half-sample noise correction, standardized asymmetry r_ij, bootstrap 95% interval and p-value of the tilt, and the collaboration-outcome class); collaboration_type_effects.csv (bilateral and multilateral effects on topic proportions, in percentage points, with 95% intervals); PROJECT_3_RQ2_tilt_null_v2.xlsx (Type I error experiment for the bootstrap tilt test). code/ — PROJECT_3_RQ2_functions.R (classification rule, half-sample noise correction, bootstrap tilt test); PROJECT_3_RQ2_NoiseCorrection_v1.R (pairwise analysis producing country_pairs_622.csv); PROJECT_3_RQ2_NoiseSimulation_v1.R (simulation with known profiles); PROJECT_3_RQ2_TiltNull_v2.R (Type I error experiment in which samples are drawn from the point equidistant from the two national profiles in Jensen–Shannon divergence, so that the true tilt is exactly zero). README.md describes every file and column. Software: R 4.x with the packages stm, dplyr, tidyr, readxl and writexl. Settings: delta = 0.10, alpha = 0.05, 30 half-sample splits, 500 bootstrap replicates (200 in the simulation). Versions 2 and 3 (October 2026) add PROJECT_3_RQ2_TiltNull_v2.R and its output PROJECT_3_RQ2_tilt_null_v2.xlsx; version 3 packages them inside Data_and_Code.zip together with the files of version 1, which are unchanged.

Civil Engineering Explained: Infrastructure, Systems & Built Environment Design is an open educational resource (OER) curriculum module establishing the core engineering principles, mathematical f…

Civil Engineering Explained: Infrastructure, Systems & Built Environment Design is an open educational resource (OER) curriculum module establishing the core engineering principles, mathematical formulations, and contemporary computational paradigms underpinning the modern built environment. The module establishes a rigorous systems engineering foundation spanning seven major sub-disciplines: Structural Engineering: Limit state design, load paths, second-order effects, ductility requirements, and advanced materials (UHPC, CFRP). Geotechnical Engineering: Soil mechanics, effective stress balance, Darcy's law, and deep/shallow foundation systems. Transportation Engineering: Macroscopic traffic flow dynamics, signal timing splits, and demand management. Water Resources Engineering: Rational method hydrology, rainfall-runoff modeling, and sustainable urban drainage systems (SuDS). Urban and Regional Planning: Transit-oriented development, microclimate adaptation, and spatial systems trade-offs. Construction Management: BIM Level 2/3 coordination, CPM/PERT scheduling, and lifecycle asset tracking. Earthquake and Disaster Engineering: Seismic base isolation, elastoplastic dynamic response, and community resilience. Key Pedagogical Features: AI-Era Computational Bridges: Integration of Physics-Informed Neural Networks (PINNs), Fourier Neural Operators (FNOs), and Kalman-filtered (IEKF/UKF) edge digital twins for real-time structural health monitoring. Systems Engineering Trade-Off Matrix: Multi-variable evaluation across structural, seismic, geotechnical, and drainage domains balancing lifecycle carbon, capital cost, and failure modes. Worked Numerical Application: Step-by-step limit state design calculation for a singly reinforced concrete beam in flexure, verifying ductility and moment capacity per ACI 318 / Eurocode 2. Interactive Tool: Algorithmic formulation of the Rational Method peak runoff and hydrograph curve dynamics. Comprehensive Assessment: Curated foundational, scenario-based, and multi-step numerical engineering problem sets.

Simulation data and posterior results for the LISA time-varying PSD study: raw time series, continuous and gapped analysis datasets, and Hagn, Horb and Hpara fits. The accompanying Jupyter notebook de…

Simulation data and posterior results for the LISA time-varying PSD study: raw time series, continuous and gapped analysis datasets, and Hagn, Horb and Hpara fits. The accompanying Jupyter notebook demonstrates loading the files, plotting WDM power and median posterior surfaces, and comparing PSD slices with simulated truth.

A Python implementation of two Monte Carlo dark matter halo merger-tree algorithms: Parkinson, Cole & Helly (2008)'s fitted branching-rate approach (multi-backend: serial/NumPy/Numba), and an exac…

A Python implementation of two Monte Carlo dark matter halo merger-tree algorithms: Parkinson, Cole & Helly (2008)'s fitted branching-rate approach (multi-backend: serial/NumPy/Numba), and an exact, barrier-agnostic approach built directly on Zhang & Hui (2006)'s exact first-crossing distribution, including a working implementation of Nadler, Benson, Driskell, Du & Gluscevic (2023)'s Brownian-bridge- constrained excursions for cheaply sampling merger trees guaranteed to reach a specified progenitor mass and redshift. Supports CDM, WDM, and FDM (via power-spectrum transfer functions and window-function choice), with an extensive validation suite cross-checking each algorithm against independent quadrature, direct Monte Carlo random walks, and each other.

This paper presents a self-observed case study of autobiographical memory reconstruction following a school reunion held after a long interval since graduation. Contemporary theories increasingly desc…

This paper presents a self-observed case study of autobiographical memory reconstruction following a school reunion held after a long interval since graduation. Contemporary theories increasingly describe memory as a reconstructive process rather than the retrieval of fixed records. The present observations provide an opportunity to examine this process in real time. Over approximately forty-four hours following the reunion, recognition of former classmates triggered the progressive re-emergence of contextual information including seasons, music, school events, interpersonal relationships, and temporally localized autobiographical episodes extending far into the past. Rather than appearing instantaneously, memories emerged through successive stages of reconstruction. Faces triggered contexts; contexts triggered environmental details; environmental details triggered music; music enabled temporal localization. The observations are interpreted within a generative framework inspired by recent developments in diffusion-based artificial intelligence models. The findings suggest that autobiographical memory may function less as retrieval from storage and more as iterative reconstruction from sparse cues. Implications for cognitive science, autobiographical memory research, music-evoked recall, reminiscence therapy, and cognitive medicine are discussed.