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The data were compiled from three database sources and made into eight figures. The primary Lyme disease data were acquired from the “Maine Tracking Network Database” website at https://data.mainepublichealth.gov/tracking/tickborne. This database offers a variety of features to isolate many variables over time and by population across various regions across Maine. The Maine state and county temperature data were acquired from the National Centers for Environmental Information (NCEI) from the National Oceanic and Atmospheric Administration (NOAA): Climate at a Glance series website at https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/statewide/time-series. The deer population estimates data were acquired from the Deer Friendly website at https://www.deerfriendly.com/deer/maine.

REPLICATION PACKAGE: "International Trade Finance and Learning Dynamics" Authors: David Kohn, Emiliano Luttini, Michal Szkup, Shengxing Zhang. The materials are split into two independent components that together reproduce every quantitative result in the paper. 1. code_paper_final/ (MATLAB). Reproduces Figures 3-11 and Tables 5 and 7, and generates a simulated firm-year panel (166,556 firms x 30 years) used as input by Package 2. Entry point: KLSZ_master_file.m, which controls binary flags for each section. The folder contains the solver, transition, and SMM routines at the root, plus: Experiments/ -- steady-state and shock drivers (foreign- and domestic-cost shocks under five risk regimes, plus delta=1 and mu=1 counterfactuals) Figures/ -- figure-generation scripts Tables/ -- model_table_5.m, model_table_7.m Output/Figures/ -- Figure_3 through Figure_11 panel PDFs Output/Tables/ -- Table_5.xlsx, Table_7.xlsx, and six simulated panels (counterparty_belief, demand_belief, oa_status, export_status, productivity, spell_status) Requirements: MATLAB R2023b+ with Parallel Computing, Optimization, and Statistics and Machine Learning toolboxes. To run, set MATLAB's working directory to the package root and execute KLSZ_master_file. See README.md. 2. Data_Replication/ (Stata). Reproduces Tables 1-4 (Section 2), Tables 5-8 (Section 4), and Tables A1-A5 (Appendix). Master script: src/do/00_main.do, which calls 01-03 (build yearly and daily customs panels and the F29 firm-year panel), 04 (ingests Package 1's CSVs and builds MergeSimData.dta), then 05-07 (estimate regressions and write LaTeX tables to out/<version>/{section_2,section_4,appendix}/). Two modes via the data_type global: "bc" -- real Chilean customs (DUS) and tax (F29) microdata from the Central Bank's PYTRFN database (DSN: MSSQL_DPM_DS02_INNO). Authorized access only; reproduces the paper's point estimates exactly. "mock" -- synthetic panel with the same schema and filters. Fixed random seeds; reproduces every script and table but not the published numbers. Data access: the firm-level microdata cannot be redistributed under the agreement with the Central Bank of Chile. Requirements: Stata, plus SSC packages reghdfe, estout, distinct, listtex, ftools, timeit, require. The six simulation CSVs from Package 1 are shipped inside each Data_Replication subfolder's dat/ so Package 2 runs without first executing Package 1. Mapping: Figures 3-11 -> Package 1 Tables 1-4 -> Package 2 (05_section_2.do) Tables 5, 7 -> Package 1 (Tables/model_table_*.m) Tables 6, 8 -> both: simulation from Package 1 -> regressions in Package 2 (06_section_4.do) Tables A1-A5 -> Package 2 (07_appendix.do) Each package ships a README and has been verified to run end-to-end from a clean install.

Here you will find the following data and code used in our paper: - BOSC Spindle Detection Code and Supporting Function Folder (BOSC2) - Traditional Spindle Detection Code - 1/f Background Signal Generation Code - Synthetic Spindle Generation Code - Single Wave Pulse Generation Code - LVS and HVS Amplitudes Needed for Synthetic Generation Codes - Synthetic Spindle Dataset - Single Wave Pulse Dataset - Raw Cortical Natural Sleep Recordings from Rats x4 - Spindle-free REM and NREM Dataset

Supplementary data for the Original Article entitled “A distinct psoriasis–atopic dermatitis overlapping phenotype in adults with dual type 2 and type 3 immune features and favorable response to JAK1 inhibition”

This dataset includes soil multitrophic community OTU tables used in the study. The data support the analysis of multitrophic interactions and their effects on soil multifunctionality in apple orchards on the Loess Plateau.

This dataset supports a study of fluid residence time in silicate weathering profiles. It includes four natural profiles: CM and ZK01 basalt profiles, and TT and DY granite profiles. Tables S1–S4 report mineral abundances, CaO and MgO concentrations, and Ca and Mg mass-transfer coefficients. Tables S5–S8 report immobile element concentrations, calculated porosity, fluid residence time, flow velocity, permeability, and Ca–Mg weathering fluxes. Mineral abundances were measured by X-ray diffraction. Hydrological parameters were calculated using a one-dimensional Darcy flow model with geochemically constrained porosity and depth-dependent permeability. The data can be used to reproduce the results and compare residence-time controls on silicate weathering across lithologies.

Digital Economy Development, Corporate Key Core Technological Innovation and New-Quality Productivity

Supplementary data

This dataset contains 16,000 high-resolution leaf images of two economically important horticultural crops — Lychee (Litchi chinensis) and Jackfruit (Artocarpus heterophyllus) — collected from multiple agricultural hubs in Bangladesh between November 5th and 27th, 2025. Images were captured across diverse field conditions using Poco F5, OnePlus Nord CE 2, and Google Pixel 7 smartphones to ensure environmental robustness and sensor variety. The dataset is distributed across eight diagnostic categories: (1) Jackfruit Healthy (1,119 original images), (2) Jackfruit Leaf Senescence (1,372 original images), (3) Jackfruit Leaf Spot (965 original images), (4) Jackfruit Pest Damage (987 original images), (5) Lychee Healthy (1,005 original images), (6) Lychee Leaf Blight (1,041 original images), (7) Lychee Pest Damage (1,030 original images), and (8) Lychee Erinose Mite (1,012 original images), totaling 8,531 original field-collected images across all categories. A strategic zero-leakage split was applied: 300 original images per class were reserved for validation and 300 for testing, while the remaining images were augmented to expand each training class to exactly 1,400 samples. The final dataset comprises 11,200 training, 2,400 validation, and 2,400 testing images across all eight categories, totaling 16,000 images. All images were standardized to 560 × 420 pixels and organized into train/val/test splits with class-wise subdirectories. Ground truth labels were validated by expert agronomists from Daffodil International University and Sylhet Agricultural University. This dataset is intended to support research in automated plant disease detection, deep learning-based image classification, and precision agriculture for tropical horticultural crops.

BDRS2025, a labeled image and video dataset collected from real road environments in Bangladesh for the purpose of road condition analysis using deep learning methods. The dataset contains 2,685 smartphone-captured images and 147 supplementary video data representing five categories of road conditions and road-related signs, such as pothole, crack, speed breaker, zebra crossing, and manhole cover. Images and videos were collected from multiple locations, including Dhaka, Chandpur, and Lakshmipur, under different weather, lighting, and traffic conditions. Each class contains an average of 500 annotated images. The images were manually cleaned and labeled to ensure consistency and accuracy. The dataset provides high-resolution road surface imagery captured in natural driving environments, making it suitable for computer vision research, infrastructure monitoring systems, and intelligent transportation applications. This dataset can be reused for tasks such as road damage classification, object detection, semantic segmentation, and benchmarking deep learning models designed for road condition monitoring in developing countries.