Mendeley Data Showcase

Content: This dataset contains delineated agricultural field boundary polygons for the Canadian Prairies covering Alberta, Saskatchewan, and Manitoba. The field boundaries were derived from automated image segmentation applied to satellite imagery and represent individual crop field extents across agricultural regions. Each polygon includes geometric properties and identifiers that link to spatial regions (RMs). The dataset comprises 457 shapefiles encompassing 656,082 field polygons across the three provinces, providing a comprehensive geospatial representation of agricultural land parcels suitable for agricultural monitoring, land management, and regional analysis applications. Location: The data were collected across the Canadian Prairies, including Alberta, Saskatchewan, and Manitoba, covering major agricultural regions between approximately 49°–55° N latitude and 96°–114° W longitude. The processed dataset is stored and maintained at the authors’ institutional affiliation. Structure : The dataset is organized as a structured repository containing vector shapefiles representing agricultural field boundaries across the Canadian Prairies. The repository consists of the following hierarchy: /Field Boundaries/ ├── /Alberta/ (field boundary shapefiles for Alberta) ├── /Saskatchewan/ (field boundary shapefiles for Saskatchewan) └── /Manitoba/ (field boundary shapefiles for Manitoba) /Metadata/ (metadata files describing the dataset) Each field boundary shapefile contains polygon geometries in ESRI Shapefile format with standard geometry files (.shp, .shx, .dbf, .prj) and is projected in a consistent coordinate reference system suitable for regional-scale analysis. The shapefiles represent delineated field boundaries derived from automated image segmentation. Format: Field boundary polygons are provided in ESRI Shapefile format with associated geometry files and projection information. Attribute tables are standardized across all shapefiles to include unique field identifiers and basic geometric properties (e.g., area and perimeter). Attribute values are numeric or categorical and are consistent across all files to facilitate integration with other geospatial datasets. How To Access: All scripts used for image preprocessing, segmentation, post-processing, dataset assembly, and GEE App development are publicly available in the associated GitHub repository: https://github.com/thuanhavan/CSA_Field_Boundary_Segmentation

This dataset provides all benchmark instances used in the article “The Multiple Trip Aircraft Refueling Problem: MILP Modeling and a Hybrid Metaheuristic Approach”, enabling the complete replication of the computational experiments reported in the article. It consists of a test instance set with five small to medium-sized instances, a set with fifteen small and medium-sized instances relating to aircraft parked in a straight line, and another with fourteen instances, also small and medium-sized, relating to aircraft not parked in a straight line. All data is available in plain text and Excel formats to facilitate analysis.

Major element, trace element, S and Fe isotope data of the Kamoya deposit

This dataset supports research examining how oil price shocks affect economic growth in African net oil-importing countries. The central hypothesis posits that increases in real oil prices expressed in domestic currency to capture both international price movements and exchange rate transmission exert negative effects on real GDP per capita growth through multiple channels: supply-side cost increases, demand-side income transfers to oil exporters, exchange rate depreciation amplification, fiscal crowding-out via energy subsidies, and investment delays due to heightened uncertainty. We employ Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) methodology to account for common exposure to global oil price shocks and regional interdependencies while accommodating heterogeneous country-specific responses.

This dataset is a replication package supporting an empirical study of adaptive resilience in a borderland tourism system at the EU’s eastern frontier during the Russian–Ukrainian war. It links daily Polish–Ukrainian border-crossing flows (01 Jan 2022–28 Feb 2023) with monthly accommodation statistics at the county level for Lubelskie and Podkarpackie voivodeships (Eastern Poland) (2021–2022). Using the accommodation data, the package provides a synthetic accommodation-utilisation index (S) combining three components: (i) tourism intensity (Defert index), (ii) foreign-visitor tourism intensity (foreign-visitor Defert index), and (iii) average length of stay. To control for seasonality, the analysis focuses on year-on-year differences for identical months (ΔS = S(2022) – S(2021)) and then aggregates them to mobility stages identified from daily border flows (day-weighted aggregation for boundary months). Spatial coverage: Eastern Poland (counties in Lubelskie & Podkarpackie). Temporal coverage: accommodation 2021–2022 (monthly); border crossings 01 Jan 2022–28 Feb 2023 (daily). Unit of analysis: county–month (accommodation), day (border flows), and stage level (day-weighted aggregates). Key variables/outputs: Defert (D), foreign-visitor Defert (F), length of stay (L), component z-scores (ZD/ZF/ZL), synthetic index (S), year-on-year change (ΔS), and a Quasi-Residential Index (QRI) capturing the dominance of length-of-stay change relative to intensity components. Interpretive note: the “foreign-visitor” component (F) is used as a contextual proxy for cross-border, war-related demand, not as a mechanical attribution to a single nationality. In a small number of suppressed/confidentiality-affected cells, simplified handling is applied as described in the methods, which may attenuate changes for very small county markets. Files included (formatted as PDF-style appendices/tables) border_crossings.pdf — daily counts of border crossings in both directions (to Poland / to Ukraine), 01 Jan 2022–28 Feb 2023; basis for mobility-stage delineation. stages_components.pdf — mobility-stage definitions and stage-level aggregates of the synthetic index change and its components (day-weighted across boundary months). components_of_the_synthetic_index.pdf — monthly county-level components D/F/L, their standardised values (z-scores), and the synthetic index S and year-on-year differences (ΔS) used for spatial-temporal analysis. Intended use: replication of index construction and staging, spatial heterogeneity analysis (corridors vs peripheries), and comparative applications to other crisis-affected border regions.

Supplementary Data

Supplementary Table 1

The computational methodology used in the present work employed a fully automated predictive-modeling pipeline implemented in R (version 4.5.1). The workflow followed a split-sample strategy for model development and external validation. A primary execution script orchestrates all steps—from CoP data preprocessing to feature extraction, model training, evaluation, and statistical reporting—ensuring complete reproducibility of the analysis.

Data for: ."Value Co-Destruction in Food Delivery Apps” published by BAR - Brazilian Administration Review - After publication