Data descriptors | Gdańsk University of Technology

This article describes the dataset series titled “Analysis of the Structure of the Register of Immovable Monuments”, which analyses data published by the National Heritage Institute in Poland. The series focuses on selected years — 2003, 2016, 2024, and 2025 — based on the availability of official records. It includes detailed measurements and classifications of immovable monuments according to various categories: by voivodeship, primary function, date of construction, ownership status, construction materials (e.g., timber-framed), and other specific characteristics of the collection, such as large-scale complexes. The primary objective of the series is to evaluate changes in the condition and structure of immovable monuments in Poland over time, using preserved registry data as a basis for longitudinal analysis.

This dataset presents a comprehensive compilation of Polish State Forest inventory data spanning 2009-2019, encompassing approximately 7 million hectares of managed forest land. The database integrates measurements from annual forest inventories published in Aktualizacja reports by Polish State Forests (Państwowe Gospodarstwo Leśne Lasy Państwowe) with calculated ecological metrics derived through AI-assisted computational methods under human oversight. The compilation includes 1,260 primary records organized across nine analytical domains: basic metrics, species-specific measurements, biodiversity indices, forest composition, growth and productivity indicators, age structure analysis, and temporal trends. Data encompasses ten dominant tree species (Pine, Spruce, Fir, Oak, Beech, Birch, Alder, Aspen, Hornbeam, and Poplar) representing major ecological and economic groups in Polish forests. Each record contains measurements of forest area (hectares), timber volume (cubic meters), age class distributions, and derived metrics following established forestry science methodologies. The database employs a transparent data provenance system distinguishing between original measurements and calculated derivatives. All calculated metrics utilize documented formulas froForestry and Environmental Sciencem peer-reviewed literature, including Shannon diversity index, Simpson index, Pielou evenness, mean annual increment, and current annual increment. Computational processing employed Claude AI (Anthropic, claude-sonnet-4-20250514) for metric calculations and database organization, with all outputs verified through human oversight and validation procedures. The dataset supports temporal analysis of forest dynamics, biodiversity assessment, sustainable management evaluation, and comparative studies within European forest monitoring systems.

The development of electrodes used in ceramic fuel cells is crucial for reducing operating temperatures and consequently operating costs. One of the key parameters influencing the electrochemical performance of solid oxide fuel cells (SOFC) is the rate of oxygen ion adsorption and incorporation or transfer by the oxygen electrode. Among the most promising SOFC oxygen electrode materials are those with mixed ionic and electronic conductivity. One of techniques for determining the chemical diffusion coefficients (D*) and chemical oxygen surface exchange coefficient (k*) in mixed ionic and electronic conductors is relaxation technique. This descriptor precisely describes how the published dataset of electrical conductivity relaxation (ECR) measurement data for the SrxTi0.30Fe0.70O3-d (STF70-x) electrode material was prepared. The described results are important because the influence of non-stoichiometry in the strontium sublattice on D* and k* has not been previously presented in the literature. As can be seen, the technique is reliable and relatively simple to apply, and such measurements should be performed for most studied materials to better understand them.

Collecting research data is a fundamental step in the scientific process, providing the basis for empirically verifying hypotheses and developing knowledge. This article aims to compare the differences and similarities in the data collection process using two distinct disciplines: the marketing orientation of healthcare facilities (non-profit organizations) and the assumptions of the tax system in Poland in the context of SMEs. The tax research, illustrated by the doctoral thesis, was designed as a case study due to sample limitations resulting from the COVID-19 pandemic. It utilized a pilot study, a survey (22 questions, Likert scale), and in-depth interviews, initially planning random sampling but ultimately collecting responses from 274 companies. Marketing research, on the other hand, requires a multi-method approach (mixed methods), combining a Systematic Literature Review (SLR) with quantitative research (surveys, primarily 7-point Likert scales, questions about multidimensional effectiveness). Key methodological differences stem from the distinct nature of the variables involved: in marketing, these are behavioral and subjective constructs (e.g., loyalty), measured using psychometric scales, whereas in taxation, objective and financial constructs predominate (e.g., tax relief amount, accounting profit), leading to the predominance of econometric analysis and archival data. The article emphasizes that, despite these differences, both processes strive to reach conclusions based on reliable empirical material, albeit with varying degrees of extrapolation of results to the population.