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Dataset on existing incentives for hospitals
No full textFLASH WP6, deliverable 6.8, a dataset, is a comprehensive resource that systematically explores financial incentives and their impacts within healthcare systems. It begins with an introduction and a dictionary section that explains key terms, methodologies, and references, providing essential context for interpreting the data. The database categorizes incentives and payment mechanisms across several dimensions. Tables 1.1 to 1.3 offer an overview of incentive categories, payment mechanisms, and their distribution by country and impact. A deeper analysis follows in Tables 2.1 to 2.4, examining incentives in relation to countries, payment mechanisms, therapeutic areas, and categories of impact. The detailed breakdown continues in the Table 2.5 and 2.6 series, where specific incentive types, such as additional payments, bonuses, penalties, and price adjustments, are analysed based on their direction, significance, quality, and population-level impacts. The dataset further assesses the broader effects of these mechanisms through Tables 3.1 to 3.4, which connect payment mechanisms and incentives to categories of impact. The Table 3.5 series covers detailed evaluations of various outcomes, including patient satisfaction, clinical quality, healthcare costs, waiting times, and financial risks. Finally, the database provides country-specific insights in Tables 4.1 to 4.3, linking nations to their payment mechanisms, incentives, and impacts on healthcare systems.This dataset serves as a valuable tool for policymakers, researchers, and healthcare administrators by offering detailed insights into how financial mechanisms influence healthcare delivery, efficiency, and outcomes, enabling informed decision-making to improve healthcare systems.</p
IFRS 17 implementation: market participants perspective
No full textThe dataset consists of 68 observations (rows) and 24 variables (columns). It contains information about opinions issued on IFRS 17 implementation challenges. The dataset includes details about the opinion issuer (e.g., whether they are a practitioner, expert, researcher, or teacher), as well as binary indicators for various IFRS 17-related issues. These issues include problems with annual cohorts, the treatment of onerous contracts, discount rate calculation methods, the Premium Allocation Approach (PAA), first-time implementation difficulties, risk adjustment for non-financial risk, IT system challenges, and interactions between IFRS 9 and IFRS 17. Each issue is coded as “1” if mentioned in the opinion and “0” otherwise. The dataset allows for analyzing how different types of opinion issuers perceive IFRS 17 challenges and which problems are most frequently reported.Genaral Variables1. Company – Name of the company being evaluated.2. no – Identification number of the company.3. Company_n – Alternative identification of the company (possibly categorization).4. Data – Date of opinion issuance.5. old_num – Number of days that have passed since the opinion was issued.6. months_num – Number of months that have passed since the opinion was issued.7. BIG4_d – Binary variable (0/1) indicating whether the company is part of the Big Four.8. cathegory – Classification of the opinion issuer. The categories include:· practitioner – The opinion was issued by a practitioner.· expert – The opinion was issued by an expert.· other – The opinion was issued by someone categorized as "other."· researcher – The opinion was issued by a researcher.· teacher – The opinion was issued by an academic or educator.Opinion Issuer Characteristics:9. practitioner_d – Binary variable (0/1) indicating whether the opinion issuer is a practitioner.10. expert_d – Binary variable (0/1) indicating whether the opinion issuer is an expert.11. other_d / researcher_d / teacher_d – Binary variables (0/1) indicating whether the opinion issuer falls into one of these categories.Variables Related to Identified Problems:From here onward, the variables indicate whether the analyzed opinion highlights a specific problem. Each is coded as “1” if the problem is mentioned in the opinion, otherwise “0.” These align with the eight identified IFRS 17-related challenges:12. cohorts – Whether the opinion discusses problems related to annual cohorts, which may cause unnecessary complexity in financial reporting.13. contract_liabilities – Whether the opinion mentions problems with contract liabilities, which may include recognition, measurement, or classification issues.14. non-financial – Whether the opinion discusses risk adjustment for non-financial risk, which affects the measurement of insurance contracts.15. First-time – Whether the opinion mentions challenges related to first-time implementation, including the need for additional provisions and adjustments.16. IFRS 9 – Whether the opinion addresses interactions between IFRS 9 and IFRS 17, particularly how the classification of financial assets affects insurance contract accounting.17. rates – Whether the opinion discusses problems with the two methods for discount rate calculation, affecting the valuation of insurance liabilities.18. PAA – Whether the opinion refers to difficulties with the Premium Allocation Approach (PAA), an alternative method for measuring liabilities under IFRS 17.19. onerous – Whether the opinion highlights issues related to the treatment of onerous contracts, i.e., contracts expected to be loss-making, requiring special accounting treatment.20. understanding – Whether the opinion raises concerns about the complexity and understanding of IFRS 17, especially regarding its interpretation and practical application.21. systems – Whether the opinion highlights issues with IT systems, such as challenges in adapting accounting systems to IFRS 17 requirements.22. comparability – Whether the opinion mentions comparability issues, indicating concerns that IFRS 17 may reduce the consistency of financial reporting across companies.</p
SEM/EDS analysis of aluminosilicate additives
No full text[ENG:]The dataset contains research results generated as part of the project titled " The influence of aluminosilicate additives on high-temperature corrosion and ash properties of animal-origin biomass", funded by the National Science Centre (NCN).The aim of the project is to investigate the influence of aluminosilicate additives on the corrosion potential and ash properties of biomass of animal origin. The project focuses on issues such as slagging tendency, high-temperature corrosion, ash particle size distribution, and the content and leachability of metals. Two types of biomass with the highest potential are being studied: poultry litter and cattle dung.The package named Additives SEM_EDS.zip contains Scanning Electron Microscope (SEM) images with Energy Dispersive Spectroscopy (EDS) analyses of the aluminosilicate fuel additives used in the combustion process of animal-derived biomass. SEM/EDS analyses were performed for halloysite, kaolin, and bentonite. Additionally, the package includes a legend in the form of a text file.[PL:]Zbiór zawiera wyniki badań wytworzone w ramach projektu pt. "Wpływ dodatków glinokrzemianowych na proces korozji wysokotemperaturowej i charakterystykę popiołu z biomasy pochodzenia zwierzęcego" finansowanego przez Narodowe Centrum Nauki.Celem projektu jest zbadanie wpływu dodatków glinokrzemianowych na potencjał korozyjny i własności popiołu z biomasy pochodzenia zwierzęcego. Projekt skupia się na problemach takich jak skłonność do żużlowania, korozja wysokotemperaturowa, rozkład ziarnowy popiołu oraz zawartość i wymywalność metali. Badane są dwa rodzaje biomasy o największym potencjale: odpady z produkcji drobiu (poultry litter) oraz z hodowli bydła (cattle dung).W paczce o nazwie Additives SEM_EDS.zip zawarto zdjęcia analiz SEM (ang. Scanning Electron Microscope) z systemem EDS (Energy Dispersive Spectroscopy) paliwowych dodatków glinokrzemianowych stosowanych w procesie spalania biomasy pochodzenia zwierzęcego. Analizy SEM/EDS wykonano dla haloizytu, kaolinu i bentonitu. Dodatkowo paczka zawiera legendę w postaci pliku tekstowego.</p
Data for publication: "Insights into the High Catalytic Activity of Li-Ion Battery Waste toward Oxygen Reduction to Hydrogen Peroxide"
No full textThe data set contains the results of studies on the composition and structure of materials recovered from spent lithium-ion batteries and the results of electrochemical studies related to the catalytic properties of the tested materials towards the electrochemical reduction of oxygen included in the publication "Insights into the High Catalytic Activity of Li-Ion Battery Waste toward Oxygen Reduction to Hydrogen Peroxide."</p
Data for publication "Tuning Electrode and Separator Sizes For Enhanced Performance of Electrical Double-Layer Capacitors"
No full textPython scripts and data files for all figures appearing in the publication "Tuning Electrode and Separator Sizes For Enhanced Performance of Electrical Double-Layer Capacitors" by Aniele Paolini, Lintymol Antony, Ganji Seeta Rama Raju, Andrij Kuzmak, Taras Verkholyak, Svyatoslav Kondrat</p
DFT Calculation Data on the Clusters of Vacancies in Gallium Nitride grown by MOCVD
No full textThis dataset contains the results of DFT calculations conducted to confirm the stability of vacancy clusters in GaN grown by MOCVD, as discussed in the article "Clusters of Vacancies in Gallium Nitride grown by MOCVD." The calculations were performed to evaluate the relative energy of various configurations of vacancy clusters in the wurtzite GaN lattice.The data include configurations for both single and paired vacancies of Ga and N atoms. Specifically, the following configurations were analyzed:Independent Ga and N vacancies: The geometric structure and energy of isolated Ga and N vacancies in GaN.VGa-VN clusters: Configurations consisting of nearest-neighbor Ga and N vacancies (denoted as the "v" and "h"configurations).2VGa-2VN clusters: Configurations of nearest-neighbor vacancies forming large cluster (denoted as the "Config-1", "Config-2", "Config-3", and "Config-4")The data include:Geometric structures of the systems in standard formats (e.g., structure.xsf files).Calculated energies for the analyzed configurations.</p
Lasing emission mapping
No full textData includes:-collected emission spectra of the starch with Rhodamine 6G sample as a scan 300x300𝜇m,-FFT of scan,-FFT of FFT of the scan,-total intensity map,-logarithm of the scan,-logarithm of the total intensity of emission,-logarithm of the maximum intensity of emission,-logarithm of the integrated emission intensity,-refractive index map. Please consult the readme.txt file for additional information.</p
Ex vivo permeation of agomelatine from DLP 3D-printed ethanol gel-coated pyramid microneedle systems
No full textRaw data from ex vivo permeation test (IVPT) of agomelatine (AGM) from microneedle systems.Microneedle system specifications:3D printing method: DLPmicroneedle shape: pyramidcoating-gel type: ethanol gelDrug permeation studies:The permeation of agomelatine (AGM) was evaluated using Franz Diffusion Cells (Teledyne Hanson 376 Research, USA) with full-thickness human skin as the diffusion membrane. The skin was thawed in PBS at room temperature, mounted on the diffusion cells, and conditioned for 30 min. After this time the microneedle system was placed onto the chamber opening (1 cm²) with full-thickness human skin as the diffusion membrane. The study lasted for 7 days and sodium azide 0.02% w/v was added to the acceptor fluid as an antimicrobial agent. At specific time points, 0.3 mL samples were taken and analyzed using High-Performance Liquid Chromatography (HPLC). The amount of the acceptor medium taken for the analysis was immediately replaced with the fresh portion of the fluid. Six replications were performed for each formulation. After the study, the epidermis and dermis from each skin sample were separated manually using scissors and forceps and inserted in tubes with 3 mm zirconium beads (Benchmark Scientific Inc., Sayreville, NJ, USA). Next, 1 mL of water and ethanol solution (50:50 v/v) was added to each tube, and the samples were homogenized for 9 min using a BeadBug Microtube Homogenizer (Benchmark Scientific Inc., USA). Then, the samples were centrifuged for 5 min at 10,000 rpm, and the supernatant was analyzed using HPLC to examine the amount of the drug in the tissue.Quantification of AGM:The amount of AGM permeated was analyzed using High-Performance Liquid Chromatography (HPLC) (Shimadzu Nexera-I LC-2040C, Japan) with LabSolution Lite software.Chromatographic Conditions:Column: Reversed-phase C18 (HyperClone BDS C18, 5 µm, 4.6 × 250 mm, Phenomenex, Torrance, CA, USA)Column Temperature: 30.0 ± 0.2°CMobile Phase: Acetonitrile and 0.05 M potassium dihydrogen phosphate solution (pH = 2.9, adjusted with 85% orthophosphoric acid) in a 35:65 ratioMode: IsocraticFlow Rate: 1.0 mL/minDetection Wavelength: 230 nmThe set contains data in LCD/ LCB format, created using the LabSolutions software (HPLC, Nexera LC 2040C). Data file (.lcd) contains all analysis results and acquisition information from the following files. Data in .txt format can be read without the need for LabSolutions software.</p
Morphophysiological adaptations of common buckwheat to silicon application under salt stress
No full textThis dataset presents experimental data on the effects of foliar silicon application on the growth and photosynthetic efficiency of common buckwheat (Fagopyrum esculentum cv. Smuga) under salt stress. The study was conducted at the Poznań University of Life Sciences, Poland, during the 2024 growing season. Buckwheat plants were subjected to 50 mM NaCl stress, and a foliar spray of a 1 mM sodium metasilicate nonahydrate (Na2SiO3·9H2O) solution was applied at a rate equivalent to 400 L ha⁻¹. The dataset includes the following files: Morphology.xlsx- contains data on growth attributes, including root and shoot length, leaf number, leaf thickness, and fresh and dry weights of roots and shoots. OJIP Curve.xlsx- provides chlorophyll a fluorescence (OJIP) transients of dark-adapted plants, recorded using a FluorPen FP 110/D instrument. OJIP Parameters.xlsx- includes various chlorophyll fluorescence parameters derived from OJIP transients. Photosynq Parameters.xlsx- contains photosynthetic parameters based on fluorescence and electrochromic shifts, measured using a MultispeQ V2.0 device connected to the PhotosynQ platform. Temperature.xlsx and PAR.xlsx- provide temperature and photosynthetic photon flux density (PPFD) data from the growing season during the experiment. This dataset is valuable for researchers studying abiotic stress tolerance mechanisms in plants. For further details on the experiment, refer to ReadMe.pdf.</p
Faecal sterols and bile acids in faeces of wild animals as a reference dataset for archaeological studies
No full textRaw data for the article:Gryczewska, N., Sulwiński, M., Chibowski, P., Krajcarz, M. T., Zegarek, M., Kot, M., Pereswiet‑Soltan, A., Szymczak, K., & Suska‑Malawska, M. 2025. Applying sterols and bile acids as biomarkers for identifying human versus wild animals’ faecal traces in cave sediments at archaeological sites. Archaeometry 67(4): 1022–1039. https://doi.org/10.1111/arcm.13067The dataset consists of nine Excel files with raw data from GC MC analysis. The methodology of the extraction, clean-up, derivatization and GC analysis is described in detail in the cited article. The data is related to the project "Chemical traces of human activity in caves of Polish Jura. Use of selected lipid biomarkers analysis and PAHs analysis in sediments from archaeological sites" (financed by NCN, Poland, no. 2021/41/N/HS3/02369), which aimed at exploring possibilities and limitations in studying human presence and activity in caves in the past through sediment analyses.Based on the raw results presented here, a reference database of faecal profiles in wild animals was created. The aim of the database was first to assess its similarity with human faecal profiles known from the literature, and then assess commonly used methods of recognizing the source of faecal matter based on the analysis of faecal sterols and bile acids. Additionally, four test samples from archaeological sites were analysed (with raw results included in the files) to assess the presence of the studied compounds in the sediment samples from cave site and feasibility of further studies. The analysis of the data in this dataset allowed for proposing an approach for studying cave sediments from archaeological sites to recognize traces of humans faecal matter.</p
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