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This dataset is the live spreadsheet model behind the compound bankability analysis reported in the related article on a proposed 1 GW utility-scale solar photovoltaic project in the Federal Capital Territory of Nigeria. The model evaluates the project under four simultaneous sources of uncertainty rather than one at a time, as a full factorial of four levers at three levels each, giving 81 deterministic scenarios to which no probabilities are assigned. The levers are energy yield at the P50, P90, and P95 exceedance levels, grid curtailment at 0, 5, and 10 percent, contracted tariff at 0.115, 0.110, and 0.105 US dollars per kilowatt hour, and capital expenditure at 845, 887, and 930 million US dollars. The headline measure is the unlevered project internal rate of return, with levered equity returns and debt service coverage reported on separate sheets. The workbook has 26 sheets and 12,713 formulas, every reported figure is a live formula with no stored constants beyond the documented inputs, and each formula also carries its computed value so the numbers display without recalculation. It reproduces every financial quantity in the article, including the four reference cases, the band counts, the driver decomposition, the hurdle-rate sensitivity, the replacement fiscal regime, the phased and levered variants, and the benchmark notes. The parameter values originate in a proprietary feasibility study that cannot be shared, so only non-confidential inputs and the derived outputs are included, matching what the article reports. Benchmark and contextual material is drawn from public sources, each listed with its verification note on the Source_Register sheet. The workbook uses standard spreadsheet functions only, with no macros and no external links, and opens in Microsoft Excel, LibreOffice Calc, and compatible applications.

This dataset provides an annual cumulative (chain-linked) price index for Ukraine, built by compounding official year-on-year CPI change (December-to-December) at three levels: national, 27 regions, and 12 COICOP-aligned commodity/service groups. The index is rebased to 100 at end-2000, so it shows the multiple by which prices have risen since then for each entity. The dataset supports estimation of the real loss of purchasing power of the hryvnia over 25 years, deflation of nominal time series (wages, budget revenues, GDP), and long-run comparative analysis across regions and commodity groups. METHODOLOGY & SOURCES The underlying annual CPI change figures (cpi_annual_growth_pct) are taken directly from Derzhstat's official Consumer Price Index publication and are not modified. The cumulative_price_index column is calculated by the author using the standard compound-growth (chain-linking) formula: index_t = index_(t-1) * (1 + cpi_annual_growth_pct_t / 100), with index_2000 = 100 For the Autonomous Republic of Crimea and the city of Sevastopol, Derzhstat has not published CPI since 2014 (see the Regional CPI Panel dataset in this series for the underlying gap). Because a valid cumulative index cannot be computed across an undocumented gap, cumulative_price_index for these two entities is left blank from 2014 onward rather than estimated or held flat. Source accessed August 2026: - National Statistical Service of Ukraine (Derzhstat), Consumer Price Index statistics, accessed August 2026: https://www.ukrstat.gov.ua/ FILE STRUCTURE - Ukraine_Cumulative_CPI_Chain_Index_2001_2025.xlsx - 00_Dictionary: defines every column, including the chain-linking formula and the gap-handling rule - Data: the full panel, 1,000 observations (1 national + 27 regions + 12 groups = 40 entities x 25 years) - ukraine_cumulative_cpi_chain_index_2001_2025.csv: the same Data sheet in plain CSV (UTF-8 with BOM) - README.txt - this file. CONTACT INFORMATION Illia Kyselov, PhD in Law Associate Professor, Department of Law, Bohdan Khmelnytsky Melitopol State Pedagogical University, Zaporizhzhia, Ukraine CMSA (R), FPWMP (R), and BIDA (R) (Corporate Finance Institute) Email: illiakyselov86@gmail.com The author is open to collaboration on Scopus-indexed publications and further research using Ukrainian economic and financial statistics. Inquiries regarding this dataset, potential co-authorship, or data requests are welcome.

This dataset aggregates Ukraine's regional annual CPI change (December-to-December) into four author-defined macroeconomic periods for each of the 27 first-level administrative regions, reporting the mean, standard deviation, minimum, maximum and coefficient-of-variation-based volatility of regional inflation within each period. Aggregation periods (author-defined periodisation, not published by Derzhstat): - P1, 2001-2007: pre-crisis growth - P2, 2008-2013: post-crisis stabilisation - P3, 2014-2021: hybrid war and macroeconomic reform - P4, 2022-2025: full-scale war and adaptation The dataset is intended as a ready basis for research on the resilience of Ukraine's regional price stability through the 2008-2009 crisis, the hybrid war and occupation from 2014, and the full-scale invasion from 2022. METHODOLOGY & SOURCES The underlying annual regional CPI change figures are taken directly from Derzhstat's official Consumer Price Index publication and are not modified (see the companion Regional CPI Panel dataset for the full annual series and its documented gaps). The four periods and all summary statistics (mean, standard deviation, min, max, coefficient of variation) in this dataset are calculated by the author; the periodisation is an analytical choice, not an official Derzhstat classification. For the Autonomous Republic of Crimea and the city of Sevastopol, no CPI has been published by Derzhstat since 2014; consequently periods P3 (2014-2021) and P4 (2022-2025) for these two entities have zero years of underlying data, and all statistics for those period-entity combinations are blank (n_years_available = 0). This is shown explicitly via n_years_available rather than silently omitted, so that users do not mistake a data gap for zero inflation. Where only one year of data is available within a period, std_dev_cpi_pct and volatility_index_cv_pct are left blank, since a standard deviation is not defined for a single observation. Source accessed August 2026: - National Statistical Service of Ukraine (Derzhstat), Consumer Price Index statistics, accessed August 2026: https://www.ukrstat.gov.ua/ FILE STRUCTURE - Ukraine_CPI_Structural_War_Shock_Stats_by_Region_2001_2025.xlsx - 00_Dictionary: defines every column, the period definitions, and the gap-handling rules - Data: 108 observations (27 regions x 4 periods) - ukraine_cpi_shock_stats_by_region_2001_2025.csv: the same Data sheet in plain CSV (UTF-8 with BOM) - README.txt - this file. CONTACT INFORMATION Illia Kyselov, PhD in Law Associate Professor, Department of Law, Bohdan Khmelnytsky Melitopol State Pedagogical University, Zaporizhzhia, Ukraine CMSA (R), FPWMP (R), and BIDA (R) (Corporate Finance Institute) Email: illiakyselov86@gmail.com The author is open to collaboration on Scopus-indexed publications and further research using Ukrainian economic and financial statistics. Inquiries regarding this dataset, potential co-authorship, or data requests are welcome.

To determine whether three dung beetle species (i.e., Kheper cupreus (Castelnau, 1840), Onitis mendax Gillet, 1918, and O. fulgidus Klug, 1855) found utilising elephant dung showed a preference for that dung, we conducted choice experiments between Africa savanna elephant (Loxodonta africana) dung and zebra (Equus quagga) or impala (Aepyceros melampus) dung.

This dataset aggregates Ukraine's national annual CPI change (December-to-December), broken down by the 12 COICOP-aligned commodity/service groups, into four author-defined macroeconomic periods, reporting the mean, standard deviation, minimum, maximum and coefficient-of-variation-based volatility of each group's inflation within each period. Aggregation periods (author-defined periodisation, not published by Derzhstat): - P1, 2001-2007: pre-crisis growth - P2, 2008-2013: post-crisis stabilisation - P3, 2014-2021: hybrid war and macroeconomic reform - P4, 2022-2025: full-scale war and adaptation The dataset is intended as a ready basis for research on which components of the consumer basket (food, energy/housing, health, etc.) drove or absorbed inflation volatility across pre-crisis, post-crisis, hybrid-war, and full-scale-war periods, and for resilience-themed publications (SSRN, Mendeley Data, Scopus). METHODOLOGY & SOURCES The underlying annual commodity-group CPI change figures are taken directly from Derzhstat's official Consumer Price Index publication and are not modified (see the companion Commodity CPI and Basket Weights Panel dataset for the full annual series). The four periods and all summary statistics in this dataset are calculated by the author; the periodisation is an analytical choice, not an official Derzhstat classification. Unlike the regional version of this dataset, no commodity group has missing years in the 2001-2025 source data, so n_years_available equals each period's full length (7, 6, 8, and 4 years respectively) throughout. The two-digit coicop_division_code values were assigned by the author to Derzhstat's 12 published group names, matching the 12 top-level divisions of the UN COICOP classification; they are not printed in the Derzhstat source itself. Source accessed August 2026: - National Statistical Service of Ukraine (Derzhstat), Consumer Price Index statistics, accessed August 2026: https://www.ukrstat.gov.ua/ FILE STRUCTURE - Ukraine_CPI_Structural_War_Shock_Stats_by_Commodity_Group_2001_2025.xlsx - 00_Dictionary: defines every column and the period definitions - Data: 48 observations (12 groups x 4 periods) - ukraine_cpi_shock_stats_by_commodity_group_2001_2025.csv: the same Data sheet in plain CSV (UTF-8 with BOM) - README.txt - this file. CONTACT INFORMATION Illia Kyselov, PhD in Law Associate Professor, Department of Law, Bohdan Khmelnytsky Melitopol State Pedagogical University, Zaporizhzhia, Ukraine CMSA (R), FPWMP (R), and BIDA (R) (Corporate Finance Institute) Email: illiakyselov86@gmail.com The author is open to collaboration on Scopus-indexed publications and further research using Ukrainian economic and financial statistics. Inquiries regarding this dataset, potential co-authorship, or data requests are welcome.

This dataset supports a PRISMA 2020-aligned systematic narrative review examining how soil redox dynamics, microbial functional groups, agricultural management practices, and climate drivers interact to control methane (CH₄) and nitrous oxide (N₂O) emissions in irrigated rice systems. The review hypothesized that no single mitigation practice fully controls the CH₄–N₂O trade-off, and that integrating water management, soil amendments, and crop/nutrient strategies would be needed to achieve substantial net greenhouse gas reductions while sustaining yield. The dataset comprises the complete search and screening documentation (search strategy across Scopus, Web of Science, PubMed, and OpenAlex; PRISMA 2020 flow diagram; inclusion and exclusion criteria) that led to the selection of 82 peer-reviewed studies (2018–2025) from an initial pool of 5,993 records, and the data extracted from those studies (list of included studies with DOIs; four extraction tables summarizing reported effects of climate/CO₂, water management, soil amendments, and crop/nutrient management on CH₄ and N₂O emissions, yield, and global warming potential). Key findings from the synthesis include: alternate wetting and drying reduces CH₄ by approximately 50% with a comparatively small N₂O trade-off; iron-based amendments are among the few practices achieving simultaneous CH₄ and N₂O mitigation; and the stimulatory effect of elevated CO₂ on emissions reverses over decadal timescales. To interpret the extraction tables, each row reports the treatment effect relative to a stated control (e.g., continuous flooding) as extracted directly from the cited primary source; percentage changes are not independently re-derived. The included studies list can be cross-referenced with the extraction tables via the citation key (author + year) column.

Raw Data for "Long-lasting analgesia by antisense-mediated switching of TRPV1 isoforms" Abstract:Chronic pain affects approximately 20-25% of the global population, yet existing treatments offer insufficient relief or pose significant safety risks. TRPV1 is fundamental to pain transmission. Conversely, its exon 7-skipped isoform TRPV1b is intrinsically insensitive to noxious stimuli. Here, we used a splice-switching strategy to suppress TRPV1 while simultaneously increasing TRPV1b expression. Stepwise screening of 172 antisense oligonucleotides (ASOs) identified a bipartite ASO that efficiently promoted TRPV1 exon 7 skipping in cultured cells and mouse tissues. Intracerebroventricular administration of this ASO elevated basal pain thresholds and produced long-term analgesia in mouse models of neuropathic, inflammatory, and cancer pain without causing motor or thermoregulatory deficits, or toxicity. Mechanistically, TRPV1b forms hetero-oligomers with full-length TRPV1, exerting a dominant-negative effect that attenuates agonist-evoked Ca²⁺ influx and channel activation. These findings demonstrate that ASO-directed splicing modulation can successfully bypass classical on-target toxicities, introducing a novel paradigm of target-specific pre-mRNA reprogramming for chronic pain intervention. Keywords: chronic pain; nociception; TRPV1; TRPV1b; antisense oligonucleotide; RNA splicing; exon skipping; analgesia. Data Structure and Content: The dataset is organized into 11 primary folders corresponding to Figure 1, 2, 5, and S1-8. Each folder contains original uncropped RT-PCR and Western blot images. Experimental Conditions: For detailed experimental protocols associated with these images, please refer to the "STAR methods" section of the main manuscript.

This dataset contains the supplementary materials for the manuscript titled "Surface Specification and Operational Depth in European SUMPs: Rethinking What Document-Level Analysis Can Explain." It includes the 25-criterion evaluation rubric used for the document-level analysis and the complete coded dataset for the 20 European Sustainable Urban Mobility Plans (SUMPs) examined in the study.

This research examines the interplay of political patronage, environmental regulation, and business dynamics in China’s governance. We reveal that local leaders’ patronage ties shield polluting firms from regulations, resulting in adverse environmental consequences. Additionally, we demonstrate that the rent-seeking behavior of politically connected officials frequently supersedes their motivations for economic growth and fiscal revenue. Further analysis shows the political patronage brings only industrial expansion of local polluters, without corresponding gains in productivity or profitability. And ultimately, it leads to the deterioration of the local business environment. Our findings extend beyond formal institutions, highlighting the impact of local leaders' informal networks on local environmental governance, as well as regional business environments and economic growth. These insights have broad implications for regulatory dynamics in autocratic and democratic contexts, emphasizing the role of power and influence.

This dataset contains supplementary figures (Figures S1–S5) and supplementary tables (Tables S1–S10) accompanying the study “Temperature effects on pollen tube progression and associated physiological and metabolic profiles in ‘Harlikar’ apple”. The study examined ‘Harlikar’ apple flowers hand-pollinated with ‘Fuji’ pollen under temperature treatments of 15, 20 and 25 °C. The supplementary figures present the experimental workflow, ethylene-release time courses, an extended heatmap of volatile features, and OPLS-DA score plots and permutation tests. The supplementary tables provide quantitative measurements, processed data and statistical results for reactive oxygen species-associated staining, ethylene release, endogenous hormones, volatile metabolites, and associations between hormone and volatile profiles. Supplementary Figures contains Figures S1–S5 and their legends. Supplementary Tables contains Tables S1–S10, a contents worksheet and an ethylene calculation-parameters worksheet. UIS denotes the unpollinated initial stage, OES denotes the first observed ovary-entry stage of pollen tubes, and HAP denotes hours after pollination. OES samples were collected at 72 HAP at 15 °C and at 48 HAP at 20 and 25 °C.