Plumbago auriculata f. alba (Pasq.) Z. X. Peng, a perennial evergreen shrub, is evaluated for both its medicinal properties and ornamental value. It is usually used to treat inflammatory disorders and skin infections, while its white flowers make it a popular landscape plant. In this study, the complete chloroplast genome of Plumbago auriculata f. alba (Pasq.) Z. X. Peng was sequenced using the Illumina HiSeq platform. The circular chloroplast genome has a total length of 169,357 (bp), exhibiting the typical quadripartite structure of angiosperms: a large single-copy (LSC) region of 92,056 bp, a small single-copy (SSC) region of 13,321 bp, and two inverted repeat (IR) regions (IRa and IRb) each of 31,990 bp. A total of 129 genes were annotated, including 84 protein-coding genes (PCGs), 37 transfer RNA (tRNA) genes, and 8 ribosomal RNA (rRNA) genes. Simple sequence repeat (SSR) analysis identified 104 SSR loci, with mononucleotide repeats (73 loci, 70.19 %) being the most abundant, attended by trinucleotide repeats (13 loci, 12.5 %). Phylogenetic tree showed that P. auriculata f. alba (Pasq.) Z. X. Peng is most closely related to Plumbago auriculata with 100 % bootstrap support. These chloroplast genome data enrich the genetic resources of Plumbaginaceae, providing a foundation for molecular breeding, genetic diversity analysis, and phylogenetic research of Plumbago auriculata complex.

For the ex-situ conservation of giant pandas, both collecting and preserving semen are important methods. The seminal plasma is rich in nutrients and bioactive substances, such as proteins, carbohydrates, lipids, amino acids, and hormones, which play an important role in the reproduction and reproductive health of the species. Understanding the composition and function of proteins in the giant panda seminal plasma proteome can provide valuable insights into their reproductive biology and help develop strategies to improve their reproductive success in captivity, which is essential for giant panda conservation. This is the first study to analyze the seminal plasma proteins of giant pandas through proteomics, identifying 1125 proteins. These proteins are related to protein turnover, translation, and metabolism. The seminal plasma proteins of giant pandas were then compared to those of humans, pigs, and sheep, and many unique proteins were found in giant panda samples. Among these proteins, the WD40 repeat-containing proteins have been identified and implicated in sperm function and fertility.

Streptomyces salinarius MPA0124, a halotolerant actinobacterium, was isolated from coastal sediments of Pondicherry, India, using Bennett's agar supplemented with 5 % NaCl. The draft genome, sequenced using the Illumina HiSeq platform, comprises 8.01 Mb across 122 contigs with a GC content of 72.22 %. Genome annotation predicted 7548 coding sequences, 70 tRNAs, and 4 rRNAs, indicating a metabolically diverse genetic repertoire. Functional analysis revealed a broad array of carbohydrate-active enzymes (CAZymes) involved in cellulose, hemicellulose, pectin, and lignin degradation, highlighting the strain's role in polysaccharide turnover and carbon recycling in saline ecosystems. Phylogenomic analysis clustered S. salinarius MPA0124 closely with S. salinarius SS06011 and S. ardesiacus NBRC15402, confirming its taxonomic placement within the Streptomyces genus. The genomic dataset enriches the current knowledge of marine-derived Streptomyces and underscores its potential for biotechnological applications in biomass conversion, enzyme discovery, and biosaline agriculture. The genome sequence has been deposited in NCBI under the accession number NZ_JBJDRC000000000.1.

Monitoring and maintaining water quality are essential for ensuring water quality for public health and environmental protection. Currently, the Philippines follows specific monitoring standards to evaluate the quality of water bodies. However, the current physicochemical properties primarily focus on individual pollutants. This can create a gap in understanding their potential biological and cumulative effects on aquatic organisms and ecosystems. In this study, possible biological effects of water samples were determined using the Danio rerio Teratogenic assay (DarT) and were correlated with selected physicochemical parameters. Water samples were collected from five sampling points along the Taytay River and nearby tributaries during the dry season. Physicochemical parameters were measured and analyzed. The malformations were observed and scored based on a malformation index, after which, the toxicity score was calculated. This dataset includes raw and processed values of malformation endpoints, toxicity scores, and corresponding physicochemical measurements. It enables the evaluation of water quality assessment using both biological effects and the corresponding physicochemical measurements. The dataset can provide valuable reference data for future studies on aquatic toxicity of local water samples. This study can also be useful in highlighting the potential application of the DarT assay for a more holistic approach to water quality monitoring.

This dataset contains raw and analyzed numerical simulation data, tables, and figures focused on the synergistic effects of nanosecond repetitively pulsed discharge (NRPD) plasma and water addition in hydrogen combustion. The data were generated using zero-dimensional (0D) plasma-assisted combustion simulations for H₂/H₂O/air mixtures. The simulation framework utilized a Design of Experiments (DoE) approach to systematically explore parameter interactions. This comprehensive numerical dataset offers insight into how plasma effects and water vapor influence hydrogen combustion and NOx formation. The data are valuable for researchers and engineers seeking to design and optimize plasma-assisted hydrogen engines and low-emission combustion systems by providing information on ignition delay, radical formation, and emission behavior under various plasma-water conditions. It also supports the development and validation of chemical kinetic models and machine-learning-based combustion optimization frameworks using well-defined simulation parameters and responses.

This data article introduces a structured pavement surface image dataset developed to advance research in automated pavement condition assessment and data-driven road infrastructure monitoring. The dataset comprises three distinct pavement condition categories: (i) alligator cracking, (ii) edge-breaking distress, and (iii) undamaged (intact) pavement surfaces, each representing a prevalent form of pavement deterioration or intact condition typically observed in flexible pavements. The dataset consists of 12,000 raw images (4000 per class) collected under real-world conditions. These images represent the primary scientific contribution of the dataset. All images were standardized through resizing and normalization, and the dataset was partitioned into training, validation, and testing subsets to ensure reproducibility and consistency in data-driven experiments. Pavement images were collected from selected segments of National Highway N6 in Pabna District, Bangladesh, under natural daylight conditions using a smartphone camera during field surveys. Image acquisition was conducted following standard safety practices without disrupting traffic flow. All images were manually reviewed and labelled to ensure annotation accuracy. This dataset is intended to support research on automated pavement crack detection and classification, benchmarking of computer vision and deep learning models, and the development of lightweight and edge-deployable inspection systems.

The stable water isotopic composition (δ18O and δ2H) of daily precipitation from north-western Croatia (near Samobor town) is presented. The dataset includes a total of 288 precipitation samples collected from December 2022 to November 2024. The collection period roughly covered a 24-hour cycle, starting predominantly early in the morning. Stable hydrogen and oxygen isotope analyses of the samples were carried out using a Picarro L2130-i Isotope analyzer, with a precision of 1 ‰ for δ2H and 0.2 ‰ for δ18O. The raw isotopic data of the precipitation samples are presented in δ-notation (‰) normalized to the international standard VSMOW (Vienna Standard Mean Ocean Water). Additionally, the d-excess values for each sample and the Local meteoric water line (LMWL) were calculated. Presented data were used to investigate the main drivers of stable isotope variability in Croatia and were published in the research article entitled “Assessing the influence of climatic parameters and moisture sources on precipitation isotope composition from central Croatia: Insights from a two-year long daily-resolved dataset“. In addition to studying the isotope effects in modern precipitation, this dataset can also be used in palaeoclimate investigations that utilize natural climate archives, as well as environmental studies, including tracking of water pollutants and water resource management.

Cities worldwide are increasingly facing the challenges of heat stress, a problem expected to worsen with ongoing climate change. The lack of detailed, city-specific data hinders effective response measures and limits the adaptive capacity of urban populations. In this data descriptor, we introduce a comprehensive database providing climate and heat stress information for 142 cities globally, covering the present and extending projections up to 2100 across three distinct climate scenarios, including two overshoot scenarios. This dataset includes 34 heat stress indicators at a spatial resolution of 100 meters, offering a unique database to identify vulnerable areas and deepen the understanding of urban heat risks. The data is presented through an accessible, user-friendly dashboard, enabling policymakers, researchers, and city planners, as well as non-experts, to easily visualise and interpret the findings, supporting more informed decision-making and urban adaptation strategies.

This dataset presents a valuable compilation of question–answer (QA) pairs derived from cultural texts and sources related to Durga mythology. A total of 21,395 QA pairs, encompassing textual materials such as scriptures, ritual narratives, temple inscriptions, and traditional storytelling records. Each entry includes the source reference, question, and corresponding answer, provided in a structured format compatible with Excel for seamless integration into downstream natural language processing (NLP) tasks. Data collection involved manual curation and annotation by domain experts, followed by preprocessing steps including text normalization, duplication removal, and verification of factual and contextual accuracy. The dataset is designed to support generative QA models, culturally aware chatbots, and digital preservation of heritage knowledge. It is particularly valuable for research in AI-driven cultural applications, educational tools, and digital humanities initiatives aiming to bridge traditional knowledge with computational methods. Researchers and practitioners may utilize the dataset for training generative models, creating interactive educational platforms, developing culturally sensitive AI agents, and supporting comparative studies in cross-cultural heritage. This openly accessible resource adheres to ethical standards, with proper attribution to source materials, and provides a foundational asset for both academic research and applied development in culturally informed artificial intelligence.