Data in Brief最新文献

Due to the success of large-scale ivermectin mass drug administration (MDA), the aim of onchocerciasis intervention efforts have shifted from control of the disease to elimination of transmission. This has necessitated a greater understanding and comparison of the performance of diagnostic tools in hypoendemic (low prevalence) settings which had not been incorporated into large-scale MDA programmes before the goal switched from onchocerciasis elimination as a public health problem to elimination (interruption) of transmission (EOT). Data on age, sex and duration of residence were collected, prior to ivermectin treatment, across Gabon in 2015 from 5,829 participants in 67 communities from 14 districts. Skin-snip samples (for detection of Onchocerca volvulus microfilariae) were obtained from 4,350 (75 %) and blood samples (for detection of presence of IgG4 antibodies against the O. volvulus Ov16 antigen) from 4,257 of those skin-snip tested (98 %).

Whole blood was tested in the field using the SD Ov16 Rapid Diagnostic Test Prototype (Ov16 RDT). Dried blood spots (DBS) were prepared for all blood-sampled individuals. After assessing DBS quality, 2,990 (70 %) samples underwent valid analysis in the lab using horseradish peroxidase (HRP) Ov16 enzyme-linked immunosorbent assay (Ov16 ELISA). The number of positive individuals varied between diagnostic tools with skin-snip microscopy, Ov16 RDT and Ov16 ELISA detecting 337/4,350 (8 %, 95 % CI =7 %–9 %), 383/4,257 (9 %, 8 %–10 %) and 348/2,990 (12 %, 10 %–13 %), respectively. Data were analysed to understand the age profiles of microfilarial and IgG4 antibody prevalence by diagnostic and mapped across Gabon.

These data have reuse potential for policy makers, test manufacturers and country programmes when making determinations at community level of the suitability of using Ov16 RDT for conducting delineation mapping or evaluating the current stage of a community or, more generally, an evaluation unit along the EOT path. Further, these data are of use to transmission dynamics modellers who can fit models to these data to better understand the stage(s) in the O. volvulus lifecycle likely responsible for IgG4 antibody seroconversion in the presence of Ov16 antigen. This is crucial for incorporation of antibody prevalence as an output of onchocerciasis transmission models to permit evaluation of currently proposed serological thresholds to inform decisions about Start- or Stop-MDA in the context of onchocerciasis elimination mapping (OEM) and verification of EOT, respectively.

This article presents the Kurdish News Question Answering Dataset (KNQAD). The texts are collected from various Kurdish news websites. The ParsHub software is used to extract data from different fields of news, such as social news, religion, sports, science, and economy. The dataset consists of 15,002 news paragraphs with question-answer pairs. For each news paragraph, one or more question-answer pairs are manually created based on the content of the paragraphs. The dataset is pre-processed by cleaning and normalizing the data. During the cleaning process, special characters and stop words are removed, and stemming is used as a normalization step. The distribution of each question type is presented in the KNQAD. Moreover, the complexity of the QA problem is analyzed in the KNQAD by using lexical similarity techniques between questions and answers.

The dataset collects and harmonizes financial data about public works in Italy, focusing on soil defence investments. The data are sourced from three distinct platforms: the Italian Ministry of Economics and Finance's open data platform OpenBDAP, the OpenCoesione website, concerned with interventions framed in cohesion policies financed by additional resources from the European and national budgets, and the ReNDiS database, provided by the Italian Institute for Environmental Protection and Research (ISPRA), that exclusively gathers information about public works in soil defence. The data records belonging to these three sources are linked by a unique project code (CUP), ensuring that there is no duplication of data. The OpenBDAP and OpenCoesione repositories report financial variables classified into various funds. In contrast, the ReNDiS database only provides the total amount of financial resources allocated to each intervention. Consequently, the merged dataset consolidates these financial variables into one, representing the total investment amount. For the first two databases this aggregate is derived by summing the financial flows from the different funds. Geographical referencing has been added to each intervention and each financial observation is associated with an Italian municipality. The database includes information on the region, province, and municipality for each record. Each database entry has also been equipped with the coordinates of the municipality's centroid and with the polygonal shape of the municipality area. Overall, the merged dataset encompasses 28 variables reporting three descriptive variables, one financial variable representing the total amount of financial resources, six geographic variables representing the codes and names of regions, provinces, and municipalities, sixteen variables referring to key dates of the process of public works, two geographical references variables respectively representing the centroids and the shape polygons of the municipality. This comprehensive dataset allows to analyse the spatial distribution of the resources allocated to soil defence investments. It offers insights to policymakers striving to allocate resources more efficiently, thereby fostering sustainable land management practices and ensuring the long-term health of the Italian ecosystems. The dataset can be complemented with additional information related to various concomitant aspects such as those pertaining to the environmental and socio-economic fields. This integration allows for broad analysis of the relationships between soil defence efforts and surrounding environmental and socio-economic contexts.

Common validation and verification test cases for compressible flow solvers are only one- or two-dimensional. Such flows, however, are inherently three-dimensional. The provided data contains simulation results of genuine three-dimensional Riemann problems computed with the open-source compressible flow solver ALPACA. The problems are designed so that each octant's constant initial state connects two neighboring states by only one elementary wave each. Thereby, initial conditions are chosen to induce three-dimensional effects. Furthermore, the cases are designed to trigger common shortcomings of compressible flow solvers, such as spurious pressure oscillations, unphysical symmetry breaking, or the onset of shock disturbances. The cases were simulated using a finite-volume scheme with HLLC and Roe Riemann solvers and fifth-order WENO reconstruction. The simulations were conducted on over 300 cores of a compute cluster. Besides the raw binary flow field data, input files are provided next to post-processing scripts and the visualizations obtained by them. The provided files ease setting up and simulating the respective cases with different solvers and allow quantitative comparisons of the obtained results.

Honey production is susceptible to manipulation by unscrupulous sellers, making honey authentication crucial to prevent fraud. The process of authenticating honey often necessitates the use of various analytical techniques, such as identifying the chemicals present in honey by means of hyphenated mass spectrometry. Here, we report on the investigation of the chemical composition of three honey samples collected at two locations in Lombok and Bali by liquid chromatography mass spectrometry (LC-MS). The three datasets include information regarding compound name, mass, retention times, as well as findings from database searches. Collectively, these data afford first insights into the compositional profile of honey samples from this specific geographical area.

Deep learning applied to raw data has demonstrated outstanding image classification performance, mainly when abundant data is available. However, performance significantly degrades when a substantial volume of data is unavailable. Furthermore, deep architectures struggle to achieve satisfactory performance levels when distinguishing between distinct classes, such as fine-grained image classification, is challenging. Utilizing a priori knowledge alongside raw data can enhance image classification in demanding scenarios. Nevertheless, only a limited number of image classification datasets given with a priori knowledge are currently available, thereby restricting research efforts in this field. This paper introduces innovative datasets for the classification problem that integrate a priori knowledge. These datasets are built from existing data typically employed for multilabel multiclass classification or object detection. Frequent closed itemset mining is used to create classes and their corresponding attributes (e.g. the presence of an object in an image) and then to extract a priori knowledge expressed by rules on these attributes. The algorithm for generating rules is described.

This dataset reports the isolation and genomic characterization of the Caudoviricetes bacteriophage MK21, a novel bacteriophage infecting Xanthomonas citri subsp. citri (XCC), collected from soil samples on Jeju Island, South Korea. The phage was isolated and enriched using double agar layer plaque assays on nutrient media. Genomic analysis revealed that the phage MK21 is a double-stranded circular DNA genome of 43,495 bp, comprising 61 genes with high coding density. The dataset includes detailed genomic information, highlighting genes related to structural components, lysis mechanisms, and DNA/RNA metabolism. Phylogenetic analysis shows a close relationship with Xanthomonas phage CP1, supporting its potential use in comparative genomic studies and the development of antibacterial agents against citrus canker. This dataset offers valuable insights for the advancement of phage therapy and sustainable agricultural practices.

Rainbow trout (Oncorhynchus mykiss) is a dominant aquaculture species of the Salmonidae family, native only to the North Pacific. Recently, the gut microbiome has been shown to reflect the health status and responses to environmental changes in farmed fish. In this analysis we investigated the microbiome composition of the intestinal tract in 20 wild-caught rainbow trout specimens sampled in Alaska, USA. The targeted 16S rRNA gene (V3-V4 region) was sequenced on the Illumina NovaSeq 6000 platform. After quality control, demultiplexing and adapter trimming reads were analyzed using the DADA2 pipeline to obtain Amplicon Sequencing Variants (ASVs) which were subsequently taxonomically assigned. We found two phyla dominating the gut ecosystem present in every sample, Firmicutes and Fusobacteria, followed by lower abundances of Cyanobacteria, Proteobacteria and Bacteroidetes. At the genus level, we found high relative abundances of Cetobacterium and Clostridium sensu stricto 1. Interestingly, we did not identify often dominant genera Mycoplasma, Pseudomonas or Weisella which were prevalent in numerous studies previously, in cultured rainbow trout. Wild fish are exposed to a plethora of unpredictable environmental challenges, ranging from fluctuating water temperatures to variable food availability, as opposed to controlled conditions in production facilities. Examining and comparing the gut ecosystem of wild and reared individuals holds great potential in optimizing management practices for commercially important species. Microbiome studies can provide novel ways to enhance the overall welfare of fish, strengthen disease prevention and increase sustainability in aquaculture production.

The dataset consists of survey data on pedestrian crosswalk usage behavior in high-density urban areas of a developing country, specifically collected from Dhaka, the capital city of Bangladesh. Data were gathered through a questionnaire survey conducted at twelve key locations, covering eight attributes related to crosswalk behavior and the demographic details of respondents. The survey yielded 682 valid responses, focusing on factors such as the suitability of crosswalk locations, guard rails, and lighting. The dataset is structured to support analyses using supervised machine learning techniques, facilitating reproducibility, secondary analysis, and policy development for pedestrian safety improvements. Furthermore, the dataset can be reused for cross-validation of future studies, comparison with pedestrian behavior in similar urban settings, and the development of predictive models to enhance pedestrian infrastructure in other developing regions.