Data in Brief最新文献

Agriculture has always played a vital role in the economic development of Bangladesh. In Agriculture, leaf diseases have become an issue because they can lead to a major drop in both quality and quantity of crops. Therefore, leveraging technology to automatically detect diseases on leaves plays an important role in farming. Malabar Spinach (Basella alba) is a well-known, widely grown leafy vegetable, which is valued for its nutritional benefits. However, there is almost no dataset that can aid in identifying diseases affecting this important crop, which often leads to decreased quality as well as financial drawback. This lack of resources makes it difficult for farmers to recognize and manage common diseases. Our purpose is to solve this problem by creating a unique dataset of Bangladesh's Malabar Spinach leaves that will ease agricultural management and disease detection. Our dataset contains both healthy and diseased samples, categorised into four common ailments: Anthracnose, Bacterial Spot, Downy Mildew, and Pest Damage. We collected 3,006 original images in total. Images were collected from various locations in Bangladesh, including Mirpur, Savar, Sirajganj and Gazipur, with photographs taken under natural lighting conditions at different times of the day. This dataset will help the researchers for further research on Malabar Spinach disease detection implementing various efficient computational models and applying advanced machine learning techniques.

This dataset contains the frequency of thousands of terms (or keywords) used by political parties in the posts they have published in their Facebook pages. The data set is composed by 20,317 keywords from posts published by 279 European political parties from 28 countries and spans 5 years, from January 2019 to December 2023. Due to the large diversity of languages in the analysed countries, we have translated every post into English to compile this dataset. We also provide an open-access web portal: EU Political Barometer, in which a wide variety of analyses can be carried out without the need of working directly with the dataset. This allows scientists without a data analysis background to access the information embedded within the dataset. The information included in the dataset may be of value for social scientists that wants to understand the evolution of the topics employed by political parties in Europe based on a widely used political communication tool such as Facebook.

Gross primary productivity (GPP) is crucial for understanding the carbon cycle and maintaining ecosystem balance under climate change. We attempt to generate a long-term global dataset for GPP of sunlit (GPP_su) and shaded leaves (GPP_sh) by a hybrid model combining the random forest (RF) submodule with the two-leaf light use efficiency (TL-LUE) model. First, the TL-LUE model was optimized by considering the seasonal differences in the clumping index on a global scale (TL-CLUE). Then, we used the RF technique to integrate various environmental stress factors, including meteorological factors, hydrological variables, soil properties, and elevation, which originate from the NASA MERRA-2 dataset, ISRIC soil Grids, and USGS data center. Furthermore, the RF submodule was embedded into the TL-CLUE model to construct the hybrid model (TL-CRF), which was trained and evaluated based on global eddy covariance (EC) site data from the AmeriFlux and FLUXNET2015 datasets. We produced a global GPP, GPP_su, and GPP_sh dataset with a spatial resolution of 0.05 × 0.05° over 2002-2020 by the TL-CRF model driven by the LP DACC leaf area index and land cover, NASA MERRA-2 incoming shortwave solar radiation, and the above environmental variables. This GPP product provides a data basis for improving our understanding of the dynamics of global vegetation productivity and its interactions with the changes in environmental conditions.

Trichoderma yunnanense strain TM10 was isolated from rhizosphere soil of rice plants cultivated under system of rice intensification (SRI) practises in West Java, Indonesia. It exhibits significant potential as a plant growth promoter and biocontrol agent in rice plants. Although this strain has shown promise in promoting plant growth and suppressing phytopathogens under in vitro and in planta conditions, there is still a lack of genomic data to elucidate the molecular mechanisms underlying its plant growth-promoting and biocontrol capabilities. This study reports the whole genome sequence of T. yunnanense strain TM10. The genome of the fungus was sequenced using the MGI DNBSEQ-G400 high-throughput sequencing platform. The assembled genome of T. yunnanense strain TM10 was approximately 36 Mbp in length, comprising 385 contigs with a GC content of 48 % and a sequencing coverage of 43.8×. This genomic data provides a foundation for harnessing the plant growth-promoting and biocontrol potential of this strain. The complete genome sequence has been deposited at the National Center for Biotechnology Information (NCBI) under Bioproject accession number PRJNA1181959, BioSample ID SAMN44575400, and genome accession number JBIYZQ000000000. These data are valuable for further research into the biotechnological potential of this strain and for exploring the molecular mechanisms underlying its plant growth-promoting and biocontrol activities.

Salinity diminishes agricultural productivity and quality, resulting in overall economic losses on a worldwide scale. Zinc oxide nanoparticles (ZnO-NPs) have been found to enhance plant physiological and metabolic processes, as well as increase overall resilience to abiotic stressors. A research study was undertaken to assess the effects of foliar application of chemically produced ZnO-NPs on tomato plants, both in the presence and absence of a NaCl stressor. The datasets were obtained through the utilization of the shallow mRNA sequencing technology. Six datasets from the SRA were uploaded to NCBI. The aforementioned datasets encompass the Transcriptome Shotgun Assembly (TSA), the contigs that underwent blasting, mapping, and annotation from the pre-processed datasets, and the count table derived from the quantification of RNA-seq reads. All the aforementioned data is encompassed under the Mendeley database. Moving forward, the utilization of databases will facilitate the examination of modifications in plant biochemical reactions at the level of gene expression.

This paper introduces "The Media Framing Dataset," a dataset developed through an in-depth examination of news articles from 140 local newspapers in Mexico and Colombia, covering events from May 2022 to August 2023. Our dataset captures a broad spectrum of topics, including politics, immigration, public opinion, and crime. The data collection involved a meticulous keyword-based search strategy designed to identify articles that illustrate various news-framing dimensions, such as Economics, Policy, Morality, and more. To construct this dataset, we employed a combination of manual and automated annotation techniques. Articles were categorized based on specific framing dimensions using a structured framework, developed in collaboration with experts in computational linguistics. The annotation process, conducted by trained annotators from Mexico's Delfin program, guarantees both precision and depth. "The Media Framing Dataset" serves as a valuable resource for NLP research with high potential for reuse. It is particularly suitable for analyzing cultural and linguistic nuances in media framing, assessing the impact of framing on public perception, and supporting the development of models that automatically detect framing techniques. Additionally, it provides a foundation for linguistic analysis and machine learning projects, enabling researchers and practitioners to explore media framing dynamics and develop innovative tools for media analysis.

Intestinal parasitism is an infection that affects people worldwide, with populations in developing countries being at a higher risk of acquiring it. This infection is contracted for various reasons, mainly related to poor sanitary conditions and inadequate food practices, leading to multiple health issues such as malnutrition, intestinal obstructions, epilepsy, and others. Identifying parasitic species is essential for establishing appropriate antiparasitic therapy, which in turn helps reduce the risk of associated morbidities. For this reason, a dataset named "ParasitoBank" was created, containing 779 images of the visual field of fresh stool samples analysed under a microscope using the serial coprological technique. These images were acquired using a Motorola G84 mobile phone, and a data-labeling process resulted in a total of 1,620 intestinal parasites, with a particular focus on intestinal protozoa. The images have an approximate aspect ratio of 1:1 with a resolution of 2100 × 2100. Label information and some metadata for the images have been included in a JSON file following the "Common Objects in Context" (COCO) format. Finally, the entire dataset and label content have been arranged in a compressed file. The presented information facilitates the use of the data for various studies, spanning education and artificial intelligence development.

This dataset contains air and surface temperature measurements taken twice daily at 11:00 and 23:00 GMT+2 from 24th June to 5th July 2024 (a total of 10 days) in the historical centre of Malaga, Spain. It includes detailed thermal readings from various street materials and the facades of historical buildings, offering insights into the thermal properties and responses of these elements at different times of day. This dataset provides valuable information on localized temperature variations within the historical centre, influenced by different materials and architectural styles. It can be used to model microclimate variations, evaluate the thermal behavior of both historical and contemporary materials, and inform urban planning and heritage conservation efforts.

This paper presents a dataset from the METICOS project pilot trials related to the acceptance of border control technologies. There was total five pilots held at Tallin Airport, Athens International Airport, Larnaca International Airport, Border Crossing Point Moravita Land Border, and Vienna International Airport. The dataset consists of the data collected using an online questionnaire (survey) to assess travellers' technology acceptance in terms of their demographics, profiles, and user perceptions along with operational information of border control technologies during their use. The questionnaire items together with their responses are paired samples collected after the travellers use the technology at the METICOS project pilot trials held in five different locations (i.e. four airports, and one land border) during the period of January to August 2023. The ABC-gates were used to assess technology acceptance during all the pilot trials provided by pilot partner of the METICOS. The total size of the dataset is 147 instances and is well-suited for quantitative analysis of assessing technology acceptance indicators for border control technologies. This information can aid policymakers and border control authorities in enhancing the acceptance and the use of these technologies at various border crossing points across Europe.

Freshwater ecosystems represent an unparalleled diversity of habitats and species, but the actual distribution of many species remains obscured or incomplete. The aim of the survey was to contribute to the knowledge on the fauna of lesser-known areas and fill the gaps in the distribution maps of the species. The dataset is based on a one-year-long study surveying 60 locations from different drying river networks that represent different ecoregions in Central Europe: Balcanic (Croatia, 15 sites), Continental (Czechia, 20 sites) and Pannonian (Hungary, 25 sites). Multihabitat sampling approach was applied for collecting stream-dwelling macroinvertebrates. Individuals were identified to the lowest possible taxonomic level, typically to species level. The dataset includes 1827 geo-referenced occurrence records based on presence-absence data of 164 taxa across Gastropoda, Hirudinea and various groups of Insecta (Coleoptera, Hemiptera, Megaloptera, Odonata, Trichoptera) along with geographical information on the sampling sites, and details of the taxonomy of the species. The data can support future studies in ecology, biogeography and nature conservation.