Data in Brief最新文献

{"title":"Dataset of Centella Asiatica leaves for quality assessment and machine learning applications","authors":"Rohini Jadhav ,&nbsp;Mayuri Molawade ,&nbsp;Amol Bhosle ,&nbsp;Yogesh Suryawanshi ,&nbsp;Kailas Patil ,&nbsp;Prawit Chumchu","doi":"10.1016/j.dib.2024.111150","DOIUrl":"10.1016/j.dib.2024.111150","url":null,"abstract":"<div><div><em>Centella asiatica</em> is a significant medicinal herb extensively used in traditional oriental medicine and gaining global popularity. The primary constituents of <em>Centella asiatica</em> leaves are triterpenoid saponins, which are predominantly believed to be responsible for its therapeutic properties. Ensuring the use of high-quality leaves in herbal medicine preparation is crucial across all medicinal practices. To address this quality control issue using machine learning applications, we have developed an image dataset of <em>Centella asiatica</em> leaves. The images were captured using Samsung Galaxy M21 mobile phones and depict the leaves in “Dried,” “Healthy,” and “Unhealthy” states. These states are further divided into “Single” and “Multiple” leaves categories, with “Single” leaves being further classified into “Front” and “Back” views to facilitate a comprehensive study. The images were pre-processed and standardized to 1024 × 768 dimensions, resulting in a dataset comprising a total of 9094 images. This dataset is instrumental in the development and evaluation of image recognition algorithms, serving as a foundational resource for computer vision research. Moreover, it provides a valuable platform for testing and validating algorithms in areas such as image categorization and object detection. For researchers exploring the medicinal potential of <em>Centella asiatica</em> in traditional medicine, this dataset offers critical information on the plantʼs health, thereby advancing research in herbal medicine and ethnopharmacology.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111150"},"PeriodicalIF":1.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate data dynamics: A high-volume real world structured weather dataset","authors":"Md Zubair ,&nbsp;Md. Nafiz Ishtiaque Mahee ,&nbsp;Khondaker Masfiq Reza ,&nbsp;Md. Shahidul Salim ,&nbsp;Nasim Ahmed","doi":"10.1016/j.dib.2024.111156","DOIUrl":"10.1016/j.dib.2024.111156","url":null,"abstract":"<div><div>The dataset at hand is a unique resource, officially procured from the Bangladesh Meteorological Department, the sole government institution that diligently monitors weather through 35 strategically placed weather stations across the nation. This dataset is a treasure trove of actual data spanning several decades, from the inception of each weather station to the present. It has been meticulously restructured and processed into four (Rainfall, Temperature, Humidity, and Sunshine) key weather parameters, presented in a highly organized and accessible format. This format not only facilitates its use in the machine-learning training process but also opens up avenues for its application in climate research, weather forecasting, and a myriad of other statistical and machine-learning applications.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111156"},"PeriodicalIF":1.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BD-freshwater-fish: An image dataset from Bangladesh for AI-powered automatic fish species classification and detection toward smart aquaculture","authors":"Pranajit Kumar Das ,&nbsp;Md. Abu Kawsar ,&nbsp;Puspendu Biswas Paul ,&nbsp;Md. Abdullah Al Mamun Hridoy ,&nbsp;Md. Sanowar Hossain ,&nbsp;Sabyasachi Niloy","doi":"10.1016/j.dib.2024.111132","DOIUrl":"10.1016/j.dib.2024.111132","url":null,"abstract":"<div><div>There are about 33,000 different species of fish and they are visually identified using variety of traits, i.e., size and shape of body, head's size and shape, skin pattern, fin pattern, mouth pattern, scale pattern, and eye pattern etc. In traditional manner, identifying these fish species is always difficult with necked eye. Identification and detection of fish species from images using deep learning and computer vision based techniques is challenging topic among researchers worldwide as an interesting problem. Automatic fish species classification and detection has practical importance for both smart aquaculture and fish industry. AI powered deep learning and computer vision based automatic fish species recognition and sorting system becoming significant factor for making aquaculture industry more productive and sustainable. However, the performance of machine learning classifier greatly depends on the size of image dataset and the quality of the images in the dataset. This article demonstrate <em>BD-Freshwater-Fish</em>, an image dataset contain 4389 images of 12 different species captured in natural environment using HD mobile camera from local fish market of Sylhet and Jessore district of Bangladesh. Twelve (12) different data classes are: Rohu (<em>Labeo rohita</em>), Catla (<em>Catla catla</em>), Mrigal (<em>Cirrhinus cirrhosus</em>), Grass Carp (<em>Ctenopharyngodon idella)</em>, Common Carp (<em>Cyprinus carpio</em>), Mirror Carp (<em>Cyprinus carpio</em> var. specularis), Black Rohu (<em>Labeo calbasu</em>), Silver Carp (<em>Hypophthalmichthys molitrix),</em> Striped Catfish (<em>Pangasius pangasius</em>), Nile Tilapia (<em>Oreochromis niloticus</em>), Long-whiskered Catfish (<em>Sperata aor</em>), Freshwater Shark (<em>Wallago attu</em>) has been included in the dataset with a different number of images of different species. The <em>BD-Freshwater-Fish</em> dataset is hosted by Department of Computer Science and Engineering mutually with the help of the Department of Aquaculture, Sylhet Agricultural University, Sylhet, Bangladesh.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111132"},"PeriodicalIF":1.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of the complete mitogenome data of collared peccary, Dicotyles tajacu (Linnaeus, 1758) (Suina: Tayassuidae) from Ucayali, Peru","authors":"Julio César Chávez-Galarza ,&nbsp;Victor Manuel Arévalo-Rojas ,&nbsp;Gladys Luz Garay Livia ,&nbsp;Rubén Dario Ferro-Mauricio ,&nbsp;Daniel Vecco ,&nbsp;Agustin Cerna-Mendoza ,&nbsp;Aldi Alida Guerra-Teixeira ,&nbsp;Dora Henriques ,&nbsp;Fredy Fabian Domínguez ,&nbsp;Wilder Macedo-Córdova ,&nbsp;Miguel Alexis Llanto-López","doi":"10.1016/j.dib.2024.111142","DOIUrl":"10.1016/j.dib.2024.111142","url":null,"abstract":"<div><div>The collared peccary (<em>Dicotyles tajacu</em> Linnaeus, 1758) is a vital resource for the subsistence and economy of the Amazonian inhabitants. Despite its importance, there is a notable lack of genetic information on Peruvian collared peccary populations. This study presents the complete mitogenome of <em>D. tajacu</em> from Peru, obtained by Illumina sequencing. The mitochondrial genome spans 16,836 bp and has a nucleotide composition of 34.2 % A, 25.5 % T, 13.5 % G, and 26.8 % C, with a GC content of 40.3 %. The genome includes 13 protein-coding genes, 22 tRNA genes, two rRNA genes, and one control region. Phylogenetic analysis of protein-coding genes indicates that Peruvian <em>D. tajacu</em> is most closely related to Bolivian <em>D. tajacu</em> within the family Tayassuidae. The annotated mitogenome of Peruvian <em>D. tajacu</em> provides valuable genomic data for evolutionary research and will serve in conservation and management strategies for the species.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111142"},"PeriodicalIF":1.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate impact dataset of 1233 ingredients to promote sustainability of food service operators in Finland","authors":"Kim Lindfors ,&nbsp;Venla Kyttä ,&nbsp;Oona Pietiläinen ,&nbsp;Merja Saarinen ,&nbsp;Virpi Vorne","doi":"10.1016/j.dib.2024.111143","DOIUrl":"10.1016/j.dib.2024.111143","url":null,"abstract":"<div><div>The food service and restaurant industry play a crucial role in promoting sustainable food consumption by offering sustainable meal options, shaping consumer preferences, and introducing eco-friendly practices. To enable the food services operating in Finland to provide more sustainable options, we created a climate impact dataset of 1233 typical ingredients used in Finnish food services. The dataset was created using Life Cycle Assessment (LCA) to assess the climate impacts of ingredients from cradle to wholesale. The climate impacts in the dataset were assessed using the characterisation factors from the IPPC's sixth assessment report, using a functional unit of 1 kg of ingredient. The final climate impacts of the ingredients include both impacts from domestic and imported products, aggregated by calculating the degree of domestic origin -weighted average. The climate impacts of Finnish plant production were assessed based on data derived from ProAgria's field plot database, and the impacts of animal and fish production were derived from recent Finnish LCA studies. The post-farm processing was assessed using data from the Agribalyse database, by changing the energy inputs of the processes to Finnish energy and modifying the transport profile to reflect Finnish conditions. The impacts of imported products were also assessed using the Agribalyse database and changing the energy inputs and transport profiles accordingly to better reflect average European production. In addition, for imported products, transportation to Finland was added. The data presented in this dataset can be utilised in other LCA studies to assess the impacts of food ingredients used in Finland in menu, or diet level assessments. As the dataset is compatible with the Finnish Food Composition Database Fineli®, it enables simultaneous assessment of nutritional value, which is crucial in achieving emission reductions without weakening the nutritional quality of food consumed.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111143"},"PeriodicalIF":1.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset for stability of high biomass and yield in maize under normal and intercropping conditions based on biplot, genotype stability index and land equivalent ratio","authors":"Mansyur ,&nbsp;D. Ruswandi","doi":"10.1016/j.dib.2024.111161","DOIUrl":"10.1016/j.dib.2024.111161","url":null,"abstract":"<div><div>Research on high-yielding and biomass of maize hibrids which adaptive to intercropping environment is important in the context of modern agriculture faced with the challenges of climate change. The field evaluation was conducted in Arjasari, West Java, Indonesia, for two seasons in three different cropping systems, namely: maize sole cropping, maize+soybean intercropping and maize+sweet potato intercropping. The evaluation applied a randomized completed block design with three replications. The article provides a data set of Combined Anova Table and biplot graphic of GGE and AMMI. Combined of Anova Table is provided to identify the effect of genotype, environment and their interaction for the traits observed. Thus biplot of GGE and AMMI is provided to identify representative environment and mega-environment for testing and development of hybrid maize; and to evaluate their adaptability in sole cropping as well as in intercropping with soybean and sweetpotato. The data in this article can be utilized by farmers to choose specific or stable maize hybrids for different cropping system.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111161"},"PeriodicalIF":1.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A database for the outer sizes of tropical cyclones over the Middle Americas.","authors":"Adolfo Perez-Estrada, Christian Dominguez","doi":"10.1016/j.dib.2024.111179","DOIUrl":"10.1016/j.dib.2024.111179","url":null,"abstract":"<p><p>Tropical cyclones (TCs) are catastrophic phenomena that constantly threaten populations settled in the tropics. Their direct effects (strong winds, storm surges, and intense precipitation) are confined near the TC center. On the other hand, the indirect effects are due to extreme rainfall events associated with rainbands distant from the TC center. Diverse TC-size definitions do not consider these indirect effects, although these definitions are used for disaster risk reduction strategies. Risk management based on TC size needs an appropriate definition of TC hazards for adequately planning early actions. Here, we developed two algorithms, Radius of the Outermost CLOUD (ROCLOUD) and Radius of Precipitation Band (RPB), to define outer TC sizes using the HURDAT data, GPM satellite imagery, and a radial wind profile. We analyzed 191 and 336 TCs over the North Atlantic and the Eastern North Pacific basins, respectively, during the 2000-2020 period. We create a database for outer TC sizes that presents 6-hourly information during the TC lifetime, from pre-cyclonic to decaying states. This database also provides the TC location, TC outer sizes defined by the two algorithms, and TC shape metrics (asymmetry, dispersion and solidity) every 6 h. The database is freely available in a text format at Mendeley data [1]. We present statistics for the four quadrant radii and mean radius defined by the ROCLOUD and RBP algorithms and the shape metrics. We expect this database can be useful to Early Warning Systems for Tropical Cyclones and regional risk management studies in the Middle Americas.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111179"},"PeriodicalIF":1.0,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11683216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface roughness and profile dataset of Ti6Al4V textured by laser ablation and spark erosion.","authors":"Victor Caso Moreira, Artur Fernando de Vito, Fabrizio Leonardi, Sergio Delijaicov, Rodrigo Magnabosco","doi":"10.1016/j.dib.2024.111185","DOIUrl":"https://doi.org/10.1016/j.dib.2024.111185","url":null,"abstract":"<p><p>The Ti6Al4V alloy is widely recognized for its extensive industrial applications, particularly in the aeronautics sector, due to its exceptional strength to-weight ratio and corrosion resistance. In this context, many industrial processes depend critically on surface area, topology, and roughness. A promising approach involves combining Ti6Al4V alloy with polymer composites, which offers significant potential for engineers to design parts that are not only high-performing but also environmentally friendly. Friction stir spot welding (FSSW) emerges as a viable technique for achieving a robust bond between the metal and polymer composite materials. However, a critical factor in this process is the surface profile of the metal, which plays a pivotal role in ensuring strong adhesion between the polymer and the titanium substrate. The data provided focus on analyzing the surface profile and roughness achieved through laser ablation, an advanced technique used for surface texturing, and explores spark erosion as an alternative method.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111185"},"PeriodicalIF":1.0,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11683252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset linking women's maternity care experiences with hospital environment and governance in Ireland.","authors":"Adegboyega Ojo, Nina Rizun, Grace Walsh, Wojciech Przychodzen, Mona Isazad Mashinchi, Conor Foley, Daniela Rohde, Manohar Rao","doi":"10.1016/j.dib.2024.111168","DOIUrl":"10.1016/j.dib.2024.111168","url":null,"abstract":"<p><p>Many scholars argue that there is a deepening crisis of trust in healthcare systems. What is not contested is the centrality of public trust in building reputational value in healthcare organisations. However, there is a dearth of research focused on better understanding how trust in healthcare institutions, and the healthcare workforce, can be sustainably cultivated. To enable the exploration of care-related factors within hospitals and their potential impacts on trust in healthcare workers, this dataset was created based on the 2020 National Maternity Experience Survey data. The survey data include responses to 68 structured, tick-box questions and three open-ended questions prepared with the participation of over 250 healthcare practitioners and experts, patients, as well as policymakers and researchers. The survey covers the full pathway of maternity care from antenatal care, through labour and birth, to postnatal care in the community. A total of 19 maternity hospitals and units participated in the survey which ran from February to April 2020, resulting in a total of 3204 women responses out of an eligible population of 6357. The survey data was extended with contextual information from a monitoring report on the National Maternity Services Standard published in 2020. The additional data includes compliance levels of maternity hospitals with established standards in four key areas including effective care support, safe care support, leadership governance and management, and workforce. This curated dataset can support investigations into a) the factors that determine overall women's care experience, b) factors contributing to building confidence and trust in the maternity care workforce among different groups of women, and c) how hospital environment, processes and governance impact both women's trust in maternity hospitals and their overall satisfaction.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111168"},"PeriodicalIF":1.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11683277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive VR dataset for machine learning: Head- and eye-centred video and positional data.","authors":"Alexander Kreß, Markus Lappe, Frank Bremmer","doi":"10.1016/j.dib.2024.111187","DOIUrl":"https://doi.org/10.1016/j.dib.2024.111187","url":null,"abstract":"<p><p>We present a comprehensive dataset comprising head- and eye-centred video recordings from human participants performing a search task in a variety of Virtual Reality (VR) environments. Using a VR motion platform, participants navigated these environments freely while their eye movements and positional data were captured and stored in CSV format. The dataset spans six distinct environments, including one specifically for calibrating the motion platform, and provides a cumulative playtime of over 10 h for both head- and eye-centred perspectives. The data collection was conducted in naturalistic VR settings, where participants collected virtual coins scattered across diverse landscapes such as grassy fields, dense forests, and an abandoned urban area, each characterized by unique ecological features. This structured and detailed dataset offers substantial reuse potential, particularly for machine learning applications. The richness of the dataset makes it an ideal resource for training models on various tasks, including the prediction and analysis of visual search behaviour, eye movement and navigation strategies within VR environments. Researchers can leverage this extensive dataset to develop and refine algorithms requiring comprehensive and annotated video and positional data. By providing a well-organized and detailed dataset, it serves as an invaluable resource for advancing machine learning research in VR and fostering the development of innovative VR technologies.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111187"},"PeriodicalIF":1.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11699299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}