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505 results for “apples”
Data on three-year flowering intensity monitoring in an apple orchard
<p>The present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020. It is directly linked to a research article entitled “Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard”. The data collection site was an apple orchard located at Randwijk, Overbetuwe, The Netherlands (51.938, 5.7068 in WGS84 UTM 31U). This dataset aims to support researchers focussing on remote sensing, machine vision, deep learning, and image classification, and the stakeholders interested in precision horticulture and orchard management. It can be used for flowering intensity estimation and prediction, and spatial and temporal flowering variability mapping by using digital photogrammetry and 3D reconstruction.</p>
Data for: Direct and indirect effects of management and landscape on biological pest control and crop pest infestation in apple orchards
<p>Biological pest control, relying on naturally occurring predator-prey dynamics, is considered a key element to achieve more sustainable farming systems. However, the combined effects of local management and landscape factors on communities of natural enemies as well as the cascading effects on pest infestations are rarely addressed, especially in perennial crops. Here, we used Piecewise Structural Equation Modelling (PSEM) to test direct and indirect effects of landscape composition, landscape configuration and local management practices on natural enemy communities, the pest control services they provide and ultimately on pest infestation and pest-related yield damage in apple crops. To this end, we surveyed 12 organic and 12 Integrated Pest Management (IPM) orchards during three consecutive years, and we also established a semi-natural benchmark to quantify the extent to which predator communities in the orchards were degraded. Natural enemies had a different community composition and were more abundant in organic orchards compared to IPM orchards. This had a small and positive effect on sentinel egg predation rates in organic orchards, but overall had very little impact on actual apple pest infestation. On the contrary, apple pest infestation levels were directly and positively affected by organic management practices and by increasing semi-natural habitat cover and landscape edge density. Compared to a semi-natural benchmark, both agricultural management systems showed degraded predator communities, which translated into an impaired delivery of biological control services. Synthesis and applications. Our results indicate that organic management and habitat conservation can enhance natural enemies and stimulate pest control, but also show that these factors can enhance pest infestations and can even lead to an overall increase in pest-related crop damage. Our study thus highlights the complex interplay of ecosystem services and disservices provided by biodiversity, which should be taken into account when advising farmers, policy makers and land managers on effective and sustainable strategies to control pest species and safeguard crop production.</p>
Conegliano Synagogue - Apple LIDAR Pointcloud
Reconstructed Synagogue of the Jewish community of Conegliano Italy. Today in Jerusalem. The Nahon Museum of Italian Jewish Art. Point Cloud 4 scans, merged in CloudCompare. Apple LIDAR scan - iPad Pro Source: Objaverse 1.0 / Sketchfab
DMP Examine the correlation between Twitter Sentiment data and the stock data of the 4 big tech companies Apple, Amazon, Google and Microsoft
<p><span>The purpose of using these specific datasets are, the possibilities they give to analyze the stock data changes, based on the sentiment analysis of the previous day. Including this output information it is possible to analyze our goal of searching for possible correlation between those two. </span></p>
Genome sequence resources from three isolates of the apple canker pathogen Neonectria ditissima infecting forest trees
<p><em>Neonectria ditissima</em> is a generalist ascomycete plant pathogen causing canker diseases on a variety of hardwood tree species and can cross-infect many of them. The fungus enters the plants through wounds throughout the year. <em>N. ditissima</em> is considered a major threat to apple production responsible for the fruit tree canker disease which damages trees and causes rotting of fruits in storage. Nearby forests and shelter belts can serve as source of inoculum for well-managed apple orchards. Thus, knowledge about the <em>N. ditissima</em> isolates infecting different host species is essential for designing integrated pest management strategies. Here, we describe the genomes of three <em>N. ditissima</em> isolates, Nd_iso34, Nd_iso35, and Nd_iso36, infecting European beech, American tulip tree, and American beech, respectively. We obtained genome assemblies of ca. 45 megabases for all isolates, covering 94% of the <em>N. ditissima</em> reference annotation, and 97% of the universal single-copy orthologs (BUSCOs). We conclude that these genome assemblies are a highly relevant resource considering the scarcity of genomic data available for <em>N. ditissima</em>.</p>
Source data for publication "A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory" in Journal of Hepatology
<p>Processed genomic and transcriptomic data for the publication <a href="https://doi.org/10.1016/j.jhep.2024.05.017">https://doi.org/10.1016/j.jhep.2024.05.017</a>.</p> <p>cnv_segmentation.tsv: CNV segmentation file from Sequenza.</p> <p>cnv_arm.tsv: Significant arm level CNV events called by GISTIC, from broad_values_by_arm.txt file.</p> <p>cnv_gene.tsv: Gene level CNV events called by GISTIC, from all_threshold_by_genes.txt file. </p> <p>RNA_raw_counts.tsv: Raw RNA-seq read counts from featureCounts.</p> <p>snv_indel.tsv: All SNV and Indel called with annotation from Funcotator.</p> <p> </p>
High-resolution cone-beam scan of an apple and pebbles with two dosage levels
<p>We release two tomographic scans with two levels of radiation dosage of two measured objects for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The objects are referred to as apple and pebbles (more specific, hydrograins), respectively. The dataset collected with higher dosage is referred to as the "<em><strong>good</strong></em>" <strong>dataset</strong>; and the other as the "<em><strong>noisy</strong></em>" <strong>dataset</strong>, as a way to distinguish between the two dosage levels.</p> <p>The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel. </p> <p> </p> <p>Both dataset were collected over a 360 degrees in circular and continuous motion with 2001 projections distributed evenly over the full circle for the good dataset and 501 projections distributed evenly over the full circle for the noisy dataset. The uploaded dataset are not binned or normalized; a single dark and two (pre- and post-) flat fields are included for each scan. Projections for both sets were collected with 100 ms exposure time with the good data projections averaged over 5 takes, and no averaging was made for the noisy data. The tube settings for the good and noisy dataset were 70kV, 45W and 70kV, 20W, respectively. The total scanning time were 20 minutes for the good; 3 minutes for the noisy scan. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings.</p> <p> </p> <p>These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl or</li> <li>m.j.lagerwerf [at] cwi.nl</li> </ul>
Images of apples for the use of the Viola-Jones method. Data set no. 1 - multicolor.
<p>The database contains pictures of apples made at different angles, from different sides and containing different varieties. In this way, two bases of apple images were created (each database contains over 1,100 images). This set is data set no. 1 - multicolor: processed images in multicolor. The photos were prepared for the best possible detection process in the Viola-Jones method. These photo bases with apples can be used to teach machines to recognize specific varieties and count apples.</p> <p>Please note that these are not raw data, but images processed for use in the algorithm in the Viola-Jones method.</p> <p> </p>
RAW data - Boric acid modified polydopamine and nanocolumnar hydrogenated TiO2 nanocomposite with improved photocatalytic performance. Appl. Surf. Sci. 2025, 686, 162118. DOI: 10.1016/j.apsusc.2024.162118 - AFM - ICONBruker - CNBM UAM - BAPDA/H:TiO2
<p>This repository contains RAW data for the experiment described in the publication: Jakub Szewczyk, Tim Tjardts, Fabian Symalla, Igor Iatsunskyi, Franz Faupel, Cenk Aktas, Emerson Coy, Salih Veziroglu, Boric acid modified polydopamine and nanocolumnar hydrogenated TiO2 nanocomposite with improved photocatalytic performance. Applied Surface Science 2025, 686, 162118.</p> <p>The remaining data supporting this study's findings are available from the corresponding author upon reasonable request. The raw AFM data is posted here in the open-access model.</p> <p> </p> <p> </p>
Diffraction data for Zajdel et al Turning Molecular Springs into Nano-Shock Absorbers ACS Appl. Mater. Interfaces 2022, 14, 26699−26713
<p>Datasets required to reproduce:</p> <p>Individual files</p> <p>Fig. 1,- XRDs of materials collected on DiscoverD, CuKa</p> <p>(BT-1.zip)</p> <p>Fig. 4a. - neutron powder diffraction of ZIF-8 + 2D2O/1H2O mix collected at the BT-1 diffractometer of the NIST Center of Neutron Research at 30C. For plot the data were normalized to a common scale in counts/h</p> <p>Fig. 4c. Fullprof PCR file and VESTA model with the volmetric data *.pgrid</p> <p>Fig. 4d Single detector file Z8221010.bt1 and raw pressure output + reduced data (Zenodo_30C_drops_Fig4d.DAT).</p> <p>Fig. 4b VSANS (110) integration results Up and Down (VSANS.zip)</p>
Data from: APPLES: Scalable distance-based phylogenetic placement with or without alignments
<p>Placing a new species on an existing phylogeny has increasing relevance to several applications. Placement can be used to update phylogenies in a scalable fashion and can help identify unknown query samples using (meta-)barcoding, skimming, or metagenomic data. Maximum likelihood (ML) methods of phylogenetic placement exist, but these methods are not scalable to reference trees with many thousands of leaves, limiting their ability to enjoy benefits of dense taxon sampling in modern reference libraries. They also rely on assembled sequences for the reference set and aligned sequences for the query. Thus, ML methods cannot analyze datasets where the reference consists of unassembled reads, a scenario relevant to emerging applications of genome-skimming for sample identification. We introduce APPLES, a distance-based method for phylogenetic placement. Compared to ML, APPLES is an order of magnitude faster and more memory efficient, and unlike ML, it is able to place on large backbone trees (tested for up to 200,000 leaves). We show that using dense references improves accuracy substantially so that APPLES on dense trees is more accurate than ML on sparser trees, where it can run. Finally, APPLES can accurately identify samples without assembled reference or aligned queries using kmer-based distances, a scenario that ML cannot handle. APPLES is available publically at github.com/balabanmetin/apples.</p>
Figure 4 in Abundance and distribution of Aculus schlechtendali on apple orchards in Southern of Brazil
Figure 4 Municipalities and its respective states (see color legend) from Southern Brazil
Figure 4 in Effects of Aculus schlechtendali (Acari: Eriophyidae) population densities on Golden Delicious apple production
Figure 4 Regression lines, data points, equations and R2 for the following response variables: A
More intraguild prey than pest species in arachnid diets may compromise biological control in apple orchards
<p>Understanding the full diet of natural enemies is necessary for evaluating their role as biocontrol agents, because many enemy species do not only feed on pests but also on other natural enemies. Such intraguild predation can compromise pest control if the consumed enemies are actually better for pest control than their predators. In this study, we used gut metabarcoding to quantify diets of all common arachnid species in Swedish and Spanish apple orchards. For this purpose, we designed new primers that reduce amplification of arachnid predators while retaining high amplification of all prey group. Results suggest that most arachnids consume a large range of putative pest species on apple but also a high proportion of other natural enemies, where the latter constitute almost a third of all prey sequences. Intraguild predation also varied between regions, with a larger content of heteropteran bugs in arachnid guts from Spanish orchards. There was also a tendency for cursorial spiders to have more intraguild prey in the gut than web spiders. Two groups that may be overlooked as important biocontrol agents in apple orchards seem to be theridiid web spiders and opilionids, where the latter had several small-bodied pest species in the gut. These results thus provide important guidance for what arachnid groups that should be targets of management actions, even though additional information is needed to quantify all direct and indirect interactions occurring in the complex arthropod food webs in fruit orchards.</p>
Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms - CaseStudy
<p>The dataset "Case Study" consists of image sequences (videos) for apple detection and tracking and its corresponding ground truth. The ground truth is presented in MOT format. This dataset is part of the paper:</p> <p>Villacrés, J., Viscaino, M., Delpiano, J., Vougioukas, S. & Cheein, F. A. (2022). Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms. <em>Computers and Electronics in Agriculture</em>.</p> <p>The article is currently accepted. For a better reference format, please refer to the journal's official website.</p> <p>If you have used the material presented in this data set, please cite the previous article.</p> <p>For more information regarding the dataset, please refer to the paper mentioned below.</p>
Data for: Hedgerows have contrasting effects on pollinators and natural enemies and limited spillover effects on apple production
<p>Agricultural intensification has resulted in a decline in insect biodiversity and threatens the provision of valuable ecosystem services. Agri-environment schemes (AESs) have been implemented in an effort to conserve biodiversity on farmland and increase agricultural sustainability, but their effectiveness can vary widely. To better determine which factors influence AES effectiveness, the relative roles of local habitat features, habitat quality, and landscape context need to be further explored. The aim of this study was to determine the most important factors influencing field margin AES effectiveness in commercial apple orchards, in terms of arthropod biodiversity conservation and ecosystem service provision. We surveyed wild bees and aphid natural enemies in field margins and apple trees in 20 orchards, ten bordered by hedgerow field margins (an AES) and ten with herbaceous field margins (no hedgerows present, not an AES). We considered field margin floral resources and the cover of semi-natural habitat in the surrounding landscape as indicators of local habitat quality and landscape context, respectively. We furthermore quantified pollination and pest control as measures of ecosystem service delivery and the relationship between arthropod communities and apple yield (initial and final fruit set) and quality. We found that hedgerow presence strongly predicted both pollinator and natural enemy communities and that these relationships were more pronounced than those with local habitat quality and landscape context. Hedgerows were negatively related to wild bee richness and abundance within the orchard, and positively related to natural enemy richness and abundance at the field margin but not within the orchard. We found no relationships between local and landscape factors and ecosystem service delivery, and no relationship between wild bee communities and apple yield, suggesting that apple is not pollen limited in our study system. There was, however, a negative relationship between natural enemy richness and initial fruit set. We conclude that annually cut hedgerows can benefit the conservation of natural enemies, but have limited arthropod-mediated private benefits for apple production, and likely need to be supplemented with additional local habitat resources for the conservation of wild bees. Our findings indicate that local habitat factors can strongly influence biodiversity regardless of landscape context, but that AESs likely need to be designed with separate biodiversity and ecosystem service targets, and specific taxonomic groups, in mind.</p>
Apple sample image
<p>An image of an apple with pesticide coated on them. This is used for identifying the level of pesticide present in them.</p>
Genetic Characterization of the Norwegian Apple Collection
<p>Commercial fruit production in Norway is located at around latitude 60° north, demanding careful choice of adapted cultivars. The most comprehensive collection of apple genetic resources in Norway is being kept in the Norwegian Apple Collection (NAC) at Njøs Fruit and Berry Centre (NJØS). The collection contains around 350 accessions and was recently genotyped with a single nucleotide polymorphism (SNP) array. Curated SNP data were used for structure assessment, diversity analysis, pedigree confirmation, and core collection development. In the subsequent SNP analysis, we identified several duplicates and parent–offspring relationships. The collection was equally diverse across the geographic regions represented. Different methods for analyzing population structure were applied. K-means clustering and a Bayesian modeling approach with prior assumptions of the data revealed five subpopulations associated with geographic breeding centers. The collection has a distinct genetic structure and low relatedness among the accessions; hence, two 100 accession core collections were created. These new core collections will allow breeders and researchers to use the NAC efficiently. The results from this study suggest that several of the accessions in the Norwegian Apple Collection could be of high importance for breeding programs.</p>
Phylogenomic conflict analyses in the apple genus Malus s.l. reveal widespread hybridization and allopolyploidy driving diversification, with insights into the complex biogeographic history in the Northern Hemisphere
<p>Phylogenomic evidence from an increasing number of studies has demonstrated that different data sets and analytical approaches often reconstruct strongly supported but conflicting relationships. In this study, 785 single-copy nuclear genes and 75 complete plastomes were used to infer the phylogenetic relationships and estimate the historical biogeography of the apple genus <em>Malus</em> sensu lato, an economically important lineage disjunctly distributed in the Northern Hemisphere and involved in known and suspected hybridization and allopolyploidy events. The nuclear phylogeny recovered the monophyly of <em>Malus</em> s.l. (including <em>Docynia</em>); however, the genus was supported to be biphyletic in the plastid phylogeny. An ancient chloroplast capture event in the Eocene in western North America best explains the cytonuclear discordance. Our conflict analysis demonstrated that ILS, hybridization, and allopolyploidy could explain the widespread nuclear gene tree discordance. One deep hybridization event (<em>Malus doumeri)</em> and one recent event (<em>Malus</em> coronaria) were detected in <em>Malus</em> s.l. Furthermore, our historical biogeographic analysis integrating living and fossil data supported a widespread East Asian-western North American origin of <em>Malus</em> s.l. in the Eocene, followed by several extinction and dispersal events in the Northern Hemisphere. We also propose a general workflow for assessing phylogenomic discordance and biogeographic analysis using deep genome skimming datasets.</p>
Fungicide-free management of Alternaria leaf blotch and fruit spot on apple indicates Alternaria spp. as secondary colonizer
<p>Raw data of the article: <strong>"Fungicide-free management of Alternaria leaf blotch and fruit spot on apple indicates <em>Alternaria spp</em>. as secondary colonizer"</strong>, DOI: 10.1038/s41598-023-35448-2</p> <p>-->see Read Me file</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.