Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
102
datasets available to search
ShareScore release 0.9.0
Dataset results
102 results for “plant disease”
Silica Nanoparticles Enhance Disease Resistance in Arabidopsis Plants - RAW DATA
<p>These datasets are used to produce the figures/graphs published in our article</p> <p><strong>Silica Nanoparticles Enhance Disease Resistance in <em>Arabidopsis</em> Plants</strong></p> <p>in <em>Nat. Nanotechnol.</em> (2020). <a href="https://doi.org/10.1038/s41565-020-00812-0">https://doi.org/10.1038/s41565-020-00812-0</a></p> <p> </p><p><strong>Correspondence: </strong></p> <p></p> <p>fabienne.schwab@alumni.ethz.ch, Tel: +41 78 736 00 19;</p> <p>m.shetehy@uky.edu, Tel. +41 76 455 56 02</p> <p>Further raw data related to qPCR and microbiology are available upon reasonable request from M.H. El‑Shetehy.</p> <p>Further raw data related to the nanoparticles and plant microscopy are available upon reasonable request by F. Schwab.</p> <p> </p> <p><strong>Abstract</strong></p> <p>In plants, pathogen attack can induce an immune response known as systemic acquired resistance (SAR) that protects against a broad spectrum of pathogens. In the search for safer agrochemicals, silica nanoparticles (SiO<sub>2</sub>‑NPs, food additive E551) have recently been proposed as a new tool. However, initial results are controversial, and the molecular mechanisms of SiO<sub>2</sub>‑NP-induced disease resistance are unknown. Here, we show that SiO<sub>2</sub>‑NPs, as well as soluble orthosilicic acid (Si(OH)<sub>4</sub>), can induce SAR in a dose-dependent manner, that involves the defence hormone salicylic acid. Nanoparticle uptake and action occurred exclusively through stomata (leaf pores facilitating gas exchange) and involved extracellular adsorption in leaf air spaces of the spongy mesophyll. In contrast to treatment with SiO<sub>2</sub>‑NPs, induction of SAR by Si(OH)<sub>4 </sub>was problematic, since high concentrations caused stress. We conclude that SiO<sub>2</sub>‑NPs have the potential to serve as an inexpensive, highly efficient, safe, and sustainable alternative for plant disease protection.</p>
Dataset Natural plant disease suppressiveness in soils extends to insect pest control
<p>This dataset is related to the study "<strong>Natural plant disease suppressiveness in soils extends to insect pest control</strong>" (Harmsen et al., 2024) and contains the raw data described therein. </p> <p>Sequencing data used in this study has been deposited in the NCBI Sequence Read Archive under the BioProject number <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1075215/">PRJNA1075215</a>.</p> <p>The scripts used to analyze the data generated in the study are available at <a href="https://github.com/nhrmsn/SuppressSoil-Data">GitHub</a>. </p>
Data for “Herbivory damage but not plant disease under experimental warming is dependent on weather for three subalpine grass species”, Rocky Mountain Biological Laboratory, Gothic, Colorado, 2015-2017.
Both theory and prior studies predict that climate warming should increase attack rates by herbivores and pathogens on plants. However, past work has often assumed that variation in abiotic conditions other than temperature (e.g., precipitation) do not alter warming responses of plant damage by natural enemies. Studies over short time periods span low variation in weather, and studies over long-time scales often neglect to account for fine-scale weather conditions. Here, we used a 20+ year field warming experiment to investigate if warming affects herbivory and disease are dependent on variation in ambient weather observed over three years. We studied three common grass species in a subalpine meadow in the Colorado Rocky Mountains, USA. We visually estimated herbivory and disease every two-weeks during the growing season and evaluated weather conditions during the previous two- or four-week time interval (two-week average air temperature, two- and four-week cumulative precipitation) as predictors of the probability and amount of damage. Herbivore attack was 13% more likely and amount of damage was 29% greater in warmed plots than controls across the focal species, but warming treatment had little affect on plant disease. Herbivory presence and damage increased the most with experimental warming when preceded by wetter, rather than drier, fine-scale weather, but preceding ambient temperature did not strongly interact with elevated warming to influence herbivory. Disease presence and damage increased, on average, with warmer weather and more precipitation regardless of warming. The effect of warming over reference climate on herbivore damage is dependent on and amplified by fine-scale weather variation, suggesting more boom-and-bust damage dynamics with increasing climate variability. However, the mean effect of regional climate change is likely reduced monsoon rainfall, for which we predict a reduction in insect herbivore damage. Plant disease was generally unrelated
Can disease resistance evolve independently at different ages? Genetic variation in age-dependent resistance to disease in three wild plant species
<p>1. Juveniles are typically less resistant (more susceptible) to infectious disease than adults, and this difference in susceptibility can help fuel the spread of pathogens in age-structured populations. However evolutionary explanations for this variation in resistance across age remain to be tested.</p> <p>2. One hypothesis is that natural selection has optimized resistance to peak at ages where disease exposure is greatest. A central assumption of this hypothesis is that hosts have the capacity to evolve resistance independently at different ages. This would mean that hosts populations have a) standing genetic variation in resistance at both juvenile and adult stages, and b) that this variation is not strongly correlated between age-classes so that selection acting at one age does not produce a correlated response at the other age</p> <p>3. Here we evaluated the capacity of three wild plant species (Silene latifolia, S. vulgaris, and Dianthus pavonius) to evolve resistance to their anther-smut pathogens (Microbotryum fungi), independently at different ages. The pathogen is pollinator-transmitted, and thus exposure risk is considered to be highest at the adult flowering stage.</p> <p>4. Within each species we grew families to different ages, inoculated individuals with anther smut, and evaluated the effects of age, family and their interaction on infection.</p> <p>5. In two of the plant species, S. latifolia and D. pavonius, resistance to smut at the juvenile stage was not correlated with resistance to smut at the adult stage. In all three species, we show there are significant age*family interaction effects, indicating that age-specificity of resistance varies among the plant families.</p> <p>6. Synthesis: These results indicate that different mechanisms likely underlie resistance at juvenile and adult stages and support the hypothesis that resistance can evolve independently in response to differing selection pressures as hosts age. Taken together our results provide new insight into the structure of genetic variation in age-dependent resistance in three well-studied wild host-pathogen systems.</p>
Figure 9 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 9. ELISA plate readings (O.D at 405nm) of leaf samples of transgenic lines (T1-T8) and control plants after 15 dpi.
Figure 7 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 7. PCR Products of the hpt gene from T0 transgenic plants. Lane 1-9 are transgenic. Lane 10, +ve control. Lane 11, control (untransformed) plant.
Figure 6. DNA bands from T0 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 6. DNA bands from T0 transgenic and Agro-infilterated plants. Lane 1-10, transgenic plants. Lane 11-13, agro-infilterated plants.
Figure 3 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 3. Symptoms development on propagative host plants after mechanical inoculation with ChiVMV isolate ATIPK. (a) N. tabacum showing mosaic, mottling and vein clearing (b) C. annum (cv. Loungi) displays the symptoms of mottling, mosaic, leaf deformation and vein clearing.
Figure 2 in Evaluation of the Chilli veinal mottle virus CP gene expressing transgenic Nicotiana benthamiana plants for disease resistance against the virus
Figure 2. Symptoms of ChiVMV on chilli leaves collected from Islamabad. (a) Shows mottling and severe vein clearing and distortion. (b) Shows reduced leaf size with mottling and distortion.
Figure 2 in In vitro study of antimicrobial activity of some plant seeds against bacterial strains causing food poisoning diseases
Figure 2. MIC's of the effective plant seeds powder against S. aureus and K. pneumonia, ± standard error.
Linked collectors and determiners for: New Zealand Fungal and Plant Disease Collection (PDD).
Natural history specimen data linked to collectors and determiners held within, "New Zealand Fungal and Plant Disease Collection (PDD)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/ee27b1b0-3b55-11dc-8c18-b8a03c50a862">https://bionomia.net/dataset/ee27b1b0-3b55-11dc-8c18-b8a03c50a862</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/ee27b1b0-3b55-11dc-8c18-b8a03c50a862">https://gbif.org/dataset/ee27b1b0-3b55-11dc-8c18-b8a03c50a862</a>. Formatted as a Frictionless Data package.
Web sourced dataset for plant disease detection
<p>The web-sourced dataset consists of plant leaf images collected from online platforms, primarily through sources like Google Images, to capture a wide range of real-world scenarios and environmental conditions. Unlike controlled laboratory datasets, these images feature diverse backgrounds, lighting variations, and different stages of plant diseases, representing how diseases appear in natural agricultural settings. The dataset includes multiple plant species and disease types, augmenting existing datasets by adding greater variability. This diversity aims to improve model robustness and generalization, enabling more accurate disease detection across varying agricultural environments.</p>
An Analysis of Plant Disease and Their Detection
<p><span>One of the biggest revolutions of modern history is the invention of agriculture for a healthier lifestyle. It significantly changed the human culture and played an important role in the development of the population and biological improvements in food production and domestication. The frequency of pests on food crops increased because environmental circumstances were changing, and diseases on crops increased rapidly. These diseases inflict catastrophic social, economic, and ecological casualties, and this extraordinary challenge is a concern for the correct and prompt detection of diseases. In this contest, technology has left its mark on the potential of farmers and is still to be exploited. As input for making the right decision, farmers need timely and credible sources of knowledge. Study into Agriculture have to be planned by improving the disease diagnostics method with the use of newer technology to enhance efficiency and quantity for agricultural production and its allied operation. In various applications of the agricultural industry, computer methodologies have been used for automation. Timely farming decisions and disease management are taken using image analysis and machinery of learning techniques in planning and creating a method for the diagnosis of diseases.</span></p>
Herbivores can benefit both plants and their pathogens through selective herbivory on diseased tissue
Open the record for dataset details and reuse information.
Data from: Disease from leaves to landscapes: Viral hotspots are determined by spatial arrangement and phytochemistry of host plants in specialist caterpillars
Open the record for dataset details and reuse information.
Can disease resistance evolve independently at different ages? Genetic variation in age-dependent resistance to disease in three wild plant species
Open the record for dataset details and reuse information.
Improving genomic prediction for plant disease using environmental covariates
Open the record for dataset details and reuse information.
Edge effects, not connectivity, determine the incidence and development of a foliar fungal plant disease
Using a model plant-pathogen system in a large-scale habitat corridor experiment, we found that corridors do not facilitate the movement of wind-dispersed plant pathogens, that connectivity of patches does not enhance levels of foliar fungal plant disease, and that edge effects are the key drivers of plant disease dynamics. Increased spread of infectious disease is often cited as a potential negative effect of habitat corridors used in conservation, but the impacts of corridors on pathogen movement have never been tested empirically. Using sweet corn (Zea mays) and southern corn leaf blight (Cochliobolus heterostrophus) as a model plant-pathogen system, we tested the impacts of connectivity and habitat fragmentation on pathogen movement and disease development at the Savannah River Site, South Carolina, USA. Over time, less edgy patches had higher proportions of diseased plants, and distance of host plants to habitat edges was the greatest determinant of disease development. Variation in average daytime temperatures provided a possible mechanism for these disease patterns. Our results show that worries over the potentially harmful effects of conservation corridors on disease dynamics are misplaced, and that, in a conservation context, many diseases can be better managed by mitigating edge effects.
Data and code for "Shrubs inhibit plant diseases through reducing herbaceous biomass in alpine meadows"
<p>Supplementary Data and code for "Shrubs inhibit plant diseases through reducing herbaceous biomass in alpine meadows"</p>
Microbiome network connectivity and composition linked to disease resistance in strawberry plants
<p>The two R data files correspond phyloseq objects used in the study. The file phyloseq_nochim_silva.RData and phyloseq_nochim_unite.RData correspond to bacterial and fungal phyloseq objects, respectively. Both objects include count table, taxonomy table and metadata information. </p>
ScienceDex guides
Understand access before you commit
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.