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3,481 results for “data set”
Data from: MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species
We developed a set of universal PCR primers (MiFish-U/E) for metabarcoding environmental DNA (eDNA) from fishes. Primers were designed using aligned whole mitochondrial genome (mitogenome) sequences from 880 species, supplemented by partial mitogenome sequences from 160 elasmobranchs (sharks and rays). The primers target a hypervariable region of the 12S rRNA gene (163–185 bp), which contains sufficient information to identify fishes to taxonomic family, genus and species except for some closely related congeners. To test versatility of the primers across a diverse range of fishes, we sampled eDNA from four tanks in the Okinawa Churaumi Aquarium with known species compositions, prepared dual-indexed libraries and performed paired-end sequencing of the region using high-throughput next-generation sequencing technologies. Out of the 180 marine fish species contained in the four tanks with reference sequences in a custom database, we detected 168 species (93.3%) distributed across 59 families and 123 genera. These fishes are not only taxonomically diverse, ranging from sharks and rays to higher teleosts, but are also greatly varied in their ecology, including both pelagic and benthic species living in shallow coastal to deep waters. We also sampled natural seawaters around coral reefs near the aquarium and detected 93 fish species using this approach. Of the 93 species, 64 were not detected in the four aquarium tanks, rendering the total number of species detected to 232 (from 70 families and 152 genera). The metabarcoding approach presented here is non-invasive, more efficient, more cost-effective and more sensitive than the traditional survey methods. It has the potential to serve as an alternative (or complementary) tool for biodiversity monitoring that revolutionizes natural resource management and ecological studies of fish communities on larger spatial and temporal scales.
Data from: Inferring complex phylogenies using parsimony: an empirical approach using three large DNA data sets for angiosperms
To explore the feasibility of parsimony analysis for large data sets, we conducted heuristic parsimony searches and bootstrap analyses on separate and combined DNA data sets for 190 angiosperms and three outgroups. Separate data sets of 18S rDNA (1,855 bp), rbc L (1,428 bp), and atp B (1,450 bp) sequences were combined into a single matrix 4,733 bp in length. Analyses of the combined data set show great improvements in computer run times compared to those of the separate data sets and of the data sets combined in pairs. Six searches of the 18S rDNA rbc L atp B data set were conducted; in all cases TBR branch swapping was completed, generally within a few days. In contrast, TBR branch swapping was not completed for any of the three separate data sets, or for the pairwise combined data sets. These results illustrate that it is possible to conduct a thorough search of tree space with large data sets, given sufficient signal. In this case, and probably most others, sufficient signal for a large number of taxa can only be obtained by combining data sets. The combined data sets also have higher internal support for clades than the separate data sets, and more clades receive bootstrap support of 50% in the combined analysis than in analyses of the separate data sets. These data suggest that one solution to the computational and analytical dilemmas posed by large data sets is the addition of nucleotides, as well as taxa.
Data from: Phylogenetic Congruence and Discordance Among One Morphological and Three Molecular Data Sets from Pontederiaceae
A morphological data set and three sources of data from the chloroplast genome (two genes and a restriction-site survey) were used to reconstruct the phylogenetic history of the Pickerelweed family Pontederiaceae. The chloroplast data are converging to a single tree, presumably the true chloroplast phylogeny of the family. Unrooted trees estimated from the three chloroplast data sets were identical or extremely similar in shape to each other, mostly robustly supported and there was no evidence of significant heterogeneity among them. The few topological differences seen among unrooted trees from each chloroplast data set are probably artifacts of sampling error on short branches. Despite well documented differences in rates of evolution for different characters in individual data sets, equally weighted parsimony therefore permits accurate reconstructions of chloroplast relationships in Pontederiaceae. A separate morphology-based data set yielded trees that were very different from the chloroplast trees. While there was substantial support by the morphological evidence for several major clades supported by chloroplast trees, most of the conflicting phylogenetic structure on the morphology trees was not robust. Nonetheless, several statistical tests of incongruence indicate significant heterogeneity between molecules and morphology. The source of this apparent incongruence appears to be a low ratio of phylogenetic signal to noise in the morphological data.
Data from: Silver nanoparticles as a medical device in healthcare settings: a five-step approach for candidate screening of coating agents
Silver nanoparticle-based antimicrobials can promote a long lasting bactericidal effect without detrimental toxic side effects. However, there is not a clear and complete protocol to define and relate the properties of the particles (size, shape, surface charge, ionic content) with their specific activity. In this paper, we propose an effective multi-step approach for the identification of a 'purpose-specific active applicability window' to maximize the antimicrobial activity of medical devices containing silver nanoparticles (Ag NPs) (such as surface coaters), minimizing any consequent risk for human health (safety by design strategy). The antimicrobial activity and the cellular toxicity of four types of Ag NPs, differing in their coating composition and concentration have been quantified. Through the implementation of flow-field flow fractionation, Ag NPs have been characterized in terms of metal release, size and shape. The particles are fractionated in the process while being left unmodified, allowing for the identification of biological particle-specific contribution. Toxicity and inflammatory response in vitro have been assessed on human skin models, while antimicrobial activity has been monitored with both non-pathogenic and pathogenic Escherichia coli. The main benefit associated with such approach is the comprehensive assessment of the maximal effectiveness of candidate nanomaterials, while simultaneously indexing their properties against their safety.
Data from: Identifying conserved genomic elements and designing universal bait sets to enrich them
Targeted enrichment of conserved genomic regions is a popular method for collecting large amounts of sequence data from non-model taxa for phylogenetic, phylogeographic and population genetic studies. For example, two available bait sets each allow enrichment of thousands of orthologous loci from >20 000 species (Faircloth et al. Systematic Biology, 61, 717–726, 2012; Molecular Ecology Resources, 15, 489–501, 2015). Unfortunately, few open-source workflows are available to identify conserved genomic elements shared among divergent taxa and to design enrichment baits targeting these regions. Those that do exist require extensive bioinformatics expertise and significant amounts of time to use. These shortcomings limit the application of targeted enrichment methods to additional organismal groups. Here, I describe a universal workflow for identifying conserved genomic regions in available genomic data and for designing targeted enrichment baits to collect data from these conserved regions. These methods require less expertise, less time and better use commonly available information to identify conserved loci and design baits to capture them. I apply this computational approach to the understudied arthropod groups Arachnida, Coleoptera, Diptera, Hemiptera or Lepidoptera to identify thousands of conserved loci in each group and design target enrichment baits to capture these loci. I then use in silico analyses to demonstrate that targeted enrichment of the conserved loci can be used to reconstruct the accepted relationships among genome sequences from the focal arthropod orders. The software workflow I created allowed me to identify thousands of conserved loci in five diverse arthropod groups and design sequence capture baits to target them. This suite of capture bait designs should enable collection of phylogenomic data from >900 000 arthropod species. Although the examples in this manuscript focus on understudied arthropod groups, the approach I describe is applicable to all organismal groups having some form of pre-existing genomic information (e.g. other invertebrates, plants, fungi and microbes). Finally, the documentation, design steps, software code and bait sets developed here are available under an open-source license for restriction-free testing, use, and additional modification by any research group.
Evaluation Data Set
<p>Evaluation data set of dissertation:</p> <p>Kersch, Mike, Evaluation von Trading-Algorithmen unter realen Marktbedingungen: Ansätze zur praktischen Anwendbarkeit, Anforderungen, Implementierung, Dissertation, Universität des Saarlandes, 2015.</p>
Data set
<p>Normalised residual levels under different test conditions.</p>
ASD specific data set An. aging
<p>These are the *.asd specific files from the spectrometer.</p>
Data_Set_JCE_PIARDMOYON_2025
<p>Data set</p> <p>1- Raw Data in French and English</p> <p>2 and 3 - Treated date</p> <p>4- ggPlot</p>
Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) - Data Set
<p>GENERAL INFORMATION</p> <p>The data and scripts used for the analysis of the paper "Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – Towards an improved representation of mountain water resources in global assessments"</p> <p><strong>When using this dataset, please refer to the original publication in addition to this Zenodo repository.</strong></p> <p><strong>Hanus, S., Schuster, L., Burek, P., Maussion, F., Wada, Y., and Viviroli, D.: Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – towards an improved representation of mountain water resources in global assessments, Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, 2024.</strong></p> <p>DATA & FILE OVERVIEW</p> <p>please have a look at readme.txt </p> <p>Don't hesitate to contact us in case of any questions (sarah.hanus@geo.uzh.ch)</p>
Data Companion Set - Bachelor's Thesis - Gabriel Gehrig
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Data Sets "Light-Induced Spiking in Proteinoids Yields Boolean Gates"
<p>The CSV files comprise the data upon which the figures in the published manuscript are based. The recorded electrical potentials (mV) in seconds (sec) for various experimental conditions on the proteinoids samples are detailed in each individual file. The timestamps enable the reconstruction of temporal dynamics, whereas the potential levels characterise the form and amplitude of the electrical responses exhibited by the proteinoids in response to different light stimuli. In the end, these measurements facilitated the implementation of logic operations that determined the behaviours of the Boolean gates.</p><p> </p>
Data set for "A large-scale integrated vector–matrix multiplication processor based on monolayer molybdenum disulfide memories"
<p>Data accompanying the paper "A large-scale integrated vector–matrix multiplication processor based on monolayer molybdenum disulfide memories" </p>
beer foam height as a function of time curves and data sets
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Data Set for Predicting the Performance of ATL Model Transformations Based on Generated Models
<p>Predicting the execution time of model transformations can help to understand how a transformation reacts to a given input model without creating and transforming the respective model.</p> <p>In our previous data set (https://doi.org/10.5281/zenodo.8385957), we have documented our experiments in which we predict the performance of ATL transformations using predictive models obtained from training linear regression, random forest and support vector regression. As input for the prediction, our approach uses a characterization of the input model. In these experiments, we only used data from real models.</p> <p>However, a common problem is that transformation developers do not have enough models available to use such a prediction approach. Therefore, in a new variant of our experiments, we investigated whether the three considered machine learning approaches can predict the performance of transformations if we use data from generated models for training. We also investigated whether it is possible to achieve good predictions with smaller training data. The dataset provided here offers the corresponding raw data, scripts, and results.</p> <p>A detailed documentation is available in documentaion.pdf.</p>
Data set for "Tracking the nearfield evolution of an initially shallow, neutrally-buoyant plane jet over a sloping bottom boundary"
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MRI data set for "Characterization of shear zones in soft granular beds by means of a novel magnetic resonance imaging technique"
<p>We introduce a new Magnetic Resonance Imaging technique to study the geometry of shear zones of soft, low-frictional and hard, frictional granular materials and their mixtures. Hydrogel spheres serve as the soft, low-frictional material component, while mustard seeds represent rigid, frictional grains. Some of the hydrogel spheres are doped with CuSO4 salt to serve as tracers. A cylindrical split-bottom cell is sheared stepwise and the shear profiles are determined from the differences of tomograms after successive shear steps, using Particle Imaging Velocimetry and Particle Tracking Velocimetry. We find that the shear zone geometry differs considerably between soft grains submersed in water and the same material without the embedding fluid.</p>
data set
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NGII Data Set for Black Ice Traffic Accident Prediction
<p>NGII Data Set for Black Ice Traffic Accident Prediction</p> <p> </p> <p> </p>
IF and SCRINSHOT image data of probe set selection for targeted spatial transcriptomics
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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.