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244
datasets available to search
ShareScore release 0.9.0
Dataset results
244 results for “Model Organisms”
Collaborative Mental Health Care Model: an Evaluation of the Implementation of a Pilot in Four Primary Care Organization
ClinicalTrials.gov study NCT05385666. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Hyperoxia Induced Pulmonary Inflammation and Organ Injury: a Human in Vivo Model
ClinicalTrials.gov study NCT05414370. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Motion analysis of non-model organisms using a hierarchical model: influence of setup enclosure dimensions on gait parameters of Swinhoe’s striped squirrels as a test case
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Data from: Pan-African phylogeography of a model organism, the African clawed frog “Xenopus laevis”
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Data from: Maiasaura, a model organism for extinct vertebrate population biology: a large sample statistical assessment of growth dynamics and survivorship
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Data from: Utility of pooled sequencing for association mapping in non-model organisms
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Data from: Spatiotemporal diversification of the true frogs (Genus Rana): a historical framework for a widely studied group of model organisms
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Natural history of model organisms: the secret (group) life of Drosophila melanogaster larvae and why it matters to developmental ecology
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Data from: Citizen science reveals unexpected continental-scale evolutionary change in a model organism
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Data from: Molecular Inversion Probes for targeted resequencing in non-model organisms
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Data from: Genome-wide single nucleotide polymorphism (SNP) identification and characterization in a non-model organism, the African buffalo (Syncerus caffer), using next generation sequencing
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Data from: Genotyping-by-sequencing for estimating relatedness in non-model organisms: avoiding the trap of precise bias
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Data from: Diversification in wild populations of the model organism Anolis carolinensis: a genome-wide phylogeographic investigation
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Data from: Correlative changes in life history variables in response to environmental change in a model organism
Global change alters the environment, including increases in the frequency of (un)favorable events and shifts in environmental noise color. However, how these changes impact the dynamics of populations, and whether these can be predicted accurately has been largely unexamined. Here we combine recently developed population modeling approaches and theory in stochastic demography to explore how life history, morphology, and average fitness respond to changes in the frequency of favorable environmental conditions and in the color of environmental noise in a model organism (an acarid mite). We predict that different life-history variables respond correlatively to changes in the environment, and we identify different life-history variables, including lifetime reproductive success, as indicators of average fitness and life-history speed across stochastic environments. Depending on the shape of adult survival rate, generation time can be used as an indicator of the response of populations to stochastic change, as in the deterministic case. This work is a useful step toward understanding population dynamics in stochastic environments, including how stochastic change may shape the evolution of life histories.
Data from: Modeling the internet of things, self-organizing and other complex adaptive communication networks: a cognitive agent-based computing approach
Background: Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose: It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method: We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results: The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.
Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)
Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.
Data from: SNP discovery in non-model organisms: strand-bias and base-substitution errors reduce conversion rates
Single nucleotide polymorphisms (SNPs) have become the marker of choice for genetic studies in organisms of conservation, commercial or biological interest. Most SNP discovery projects in nonmodel organisms apply a strategy for identifying putative SNPs based on filtering rules that account for random sequencing errors. Here, we analyse data used to develop 4723 novel SNPs for the commercially important deep-sea fish, orange roughy (Hoplostethus atlanticus), to assess the impact of not accounting for systematic sequencing errors when filtering identified polymorphisms when discovering SNPs. We used SAMtools to identify polymorphisms in a velvet assembly of genomic DNA sequence data from seven individuals. The resulting set of polymorphisms were filtered to minimize 'bycatch'—polymorphisms caused by sequencing or assembly error. An Illumina Infinium SNP chip was used to genotype a final set of 7714 polymorphisms across 1734 individuals. Five predictors were examined for their effect on the probability of obtaining an assayable SNP: depth of coverage, number of reads that support a variant, polymorphism type (e.g. A/C), strand-bias and Illumina SNP probe design score. Our results indicate that filtering out systematic sequencing errors could substantially improve the efficiency of SNP discovery. We show that BLASTX can be used as an efficient tool to identify single-copy genomic regions in the absence of a reference genome. The results have implications for research aiming to identify assayable SNPs and build SNP genotyping assays for nonmodel organisms.
Data from: De novo transcriptomic analyses for non-model organisms: an evaluation of methods across a multi-species data set
High-throughput sequencing (HTS) is revolutionizing biological research by enabling scientists to quickly and cheaply query variation at a genomic scale. Despite the increasing ease of obtaining such data, using these data effectively still poses notable challenges, especially for those working with organisms without a high-quality reference genome. For every stage of analysis – from assembly to annotation to variant discovery – researchers have to distinguish technical artefacts from the biological realities of their data before they can make inference. In this work, I explore these challenges by generating a large de novo comparative transcriptomic data set data for a clade of lizards and constructing a pipeline to analyse these data. Then, using a combination of novel metrics and an externally validated variant data set, I test the efficacy of my approach, identify areas of improvement, and propose ways to minimize these errors. I find that with careful data curation, HTS can be a powerful tool for generating genomic data for non-model organisms.
Data from: General models for the spectra of surface area scaling strategies of cells and organisms: fractality, geometric dissimilitude, and internalization
Surface areas and volumes of biological systems—from molecules to organelles, cells, and organisms—affect their biological rates and kinetics. Therefore, surface-area-to-volume ratios and the scaling of surface area with volume profoundly influences ecology, physiology, and evolution. The zeroth-order geometric expectation is that surface area scales with body mass or volume as a power law with an exponent of two-thirds, with consequences for surface-area-to-volume (SA:V) ratios and constraints on size; however, organisms have adaptations for altering the surface area scaling and SA:V ratios of their bodies and structures. The strategies fall into three groups: (i) fractal-like surface convolutions and crinkles; (ii) classic geometric dissimilitude through elongating, flattening, fattening, and hollowing; and (iii) internalization of surfaces. Here I develop general quantitative theory to model the spectra of effects of these strategies on SA:V ratios and surface area scaling, from exponents of less than two-thirds to superlinear scaling and mixed-power laws. Applying the theory to cells helps quantitatively evaluate the effects of membrane fractality, shape-shifting, vacuoles, vesicles, and mitochondria on surface area scaling, informing understanding of cell allometry, morphology, and evolution. Analysis of compiled data indicates that through hollowness and surface internalization eukaryotic phytoplankton increase their effective surface area scaling, attaining near-linear scaling in larger cells. This unifying theory highlights the fundamental role of biological surfaces in metabolism and morphological evolution.
Data from: Using viromes to predict novel immune proteins in non-model organisms
Immunity is mostly studied in a few model organisms, leaving the majority of immune systems on the planet unexplored. To characterize the immune systems of non-model organisms alternative approaches are required. Viruses manipulate host cell biology through the expression of proteins that modulate the immune response. We hypothesized that metagenomic sequencing of viral communities would be useful to identify both known and unknown host immune proteins. To test this hypothesis, a mock human virome was generated and compared to the human proteome using tBLASTn, resulting in 36 proteins known to be involved in immunity. This same pipeline was then applied to reef-building coral, a non-model organism that currently lacks traditional molecular tools like transgenic animals, gene-editing capabilities, and in vitro cell cultures. Viromes isolated from corals and compared with the predicted coral proteome resulted in 2503 coral proteins, including many proteins involved with pathogen sensing and apoptosis. There were also 159 coral proteins predicted to be involved with coral immunity but currently lacking any functional annotation. The pipeline described here provides a novel method to rapidly predict host immune components that can be applied to virtually any system with the potential to discover novel immune proteins.
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.