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datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “Network Motif”
Seismic Network and Motifs Vrancea
<p>A seismic network for earthquakes with magnitude larger than 4 in Vrancea, Romania seismic region. Here are also presented the network's 3 nodes motifs (triangles)</p>
Neural network ensembles and FEFF spectra for multi-modal small molecule chemical motif prediction
<p><strong>Data</strong></p> <ul> <li><strong>22-12-05-data</strong>: original molecular XANES data created from <a href="https://doi.org/10.1103/PhysRevResearch.5.013180">Ghose <em>et al.</em></a></li> <li><strong>23-04-26-ml-data</strong>: machine learning-ready data which is prepared in the format required by <a href="https://github.com/matthewcarbone/Crescendo">Crescendo</a>.</li> <li><strong>23-05-03-hp</strong>: hyper-parameter tuning results from 23-04-26-ml-data.</li> <li><strong>23-05-05-ensembles</strong>: ensemble results from 23-04-26-ml-data.</li> <li><strong>23-05-11-ml-data-CUTOFF8</strong>: a special machine learning-ready dataset constructed by a unique partitioning: only molecules with less than or equal to 8 atoms/molecule are used for training/validation, the rest are used for testing.</li> <li><strong>23-12-06_torch_models</strong>: torch only models which can be easily used independently of our ML helper repository, Crescendo. Instead, it can be used with a few lines of code found in multimodal_molecules/core.py, in our <a href="https://github.com/AI-multimodal/multimodal-molecules">GitHub respository</a>.</li> </ul> <p><strong>Funding</strong></p> <p>This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under Contract No. DE-SC0012704.</p>
Hybrid networks reveal contrasting effects of agricultural intensification on antagonistic and mutualistic motifs
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Data from: Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance
Systems biology is accumulating a wealth of understanding about the structure of genetic regulatory networks, leading to a more complete picture of the complex genotype-phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network-based view of genetic variation. Here we model a set of two-node, two-phenotype genetic network motifs, covering a full range of regulatory interactions. We find that network interactions result in different patterns of mutational (co)variance at the phenotypic level (the M-matrix), not only across network motifs but also across phenotypic space within single motifs. This effect is due almost entirely to mutational input of additive genetic (co)variance. Variation in M has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space-time under general relativity, and similar mathematical tools may apply in each case. We explored the consequences of curvature in mutational variation by simulating adaptation under divergent selection with gene flow. Both standing genetic variation (the G-matrix) and rate of adaptation are constrained by M, so that G and adaptive trajectories are curved across phenotypic space. Under weak selection the phenotypic mean at migration-selection balance also depends on M.
PhD Thesis - Emergence of cortical network motifs
<p>Annotated representative video of mice performing sensorimotor task.</p>
Data from: The network motif architecture of dominance hierarchies
The widespread existence of dominance hierarchies has been a central puzzle in social evolution, yet we lack a framework for synthesizing the vast empirical data on hierarchy structure in animal groups. We applied network motif analysis to compare the structures of dominance networks from data published over the past 80 years. Overall patterns of dominance relations, including some aspects of non-interactions, were strikingly similar across disparate group types. For example, nearly all groups exhibited high frequencies of transitive triads, whereas cycles were very rare. Moreover, pass-along triads were rare, and double-dominant triads were common in most groups. These patterns did not vary in any systematic way across taxa, study settings (captive or wild) or group size. Two factors significantly affected network motif structure: the proportion of dyads that were observed to interact and the interaction rates of the top-ranked individuals. Thus, study design (i.e. how many interactions were observed) and the behaviour of key individuals in the group could explain much of the variations we see in social hierarchies across animals. Our findings confirm the ubiquity of dominance hierarchies across all animal systems, and demonstrate that network analysis provides new avenues for comparative analyses of social hierarchies.
Data from: The network motif architecture of dominance hierarchies
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Data from: Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance
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microRNAs with an AAGUGC seed motif constitute an integral part of a signaling network driving NSCLC cell proliferation
GEO Series GSE81417. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Mapping gene regulatory networks in Drosophila eye development by large-scale transcriptome perturbations and motif inference. [RNA-seq]
GEO Series GSE59059. Drosophila melanogaster. 80 samples. Type: Expression profiling by high throughput sequencing.
Mapping gene regulatory networks in Drosophila eye development by large-scale transcriptome perturbations and motif inference
GEO Series GSE62558. Drosophila melanogaster. 82 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network
GEO Series GSE13219. Saccharomyces cerevisiae. 73 samples. Type: Expression profiling by array.
Mapping gene regulatory networks in Drosophila eye development by large-scale transcriptome perturbations and motif inference [ChIP-Seq]
GEO Series GSE62557. Drosophila melanogaster. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
From gene networks to network motif dynamics: how the presense of positive feed-back and -forward loops between PERIANTHIA, WUSCHEL-RELATED HOMEOBOX5 and GRF-INTERACTING FACTOR 1 modulates gene expres
GEO Series GSE118102. Arabidopsis thaliana. 13 samples. Type: Expression profiling by high throughput sequencing.
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.