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364
datasets available to search
ShareScore release 0.9.0
Dataset results
364 results for “Network interaction”
Dazl regulates germ cell survival through a network of polA-proximal mRNA interactions [GC1-spg cells (parental line) iCLIP]
GEO Series GSE120098. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
A Genome-Scale TF-DNA Interaction Network for Transcriptional Regulation of Arabidopsis Primary and Specialized Metabolism
GEO Series GSE137623. Arabidopsis thaliana. 64 samples. Type: Expression profiling by high throughput sequencing.
Hfq orchestrates a robust RNA-RNA interaction network in Acinetobacter baumannii. [RIL-Seq]
GEO Series GSE295534. Acinetobacter baumannii. 12 samples. Type: Other.
Dazl maintains proliferating germ cells through a network of polyA-proximal mRNA interactions [P6 iCLIP]
GEO Series GSE108183. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
The Properties of Genome Conformation and Spatial Gene Interaction and Regulation Networks of Normal and Malignant Human Cell Types
GEO Series GSE73924. Homo sapiens. 3 samples. Type: Other.
Functional network of the long non-coding RNA growth arrest specific transcript 5 (GAS5) and its interacting proteins in senescence
GEO Series GSE163237. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
Mapping nucleolus-associated chromatin interactions by nucleolus-Hi-C reveals repression network
GEO Series GSE90003. Homo sapiens. 22 samples. Type: Other.
The dependency factor RRM2 at the nexus of a copy number driven regulatory network and a target for synthetic lethal interactions with replication stress checkpoint addiction in high-risk neuroblastom
GEO Series GSE162202. Homo sapiens. 28 samples. Type: Expression profiling by high throughput sequencing.
A protein interaction network of mental disorder factors in neural stem cells
GEO Series GSE70872. Mus musculus. 17 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
CEBP-β and PLK1 as Potential Mediators of the Breast Cancer/Obesity Crosstalk: ‘in vitro’ and ‘in silico’ approaches inside a spiderweb interaction network
GEO Series GSE223853. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
OCEAN-C: mapping hubs of open chromatin interactions across the genome reveals gene regulatory networks
GEO Series GSE100832. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
Gene Network Transitions in Embryos Depend Upon Interactions between a Pioneer Transcription Factor and Core Histones [ATAC-Seq]
GEO Series GSE134464. Mus musculus. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Changing expression profiles of circular RNA reveal the key regulators and interaction networks of competing endogenous RNA in neurogenic bladder after suprasacral spinal cord injured
GEO Series GSE227447. Rattus norvegicus. 12 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Genetic interaction network has a very limited impact on the evolutionary trajectories in continuous culture-grown populations of yeast
GEO Series GSE167397. Saccharomyces cerevisiae. 17 samples. Type: Expression profiling by array.
Data from: Refuges from fire maintain pollinator-plant interaction networks
Fire is a major disturbance factor in many terrestrial ecosystems, leading to landscape transformation in fire‐prone areas. Species in mutualistic interactions are often highly sensitive to disturbances like fire events, but the degree and complexity of their responses are unclear. We use bipartite insect–flower interaction networks across a recently burned landscape to explore how plant–pollinator interaction networks respond to a recent major fire event at the landscape level, and where fire refuges were present. We also investigate the effectiveness of these refuges at different elevations (valley to hilltop) for the conservation of displaced flower‐visiting insects during fire events. Then, we explore how the degree of specialization of flower‐visiting insects changes across habitats with different levels of fire impact. We did this in natural areas in the Greater Cape Floristic Region (GCFR) biodiversity hotspot, which is species rich in plants and pollinators. Bees and beetles were the most frequent pollinators in interactions, followed by wasps and flies. Highest interaction activity was in the fire refuges and least in burned areas. Interactions also tracked flower abundance, which was highest in fire refuges in the valley and lowest in burned areas. Interactions consisted mostly of specialized flower visitors, especially in refuge areas. The interaction network and species specialization were lowest in burned areas. However, species common to at least two fire classes showed no significant difference in species specialization. We conclude that flower‐rich fire refuges sustain plant–pollinator interactions, especially those involving specialized species, in fire‐disturbed landscape. This may be an important shelter for specialized pollinator species at the time that the burned landscape goes through regrowth and succession as part of ecosystem recovery process after a major fire event.
Data from: The evolution of generalized reciprocity on social interaction networks
Generalized reciprocity ("help anyone, if helped by someone") is a minimal strategy capable of supporting cooperation between unrelated individuals. Its simplicity makes it an attractive model to explain the evolution of reciprocal altruism in animals that lack the information or cognitive skills needed for other types of reciprocity. Yet, generalized reciprocity is anonymous and thus defenseless against exploitation by defectors. Recognizing that animals hardly ever interact randomly, we investigate whether social network structure can mitigate this vulnerability. Our results show that heterogeneous interaction patterns strongly support the evolution of generalized reciprocity. The future probability of being rewarded for an altruistic act is inversely proportional to the average connectivity of the social network when cooperators are rare. Accordingly, sparse networks are conducive to the invasion of reciprocal altruism. Moreover, the evolutionary stability of cooperation is enhanced by a modular network structure. Communities of reciprocal altruists are protected against exploitation, because modularity increases the mean access time, i.e., the average number of steps that it takes for a random walk on the network to reach a defector. Sparseness and community structure are characteristic properties of vertebrate social interaction patterns, as illustrated by network data from natural populations ranging from fish to primates.
Dataset for efficient modelling of ionic and electronic interactions by resistive memory- based reservoir graph neural network
<p>Dataset for training the resistive memory-based reservoir graph neural network.</p> <p>In the atomic force calculation experiment, <span lang="EN-HK"><span>a Li</span><sub>3</sub><span>PO</span><sub>4</sub><span> dataset is derived from the melting and quenching trajectory via AIMD simulations. The training, validation, and testing datasets consist of 40,000, 5,000, and 5,000 samples, respectively. </span></span></p> <p><span lang="EN-HK"><span>In the Hamiltonian calculation, a dataset </span><span lang="EN-HK">of various graphene (72 atoms) configurations are generated by AIMD simulations at room temperature, with Hamiltonian data calculated via the OpenMX code</span><span lang="EN-HK">.</span><span lang="EN-HK"> <span>The training, validation, and testing datasets consist of 270, 90, and 90 samples (including atomic structure and Hamiltonian matrix), respectively.</span></span></span></p> <p>Code: https://github.com/hustmeng/RGNN.git</p> <p>1-Atomic_force_dataset.zip and 2-Hamiltonian_dataset.zip are original data.</p> <p>3-Graph_atomic_force.zip and 4-Graph_training_Hamiltonian.zip are graphs. </p> <p> </p> <p>References:</p> <p> </p> <p>1. C.W. Park, M. Kornbluth, J. Vandermause, C. Wolverton, B. Kozinsky, J.P. Mailoa, Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture, npj Comput. Mater. 7(1) (2021) 73. https://github.com/ken2403/gnnff.git</p> <p>2. H. Li, Z. Wang, N. Zou, M. Ye, R. Xu, X. Gong, W. Duan, Y. Xu, Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation, Nat. Comput. Sci. 2(6) (2022) 367-377. https://github.com/mzjb/DeepH-pack.git</p> <p>3. D. Pfau, J.S. Spencer, A.G.D.G. Matthews, W.M.C. Foulkes, Ab initio solution of the many-electron Schrödinger equation with deep neural networks, Phys. Rev. Res. 2(3) (2020) 033429. https://github.com/google-deepmind/ferminet.git</p> <p> </p>
Dataset for "Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine"
<p>Dataset:</p> <p>Data1. Host gene expression profile</p> <p>Data2. Microbiome gene expression profile</p>
Networks of Bacterium-Metabolite Interactions in the Small Intestine
ClinicalTrials.gov study NCT04978077. IPD Sharing: YES. Countries: 1. Publications: 0.
Investigating Dynamic Interactions in Distributed Cognitive Control Networks
ClinicalTrials.gov study NCT05927974. IPD Sharing: NO. Countries: 1. Publications: 0.
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.