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.
151
datasets available to search
ShareScore release 0.9.0
Dataset results
151 results for “network scaling”
Data from Genome scale metabolic network modelling for metabolic profile predictions
<p>Data used to produce figures 4, 5 and 6 in the paper Genome scale metabolic network modelling for metabolic profile predictions.</p>
Data from "Quantification of major particulate matter species from a single filter type using infrared spectroscopy – Application to a large-scale monitoring network"
Open the record for dataset details and reuse information.
Data from: Drivers of viral prevalence in landscape-scale pollinator networks across Europe: Honey bee viral density, niche overlap with this reservoir host and network architecture
Open the record for dataset details and reuse information.
Network structure variation across scales offers clues to the macroevolutionary persistence of specialised mutualisms
Open the record for dataset details and reuse information.
Data from: Latest Ordovician (Hirnantian) brachiopod faunal lists used for non-matric multidimensional scaling (NMDS) and network analyses
Open the record for dataset details and reuse information.
Data from: Dynamics of mesoscale brain network during decision-making learning revealed by chronic, large-scale single-unit recording
Open the record for dataset details and reuse information.
Variations in ecosystem-scale methane fluxes across a boreal mire complex assessed by a network of flux towers
Open the record for dataset details and reuse information.
Data from: Temporal scale-dependence of plant-pollinator networks
Open the record for dataset details and reuse information.
Dataset for: Indirect nitrous oxide emission factors of fluvial networks can be predicted by dissolved organic carbon and nitrate from local to global scales
Open the record for dataset details and reuse information.
Unraveling the cavity-nesting network at large spatial scales: The biogeographic role of woodpeckers as ecosystem engineers
Open the record for dataset details and reuse information.
Data of A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " 113234", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2020.113234", author = "Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic"</pre>
Large-scale metabolic interaction network of the mouse and human gut microbiota
<p>The role of our gut microbiota in health and disease is largely attributed to the collective metabolic activities of the inhabitant microbes. A system-level framework of the microbial community structure, mediated through metabolite transport, would provide important insights into the complex microbe-microbe and host-microbe chemical interactions. This framework, if adaptable to both mouse and human systems, would be useful for mechanistic interpretations of the vast amounts of experimental data from gut microbiomes in murine animal models, whether humanized or not. Here, we constructed a literature-curated, interspecies network of the mammalian gut microbiota for mouse and human hosts, called NJC19. This network is an extensive data resource, encompassing 838 microbial species (766 bacteria, 53 archaea, and 19 eukaryotes) and 6 host cell types, interacting through 8,224 small-molecule transport and macromolecule degradation events. Moreover, we compiled 912 negative associations between organisms and metabolic compounds that are not transportable or degradable by those organisms. Our network may facilitate experimental and computational endeavors for the mechanistic investigations of host-associated microbial communities.</p>
Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs
<p><strong>Background: To mitigate the effects of climate change on public health and conservation, we need to better understand the dynamic interplay between biological processes and environmental effects. Machine learning (ML) methods in general, and Deep Learning (DL) methods in particular, are a potential way forward because they are able to cope with the nonlinearity of natural systems. However, there are several barriers that exist, including the absence of ML-ready data. We propose to develop a machine learning framework capable of predicting phenotypes based on multi-scale data about genes and environments. A critical part of this framework are data transformation methods that map the heterogeneous input data into formats that are consumable by the ML techniques. The central hypothesis of this research is that deep learning algorithms and biological knowledge graphs will predict phenotypes more accurately across more taxa and more ecosystems than do current numerical and traditional statistical modeling methods. Our long term goal is to develop predictive analytics for organismal response to environmental perturbations using innovative data science approaches. This pilot project on predicting emergent properties of complex systems and multidimensional interactions is funded by the NSF (Award # 1939945, 1940059, 1940062, 1940330). </strong></p> <p> </p> <p><strong>Results: We have established shared project governance, communication channels, project timeline, and data and computing environment across four universities. We have successfully reached out to three other projects for broader collaboration.</strong></p>
Data from: Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks
<p>Inferring the frequency and mode of hybridization among closely related organisms is an important step for understanding the process of speciation and can help to uncover reticulated patterns of phylogeny more generally. Phylogenomic methods to test for the presence of hybridization come in many varieties and typically operate by leveraging expected patterns of genealogical discordance in the absence of hybridization. An important assumption made by these tests is that the data (genes or SNPs) are independent given the species tree. However, when the data are closely linked, it is especially important to consider their non-independence. Recently, deep learning techniques such as convolutional neural networks (CNNs) have been used to perform population genetic inferences with linked SNPs coded as binary images. Here we use CNNs for selecting among candidate hybridization scenarios using the tree topology (((P<sub>1</sub>,P<sub>2</sub>),P<sub>3</sub>),Out) and a matrix of pairwise nucleotide divergence (d<sub>XY</sub>) calculated in windows across the genome. Using coalescent simulations to train and independently test a neural network showed that our method, HyDe-CNN, was able to accurately perform model selection for hybridization scenarios across a wide-breath of parameter space. We then used HyDe-CNN to test models of admixture in <em>Heliconius</em> butterflies, as well as comparing it to a random forest classifier trained on introgression-based statistics. Given the flexibility of our approach, the dropping cost of long-read sequencing, and the continued improvement of CNN architectures, we anticipate that inferences of hybridization using deep learning methods like ours will help researchers to better understand patterns of admixture in their study organisms.</p>
Data from: Frugivore biodiversity and complementarity in interaction networks enhance landscape-scale seed dispersal function
1. Animal biodiversity matters for the provision of ecosystem functions derived from trophic activity. However, the mechanisms underlying this pattern remain elusive since animal abundance and diversity, which are the components commonly used for representing biodiversity, provide poor information about ecological complementarity in species assemblages. An approach based on species interaction networks may overcome this constraint. 2. Here, we relate frugivore biodiversity and frugivore-plant network structure with landscape-scale seed dispersal function. We sampled, for two years, and at fourteen plots with variable assemblages of frugivores and plants in the Cantabrian Range (N Spain), data on the abundance and diversity of frugivorous birds, the consumption of fleshy fruits of woody plants, and the landscape-scale patterns of avian seed deposition. As a measure of interaction complementarity in seed dispersal networks, we estimated the degree to which frugivore and plant species specialize in their interacting partners. 3. Specialization varied strongly across the seed dispersal networks of the different plots, being higher in networks harboring smaller bird species that dispersed mostly small-fruited plants, and also in networks with late-ripening, dominant fruiting species dispersed mostly by wintering birds. 4. Bird abundance markedly affected seed deposition. Plots harboring more birds received a higher density of dispersed seeds, and showed higher probabilities of seed arrival and seed deposition in open microhabitats. Bird diversity also had a positive effect on the density of dispersed seed and, to a lesser extent, seed arrival probability. Independently of frugivore abundance and diversity, the density of dispersed seeds increased in plots where seed dispersal networks showed a higher degree of specialization. 5. This study considers the structure of interaction networks to re-address the relationship between biodiversity and ecosystem functionality, evidencing that specialization in frugivore-plant networks drives the large-scale process of seed dispersal. These results encourage the consideration of interaction complementarity as an underlying mechanism linking animal biodiversity and trophic-related functions.
Data from: Functional outcomes of mutualistic network interactions: a community-scale study of frugivore gut passage on germination
1. Current understanding of mutualistic networks is grounded largely in data on interaction frequency, yet mutualistic network dynamics are also shaped by interaction quality—the functional outcomes of individual interactions on reproduction and survival. The difficulty of obtaining data on functional outcomes has resulted in limited understanding of functional variation among a network's pairwise species interactions, of the study designs that are necessary to capture major sources of functional variation, and of predictors of functional variation that may allow generalization across networks. 2. In this community-scale study, we targeted a key functional outcome in plant-frugivore networks: the impact of frugivore gut passage on seed germination. We used captive frugivore feeding trials and germination experiments in an island ecosystem, attaining species-level coverage across all extant native frugivores and the plants they consume to 1) assess sources of functional variation, 2) separate effects of pulp removal from those of scarification via gut passage, and 3) test trait-based correlates of gut passage effect sizes. 3. We found antagonistic seed predation effects of a frugivore previously assumed to be a seed disperser, highlighting the need to consider functional outcomes rather than interaction frequency alone. The other frugivores each exhibited similar impacts for individual plant species, with benefits primarily caused by pulp removal rather than scarification, supporting the use of animal functional groups in this context. In contrast, plant species varied widely in impacts of gut passage on germination. Species with smaller seeds and more frugivore partners had larger benefits of gut passage, showing promise for network metrics and functional traits to predict functional variation among plants. 4. Synthesis. Combining network and demographic approaches, we assessed the degree and sources of variation in a key functional outcome of plant-frugivore interactions across an entire network. Using a detailed study design, our work shows how simpler study designs can capture primary sources of functional variation and that functional traits and network metrics may allow generalization across networks. Efficiently measuring and generalizing sources of functional variation within mutualistic networks will strengthen our ability to model network dynamics and predict mutualist responses to global change.
Data from: Benefits and challenges of scaling up expansion of marine protected area networks in the Verde Island Passage, Central Philippines
Locally-established marine protected areas (MPAs) have been proven to achieve local-scale fisheries and conservation objectives. However, since many of these MPAs were not designed to form ecologically-connected networks, their contributions to broader-scale goals such as complementarity and connectivity can be limited. In contrast, integrated networks of MPAs designed with systematic conservation planning are assumed to be more effective—ecologically, socially, and economically—than collections of locally-established MPAs. There is, however, little empirical evidence that clearly demonstrates the supposed advantages of systematic MPA networks. A key reason is the poor record of implementation of systematic plans attributable to lack of local buy-in. An intermediate scenario for the expansion of MPAs is scaling up of local decisions, whereby locally-driven MPA initiatives are coordinated through collaborative partnerships among local governments and their communities. Coordination has the potential to extend the benefits of individual MPAs and perhaps to approach the potential benefits offered by systematic MPA networks. We evaluated the benefits of scaling up local MPAs to form networks by simulating seven expansion scenarios for MPAs in the Verde Island Passage, central Philippines. The scenarios were: uncoordinated community-based establishment of MPAs; two scenarios reflecting different levels of coordinated MPA expansion through collaborative partnerships; and four scenarios guided by systematic conservation planning with different contexts for governance. For each scenario, we measured benefits through time in terms of achievement of objectives for representation of marine habitats. We found that: in any governance context, systematic networks were more efficient than non-systematic ones; systematic networks were more efficient in broader governance contexts; and, contrary to expectations but with caveats, the uncoordinated scenario was slightly more efficient than the coordinated scenarios. Overall, however, coordinated MPA networks have the potential to be more efficient than the uncoordinated ones, especially when coordinated planning uses systematic methods.
Data from: Integrated genome-scale analysis identifies novel genes and networks underlying senescence in maize
Premature senescence in annual crops reduces yield while delayed senescence, termed stay-green, is known to impose both positive and negative impact on yield and nutrition quality. Despite the importance, scant information is available on the genetic architecture of senescence in maize (Zea mays L.) and other cereals. We combined a systematic characterization of natural diversity for senescence in maize and co-expression networks derived from transcriptome analysis of normally senescing and stay-green lines. Sixty-four candidate genes were identified by GWAS, and 14 of these are supported by additional evidence for involvement in senescence-related processes including proteolysis, sugar transport and signaling, and sink activity. Eight of the GWAS candidates, independently supported by a co-expression network underlying stay-green, include a trehalose-6-phosphate synthase, a NAC transcription factor, and two xylan biosynthetic enzymes. Source-sink communication and the activity of cell walls as a secondary sink emerge as key determinants of stay-green. Mutant analysis supports the role of a candidate encoding cysteine protease in stay-green in Arabidopsis (Arabidopsis thaliana), and analysis of natural alleles suggest a similar role in maize. This study provides a foundation for enhanced understanding and manipulation of senescence for increasing carbon yield, nutritional quality, and stress tolerance of maize and other cereals.
Data from: Network-scale effects of invasive species on spatially-structured amphibian populations
<p>Understanding the factors affecting the dynamics of spatially-structured populations (SSP) is a central topic of conservation and landscape ecology. Invasive alien species are increasingly important drivers of the dynamics of native species. However, the impacts of invasives are often assessed at the patch scale, while their effects on SSP dynamics are rarely considered. We used long-term abundance data to test whether the impact of invasive crayfish on subpopulations can also affect the whole SSP dynamics, through their influence on source populations. From 2010 to 2018, we surveyed a network of 58 ponds and recorded the abundance of Italian agile frog clutches, the occurrence of an invasive crayfish, and environmental features. Using Bayesian hierarchical models, we assessed relationships between frog abundance in ponds and a) environmental features; b) connectivity within the SSP; c) occurrence of invasive species at both the patch- and the SSP-levels. If spatial relationships between ponds were overlooked, we did not detect effects of crayfish presence on frog abundance or trends. When we jointly considered habitat, subpopulation, and SSP features, processes acting at all these levels affected frog abundance. At the subpopulation scale, frog abundance in a year was related to habitat features, but was unrelated to crayfish occurrence at that site during the previous year. However, when we considered the SSP level, we found a strong negative relationship between frog abundance in a given site and crayfish frequency in surrounding wetlands during the previous year. Hence, SSP-level analyses can identify effects that would remain unnoticed when focussing on single patches. Invasive species can affect population dynamics even in not invaded patches, through the degradation of subpopulation networks. Patch-scale assessments of the impact of invasive species can thus be insufficient: predicting the long-term interplay between invasive and native populations requires landscape-level approaches accounting for the complexity of spatial interactions.</p>
Data from: Scale-dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks
The network architecture of streams and rivers constrains evolutionary, demographic, and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho giant salamander, Dicamptodon aterrimus, in stream networks of Idaho and Montana, USA. We used microsatellite data to test population structure models by (1) examining hierarchical partitioning of genetic variation in stream networks and (2) testing for genetic isolation by distance along stream corridors versus overland pathways. Replicated sampling of streams within catchments within three river basins revealed that hierarchical scale had strong effects on genetic structure and gene flow. AMOVA identified significant structure at all hierarchical scales (among streams, among catchments, among basins), but divergence among catchments had the greatest structural influence. Isolation by distance was detected within catchments, and in-stream distance was a strong predictor of genetic divergence. Patterns of genetic divergence suggest that differentiation among streams within catchments was driven by limited migration, consistent with a stream hierarchy model of population structure. However, there was no evidence of migration among catchments within basins, or among basins, indicating that gene flow only counters the effects of genetic drift at smaller scales (within rather than among catchments). These results show the strong influence of stream networks on population structure and genetic divergence of a salamander, with contrasting effects at different hierarchical scales.
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.