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.
9,152
datasets available to search
ShareScore release 0.9.0
Dataset results
9,152 results for “Networking”
River network rearrangements promote speciation in lowland Amazonian birds
<p>Large Amazonian rivers impede dispersal for many species, but lowland river networks frequently rearrange, thereby altering the location and effectiveness of river-barriers through time. These rearrangements may promote biotic diversification by facilitating episodic allopatry and secondary contact among populations. We sequenced genome-wide markers to evaluate histories of divergence and introgression in six Amazonian avian species-complexes. We first tested the assumption that rivers are barriers for these taxa and found that even relatively small rivers facilitate divergence. We then tested whether species diverged with gene flow and recovered reticulate histories for all species, including one potential case of hybrid speciation. Our results support the hypothesis that river dynamics promote speciation and reveal that many rainforest taxa are micro-endemic, unrecognized and thus threatened with imminent extinction. We propose that Amazonian hyper-diversity originates in part from fine-scale barrier displacement processes –including river dynamics– which allow small populations to differentiate and disperse into secondary contact.</p>
Data from: Spatial familial networks to infer demographic structure of wild populations
<p class="List1">In social species, reproductive success and rates of dispersal vary among individuals resulting in spatially structured populations. Network analyses of familial relationships may provide insights on how these parameters influence population-level demographic patterns. These methods have however rarely been applied to genetically-derived pedigree data from wild populations.</p> <p class="List1">Here we use parent-offspring relationships to construct familial networks from polygamous boreal woodland caribou (<i>Rangifer tarandus caribou</i>) in Saskatchewan, Canada, to inform recovery efforts. We collected samples from 933 individuals at 15 variable microsatellite loci along with caribou-specific primers for sex identification. Using network measures, we assess the contribution of individual caribou to the population with several centrality measures and then determine which measures are best suited to inform on the population demographic structure. We investigate the centrality of individuals from eighteen different local areas, along with the entire population.</p> <p class="List1">We found substantial differences in centrality of individuals in different local areas, that in turn contributed differently to the full network, highlighting the importance of analyzing networks at different scales. The full network revealed that boreal caribou in Saskatchewan form a complex, interconnected familial network, as the removal of edges with high betweenness did not result in distinct subgroups. Alpha, betweenness, and eccentricity centrality were the most informative measures to characterize the population demographic structure and for spatially identifying areas of highest fitness levels and family cohesion across the range. We found varied levels of dispersal, fitness and cohesion in family groups.</p> <p class="List1"><i>Synthesis and applications</i>: Our results demonstrate the value of different network measures in assessing genetically-derived familial networks. The spatial application of the familial networks identified individuals presenting different fitness levels, short and long-distance dispersing ability across the range in support of population monitoring and recovery efforts.</p>
Data from: Evolutionary transition from a single RNA replicator to a multiple replicator network
<p><span>In prebiotic evolution, self-replicating molecules are believed to have evolved into complex living systems by expanding their information and functions open-endedly. Theoretically, such evolutionary complexification could occur through successive appearance of novel replicators that interact with one another to form replication networks. Here we perform long-term evolution experiments of RNA that replicates using a self-encoded RNA replicase. The RNA diversifies into multiple coexisting host and parasite lineages, whose frequencies in the population initially fluctuate and gradually stabilize. The final population, comprising five RNA lineages, forms a replicator network with diverse interactions, including cooperation to help the replication of all other members. These results support the capability of molecular replicators to spontaneously develop complexity through Darwinian evolution, a critical step for the emergence of life.</span></p>
Data from: Anderson lab experiments from synthesizing the effects of spatial network structure on predator prey dynamics
<p>Predator-prey persistence is thought to be enhanced by spatial heterogeneity. Theory predicts that metacommunity size, spatial connectivity, network synchrony, predator identity, and productivity influence predator-prey persistence, through a variety of mechanisms such as statistical stabilization, colonization-extinction dynamics, and trophic interactions. However, comparative tests and synthesis of the multiple factors and mechanisms across different spatial networks are needed to understand which factors and mechanisms of spatial network structure promote predator-prey persistence. To address this gap between theory and empirical work, we synthesized data from 22 microcosm experiments of protist predator-prey communities differing the productivity, connectivity, and size of spatial habitat structure. Prey time to extinction was better explained by productivity and spatial factors than predator time to extinction. At the local and regional scale, metacommunity size and productivity had positive effects on prey occupancy, whereas connectivity negatively influenced prey occupancy. For predators, metacommunity size and connectivity had positive effects on predator occupancy, network synchrony had negative influences, and productivity showed a hump-shaped relationship with predator occupancy. Further, trophic interactions drove variation in the way species were spatially structured, where the strength and direction of predator and prey occupancy relationships varied among productivity levels and predator-prey combinations. In predator-prey interactions that were stronger, prey occupancy showed negative relationship with predator occupancy regardless of productivity. However, in predator-prey interactions that were weaker, prey occupancy was positively related to predator occupancy at low productivity, and this relationship disappeared at higher productivity treatments where prey occupancy was high regardless of predator occupancy. Predictions from metapopulation theory explained predator occupancy, while prey were better explained by trophic dynamics. Taken together, these results highlight that spatial network structure has a complex, spatially contingent relationship with predator-prey dynamics.</p>
Pollinator richness, pollination networks and diet adjustment along local and landscape gradients of resource diversity
<p>Loss of habitats and native species, introduction of invasive species, and changing climate regimes lead to the homogenization of landscapes and communities, affecting the availability of habitats and resources for economically important guilds, such as pollinators. Understanding how pollinators and their interactions vary along resource diversity gradients at different scales may help to determine their adaptability to current diversity loss related to global change. We used data on 20 plant-pollinator communities along gradients of flower richness (local diversity) and landscape heterogeneity (landscape diversity) to understand how the diversity of resources at local and landscape scales affected: (1) wild pollinator abundance and richness (accounting also for honeybee abundance); (2) the structure of plant-pollinator networks; (3) the proportion of actively selected interactions (those not occurring by neutral processes) and (4) pollinator diet breadth and species' specialization in networks. Wild pollinator abundance was higher overall in flower-rich and heterogeneous habitats, while wild pollinator richness increased with flower richness (more strongly for beetles and wild bees) and decreased with honeybee abundance. Network specialization (H2'), modularity, and functional complementarity were all positively related to floral richness and landscape heterogeneity, indicating niche segregation as the diversity of resources increases at both scales. Flower richness also increased the proportion of actively selected interactions (especially for wild bees and flies), whereas landscape heterogeneity had a weak negative effect on this variable. Overall, network-level metrics responded to larger landscape scales than pollinator-level metrics did. Higher floral richness resulted in a wider taxonomic and functional diet for all the study guilds, while functional diet increased mainly for beetles. Despite this, specialization in networks (d') increased with flower richness for all the study guilds, because pollinator species fed on a narrower subset of plants as communities became richer in species. Our study indicates that pollinators are able to adapt their diet to resource changes at local and landscape scales. However, resource homogenization might lead to poor and generalist pollinator communities, where functionally specialized interactions are lost. This study highlights the importance of including different scales to understand the effects of global change on pollination service through changes in resource diversity.</p>
Automated detection, segmentation and classification of pericardial effusions on chest CT using a deep convolutional neural network
<p>Trainingsdata for chest CT pericard effusion and the finish trained nnU-Net model. </p>
Supplementary material 1 from: Conan A, Fleitz J, Garnier L, Le Brishoual M, Handrich Y, Jumeau J (2022) Effectiveness of wire netting fences to prevent animal access to road infrastructures: an experimental study on small mammals and amphibians. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 271-281. https://doi.org/10.3897/natureconservation.47.71472
Supplementary materials and methods
Supplementary material 1 from: Sur S, Saikia PK, Saikia MK (2022) Speed thrills but kills: A case study on seasonal variation in roadkill mortality on National highway 715 (new) in Kaziranga-Karbi Anglong Landscape, Assam, India. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 87-104. https://doi.org/10.3897/natureconservation.47.73036
Figures S1–S5
Supplementary material 1 from: Paquet J-Y, Swinnen K, Derouaux A, Devos K, Verbelen D (2022) Sensitivity mapping informs mitigation of bird mortality by collision with high-voltage power lines. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 215-233. https://doi.org/10.3897/natureconservation.47.73710
Table S1
Supplementary material 1 from: Ferreira EM, Valerio F, Medinas D, Fernandes N, Craveiro J, Costa P, Silva JP, Carrapato C, Mira A, Santos SM (2022) Assessing behaviour states of a forest carnivore in a road-dominated landscape using Hidden Markov Models. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 155-175. https://doi.org/10.3897/natureconservation.47.72781
Figures S1–S3
Supplementary material 1 from: Cirino DW, Lupinetti-Cunha A, Freitas CH, de Freitas SR (2022) Do the roadkills of different mammal species respond the same way to habitat and matrix? In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 65-85. https://doi.org/10.3897/natureconservation.47.73010
Correlation plot and R script for building and selecting best models
Supplementary material 4 from: Paquet J-Y, Swinnen K, Derouaux A, Devos K, Verbelen D (2022) Sensitivity mapping informs mitigation of bird mortality by collision with high-voltage power lines. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 215-233. https://doi.org/10.3897/natureconservation.47.73710
Figures S1–S17
Network origin-destination data
<p>The first sheet has the tonnage data for each of the 440 routes. Each of these routes is connecting some of the 72 segments, which are entered in Columns B and C.</p> <p>Name of the routes/segments in the original format are provided in the second sheet. Column C has the names of the segments.</p> <p>The third sheet has the cost of dredging each of the segments entered in Column A at different depths that are entered in Columns B to L</p> <p>The fourth sheet has the tonnage capacity of each route entered in Column A after dredging at different depths entered in Columns B to L</p> <p>The fifth sheet has the required number of vessels to meet the demand after dredging each of the routes entered in Column A at different depths entered in Columns B to L</p> <p>The sixth and seventh sheets have water routes entered in Column A that carries some demand to the destination and from the origin</p> <p>The eighth sheet has the freight demand on each OD entered in Column A</p> <p> </p>
Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)
<p>This repository includes the input and output dataset, and python scripts used in the article, "Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)," of J. Chem. Phys. 156, 154108 (2022) [DOI: <a href="http://doi.org/10.1063/5.0087310">10.1063/5.0087310</a>] The repository also includes source data of figures in the article.</p>
Daily profiles (2020) of load of a rural synthetic electricity distribution network from UK – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a synthetic UK Distribution network located in a rural area (Exchange St.) as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 6.6 kV, has 66 nodes and 66 branches. The auxiliary load data comprises 1 typical winter day gathering the active and reactive consumption at each node of the network in intervals of 1 hour.</p>
Daily profiles (2020) of load of a electricity distribution network (40 nodes) from Croatia – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Distribution network, totally anonymized, located in Croatia as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 400 V, 35 kV, 20 kV and 10 kV, has 40 nodes, 59 branches and 14 generators modelled as negative loads. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at specific feeders of the network in intervals of 15 minutes. Besides, a single line diagram of the network is also provided.</p>
Daily profiles (2020) of load of a electricity distribution network (25 nodes) from Croatia – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Distribution network, totally anonymized, located in Croatia as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 110 kV, 35 kV, 20 kV and 10 kV, has 25 nodes, 34 branches and 107 generators modelled as negative loads. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at specific feeders of the network in intervals of 15 minutes. Besides, a single line diagram of the network is also provided.</p>
Daily profiles (2020) of load of a electricity distribution network (24 nodes) from Croatia – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Distribution network, totally anonymized, located in Croatia as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 110 kV, 35 kV, 10 kV and 400 V, has 24 nodes, 32 branches and 2 generators modelled as negative loads. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at specific feeders of the network in intervals of 15 minutes. Besides, a single line diagram of the network is also provided.</p>
Daily profiles (2020) of load of a electricity distribution network (26 nodes) from Croatia – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Distribution network, totally anonymized, located in Croatia as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 220 kV, 110 kV, 35 kV, 10 kV and 400 V, has 26 nodes, 32 branches and 97 generators modelled as negative loads. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at specific feeders of the network in intervals of 15 minutes. Besides, a single line diagram of the network is also provided.</p>
An urban synthetic electricity distribution network from Spain, third version
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about the third version of a synthetic Distribution network from Spain built using a Reference Network Model. It models three high to medium voltage substations, high and medium voltage power lines. Includes a network model (MatPower format) with a “snapshot” or “steady-state” at a given time with a converged power flow solution. Furthermore, it also comprises 5 auxiliary text files with detailed information about buses, branches, switching devices, coordinates and power lines. Besides that, a graphical representation of the synthetic distribution network is also provided along with a “readme” help file.</p>
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.