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73
datasets available to search
ShareScore release 0.9.0
Dataset results
73 results for “Network robustness”
Data from: The robustness and restoration of a network of ecological networks
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Data from: Selective logging in tropical forests decreases the robustness of liana-tree interaction networks to the loss of host tree species
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Data from: Removing interactions, rather than species, casts doubt on the high robustness of pollination networks
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Data from: The impact of gene expression variation on robustness and evolvability of a developmental gene regulatory network
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Robustness of a meta‐network to alternative habitat loss scenarios
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Data from: Learning and robustness to catch-and-release fishing in a shark social network
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Data and code from: Network theory predicts ecosystem robustness across environmental conditions
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Data from: Resilience or robustness: identifying topological vulnerabilities in rail networks
Many critical infrastructure systems have network structure and are under stress. Despite their national importance, the complexity of large-scale transport networks means we do not fully understand their vulnerabilities to cascade failures. The research in this paper examines the interdependent rail networks in Greater London and surrounding commuter area. We focus on the morning commuter hours, where the system is under the most demand stress. There is increasing evidence that the topological shape of the network plays an important role in dynamic cascades. Here, we examine whether the different topological measures of resilience (stability) or robustness (failure) are more appropriate for understanding poor railway performance. The results show that resilience and not robustness has a strong correlation to the consumer experience statistics. Our results are a way of describing the complexity of cascade dynamics on networks without the involvement of detailed agent-based-models, showing that cascade effects are more responsible for poor performance than failures. The network science analysis hints at pathways towards making the network structure more resilient by reducing feedback loops.
Robust and fast post-processing of single-shot spin qubit detection events with a neural network
<p>Dataset to the paper 'Robust and fast post-processing of single-shot spin qubit detection events with a neural network'</p>
Optimal network sizes for most robust Turing patterns
<p>Dataset for Figures 3 and 4.</p>
Data from: Cold adaptation shapes the robustness of metabolic networks in Drosophila melanogaster.
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Data from: Resilience or robustness: identifying topological vulnerabilities in rail networks
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Data from: Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks
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Data from: Food dissemination in ants: robustness of the trophallactic network against resource quality
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Mutation of Vsx genes in zebrafish highlights the robustness of the retinal specification network [RNA-Seq]
GEO Series GSE189737. Danio rerio. 9 samples. Type: Expression profiling by high throughput sequencing.
Transcriptional network analysis of PTEN protein-deficient prostate tumors reveals robust stromal reprogramming and signs of senescent paracrine communication
GEO Series GSE288484. Homo sapiens. 197 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.
Mutation of Vsx genes in zebrafish highlights the robustness of the retinal specification network
GEO Series GSE189739. Danio rerio. 13 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
[LS2N_IPI_DisFER] Comparing the Robustness of Humans and Deep Neural Networks on Facial Expression Recognition
<h2>DisFER</h2> <p>Distorted-FER (DisFER), a new facial expression recognition (FER) dataset composed of a wide number of distorted images of faces.</p> <p> </p> <h2>Materials and Methods</h2> <h4>Dataset</h4> <div>The source images used in our experiment come from the Facial Expression Recognition 2013 (FER-2013) dataset [1]. This dataset was firstly introduced in 2013 at the International Conference on Machine Learning, and has been used in a large number of research works since then, as it encompasses naturalistic conditions and challenges. This dataset consists of 35,887 images of faces in 48 × 48 format, collected thanks to a Google search. Human accuracy on FER-2013 was estimated by its authors around 65.5% [1].</div> <div>To build the Distorted-FER (DisFER) dataset, we randomly selected, from FER-2013, twelve images per basic emotion, as defined by Ekman [2] (i.e., anger, disgust, fear, happiness, neutral, sadness, and surprise). This yields a total of 84 source images. Each original stimulus was then distorted using three different types of distortions, i.e., Gaussian blur (GB), Gaussian noise (GN), and salt-and-pepper noise (SP). Each distortion was applied at distinct levels: three standard deviation values were tested for GB, i.e., 0.8, 1.1, and 1.4; similarly for GN with standard deviation values equal to 10, 20, and 30; while probability levels of 0.02, 0.04, and 0.06 were chosen for SP; corresponding to low, medium, and high distortions, respectively. </div> <div> </div> <div> <h4>Crowdsourcing Experiment</h4> <div>In order to collect as many votes as possible on our dataset, and because rating 840 images is time-consuming and can be extremely tiring for a single participant, we decided to set up a crowdsourcing experiment. Such experiments indeed allow the conduct of large-scale subjective tests with reduced costs and efforts.</div> <div>The DisFER dataset was therefore split into twenty-one playlists of forty images each, with a view to keep the tests as fast as possible—as crowdsourcing experiments should not last more than ten minutes or so. Playlists were carefully designed to contain the same numbers of images of a given configuration (i.e., emotion, distortion types, and distortion levels). Among a playlist, images were randomly displayed to participants.</div> <div>Each participant was asked to choose which emotion (i.e., anger, disgust, fear, happiness, neutral, sadness, or surprise) they recognized in the displayed image. No time constraint was imposed on participants to fulfill the task.</div> <div> </div> <div>A total of 1051 participants (including 50% of females) were recruited using the Prolific platform [3]. Prolific takes into consideration researchers’ needs by maintaining a subject recruitment process that is similar to that of a laboratory experiment. Indeed, participants are fully informed that they are being recruited for a research study. Consequently, this platform allows researchers to eliminate ethical concerns, and it further improves the reliability of collected data.</div> <div> </div> Participants were aged between 19 and 75 years old (with a mean of 30±8.53 -- note that three participants did not wish to respond). Twenty playlists out of twenty-one were entirely watched and rated by fifty distinct participants, whereas one playlist was watched and evaluated by fifty-one participants.</div> <div> </div> <div> <h2>References</h2> </div> <div>[1] Goodfellow, I.J.; Erhan, D.; Carrier, P.L.; Courville, A. Challenges in Representation Learning: A Report on Three Machine Learning Contests. In Proceedings of the Neural Information Processing, Daegu, South Korea, 3–7 November 2013; Springer: Berlin/Heidelberg, Germany, 2013; pp. 117–124</div> <div>[2] Ekman, P. An argument for basic emotions. Cogn. Emot. <strong>1992</strong>, 6, 169–200</div> <div>[3] https://www.prolific.com/</div> <div> </div>
Adaptation to diverse transcription factor knockouts reveals robustness of transcription regulatory network
GEO Series GSE266148. Escherichia coli str. K-12 substr. MG1655; Escherichia coli BW25113. 46 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.