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33 results for “distributed generation”
Data from: Variability in DNA methylation and generational plasticity in the Lombardy poplar, a single genotype worldwide distributed since the eighteenth century
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Investigating Distributional Robustness: Semantic Perturbations Using Generative Models (ImageNet Examples)
<p>This dataset contains examples of semantically-perturbed images, for NeurIPS 2020 submission #4915.</p> <p>There are four top-level folders, each containing results for semantic perturbations restricted to adjust the activation values at only certain layers of the BigGAN generative network: the first six layers, the middle six layers, the last six layers, and all layers.</p> <p>Within each top-level folder, there are a further four folders, each corresponding to a classifier neural network whose evaluation is being evaluated. These are EfficientNet-B4 with NoisyStudent training [1], the standard ResNet50 [2], a pixel-perturbation-robust ResNet50 trained by Engstrom et al. [3] and another trained by Wong et al. [4], using their "Fast is better than free" technique.</p> <p>Within each of these, there are many folders, named 'version_$N'. Each one of these contains three images: the unperturbed generated image, named unpert_generated_x_grid_0.png; the semantically-perturbed generated image, named generated_x_grid_0.png; and an image named semantic_pert_diffs_grid_0.png showing the pixel-space effect of the semantic perturbation, that is, the diff between the perturbed and unperturbed images. Note that if the perturbed and unperturbed images are identical, and the classifier misclassifies the unperturbed images, and so we skip this example.</p> <p>Along with the 'version_$N' folders containing the images, there exists a file for each classifier named results.json. Each top-level item in this JSON file corresponds to one 'version_$N' example. There are 5 attributes: 'label', indicating the target label of the unperturbed image; 'magnitude', which gives the magnitude of the semantic perturbation found; 'skipped_cla', which is 1 if the example is skipped because the classifier did not correctly classify the unperturbed image; 'skipped_judge', which is 1 if the human judged that the unperturbed image did not match its label, so this example is skipped; and 'pert_judgement', which is 1 if the semantically-perturbed image is judged by the human to be of the same class as the unperturbed image. These judgements on these images were used to construct the main graphs in the paper.</p> <p> </p> <p>[1] Qizhe Xie, Eduard H. Hovy, Minh-Thang Luong, and Quoc V. Le. Self-training with Noisy Student improves ImageNet classification. CoRR, abs/1911.04252, 2019. URL http://arxiv.org/abs/1911.04252.</p> <p>[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 770–778. IEEE Computer Society, 2016. doi: 10.1109/CVPR.2016.90. URL https://doi.org/10.1109/CVPR.2016.90.</p> <p>[3] Logan Engstrom, Andrew Ilyas, Shibani Santurkar, and Dimitris Tsipras. Robustness (Python library), 2019. URL ttps://github.com/MadryLab/robustness.</p> <p>[4] Eric Wong, Leslie Rice, and J. Zico Kolter. Fast is better than free: Revisiting adversarial training. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020. URL<br> https://openreview.net/forum?id=BJx040EFvH.</p>
Figure 4. Seasonal range generated from a in Demographic characteristics, seasonal range and habitat topography of Balkan chamois population in its southernmost limit of its distribution (Giona mountain, Greece)
Figure 4. Seasonal range generated from a Fixed Kernel Density Estimator (FKDE) (95% probability) and respective core areas of Balkan chamois in Giona Mt for (A) winter, (B) spring, (C) summer and (D) autumn. In the upper right corner the diagram presents the delineation of the probability of species occurrence within the core area.
Daily profiles (2020) of load, generation and flexibility of a rural distribution network from Portugal – 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 Portuguese Distribution test network, totally anonymized, located in a rural area 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 10 kV, 30 kV and 60 kV, has 103 nodes (46 with consumption) and 102 branches. Both auxiliary load data and auxiliary generation data comprise 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 each node of the network in intervals of 15 minutes. The auxiliary flexibility files comprises 4 typical days (business day of summer, Sunday of summer, business day of winter and Sunday of winter) gathering the upward and downward active power of flexibility at each node of the network in intervals of 15 minutes. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly. For the sake of coherence, each generation file and flexibility file should be used together with the respective load data file.</p>
Daily profiles (2050) of load and generation of a semi-urban electricity distribution network from Portugal – 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 Portuguese Distribution test network, totally anonymized, located in a semi-urban area as in 2050. 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 15 kV, 30 kV and 60 kV, has 229 nodes (118 with consumption) and 229 branches. 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 each node of the network in intervals of 15 minutes. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly. For the sake of coherence, each generation file should be used together with the respective load data file.</p>
Daily profiles (2020) of load and generation of a semi-urban electricity distribution network from Portugal – 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 Portuguese Distribution test network, totally anonymized, located in a semi-urban area 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 15 kV, 30 kV and 60 kV, has 229 nodes (118 with consumption) and 229 branches. 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 each node of the network in intervals of 15 minutes. The auxiliary generation files comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive generation at each node with production in intervals of 15 minutes. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly. For the sake of coherence, each generation file should be used together with the respective load data file.</p>
Daily profiles (2040) of load and generation of a semi-urban electricity distribution network from Portugal – 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 Portuguese Distribution test network, totally anonymized, located in a semi-urban area as in 2040. 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 15 kV, 30 kV and 60 kV, has 229 nodes (118 with consumption) and 229 branches. 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 each node of the network in intervals of 15 minutes. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly. For the sake of coherence, each generation file should be used together with the respective load data file.</p>
Linked collectors and determiners for: First detection of the adventive large rove beetle Ocypus nitens (Schrank) in Canada and an update of its Nearctic distribution using data generated by the public.
Natural history specimen data linked to collectors and determiners held within, "First detection of the adventive large rove beetle Ocypus nitens (Schrank) in Canada and an update of its Nearctic distribution using data generated by the public". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e5e13b4c-e903-4507-9f00-47d764c2fe16">https://bionomia.net/dataset/e5e13b4c-e903-4507-9f00-47d764c2fe16</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e5e13b4c-e903-4507-9f00-47d764c2fe16">https://gbif.org/dataset/e5e13b4c-e903-4507-9f00-47d764c2fe16</a>. Formatted as a Frictionless Data package.
Data from: Distributed rhythm generators underlie Caenorhabditis elegans forward locomotion
Coordinated rhythmic movements are ubiquitous in animal behavior. In many organisms, chains of neural oscillators underlie the generation of these rhythms. In C. elegans, locomotor wave generation has been poorly understood; in particular, it is unclear where in the circuit rhythms are generated, and whether there exists more than one such generator. We used optogenetic and ablation experiments to probe the nature of rhythm generation in the locomotor circuit. We found that multiple sections of forward locomotor circuitry are capable of independently generating rhythms. By perturbing different components of the motor circuit, we localize the source of secondary rhythms to cholinergic motor neurons in the midbody. Using rhythmic optogenetic perturbation, we demonstrate bidirectional entrainment of oscillations between different body regions. These results show that, as in many other vertebrates and invertebrates, the C. elegans motor circuit contains multiple oscillators that coordinate activity to generate behavior.
Data from: Distributed rhythm generators underlie Caenorhabditis elegans forward locomotion
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Data used to generate results for Meireles et al. 2024. The Miners of South America: impacts of climate change on the distribution of Geositta miners along elevational gradients
<p><span>Data used to generate results for Meireles et al. 2024. The Miners of South America: impacts of climate change on the distribution of <em>Geositta miners</em> along elevational gradients </span></p> <p><span> </span><span>Here we included all the data used in this paper.</span></p> <p><strong><span>Table1</span></strong><span>. Occurrence records for the seven species of <em>Geositta</em> miners, their elevation and climate suitability values per record for the present and future in different climate models (GCM: MPI-ESM1-2-HR and MRI-ESM2-0) and different scenarios (Optimistic: ssp245 and Pessimistic: ssp585).</span></p>
DeepStruc: Towards structure solution from pair distribution function data using deep generative models
<p>XYZ files, PDF dataset and XGB model to use MetalFinder which is one of the baseline models in the paper.</p>
Supplementary Material for 'Generation Z Doctors' Preferences Towards Pharmaceutical Marketing': Survey Instrument and Response Distribution
<p>This supplementary material accompanies the study 'Generation Z Doctors and the Future of Pharma Marketing in India.' It includes the complete survey instrument used to gather insights from Generation Z doctors, as well as detailed response distribution tables. The material provides additional data that supports the quantitative and qualitative analyses presented in the main manuscript, offering a comprehensive understanding of the engagement preferences, decision-making paradigms, and ethical considerations influencing Generation Z doctors' interactions with pharmaceutical marketing. This dataset is intended to facilitate further research and enhance the transparency of the study's findings.</p>
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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.