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472 results for “Continuous data”
Figure Data for "Continuous Estimates of Glacier Mass Balance in High Mountain Asia Based on ICESat-1,2 and GRACE/GRACE Follow-On"
<p>Figure Data for "Continuous Estimates of Glacier Mass Balance in High Mountain Asia Based on ICESat-1,2 and GRACE/GRACE Follow-On" Data" </p>
Dataset: Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks
<p>Dataset for the paper E. Rinaldi, M. González Lastre, S. García Herreros, S. Ahmed, M. Khanahmadi, F. Nori, and C. Sánchez Muñoz (2023), <a href="https://arxiv.org/abs/2310.02309">¨Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks¨, arxiv: 2310.02309</a></p> <p>This dataset can be used to populate the [datapath] folder in the repository <strong>ParamEst-NN</strong> (<a href="https://github.com/CarlosSMWolff/ParamEst-NN">github.com/CarlosSMWolff/ParamEst-NN</a> ) and reproduce the results shown in the paper.</p> <p>The dataset consist of four folders:</p> <ol> <li><strong>Training trajectories.</strong> Records of quantum-jump trajectories simulated with the Monte-Carlo solver of the <a href="https://qutip.org/">QuTiP</a> library, used to train neural networks for the problem of quantum parameter estimation. The records consist of time delays between quantum jumps.</li> <li><strong>Models. </strong>Models trained with the training trajectories provided, and used to obtain the results shown in the paper.</li> <li><strong>Validation trajectories. </strong>Trajectories used to benchmark the trained models. For the 2D case, the same trajectories are provided as a single .npy file, and split in 10 separated batches inside a ```batches``` folder. These are the batches that we used to generate Bayesian estimations on a cluster using nested sampling (see README file of the <a href="http://github.com/CarlosSMWolff/ParamEst-NN">repository</a>).</li> <li><strong>Cached results. </strong>Here we provide pre-computed Bayesian estimations for the 2D multi-parameter estimation case using nested sampling.</li> </ol> <p> </p> <p><em>E.R. was supported by Nippon Telegraph and Telephone Corporation (NTT) Research during the early stages of this work.<br> C.S.M. acknowledges that the project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No.847648, with fellowship code LCF/BQ/PI20/11760026, and financial support from the MCINN project PID2021-126964OB-I00 (QENIGMA) and the Proyecto Sinérgico CAM 2020 Y2020/TCS- 6545 (NanoQuCo-CM).</em></p>
Data from: Semi-natural habitat, but not aphid amount or continuity, predicts lady beetle abundance across agricultural landscapes
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Data from: When discrete characters are wanting: Continuous character integration under the phylospecies concept informs the revision of the Australian land snail <em>Thersites</em> (Eupulmonata, Camaenidae)
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Habitats as predictors in species distribution models: Shall we use continuous or binary data?
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Data and code from: The continuity-contiguity problem in fragmentation-biodiversity research
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Data from: Refinement of the Antarctic fur seal (Arctocephalus gazella) reference genome increases continuity and completeness
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Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey
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Data from: Experimental study on the impact of continuous and dynamic artificial light at night on moths originating from different skyglow conditions
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Data from: Linking continuous and discrete models of cell birth and migration
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Assembled file of one minute averages for high resolution surface meteorological (Met) and sea water intake (SWI) data from continuous underway measurements from CCE LTER process cruises in the CCE region, 2006 - 2019.
As the research vessel is underway for the duration of a CCE Process Cruise (since 2006, ongoing), 30 parameters are continuously measured regarding the oceanographic surface and atmospheric and navigational environment of the vessel, along the ship's trackline in the CCE region.
Continuous Revegetation Survey Data, Watershed 5, Hubbard Brook Experimental Forest, 1984 - 1989
During the first five years after harvest of Watershed 5, detailed measurements of recovering vegetation were conducted on a large set of permanent plots. Results are summarized in Hughes and Fahey (1991) and Pu et al. (1993). Measurements on these plots were discontinued in 1989 and replaced by measurements on a smaller set of larger plots. Those data are not included in this dataset. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Data supplement for "Adaptive stochastic continuation with a modified lifting procedure applied to complex systems"
<p>This dataset contains the data and source files for the diagrams of the following preprint:</p> <p><em>Clemens Willers, Uwe Thiele, Andrew J. Archer, David J. B. Lloyd, and Oliver Kamps<br> Adaptive stochastic continuation with a modified lifting procedure applied to complex systems<br> arXiv preprint arXiv:2002.01705, 2020 </em></p> <p>Please follow the instructions given in 'Readme.txt'.</p>
Data from: A Bayesian approach for inferring the impact of a discrete character on rates of continuous-character evolution in the presence of background-rate variation
Understanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; e.g., does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus non-reef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a "straw-man" effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation ("background-rate variation"). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae).
Data from: Inferring continuous and discrete population genetic structure across space
A classic problem in population genetics is the characterization of discrete population structure in the presence of continuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignment methods may incorrectly ascribe differentiation due to continuous processes (e.g., geographic isolation by distance) to discrete processes, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of current methods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributed populations. Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns of population structure. The method estimates ancestry proportions for each sample from a set of two-dimensional population layers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addresses the "clines versus clusters" problem in modeling population genetic variation, and remedies some of the overfitting to which nonspatial models are prone. The method produces useful descriptions of structure in genetic relatedness in situations where separated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstrate the utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America.
Data for: Including Performance Benchmarks into Continuous Integration to Enable DevOps
<p>The DevOps movement intends to improve communication, collaboration, and integration between software developers (Dev) and IT operations professionals (Ops). Automation of software quality assurance is key to DevOps success. We present how automated performance benchmarks may be included into continuous integration. As an example, we report on regression benchmarks for application monitoring frameworks and illustrate the inclusion of automated benchmarks into continuous integration setups.</p> <p>This dataset supplements the paper and contains the raw experimental data for each experiment.</p>
Additional materials used in the paper "Towards Continuous Scientific Data Analysis and Hypothesis Evolution" on the Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
<p>This bundle contains a web page describing the materials used in the evaluation of the paper, along with references to the software and datasets, provenance metadata and workflows used. All the scripts and descriptions are included as well.</p>
Dataset: A continuous open source data collection platform for architectural technical debt assessment
<p>The dataset and replication package of the study "A continuous open source data collection platform for architectural technical debt assessment".</p> <p> </p> <p>Abstract</p> <p>Architectural decisions are the most important source of technical debt. In recent years, researchers spent an increasing amount of effort investigating this specific category of technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.</p> <p> </p> <p>However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.</p> <p>In response to this concern, researchers strive to expand the scope of their study by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.</p> <p> </p> <p>To address this issue, this paper presents our initial attempt at tackling this problem and enabling researchers to study architectural smells at large scale, a well-known indicator of architectural technical debt. Specifically, we introduce a novel approach to data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets using Arcan, a tool for architectural smells detection (or any other tool).</p> <p>Finally, we present the publicly-available dataset resulting from the first three months of execution of the pipeline, that includes over 30,000 analysed commits and releases from over 10,000 open source GitHub projects written in 5 different programming languages and amounting to over a billion of lines of code analysed.</p>
Global continuous 0.05 degree atmospheric carbon dioxide dataset (GCXCO2) based OCO-2 satellite, CAMS and CarbonTracker simulation data from 2000 to 2020
<p>This dataset provides global seamless 8-day XCO2 (column-averaged CO2 dry air mole fraction) with a spatial resolution of 0.05 degree from 2000 to 2020. The unit is ppm. The detailed process and product validation accuracy can be found in our paper at https://doi.org/10.1016/j.scitotenv.2024.177051</p>
Data from: Continuous variation in an aposematic pattern affects background contrast, but is not associated with differences in microhabitat use
<p>Variation in aposematic signals was once predicted to be rare, yet in recent years it has become increasingly well-documented. Despite increases in the frequency with which polytypism and polymorphism have been suggested to occur, population-wide variance is rarely quantified. We comprehensively sampled a subpopulation of the poison frog <em>Oophaga sylvatica</em>, a species which is polytypic across its distribution and also shows considerable within-population polymorphism. On one hand, color pattern polymorphism could be the result of multifarious selection acting to balance different signaling functions and leading to the evolution of discrete sub-morphs which occupy different fitness peaks. Alternatively, variance could simply be due to relaxed selection, where variation would be predicted to be continuous. We used visual modeling of conspecific and heterospecific observers to quantify the extent of within-population phenotypic variation and assess whether this variation produced distinct signals. We found that, despite considerable color pattern variation, variance could not be partitioned into distinct groups, but rather all viewers would be likely to perceive variation as continuous. Similarly, we found no evidence that frog color pattern contrast was either enhanced or diminished in the frogs' chosen microhabitats compared to alternative patches in which conspecifics were observed. Within population phenotypic variance therefore does not seem to be indicative of strong selection towards multiple signaling strategies, but rather pattern divergence has likely arisen due to weak purifying selection, or neutral processes, on a signal that is highly salient to both conspecifics and predators.</p>
ScienceDex guides
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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.