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562 results for “bets”
CS:GO Bets Time series
<p>This is a dataset made taking the data from hltv about the odds which come from different Online Gambling Companies (OGCs). It shows the bet data, the result of the match and the timestamp.<br> </p>
Data from: Bet hedging is not sufficient to explain germination patterns of a winter annual plant
<div> <div> <div> <p>Bet hedging consists of life history strategies that buffer against environmental variability by trading off immediate and long-term fitness. Delayed germination in annual plants is a classic example of bet hedging and is often invoked to explain low germination fractions. We examined whether bet hedging explains low and variable germination fractions among 20 populations of the winter annual plant <em>Clarkia</em> <em>xantiana</em> ssp. <em>xantiana</em> that experience substantial variation in reproductive success among years. Leveraging 15 years of demographic monitoring and 3 years of field germination experiments, we assessed the fitness consequences of seed banks and compared optimal germination fractions from a density-independent bet-hedging model to observed germination fractions. We did not find consistent evidence of bet hedging or the expected trade-off between arithmetic and geometric mean fitness, though delayed germination increased long-term fitness in 7 of 20 populations. Optimal germination fractions were 2 to 5 times higher than observed germination fractions, and among-population variation in germination fractions was not correlated with risks across the life cycle. Our comprehensive test suggests that bet hedging is insufficient to explain the observed germination patterns. Understanding variation in germination strategies will likely require integrating bet hedging with complementary forces shaping the evolution of delayed germination.</p> </div> </div> </div>
Data from: Bet hedging is not sufficient to explain germination patterns of a winter annual plant
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Multiple-batch spawning as a bet-hedging strategy in highly stochastic environments: an exploratory analysis of Atlantic cod
<p><span><span><span><span><span><span><span><span><span><span><span>Stochastic environments shape life-history traits and can promote selection for risk-spreading strategies, such as bet-hedging. Although the strategy has often been hypothesised to exist for various species, empirical tests providing firm evidence have been rare, mainly due to the challenge in tracking fitness across generations. Here, we take a 'proof of principle' approach to explore whether the reproductive strategy of multiple-batch spawning constitutes a bet-hedging. We used Atlantic cod (<i>Gadus morhua</i>) as the study species and parameterised an eco-evolutionary model, using empirical data on size-related reproductive and survival traits. To evaluate the fitness benefits of multiple-batch spawning (within a single breeding period), the mechanistic model separately simulated multiple-batch and single-batch spawning populations under temporally varying environments. We followed the arithmetic and geometric mean fitness associated with both strategies and quantified the mean changes in fitness under several environmental stochasticity levels. We found that, by spreading the environmental risk among batches, multiple-batch spawning increases fitness under fluctuating environmental conditions. The multiple-batch spawning trait is, thus, advantageous and acts as a bet-hedging strategy when the environment is exceptionally unpredictable. Our research identifies an analytically flexible, stochastic, life-history modelling approach to explore the fitness consequences of a risk-spreading strategy and elucidates the importance of evolutionary applications to life-history diversity. </span></span></span></span></span></span></span></span></span></span></span></p>
Diapause is not selected as a bet-hedging strategy in insects: a meta-analysis of reaction norm shapes
<p>Many organisms escape from lethal climatological conditions by entering a resistant resting stage called diapause, and it is essential that this strategy remains optimally timed with seasonal change. Climate change therefore exerts selection pressure on phenology, which is expected to cause the evolution of mean diapause timing, but also phenotypic plasticity and bet-hedging strategies. Especially the latter as a strategy to cope with unpredictability is so far little considered in the context of climate change, and it is unknown whether it can readily evolve.</p> <p>Contemporary patterns of phenological strategies across a geographic range may provide information about their evolvability. We thus extracted 458 diapause reaction norms from 60 studies. First, we correlated mean diapause timing with mean winter onset. Then we partitioned the reaction norm variance into a temporal component (phenotypic plasticity) and among-offspring variance (diversified bet-hedging) and correlated this variance composition with predictability of winter onset. Contrary to our expectation, mean diapause timing correlated only weakly with mean winter onset, as populations at high latitudes failed to track early onsets. Variance among offspring was also limited and correlated only weakly with environmental predictability, indicating little scope for bet-hedging. We conclude that constraints may limit the evolution of phenology in a rapidly changing climate.</p>
Data for: Variability in thermal and phototactic preferences in Drosophila may reflect an adaptive bet-hedging strategy
<p>Data and code for "Variability in thermal and phototactic preferences in <em>Drosophila</em> may reflect an adaptive bet-hedging strategy." </p> <p>.zip file contains .mats with data, a LabView .vi for the acquisition instrument, and Matlab .m analysis and visualization scripts. </p> <p>The included index.html file acts as a readme and contains descriptions of all other files and links.</p>
GR-SCBET-Y03Y07 Soccer bets and teams DB from coupons, Greece, Aug.2003 - Mar.2007
<p>========================================================</p> <p> Dataset: GR-SCBET-Y03Y07</p> <p> Soccer bets and teams DB from coupons<br /> Greece, Aug.2003 - Mar.2007<br /> <br /> Release Notes</p> <p> Copyright (c) 2016 by Harris V. Georgiou</p> <p>========================================================<br /> Release: Aug 3, 2016</p> <p> - Version: 1.1a<br /> - Format: .xls<br /> ========================================================</p> <p><br /> This file contains important information about the current version of the dataset package. Downloading and using this material hints that you accept the EULA/Terms-of-Use (please read carefully).</p> <p>We welcome your comments and suggestions.</p> <p>_______________________________________________<br /> WHAT'S IN THIS PACKAGE?</p> <p>- Overview<br /> - Available file formats<br /> - Files and Datasets<br /> - License Agreement</p> <p>_______________________________________________<br /> OVERVIEW</p> <p>Athletic betting and successful risk management is one of the most challenging tasks in Machine Learning and A.I. in general. With only limited information and very sparsely distributed data, an algorithm has to model the teams' competency and predict outcomes of games, even when two teams face each other for the first time.</p> <p>This package contains two main datasets, generated from soccer betting coupons in Greece throughout a 3.5 year period and including every game that was included for any championship and any team. The first dataset corresponds to 43.758 matches with complete odds, final result and goals. The second is team-based statistics from these matches for a total of 1.126 teams from various championships (European and other).</p> <p>These datasets can be used as benchmark training sets for odds-oriented or team-oriented predictive models and algorithms, relevant not only to athletic betting but also other contexts, lile in in financial risk management. In the case of team-oriented modeling, special ELO-like ranking algorithms can be developed and tested against the official team/country rankings, as in UEFA and FIFA, but tailored to specific teams or championships, in order to evaluate and fine-tune the models.</p> <p>Note: Team names are in Greek, but they can be used as-is for proper referencing to unique IDs, i.e., practically they can be ignored.</p> <p>_______________________________________________<br /> AVAILABLE FILE FORMATS</p> <p>The datasets are available in the following formats (included):</p> <p>*.xls : MS-Excel/LibreOffice worksheet data file (exportable)</p> <p> </p>
BET surfaces of selected CNTs
<p>BET surfaces measured for various commercially available carbon nanotubes.</p>
Bet Alpha Synagogue
Bet Alpha is a sixth-century CE synagogue located at the foot of Mt. Gilboa in the Galilee of Israel. The Mosaic has three primary sections in addition to a dedicatory insciption, mentioning the name of the mosasist who completed the work. The three scenes are: 1. The binding of Isaac 2. The four seasons, zodiac, and Helios (sun god) riding in chariot 3. The ark of the covenant flanked by two menorahs. Source: Objaverse 1.0 / Sketchfab
Supplementary materials for "Forecasting Soccer Matches With Betting Odds: A Tale of Two Markets"
<p>Replication package for International Journal of Forecasting article "Forecasting Soccer Matches With Betting Odds: A Tale of Two Markets".</p>
Results of Bet-and-Run Strategies with Different Decision Makers on the Traveling Salesman Problem and the Minimum Vertex Cover Problem
<p><em><strong>Results of Bet-and-Run Strategies with Different Decision Makers on the Traveling Salesman Problem and the Minimum Vertex Cover Problem</strong></em></p> <p><strong>1. Introduction</strong></p> <p>In this repository, we provide the implementation and results of an improved generic Bet-and-Run strategy for black-box optimization.<br> The goal our new Bet-and-Run method is to obtain the best possible results within a given time budget <em>T</em> using a given black-box optimization algorithm.<br> If no prior knowledge about problem features and algorithm behavior is available, the question about how to use the time budget most efficiently arises. We propose to first start <em>n>=1</em> independent runs of the algorithm during an initialization budget <em>T1<T</em>, pausing these runs, then apply a decision maker <em>D</em> to choose <em>1<=m<n</em> runs from them (consuming <em>T2>=0</em> time units in doing so), and then continuing these runs for the remaining <em>T3=T-T1-T2</em> time units.</p> <p>In previous bet-and-run strategies, the decision maker <em>currentBest</em> would simply select the run with the best-so-far results at negligible time.<br> We propose using more advanced methods and test several different approaches, including neural networks trained or polynomials fitted on the current trace of the algorithm to predict which run may yield the best results if granted the remaining budget.<br> Applying this implementation to run "virtual experiments," one can find that this approach can yield better results than the previous methods, but also find that the `currentBest` method is a very reliable and robust baseline approach.</p> <p>Here you can find the results of such experiments on the Traveling Salesman Problem and the Minimum Vertex Cover Problem. Both betAndRun_tsp.tar.xz and betAndRun_vertex_cover.tar.xz are extracted in the scale of 30 GiB of size.</p> <p><strong>2. Copyright</strong></p> <p>The source code in this repository is under MIT License and is published in the most recent version at http://github.com/thomasWeise/betAndRun, while the results are under the Creative Commons Attribution 4.0 License.</p> <p>The code on bet-and-run (mainly under cn.edu.hfuu.iao.betAndRun) is jointly developed by Dr. Thomas Weise (http://iao.hfuu.edu.cn), tweise@hfuu.edu.cn, tweise@gmx.de) and Dr. Markus Wagner (http://cs.adelaide.edu.au/~markus/, markus.wagner@adelaide.edu.au).</p> <p>The <em>jpack</em> (http://github.com/marmakoide/jpack) code for Artificial Neural Networks, Linear Algebra, and Evolution Strategies (e.g., CMA-ES) has originally been developed by Dr. Alexandre Devert (http://www.marmakoide.org, marmakoide@hotmail.fr, and http://github.com/marmakoide), who kindly granted us the permission to include it in our repository. The code published here is a slightly modified version of his code, but the copyright and authorship remains entirely with Dr. Devert, who provides it under the MIT license at GitHub under http://github.com/marmakoide/jpack. Please contact Dr. Devert for any questions, in particular regarding licensing and (re-)distribution.</p>
Quercetin enhances the anti-tumor effects of BET inhibitors Supplementary Figures
<p>Supplementary Figures to "Quercetin enhances the anti-tumor effects of BET inhibitors" manuscript</p>
Targeted protein degradation reveals BET bromodomains as the cellular target of Hedgehog Pathway Inhibitor-1
<p>Raw experimental data for the manuscript entitled 'Targeted protein degradation reveals BET bromodomains as the cellular target of Hedgehog Pathway Inhibitor-1'. This includes the data underlying all figures, DESeq2 files, modeling and MD simulations, and chemical compound characterization (NMR, LC-MS, HRMS). Also, the custom Matlab script to analyse cilium intensity from microscopy images is included (cilia-track_v4.0).</p>
A Trial Comparing Two Therapies: Basal Insulin/Glargine, Exenatide and Metformin Therapy (BET) or Basal Insulin/Glargine, Bolus Insulin Lispro and Metformin Therapy (BBT) in Subjects With Type 2 Diabe
ClinicalTrials.gov study NCT00960661. IPD Sharing: Not stated. Countries: 18. Publications: 1.
ACTIV-5 / Big Effect Trial (BET-A) for the Treatment of COVID-19
ClinicalTrials.gov study NCT04583956. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Novel BET Inhibitor PLX51107 for Steroid-Refractory Acute GVHD
ClinicalTrials.gov study NCT04910152. IPD Sharing: NO. Countries: 1. Publications: 0.
ACTIV-5 / Big Effect Trial (BET-B) for the Treatment of COVID-19
ClinicalTrials.gov study NCT04583969. IPD Sharing: Not stated. Countries: 2. Publications: 1.
FTIR, TGA/DSC, BET, and WCA analyses of materials, as well as effects of parameters on isolating cellulose fibers from reed
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Data from: Shifting precipitation regimes influence optimal germination strategies and population dynamics in bet-hedging desert annuals
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Diapause is not selected as a bet-hedging strategy in insects: a meta-analysis of reaction norm shapes
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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.