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.
768
datasets available to search
ShareScore release 0.9.0
Dataset results
768 results for “reinforcement”
Supporting data for increasing fire activity reinforces shrub conversion in Southwestern US forests
<p>Fire-exclusion in historically frequent-fire forests of the southwestern United States has altered forest structure and increased the probability of high-severity fire. Warmer and drier conditions, coupled with dispersal distance limitations are limiting tree seedling establishment and survival following high-severity fire. Post-fire conversion to non-forest vegetation can be reinforced by subsequent fire events. We sought to determine the influence of fire probability on post-fire vegetation development in a severely burned landscape in New Mexico, USA. We used LANDIS-II to simulate three fire probability scenarios—contemporary mean fire return interval (CMFRI), and 1.5 times and 2 times CMFRI—with contemporary climate. As fire probability increased, the mean size of the largest fires and the mean landscape fire severity increased. These changes in fire characteristics resulted in a net decrease in total above ground biomass and photosynthetic capacity on the landscape. Additionally, the distribution of individual species biomass shifted, with early successional species, especially those that resprout after fire, increasing as a fraction of total biomass with increasing fire occurrence. Continued increases in fire frequency are likely to favor resprouting species and result in a loss of forest biomass and ecosystem productivity in this southwestern forest landscape.</p>
Reinforcement learning theory reveals the cognitive requirements for solving the cleaner fish market task
<p>Learning is an adaptation that allows individuals to respond to environmental stimuli in ways that improve their reproductive outcomes. The degree of sophistication in learning mechanisms potentially explains variation in behavioural responses. Here, we present a model of learning that is inspired by documented intra- and interspecific variation in the performance in a simultaneous two-choice task, the 'biological market task'. The task presents a problem that cleaner fish often face in nature: the decision of choosing between two client types; one that is willing to wait for inspection and one that may leave if ignored. The cleaners' choice hence influences the future availability of clients, i.e. it influences food availability. We show that learning the preference that maximizes food intake requires subjects to represent in their memory different combinations of pairs of client types rather than just individual client types. In addition, subjects need to account for future consequences of actions, either by estimating expected long-term reward or by experiencing a client leaving as a penalty (negative reward). Finally, learning is influenced by the absolute and relative abundance of client types. Thus, cognitive mechanisms and ecological conditions jointly explain intra and interspecific variation in the ability to learn the adaptive response.</p>
The population genetics of speciation by cascade reinforcement
<p>Species interactions drive diverse evolutionary outcomes. Speciation by cascade reinforcement represents one example of how species interactions can contribute to the proliferation of species. This process occurs when the divergence of mating traits in response to selection against interspecific hybridization incidentally leads to reproductive isolation among populations of the same species. Here, we investigated the population genetic outcomes of cascade reinforcement in North American chorus frogs (Hylidae: <em>Pseudacris</em>). Specifically, we estimated the frequency of hybridization among three taxa, assessed genetic structure within the focal species, <em>P. feriarum</em>, and ascertained the directionality of gene flow within <em>P. feriarum</em> across replicated contact zones via coalescent modeling. Through field observations and preliminary experimental crosses, we assessed whether hybridization is possible under natural and laboratory conditions. We found that hybridization occurs among <em>P. feriarum</em> and two conspecifics at a low rate in multiple contact zones and that gene flow within the former species is unidirectional from allopatry into sympatry with these other species in three of four contact zones studied. We found evidence of substantial genetic structuring within <em>P. feriarum</em> including a divergent western allopatric cluster, a behaviorally-distinct sympatric South Carolina cluster, and several genetically-overlapping clusters from the remainder of the distribution. Furthermore, we found sub-structuring between reinforced and non-reinforced populations in the two most intensely-sampled contact zones. Our literature review indicated that <em>P. feriarum</em> hybridizes with at least five heterospecifics at the periphery of its range providing a mechanism for further intraspecific diversification. This work strengthens the evidence for cascade reinforcement in this clade, revealing the geographic and genetic landscape upon which this process can contribute to the proliferation of species. </p>
Michalis Panayides thesis: Reinforcement learning dataset
<p>A dataset that consists of the reinforcement learning experiments used in my thesis. There are 5 main subdirectories. Each subdirectory consists of scripts, data, jupyter notebooks and plots for the corresponding utility function of the reinforcement learning algorithm. See README.md for more information.</p>
Policies used in "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark"
<p>Policies used in the paper "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark". They can be tested using the software "OTTO-benchmark" available at https://github.com/auroreloisy/otto-benchmark.</p>
Dataset for scanning electron microscopy based local fiber volume fraction analysis of non-crimp fabric glass fiber reinforced composites
<p>SEM data sets, Matlab codes, and output from the local fiber volume fraction analysis from the publications:</p> <p>Mortensen, U.A., Rasmussen, S., Mikkelsen, L.P., Fraisse, A., Andersen, T.L. The impact of the fibre volume fraction on the fatigue performance of glass fiber composites used in the wind turbine industry, <em>Composites Part A</em>, submitted Dec 2022.</p> <p>Mikkelsen, Lars P., Fæster, S., Dahl, V.A. Dataset for scanning electron microscopy based local fiber volume fraction analysis of non-crimp fabric glass fiber reinforced composites. <em>Data in Brief</em>, Submitted, 2023.</p> <p>to where a reference should be given. </p> <p>The files are structured in the following way.</p> <ul> </ul> <p>L1, L2: Low FVF cases 1 and 2; see the publication.<br> H1, H2: High FVF case 1 and 2; see the publication.</p> <p>SEM images:</p> <ul> <li>*.bmp: Single SEM scan</li> <li>*.bmp.hdr: settings for single SEM scan</li> <li>*.tif: Stitched SEM scan</li> <li>*.tif.hdr: settings for stitched SEM scan</li> </ul> <p>*_BundleAnalysis.m: Script for manual segmentation of the individual bundles.</p> <ul> <li>*.zip files: Functions used by the ..._BundleAnalysis.m scripts</li> <li>*-CURVES_AND_AREAS.mat: Matlab saving of bundle definitions used by the ...BUndlesAnalysis.m script</li> <li>*BWbundles.mat: Output from the _BundleAnalysis.m script</li> </ul> <p>*.m: Local fiber volume fraction analysis script of SEM-scan based on output from BundleAnalysis tool</p> <ul> <li>Figxx.png: Reference to specific figures in the publications shown for all 4 cases</li> <li>Fig7.m: Matlab script for plotting figure 7</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Unpaired fast- and slow-acquisition microCT scans of carbon-fibre-reinforced composites
<p>data.zip contains tomographic images of carbon-fibre-reinforced composites with <a href="https://www.toraycma.com/wp-content/uploads/T700S-Technical-Data-Sheet-1.pdf.pdf">Toray T700S</a> fibres in a <a href="http://sicomin.com/datasheets/product-pdf1123.pdf">Sicomin SR8500 KTA313</a> epoxy matrix.</p> <p>The datasets accompany the paper <a href="https://doi-org.remotexs.ntu.edu.sg/10.1016/j.compscitech.2023.110278" target="_blank" rel="noopener"><em>Deep-learning image enhancement and fibre segmentation from time-resolved computed tomography of fibre-reinforced composites</em></a>.</p> <p>They are used in a two-step process:<br>1) to train a 3DCycleGAN enhancement algorithm available on <a href="https://github.com/pvilla/3DCycleGaN">https://github.com/pvilla/3DCycleGaN</a>.<br>2) to train a UNetID segmentation algorithm available on <a href="https://github.com/CMG-KULeuven/UnetID-FibreSeg">https://github.com/CMG-KULeuven/UnetID-FibreSeg</a>.</p> <p>The tomograms were acquired at the TOMCAT Beamline at the Swiss Light Source in October 2021.</p> <p>Acquisition specifications:</p> <p>T700_GF_0p8um_1ms:</p> <ul> <li>detector: GigaFRoST</li> <li>effective voxel size: 800x800x800 nm³</li> <li># projections: 500</li> <li>exposure time: 1 ms</li> <li>total acquisition time: 500 x 1 ms = 0.5 s</li> <li>beam: polychromatic</li> <li>datasets: (same sample, different regions)<br>T700-T-21_GF_0p8um_1ms_1.h5<br>T700-T-21_GF_0p8um_1ms_2.h5<br>T700-T-21_GF_0p8um_1ms_3.h5</li> </ul> <p>T700_GF_1p6um_0p5ms:</p> <ul> <li>detector: GigaFRoST</li> <li>effective voxel size: 1600x1600x1600 nm³</li> <li># projections: 500</li> <li>exposure time: 0.5 ms</li> <li>total acquisition time: 500 x 0.5 ms = 0.25 s</li> <li>beam: polychromatic</li> <li>files: (same sample, different regions)<br>T700-T-21_GF_1p6um_0p5ms_1.h5<br>T700-T-21_GF_1p6um_0p5ms_2.h5</li> </ul> <p>T700_GF_1p6um_3ms:</p> <ul> <li>detector: GigaFRoST</li> <li>effective voxel size: 1600x1600x1600 nm³</li> <li># projections: 500</li> <li>exposure time: 3 ms</li> <li>total acquisition time: 500 x 3 ms = 1.5 s</li> <li>beam: polychromatic</li> <li>files: (same sample, different regions)<br>T700-T-21_GF_1p6um_3ms_1.h5<br>T700-T-21_GF_1p6um_3ms_2.h5<br>T700-T-21_GF_1p6um_3ms_3.h5<br>T700-T-21_GF_1p6um_3ms_4.h5<br>T700-T-21_GF_1p6um_3ms_5.h5</li> </ul> <p>T700_pco_0p4um:</p> <ul> <li>detector: pco.edge 5.5</li> <li>effective voxel size: 400x400x400 nm³ (scaled from 325x325x325 nm³)</li> <li># projections: 2000</li> <li>exposure time: 250 ms</li> <li>total acquisition time: 2000 x 250 ms = 500 s</li> <li>beam: 15 keV</li> <li>files: (different samples)<br>T700-T-02_pco_0p4um.h5<br>T700-T-08_pco_0p4um.h5<br>T700-T-21_pco_0p4um_reference.h5<br>T700-T-26_pco_0p4um.h5</li> </ul> <p>3DCycleGAN_trained_models.zip contains pre-trained models for the 3DCycleGAN enhancement algorithm.</p> <p>UnetID_trained_models.zip contains pre-trained models for the UNetID segmentation algorithm.</p>
Dataset: Scatter of mechanical properties of 2D biaxial and triaxial braided reinforced composites and their correlation with visual features
<p>The data represents tensile tests of 2D braided biaxial and triaxial composite laminates. Braids were produced on a Herzog radial braiding machine RF 1-176-100 with carbon fiber yarns (Toho Tenax F13 HTS40 12K 800 tex). Test laminates are infused with a VARI process with the 2-component epoxy resin system Hexion RIM235/RIMH237. The infusion is performed at vacuum pressure leves of 20-30 mbar and the resin is cured at room temperature for 24 hours at a pressure level of 250 mbar. A subsequent post-cure step maximizes matrix properties at 70°C for 10 hours.<br> The braid setups have been chosen to achieve four different braids with a fiber angle of 45° on braid mandrels with a diameter of 120 mm, two biaxial ones and two triaxial ones. By using different sizes of braiding ring diameters and yarn forces, homogeneous and inhomogeneous fiber architectures are generated.<br> Braiding setups 1 (biaxial) and 2 (triaxial) with 130 mm braiding ring diameter and 350 g yarn force result in braids with homogeneous fiber architecture. 3 (biaxial, 400 mm braiding ring, 600 g yarn force) and 4 (triaxial, 250 mm braiding ring, 600 g yarn force) in braids with inhomogeneous scattering of visual characteristics. Single layers are braided on mandrels with a length of 1500 mm and laminates with 6 layers of biaxial and 4 layers of triaxial braids are subsequently infused.<br> Tensile tests are performed on a Schenck-Trebel RM250 universal testing machine according to AITM 1-0007. For each specimen, the individual fiber orientation in each layer is measured prior to infusion by image analysis performed on scans of the braided layers. The results show a strong dependence of the individual fiber orientation of single test specimens on their mechanical properties in case of the presented tensile tests</p>
YudengLin/memristorBDNN: Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning
<p>This code repository is partly to support risk-sensitive reinforcement learning experiment in the manuscript "Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning" submitted to Nature Machine Intelligence.</p>
image classification dataset on carbon fiber reinforcement quality control
<p>Image classification dataset on carbon fiber quality control.</p> <p>To represent a practical quality control problem, a dataset was generated using carbon plain weave with a grammage of 200g/m². Pieces of the weave, measuring 300x300 mm², were cut using a CNC cutter table. Two such pieces were stacked, with a binder applied between them for shape stability after the forming process. The formed stacks were then scanned using a high-resolution camera mounted on a robotic arm, resulting in 500 images of the textiles' surfaces in three-dimensional shape. The images were then cropped to 341x384 pixels patches and transformed to grayscale.</p> <p>Each patch was classified into one of three classes: normal textile, gap, or fold. Images that were blurred, out of focus, or had bad contrast were sorted out. The dataset has not yet been released, and will be available upon the acceptance of the document.</p>
Fighting investment of Neolamprologus pulcher after receiving single, reinforcing, and contradictory contest experiences
<p>When animals live in long-term groups, the potential for conflict is high. Conflict is costly, so an individual's decision to engage depends on the information it has about the costs and benefits of fighting. One source of information could be past contest experience, where previous winners/losers typically become more likely to win/lose in the future. However, repeated interactions can familiarize individuals with conflict and provide opportunities to learn to become better fighters, regardless of outcome. We explored how individuals integrate information from previous contests to inform future encounters in a group-living fish, <em>Neolamprologus pulcher</em>. We gave contestants single, reinforcing, and contradictory experiences and measured behavior and post-fight water-borne levels of androgenic steroids (testosterone, 11-ketotestosterone). Contradictory outcomes were associated with reduced investment in fighting. More fighting experience did not lead to greater investment in fighting, as consecutive losses resulted in reduced aggression. Also, there was no effect of fighting treatment on water-borne androgen concentrations. Interestingly, there were sex differences in which behaviors were influenced by experience, and in whether body mass was associated with androgen concentrations, which could indicate that males and females vary in how perceived fighting ability changes with contest experience. Our data reveal the complex ways in which repeated experiences can alter an individual's propensity to invest in conflict. Repeated interactions associated with predictable changes in behavior can contribute to rank stability in groups and our results indicate that whether and how they do depend on the quality and quantity of interactions plus individual factors such as sex.</p>
Dataset_Multiscale Fast Fourier Transform homogenization of additively manufactured fiber reinforced composites from component-wise description of morphology
<p>Original micro-CT imaging data and output of processing via the OpenFiberSeg software of additively manufactured carbon fiber reinforced composites. </p> <p>This dataset accompanies the publication in Composite Science and Technology available at https://doi.org/10.1016/j.compscitech.2023.110261</p> <p> </p>
Dataset for the paper: Emergent Resource Exchange and Tolerated Theft Behavior using Multi-Agent Reinforcement Learning
<p>Data generated from the experiments for the paper "Emergent Resource Exchange and Tolerated Theft Behavior using Multi-Agent Reinforcement Learning"</p>
Outcomes of Anterior Colporrhaphy Versus Graft Reinforced Anterior Prolapse Repair
ClinicalTrials.gov study NCT00535301. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Reinforcing Mechanisms of Smoking in Adult ADHD
ClinicalTrials.gov study NCT00573859. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Clinical and Microbial Study of Reinforced 3D Printed Maxillary Denture Base Resin
ClinicalTrials.gov study NCT06921577. IPD Sharing: NO. Countries: 1. Publications: 1.
Impact of Buspirone Maintenance on the Reinforcing Effects of Cocaine
ClinicalTrials.gov study NCT01639157. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Employment-based Reinforcement to Motivate Drug Abstinence in the Treatment of Drug Addiction. - 1
ClinicalTrials.gov study NCT00249496. IPD Sharing: NO. Countries: 1. Publications: 2.
The Effect of Sweet Flavoring on the Rewarding and Reinforcing Value of Cigarillo Use Among Young Adults
ClinicalTrials.gov study NCT05092919. IPD Sharing: YES. Countries: 1. Publications: 1.
Reinforced Staple Line on Leak Rate in Distal Pancreatectomy
ClinicalTrials.gov study NCT01359410. IPD Sharing: NO. Countries: 1. Publications: 4.
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.