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1,855 results for “Framework”
Dataset for "Roles of surface forcing in the Southern Ocean temperature and salinity changes under increasing CO2: perspectives from model perturbation experiments and a theoretical framework"
<p>Reference: Kewei Lyu, Xuebin Zhang, John A. Church, Quran Wu, Russell Fiedler, and Fabio Boeira Dias (2022), Roles of surface forcing in the Southern Ocean temperature and salinity changes under increasing CO<sub>2</sub>: perspectives from model perturbation experiments and a theoretical framework, <em>Journal of Physical Oceanography</em>, <a href="https://doi.org/10.1175/JPO-D-22-0095.1">https://doi.org/10.1175/JPO-D-22-0095.1</a></p>
Arbitrary-shape dielectric particles interacting in the linearized Poisson-Boltzmann framework: an analytical treatment
<p>h_kmlsn.mat --</p> <p>the coefficients h<sub>kmls,n</sub> calculated for n_max<=50: one has h<sub>kmls,n</sub> = h<sub>kmlsn</sub>(k+1,m+1,l+1,s+1,n+1), where h<sub>kmlsn</sub> is a 5D array contained in h_kmlsn.mat (note that MATLAB indices must start from 1, while mathematically indices k, m, l, s, and n are >=0).</p> <p>b_nml_approx.m -- it approximates $b_{nml}(\tilde r,\tilde R)$ with a given parameter n_max;</p> <p>derivative_b_nml_approx.m -- it approximates the derivative of $b_{nml}(\tilde r,\tilde R)$ with respect to $\tilde r$ with a given parameter n_max;</p> <p>linear_system_two_bodies_direct_backslash.m -- a simple example of forming the global linear system (14) and solving it directly (without regularization) using the MATLAB "mldivide" (or backslash) operation</p> <p>GSK3beta.zip -- zipped directory with DelPhi files related to Sec. 5.1.3 (see ReadmeGSK3beta.txt);</p> <p>Arginine-Glutamate -- zipped directory with DelPhi files related to Sec. 5.1.4 (see ReadmeARGGlu.txt)</p>
Dataset supporting publication: Design Framework and Laboratory Experiments for Helix and Slinky Type Ground Source Heat Exchangers for Retrofitting Projects
<p>Dataset supporting publication: “Design Framework and Laboratory Experiments for Helix and Slinky Type Ground Source Heat Exchangers for Retrofitting Projects” (publication available for download: <a href="https://zenodo.org/record/7436458">GEOFIT Zenodo</a>).</p> <p>The focus of the experimental work was on shallow spiral geothermal heat exchanger configurations. Real-scale experiments were carried out for vertically oriented spiral collectors (helix) in sand and soil. One objective was to develop a measurement concept in laboratory environment to create a framework for a validated database. This database serves as the basis for further and new development of engineering design tools. To achieve the highest possible data-point density in the observed environment, temperature sensors and a fiber-optic temperature measurement system (DTS) were used. Soil probes were taken in situ before and after the measurements and analyzed at a thermophysical laboratory to determine material properties. The heat flow was controlled by an electric heating cable, which was installed in the form of a spiral-shaped heat exchanger in a 1 m³ container. To guarantee constant boundary conditions, the measurements were carried out in a climate chamber at a defined ambient temperature. The evaluation of the transient response behavior is spatially resolved. The results are coordinate-based temperature points, which describe temperature gradients in all axes of the container over time, which are combined with known soil properties. The collected data was used to develop computational fluid dynamic (CFD) models, which are used to extend the variety of geometry and soil configurations for developing new design tools.</p>
Framework for Assessing Changes To Sea-level (FACTS) Module Data
<p>Input module data sets from the Framework for Assessing Changes To Sea-level. These files should be installed in the modules-data/ directory. See https://github.com/radical-collaboration/facts for more information.</p>
Supplemental Material for "Make Your Tools Sparkle with Trust: The PICSE Framework for Trust in Software Tools"
<p>This package contains the materials that were used for the paper "Make Your Tools Sparkle with Trust: The PICSE Framework for Trust in Software Tools".</p> <ul> <li><strong>Interview Script.docx</strong> contains the script that was used for the semi-structured interviews.</li> <li><strong>Codebook.xlsx </strong>contains the final codebook of the study.</li> </ul>
Raw data files for the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse"
<p>Datasets for the data presented in the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse" published in ACS Applied Materials and Interfaces.</p>
Artifact for Paper ParaQooba: A Fast and Flexible Framework for Parallel and Distributed QBF Solving
<p>The artifact for the paper "ParaQooba: A Fast and Flexible Framework for Parallel and Distributed QBF Solving" submitted to the <a href="https://tacas.info/artifacts-23.php">TACAS2023 artifact evaluation</a>.</p> <p>We thank the reviewers for their comments and added the missing dependencies to this revised version. No other parts have been changed.</p>
Datasets of Antiaromatic COFs associated with the publication "Antiaromatic Covalent Organic Frameworks Based on Dibenzopentalenes"
<p>X-ray scattering and sorption data associated with the publication "Antiaromatic Covalent Organic Frameworks Based on Dibenzopentalenes". X-ray scattering data is provided for COF and POP materials, including data from stability tests, as three-column ascii files with columns q (nm<sup>-1</sup>), I (m<sup>-1</sup>) and uncertainty on I, as well as being provided in 2θ.</p>
Datasets of evaluated Covalent Organic Frameworks for photocatalysis
<p>We developed a workflow to evaluate covalent organic frameworks (COFs) for photocatalysis. As an input, we chose the CURATED COFs (doi:<a href="https://doi.org/10.24435/materialscloud:z6-jn">10.24435/materialscloud:z6-jn</a>), a database of experimental COFs. We performed density functional theory (DFT) calculations, and post-processed the result with in-house codes available on GitHub (see <a href="https://github.com/bmourino/cof_photocatalysis">GitHub repository</a>). The dataset in cofs_descriptors.csv is the full dataset with all of our computed descriptors, and an application was developed to allow for interactively exploring the results (see <a href="https://github.com/bmourino/cofs_photocat_app">GitHub repository</a>). The bootstrapped_statistics.csv is the result of the statistical analysis with DABEST (Data Analysis with Bootstrap-coupled ESTimation, doi:<a href="https://doi.org/10.1038/s41592-019-0470-3">10.1038/s41592-019-0470-3</a>).</p>
Open datasets used in the analysis of impact of European Union Funding Programmes (Framework Programmes).
<p>This repository will hold datasets used in the various analysis on results, achievements and impact of European Union Funding Programmes (Framework Programmes:, FP). It supports the objective of using openly available dataset to study the results of the FP funding.</p> <p>The repository will be periodically updated with new datasets and links to the analysis.</p> <p><strong>Disclaimer:</strong></p> <p><em>The views expressed in this paper are the author’s. They do not reflect the views or official positions of the European Commission or the European Research Council.</em></p> <p> </p> <p><strong>List of datasets included :</strong></p> <p>1. dataset for the analysis: <a href="https://data.europa.eu/doi/10.2777/235579">Contribution of the Framework Programmes to IPCC: Insights from the References of IPCC Reports</a><em><strong> </strong></em></p> <p> The analysis is an update of an earlier report titled <em><strong><a href="https://data.europa.eu/doi/10.2777/928125">Informing global climate action: Contribution of the Framework Programmes (FP7 and H2020) to recent IPCC reports</a></strong></em></p> <p>the dataset is provided in json_l format </p> <p> </p>
Preliminary biohybrid experiments with the Behavioral Observation & Biohybrid Interaction framework (featuring the LureBot)
<p>This is the dataset that corresponds to preliminary biohybrid interaction experiments conducted with the Behavioral Observation & Biohybrid Interaction (BOBI) framework, featuring the LureBot.</p> <p> </p> <p>The directory contains:</p> <ol> <li>Trajectories of fish and/or robot agents for different scenarios (<filename>.dat).</li> <li>A robot index file (<filename>_ridx.dat) that corresponds to each trajectory file, that contains the robot's ID (starting from 0) in the experiment, or a negative ID in the case of fish-only experiments.</li> </ol> <p> </p> <p>A brief explanation of the directory's contents:</p> <ul> <li>[Open-loop dynamics] <strong>Circular</strong> trajectories <ul> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure performing a circular trajectory while a single H. rhodostomus is in the tank.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure performing a circular trajectory while a single H. rhodostomus is in the tank.</li> </ul> </li> </ul> <p> </p> <ul> <li>[Open-loop dynamics] <strong>Eightfold rose</strong> trajectories <ul> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure performing an eightfold rose trajectory while a single H. rhodostomus is in the tank.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure performing an eightfold rose trajectory while a single H. rhodostomus is in the tank.</li> </ul> </li> </ul> <p> </p> <ul> <li>[Closed-loop dynamics] <strong>Biomimetic Interaction Model</strong> trajectories <ul> <li><strong>single_agent</strong>: <ul> <li><strong>Fish</strong>: 1-hour-long trajectories of a single H. rhodostomus interacting with the wall alone.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of a single robot following a biomimetic model.</li> </ul> </li> <li><strong>pair_agents</strong>: <ul> <li><strong>Fish-only</strong>: 1-hour-long trajectories of a pair of H. rhodostomus interacting with the wall and with each other.</li> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure biomimetically interacting with a single H. rhodostomus.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure biomimetically interacting with a single H. rhodostomus.</li> </ul> </li> <li><strong>group5_agents</strong>: <ul> <li><strong>Fish-only</strong>: 1-hour-long trajectories of 5 H. rhodostomus interacting with the wall and with each other.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure biomimetically interacting with 4 H. rhodostomus.</li> </ul> </li> </ul> </li> </ul>
Dataset: A unified modelling framework for projecting sectoral greenhouse gas emissions
<p>Data provided includes results from the unified framework described in "A unified modelling framework for projecting sectoral greenhouse gas emissions". Contains posterior draws of emission intensities and resulting emissions for 173 countries, five main sectors up to the year 2050. Historical GHG emissions data based on <a href="https://doi.org/10.5194/essd-13-5213-2021">Minx et al (2021)</a>.</p> <p>Code for the processing of results can be found on <a href="https://github.com/oDNAudio/GHG_sector_projections">Github</a>.</p>
Dataset for the paper "An end-to-end deep learning framework for wideband signal recognition"
<p>This dataset comprises a collection of synthetically generated wideband signals, which were used in experiments conducted for the paper "An end-to-end deep learning framework for wideband signal recognition," submitted for publication to IEEE <em>Access</em>. In this work, the proposed learning-based approach for signal detection, localization, and classification was evaluated on the public wideband signal recognition dataset introduced by West <em>et al.</em> (<a href="https://ieeexplore.ieee.org/document/9593265">https://ieeexplore.ieee.org/document/9593265</a>). We note that the dataset provided here contains only additional auxiliary data that were generated to complement the public benchmark dataset in experiments that employ data mixing and transfer learning techniques. As such, the synthetic wideband signals in our dataset are designed to mimic those in the benchmark dataset.<br> <br> Specifically, in our simulations, a wideband signal is modeled as a superposition of several narrowband emissions and receiver noise. Similar to the benchmark dataset, all wideband signals have a normalized sampling frequency of <span class="math-tex">\(F_\text{s} = 1\)</span> sample per second (sps), duration of <span class="math-tex">\(L = 1 000 000\)</span> samples, and a center frequency of <span class="math-tex">\(F_\text{c} = 0\)</span> Hz. The narrowband signals have varying center frequencies, bandwidths, start times, and durations, and are modulated with a range of modulation classes, including:</p> <ol> <li>Amplitude Modulation - Double Sideband (AM-DSB)</li> <li>Amplitude Modulation - Single Sideband (AM-SSB)</li> <li>Frequency Modulation (FM)</li> <li><span class="math-tex">\(M\)</span>-Frequency-Shift Keying (<span class="math-tex">\(M\)</span>-FSK) for <span class="math-tex">\(M \in \{2, 4\}\)</span></li> <li><span class="math-tex">\(M\)</span>-Continuous Phase Frequency-Shift Keying (<span class="math-tex">\(M\)</span>-CPFSK) for <span class="math-tex">\(M \in \{2, 4\}\)</span></li> <li>Gaussian Minimum Shift Keying (GMSK)</li> <li>On-Off Keying (OOK)</li> <li><span class="math-tex">\(M\)</span>-Phase-Shift Keying (<span class="math-tex">\(M\)</span>-PSK) for <span class="math-tex">\(M \in \{2, 4, 8\}\)</span></li> <li><span class="math-tex">\(M\)</span>-Quadrature Amplitude Modulation (<span class="math-tex">\(M\)</span>-QAM) for <span class="math-tex">\(M \in \{16, 64, 256\}\)</span></li> </ol> <p>The modulation parameters for each narrowband signal are randomly selected from predefined typical ranges. For all wideband signals, the value of the power spectral density of the added white Gaussian noise has been initially set to -174 dBm/Hz. Further noise addition according to the desired signal-to-noise ratio (SNR) level can be applied by the user. <br> <br> The data are stored and documented according to the Signal Metadata Format (SigMF) standard (<a href="https://github.com/sigmf/SigMF">https://github.com/sigmf/SigMF</a>). Each wideband signal in the dataset is represented by a data file (a binary file containing digital I/Q samples of the signal) and a metadata file (in JSON format) that annotates the signal. The annotations include the general properties of the wideband recording (sampling rate, center frequency, signal duration, and noise power spectral density), as well as specific information for each narrowband signal (start time, duration, lowest and highest frequency, modulation class label, and power). For every signal in the dataset, the data file and the annotations file share the same name. For example, the first training wideband signal is stored in the file "train_1.sigmf-data" and is annotated by the file "train_1.sigmf-meta".</p>
Dataset for "Single-Step Selective Oxidation of Methane by Iron-Oxo Species in the Metal-Organic Framework MFU-4l"
<p>Dataset belonging to publication "Single-Step Selective Oxidation of Methane by Iron-Oxo Species in the Metal-Organic Framework MFU-4l"</p>
Data from: A framework to apply trait-based ecological restoration at large scales
<p>R scripts and data to run the proposed framework at</p> <p>Coutinho, A. G., Carlucci, M. B., Cianciaruso, M. V. (2023) A framework to apply trait-based ecological restoration at large scales. Journal of Applied Ecology.</p>
Extending the application of connectivity metrics within the framework of the characterization of the dynamic behaviour of a WDS subjected to users' activity
<p>Water distribution networks (WDNs) are complex combinations of nodes and links, and the current tendency is to modify their topological structure through the closure of isolation valves for monitoring and water quality reasons. For their analysis, several approaches based on graph theory have recently been proposed, mainly considering steady-state flow conditions. However, in their real functioning, WDNs are continuously subjected to pressure transients generated by manoeuvres on regulation devices or by users’ activity. This study investigates the application of some metrics from graph theory, already used in the context of steady-state analysis, for assessing the effects of changes in the topological structure of a network ‒ due for example to sectorization or branching operations ‒ on its transient response when subjected to manoeuvres on devices such as hydrants, pumps, etc. or users’ activity. The analysis shows that some connectivity metrics can effectively reflect the dynamic pressure behaviour of the network and, thus, provide useful indications for design and management operations taking into account unsteady flow features.</p>
Hypertuned Beta parameter SDOT Framework for Robust Forensic File Classification
<p>This dataset contains code for implementing our proposed Forensic File classification system with the help of Guided LDA algorithm and SDOT forensic Framework. Along with the code, we have provided auxiillary files that are used in this research.</p>
SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data (User guide V2.4)
<p>The<strong> User Guide V2.4 </strong>of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: <strong><a href="https://doi.org/10.1016/j.isprsjprs.2022.08.008">L2HNet</a></strong>. A more detailed description of the data can be found in the<strong> <a href="https://doi.org/10.5194/essd-15-4749-2023">paper</a>.</strong> More related work can be found at my <strong><a href="https://lizhuohong.github.io/lzh/">homepage</a>.</strong></p> <p><a href="https://zenodo.org/search?q=parent.id%3A7707461&f=allversions%3Atrue&l=list&p=1&s=10&sort=version"><strong>Click to check all the data versions and download the data (点击查看/下载所有数据版本)</strong></a></p> <p><strong>NOTE: If you have any data needs, questions, or technical issues, contact us at </strong><a href="http://ashelee@whu.edu.cn"><strong>ashelee@whu.edu.cn</strong></a><strong> (Zhuohong Li, 李卓鸿).</strong></p> <p>The land-cover mapping method with Python code is open-access at <a href="https://github.com/LiZhuoHong/Paraformer/"><strong>Code link</strong></a>. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (<strong><a href="https://arxiv.org/abs/2403.02746">Paper link</a></strong>).</p> <p><strong>我们的最新制图算法被计算机视觉顶会CVPR2024接收(<a href="https://arxiv.org/abs/2403.02746">Paper link</a>),代码开源在:<a href="https://github.com/LiZhuoHong/Paraformer/">Code link</a>,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。</strong></p> <p><strong>Citation format of the paper:</strong><br>Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023. </p> <p>Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>. <em>192</em>, pp.244-267, 2022.</p> <p><strong>BibTex format of the paper:</strong></p> <blockquote> <pre>@article{li2023sinolc, title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data}, author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan}, journal={Earth System Science Data}, volume={15}, number={11}, pages={4749--4780}, year={2023}, publisher={Copernicus Publications G{\"o}ttingen, Germany} }</pre> <pre>@article{li2022breaking, title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels}, author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={192}, pages={244--267}, year={2022}, publisher={Elsevier} }</pre> </blockquote>
There and back to the present: a model-based framework to estimate phylogenetically constrained alpha diversity gradients
<p>The imprint left by niche evolution on the variation of biological diversity across spatial and environmental gradients is still debated among ecologists. Furthermore, understanding to what extent dispersal limitation may reinforce or blur such an imprint is still a gap in our ecological knowledge. In this article we introduce a simulation approach coupled to Approximate Bayesian Computation (ABC) that parameterizes both the adaptation rate of species' niche positions over the evolution of a monophyletic lineage and the intensity of dispersal limitation associated with the variation of species alpha diversity among assemblages distributed across spatial and environmental gradients. The analytical tool was implemented in the R package <em>mcfly</em>. We evaluated the statistical performance of the analytical framework using simulated datasets, which confirmed the suitability of the analysis to estimate the adaptation rate parameter but showed to be less precise in relation to the dispersal limitation parameter. Also, we found that increased dispersal limitation levels improved the parameterization of the adaptation rate of species' niche positions in simulated datasets. Further, we evaluated the role played by niche evolution and dispersal limitation on species alpha diversity variation of Phyllostomidae bats across the Neotropics. The framework proposed here sheds light on the links between niche evolution, dispersal limitation and gradients of biological diversity, and thereby improved our understanding of evolutionary imprints on current biological diversity patterns.</p>
Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
<h2>Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</h2><p>This is our replication package for our study on <i>Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</i>.</p><p>All scalability experiments are performed with the scalability benchmarking framework <a href="https://www.theodolite.rocks/">Theodolite</a> at <a href="https://www.se.informatik.uni-kiel.de/en/research/software-performance-engineering-lab-spel">Kiel University's Software Performance Engineering Lab (SPEL)</a> or Google Cloud.</p><p>With this replication package, we provide:</p><ul><li><a href="https://www.theodolite.rocks/concepts/benchmarks-and-executions.html">Benchmark execution files</a> in <i>executions</i>,</li><li>our benchmark (raw) results in <i>results</i>, and</li><li>analysis script for our results in <i>analysis</i>.</li></ul><h3>Repeating Benchmark Executions</h3><p>All our Theodolite executions are tailored to either the SPEL cluster or the Google Cloud.</p><h4>Kiel University's Software Performance Engineering Lab (SPEL)</h4><p>The SPEL cluster consists of 5 nodes, named <i>kube1-1</i> to <i>kube1-5</i> and labeled with <i>env=dev</i>. To run them in your local cluster, make sure to provide the same infrastructure or rename node selectors in the execution files accordingly.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml</p></blockquote><p>or for the vertical scalability experiment:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml -f se-cluster-dev-vertical.yaml</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h4>Google Cloud</h4><p>In the public cloud baseline experiments, the cluster consists of 5 e2-standard-32 nodes.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-dev.yaml</p></blockquote><p>For the experiments testing higher load intensities, the cluster consists of 4 e2-standard-16 nodes labeled with <i>type=infra</i> and 4 or 8 e2-standard-16 nodes with label <i>type=sut</i>. To install Theodolite in this cluster, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-stress.yaml</p></blockquote><p>In both cases, change the maximum load generated per load generator instance:</p><blockquote><p># Generate max. 100000 rec/sec per load generator instance export MAX_RECORDS_PER_INSTANCE=100000 kubectl patch benchmarks uc1-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]"</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h3>Repeating Results Analysis</h3><p>To inspect, repeat, or extend our results analysis, see <i>results</i> or run the corresponding notebooks in <i>analysis</i>.</p><p>For analyzing and visualizing benchmark results, either Docker or a Jupyter installation with Python 3.7 or 3.8 is required (e.g., in a virtual environment). Moreover, we require some Python libraries, which can be installed by:</p><blockquote><p>python3.8 -m venv .venv # source .venv/bin/activate pip install -r analysis/requirements.txt</p></blockquote><p> </p>
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.