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80 results for “evaluation framework”
Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems - Image Dataset
<p>This dataset contains data used in the research published by MLabs Optronics in the paper:</p> <p>Medina Heierle, Victor, María Tejada Casado, Alberto Briasco González, Hugo Jestes Zoilo, Jesús Martín Tapia, Adeodato Altamirano Aguilar, and Javier Muñoz De Luna Clemente. Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 274-280. IEEE, 2018.</p> <p><br> The dataset is classified into 3 folders:</p> <p>- IR_VIS: Contains 28 pairs of images in the IR (some images may be in the NIR spectrum instead) and Visual spectrum, taken from different public repositories off the internet, which are typically used in multispectral fusion research.<br> <br> - Fusion: Contains 8 sets with the results of applying each of the 4 fusion algorithms described in the paper on some of the images in folder "IR_VIS".</p> <p>- VIS haze filtering: Contains 24 images taken with a CCD camera of a contrast target inside a fog simulation cabin in a laboratory. For comparison purposes, all images have been taken with a similar amount of fog, which is as much as was possible while still being able to see the target with the camera through the fog. Each image has been taken with a different type of filter (filter information is provided in another image inside the folder).</p> <p> </p> <p>Mlabs Optronics<br> PTA<br> Calle Pierre Laffitte, 8<br> 29590 Málaga (Spain)</p> <p>www.mlabsoptronics.com<br> info@mlabsoptronics.com</p>
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <p> </p>
An Integrated Usability Framework for Evaluating Open Government Data Portals and Analysis of EU and GCC OGD Portals
<p><span>This dataset contains data collected during a study (<em><strong>"<a href="https://arxiv.org/ftp/arxiv/papers/2403/2403.08451.pdf">An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries</a>"</strong></em>) conducted by Fillip Molodtsov and Anastasija Nikiforova (University of Tartu).</span></p> <p><span> </span><span>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and develop user-friendly, collaborative, robust, and sustainable open data portals.</span></p> <p><span>***Purpose of the study***</span></p> <p><span>This paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. </span></p> <p><span>The framework is validated by applying it to 33 national portals across European Union (EU) and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings.</span></p> <p><span>***Methodology***</span></p> <p><span>(1) systematic literature review to establish a knowledge base and identify frameworks have been used to evaluate OGD portals, we conducted a systematic literature review - Dataset_ Usability_Framework_SLR;</span></p> <p><span>(2) development of the Integrated Usability Framework for Evaluating Open Government Data Portals, which content is based on the outputs of the first step, along with selected articles of experts in portal design, and an exploratory assessment of the French, Irish, Estonian and Spanish portals - Dataset_Integrated_Usability_Framework;</span></p> <p><span>(3) data collection, that is a completion of the protocol developed in the previous step by analysing 34 national OGD portals of the EU and GCC countries. When all individual protocols were collected, the total score are calculated using the weighting system. The average scores are calculated for the EU and GCC. The portals are ranked. The top portals (best performers) are determined for each dimension - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p><span>(4) identification of relationships and patterns among different portals based on their performance metrics as a result of the cluster analysis. By calculating the average dimensional scores of portals from both types of clusters, their performance across multiple dimensions is evaluated - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p> </p> <p><strong><em><span>For more details see Molodtsov, F., Nikiforova, A. (2024). “An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries”. In Proceedings of the 25th Annual International Conference on Digital Government Research (DGO 2024), June 11--14, 2024, Taipei, Taiwan, 10.1145/3657054.3657159</span></em></strong></p> <p><span>***Format of the file***</span></p> <p><span>.xls, .csv</span></p> <p><span>***Licenses or restrictions***</span></p> <p><span>CC-BY</span></p>
Datasets of synthetic task graphs for evaluating a reliability and latency multi-objective task allocation framework
<p>These datasets of synthetic task graphs were generated to evaluate the performance and scalability of a multi-objective task allocation approach for workflow applications of various structures and sizes in a system based on the edge-hub-cloud paradigm. The targeted architecture comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objectives were the maximization of the overall reliability and the minimization of the overall latency of the application, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device. Each task had a different vulnerability factor (i.e., probability of failure) on each device.</p> <p>We generated nine task graphs of serial, parallel, and mixed (a combination of serial and parallel) structure with 10, 100, and 1000 nodes, utilizing the Task Graphs For Free (TGFF) random task graph generator [1]. Additional task parameters (e.g., execution time, power consumption, vulnerability factor, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt.</p> <p>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10357101.</p> <p>References:</p> <p>[1] R. P. Dick, D. L. Rhodes and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE'98), Seattle, WA, USA, 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p>
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2nd Web-Delphi process to HTA stakeholders, organized in a single panel
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2<sup>nd</sup> Web-Delphi process to HTA stakeholders, organized in a single panel (all stakeholder groups in a single panel, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</p>
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1st Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1<sup>st</sup> Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels (one panel per stakeholder group, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</p>
Scalability, dynamicity and performance evaluation results of Mantus framework
<p>Datasets used for experimental results (Figure 5): (a) Compositional weaver efficiency; (b) incremental weaving efficiency; (c) relative overhead of weaving in workflow; (d) weaver efficiency vs. aspect complexity.</p> <p>Type of data: raw and processed</p> <p>Hardware/software used: Intel Xeon E5-2650 Haswell at 2.60GHz with 64 GB of RAM; Testing input for all Mantus benchmarks: OpenStack-based ORBITS template described in paper, composed of a controller node and of 3 different group instances of compute nodes (Xen, KVM, LXC), with two virtual networks and relative network resources.</p> <p>Data format: CSV</p> <p>Source: Experiments</p> <p> </p>
The Kconfig Variability Framework as a Feature Model: Sampled Configurations for Manual Evaluation
<p>This dataset contains plain text files with sampled solutions used during the manual evaluation of the transformation rules presented in https://doi.org/10.5445/IR/1000162110. To reproduce the manual evaluation process yourself, please copy over the respective Kconfig files in a local copy of the Linux kernel Git repository and run `make menuconfig`. You need to insert an invisible `MODULES` configuration symbol to ensure that tristate configuration symbols are handled correctly by Kconfig. Additionally, you need to remove the default Linux Kconfig file and rename the Kconfig file for which you want to reproduce the evaluation process accordingly (simply remove the number prefix).</p><p>Configurations marked with KCONFIG_NONSOLUTION cannot be reconstructed in `menuconfig`, wherein configurations marked with KCONFIG_SOLUTION should be reproducable in the `menuconfig` interface.</p><p>We additionally provide the generated feature models for the 9 selected Kconfig files, alongside with the Kconfig files themselves. Kconfig{1,2,3,4,5} can be automatically evaluated with Kfeature, as they contain no tristate confsyms.</p><p>The upstream version of Kfeature can be found on Codeberg: https://codeberg.org/6b6279/Kfeature</p>
ADAPTING SYSTEMS ENGINEERING TO EVALUATE TECH STARTUPS: AN INNOVATIVE FRAMEWORK BASED ON OMG ESSENCE
<p>The adaptation of system engineering to evaluate technical startups through an innovative framework based on the OMG Essence standard is a modern approach to simplify the process of analyzing and managing startups at various stages of their development. In this study, a universal framework was proposed that allows evaluating technical startups from the point of view of system engineering. This approach takes into account key aspects of startup development, such as requirements, stakeholders, technology, and team, which makes it an important tool for evaluating innovative projects.</p> <p>The main purpose of the proposed framework is to apply the principles of systems engineering to the evaluation of startups, focusing on technical aspects such as system architecture, integration capabilities, and the ability of the team to solve complex tasks. Traditional methods of evaluating startups, often focusing on business aspects, may not always take into account all the technical difficulties and risks that arise when developing software products. The OMG Essence-based framework fills this gap by providing a more comprehensive tool for analyzing and managing startup development.</p> <p>OMG Essence, as a standardized method, acts as a basis for formalizing practices and facilitating interaction between various project participants. The framework based on it offers the possibility of modular adaptation, which allows you to evaluate startups regardless of their complexity and current stage of development. An important feature of OMG Essence is the ability to integrate with other methodologies, which makes it universal for various fields. The application of this standard in the evaluation of startups allows you to obtain more objective results and improve the decision-making process.</p>
Evaluating the Usability of Open Source Frameworks in Energy System Modelling (Supplementary Material)
<p>Dataset and source code for analysis of the Energy System Modelling Usability Testing (ESMUT) procedure applied in the open_MODEX project.</p> <p>This is supplementary material for the publication:</p> <pre>Berendes et al. (2022). Evaluating the Usability of Open Source Frameworks in Energy System Modelling. <em>Renewable and Sustainable Energy Reviews. DOI: </em><a href="https://doi.org/10.1016/j.rser.2022.112174">https://doi.org/10.1016/j.rser.2022.112174</a></pre> <p> </p> <p> </p>
Multinational evaluation of genetic diversity indicators for the Kunming-Montreal Global Biodiversity Framework
<p>Under the recently adopted Kunming-Montreal Global Biodiversity Framework, 196 Parties committed to report the status of genetic diversity for all species. To facilitate reporting, three genetic diversity indicators were developed, two of which focus on processes contributing to genetic diversity conservation: maintaining genetically distinct populations and ensuring populations are large enough to maintain genetic diversity. The major advantage of these indicators is that they can be estimated with or without DNA-based data. However, demonstrating their feasibility requires addressing the methodological challenges of using data gathered from diverse sources, across diverse taxonomic groups, and for countries of varying socioeconomic status and biodiversity levels. Here, we assess the genetic indicators for 919 taxa, representing 5,271 populations across nine countries, including megadiverse countries and developing economies. Eighty-three percent of taxa assessed had data available to calculate at least one indicator. Our results show that although the majority of species maintain most populations, 58% of species have populations too small to maintain genetic diversity. Moreover, genetic indicator values suggest that IUCN Red List status and other initiatives fail to assess genetic status, highlighting the critical importance of genetic indicators.</p>
Evaluation datasets and results of the paper "A Framework for Measuring the Quality of Business Process Simulation Models"
<p>Datasets and files used in the evaluation of the publication entitled "A Framework for Measuring the Quality of Business Process Simulation Models", where:</p> <ul> <li><strong><em>BPS-models/</em></strong>: folder containing the BPS models used in the evaluation (the BPS models discovered by ServiceMiner are not included due to privacy reasons). <ul> <li>The BPS models discovered by SIMOD are composed of <em>i)</em> a BPMN file with the process model structure, and <em>ii)</em> a JSON file with the parameters of the simulation. These files correspond to the format of Prosimos simulation engine (<a href="https://prosimos.cloud.ut.ee/">https://prosimos.cloud.ut.ee/</a>).</li> <li>The BPS models of the Loan Application and Procure to Pay processes are composed of a BPMN file with both the process model structure and parameters, corresponding to the format of the BIMP simulator used in APROMORE (<a href="https://apromore.com/">https://apromore.com/</a>).</li> </ul> </li> <li><em><strong>measures/</strong></em>: folder containing the distance values of each measure reported in the paper.</li> <li><em><strong>original-event-logs/</strong></em>: folder containing the (train and test) event logs used in the evaluation.</li> <li><em><strong>simulated-logs/</strong></em>: folder containing the simulated logs evaluated in the paper (synthetic, SIMOD, and ServiceMiner).</li> <li><em><strong>ComputeLogDistance.py</strong></em>: script to compute the distance measures proposed in the paper.</li> </ul> <p> </p> <p>To evaluate the distance measures of a set of simulated event logs in the folder <em>simulated_logs/</em> against the test log <em>test_event_log.csv.gz</em>, run:<br><em> python ComputeLogDistance.py -cfld test_event_log.csv.gz simulated_logs/</em></p> <p>*The flag <em>-cfld</em> is optional, due to the high computational complexity of the CFLD measure.</p> <p><strong>WARNING</strong>: set the column names of each log accordingly (where <em>log_1_ids</em> are the IDs of the test log, and <em>log_2_ids</em> the IDs of the simulated logs). Examples:</p> <pre><code># Column IDs for the (train/test) real-life logs, and the SIMOD simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='start_time', end_time='end_time', resource='resource' ) # Column IDs for the Loan Application and Procure to Pay simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='Start_Time', end_time='End_Time', resource='resource' ) # Column IDs for the ServiceMiner simulated logs. EventLogIDs( case='case_id', activity='Activity', start_time='start_time', end_time='end_time', resource='Resource' )</code></pre> <p> </p>
ENTICE Multi-objective optimization framework synthetic evaluation data-sets
<p>This dataset contains synthetic usage data for evaluation of the multi-objective redistribution framework for distributed VMI repositories. </p> <p>The data is stored in a Java object and it should be directly loaded. </p> <p> </p>
A Layer-averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation and Sensitivity Exploration
<p>Selected model output data for supporting this paper.</p> <p>List of Files:</p> <p>2dtracer.tar.gz: correlated tracer test</p> <p>rh3d.tar.gz: 3D Rossby-Haurwitz Wave</p> <p>modon.tar.gz: Colliding Modons</p> <p>jwss.tar.gz: Jablonowski-Williamson Baroclinic Steady State</p> <p>jwbw_1d.tar.gz: 1D data output from Jablonowski-Williamson Baroclinic Wave</p> <p>jwbw_2d.tar.gz: 2D data output from Jablonowski-Williamson Baroclinic Wave</p> <p>dcmip31.tar.gz: DCMIP3-1 nonhydrostatic gravity wave</p> <p>Klemp15.tar.gz: Nonhydrostatic Mountain Waves in Klemp et al. 2015</p> <p>held-suarez.tar.gz: Held-Suarez dry climate (post-processed data for plotting, the raw daily data are too large to upload)</p> <p>jwbwvr.tar.gz: Variable-Resolution modeling of the Jablonowski-Williamson Baroclinic Wave</p> <p> </p> <p>see https://doi.org/10.5281/zenodo.3544795 for a companion work</p> <p>References:</p> <p>Zhang, Y., J. Li, R. Yu, S. Zhang, Z. Liu, J. Huang, and Y. Zhou, 2019: A Layer-Averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation, and Sensitivity Exploration. <em>Journal of Advances in Modeling Earth Systems</em>, <strong>11,</strong> 1685-1714.</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>
Multinational evaluation of genetic diversity indicators for the Kunming-Montreal Global Biodiversity Framework
Open the record for dataset details and reuse information.
Data from: Using a participatory impact assessment framework to evaluate a community-led mangrove and fisheries conservation approach in West Kalimantan, Indonesia
<ol> <li>Community-based conservation (CBC) has been identified as a solution to biodiversity loss, climate change, and the reduction of rural poverty. The heterogeneity in social and economic inequalities often acts as a barrier to community engagement in resource management and further inhibits the distributional equity of social and ecological outcomes.</li> <li>This study presents a participatory impact assessment (PIA) framework that evaluated the outcomes of a cross-sector community-led conservation initiative. Community members involved in the program identified activities and outcomes for the Conservation Cooperative (CC), ranking the influence of the former on the latter as well as their daily life through multiple focus group discussions (FGDs). Participants were asked to rank the impact of activities on outcomes and the scale of the outcome which was totaled to identify the most impactful program activities and outcomes during the project period.</li> <li>Community members reported improved income, health, education and the creation of a locally-led natural resource management system. Members also reported improved crab harvest rates and reduced mangrove deforestation. Environmental outcomes identified by community members through the PIA were verified through a secondary spatial analysis and mud-crab independent fisheries monitoring.</li> <li>The results support the hypothesis that environmental NGOs need to consider a multi-dimensional view of human well-being, and that cross-sector integrated interventions may be effective at improving multiple outcomes.</li> <li>Future steps should focus on spatial replication of the CC program which will provide further insights by testing for differences in outcomes between villages, how those are impacted by preexisting social and ecological systems, and comparing outcomes between control sites that did not receive interventions.</li> </ol>
Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review (Article Pool)
<p>This pdf includes all of the articles that analyzed in the study: "Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review".</p>
The Blockchain Trilemma: an Evaluation Framework (Replication Package)
<p>This repository contains essential data files used to generate graphs and statistics related to various blockchain ecosystems. These files are the result of aggregation and cleaning procedures applied to raw data collected from reputable sources within each respective blockchain network.<br> </p> <h2><strong>Transaction Per Second</strong></h2> <ol> <li><strong>Cardano</strong>: <ol> <li>Theoretical: 5.35</li> <li>Maximum: 5.74</li> </ol> </li> <li><strong>Solana</strong>:<br> <ol> <li>Theoretical: 710,000</li> <li>Maximum: 1763</li> </ol> </li> <li><strong>Arbitrum</strong>: <ol> <li>Theoretical: 40000</li> <li>Maximum: 3.09</li> </ol> </li> <li><strong>zkSync</strong>: <ol> <li>Theoretical: 2000</li> <li>Maximum: 0.521</li> </ol> </li> <li><strong>Polygon:</strong> <ol> <li>Theoretical: 7200</li> <li>Maximum: 101.97 </li> </ol> </li> <li><strong>Bitcoin</strong>: <ol> <li>Theoretical: 27</li> <li>Maximum: 4.53</li> </ol> </li> <li><strong>Ethereum</strong>: <ol> <li>Theoretical: 30</li> <li>Maximum: 19.86</li> </ol> </li> </ol> <h2>Nakamoto Coefficients</h2> <ol> <li><strong>arbitrum_aggregators.json</strong> <ol> <li><strong>Description:</strong> presents information on Arbitrum network aggregators' transaction count</li> <li><strong>Data Source: </strong>https://etherscan.io/apis</li> </ol> </li> <li><strong>cardano_stake.json</strong> <ol> <li><strong>Description:</strong> details stake-related data pertinent to the Cardano blockchain. For each exchange address the amount of holded stake</li> <li><strong>Data Source: </strong>https://pooltool.io/</li> </ol> </li> <li><strong>polygon_validators.json</strong> <ol> <li><strong>Description:</strong> provides insights into validators within the Polygon network. For each validator's name, the amount of held stake.</li> <li><strong>Data Source: </strong>https://wallet.polygon.technology</li> </ol> </li> <li><strong>solana_validators.json</strong> <ol> <li><strong>Description: </strong>provides information on validators operating within the Solana network. For each validator, the amount of held stake and details about the validator node.</li> <li><strong>Data Source: </strong>https://www.validators.app/</li> </ol> </li> <li><strong>zksync_validators.json</strong> <ol> <li><strong>Description: </strong>Contains data on validators associated with the zkSync protocol. For each validator's address, the amount of transactions processed.</li> <li><strong>Data Source: </strong>https://etherscan.io/apis</li> </ol> </li> </ol> <h2>Security Quantitative Evaluation</h2> <ol> <li><strong>Cardano</strong><br> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 119</li> <li>33% Stake: 895,706,097.1264836 ADA</li> <li>ADA Value @ 13 Dec-2023 = 0.59</li> <li>Cost of Attack: 895,706,097.1264836 * 0.59 = 528,466,597.3046253 $</li> </ol> </li> <li><strong>Solana</strong> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 18</li> <li>33% Stake: 126,950,233.4364128 SOL</li> <li>SOL Value @ 13 Dec-2023 = 71.78</li> <li>Cost of Attack: 126,950,233.4364128 * 71.78 = 9,112,487,756.06571 $</li> </ol> </li> <li><strong>Polygon</strong><br> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 2</li> <li>33% Stake: 1,016,718,726.6246895 MATIC</li> <li>MATIC Value @ 13 Dec-2023 = 0.8317</li> <li>Cost of Attack: 1,016,718,726.6246895 * 71.78 = 845,604,964.9337542 $</li> </ol> </li> <li><strong>Ethereum</strong> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 1</li> <li>33% Stake: 28907872.7327942/3 = 9,635,957.577598067 ETH</li> <li>ETH Value @ 08 Jan-2024 = 2133,90</li> <li>Cost of Attack: 9,635,957.577598067 * 2133,90 = 20,562,169,874.8365 $</li> </ol> </li> <li><strong>Bitcoin: </strong>$7.9 billion <a href="https://www.investopedia.com/terms/1/51-attack.asp#:~:text=4-,What%20Is%20a%2051%25%20Attack%3F,total%20hashing%20or%20validating%20power.">source</a></li> </ol>
Datasets of synthetic task flow graphs for evaluating a latency/energy optimization task allocation framework
<p>These datasets of synthetic task flow graphs were generated to evaluate the performance and scalability of an optimal task allocation approach for applications of various structures and sizes in an environment following the edge/hub/cloud paradigm. The system under study comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objective was the minimization of either overall latency or overall energy consumption, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device.<br> <br>We generated 18 task flow graphs of parallel, serial, and mixed (a combination of parallel and serial) structure with 10, 100, and 1000 nodes, and various in/out degrees, utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt and in [3].<br> <br>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10654551.<br> <br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.<br>[3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, "An optimization framework for task allocation in the edge/hub/cloud paradigm," Future Generation Computer Systems, vol. 155, pp. 354-366, Jun. 2024, doi: 10.1016/j.future.2024.02.005.</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.