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Sample Research Object for Zenodo
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque sodales, dolor imperdiet viverra gravida, turpis est aliquet nisi, vel fermentum augue nisi ut mi. Curabitur placerat egestas scelerisque. Nunc sit amet justo non eros rhoncus egestas a semper dui. Sed at ipsum nec elit sodales rhoncus. Fusce vel ante id mi consectetur tincidunt. Proin lobortis dui vel nunc lacinia pellentesque. Sed pretium justo quis dolor ornare, in egestas turpis eleifend. Sed tellus tellus, fermentum cursus ligula vitae, tincidunt consectetur velit. Vivamus a magna varius, pulvinar purus quis, molestie odio. Nam auctor semper nisi at vehicula. Vestibulum et dui quis metus fringilla tempus. Integer vitae purus nisi. Curabitur a elit at eros mattis lobortis et at lectus.
Supplementary Data for Multiform: Multi-objective Evolution of Requirements Models Constrained by Formal Verification Results
<p>This data set provides supplementary material for the article "<em>Multiform: Multi-objective Evolution of Requirements Models Constrained by Formal Verification Results</em>" (to appear). It contains the following files:</p> <ul> <li><strong>experiment-input-models.zip</strong> which contains the SML input models for the EBEAS and production cell examples that were used to conduct the experiment</li> <li><strong>experiment-results.tar</strong> which contains the computed candidate SML models as well as H2 database files that contain measurements.</li> <li><strong>experiment-results.pdf</strong> which summarizes the conducted controlled experiment and results.</li> </ul> <p> </p> <p><strong>Input models</strong> (example for EBEAS)</p> <ul> <li><strong>ebeas.sml</strong> contains the actual SML input model to be evolved</li> <li><strong>ebeas.ecore</strong> contains the metamodel of the EBEAS example</li> <li><strong>ebeas.xmi</strong> contains the object system of the EBEAS example that is used for the SML realizability check</li> <li><strong>ebeas.runconfig</strong> contains the runtime configuration for ScenarioTools that binds the SML input model with the object system</li> <li><strong>ebeas.cspec</strong> contains the solution space model used by Multiform.</li> </ul> <p> </p> <p><strong>Measurements</strong> are stored in an <a href="http://h2database.com/html/main.html">H2 database</a> file. To open one of the database files for the EBEAS or production cell examples extract the appropriate zip file to a local folder, navigate to the folder in a terminal, and start H2 with the appropriate database file as follows:</p> <pre><code>java -jar h2-1.4.199.jar -url jdbc:h2:./Statistics</code></pre> <p>A web-based SQL client will open in your browser. H2 cann be obtained free of charge from their homepage.</p> <p> </p> <p>The <strong>database schema</strong> consists of three simple tables:</p> <p><strong>SMLCANDIDATESTATISTICS</strong> contains measurements for each evolved candidate SML model and consists of the following columns:<br> <strong>ALGORITHM </strong>- one of 'Random', 'NSGA2', 'tabu-75-intensify'<br> <strong>SEED </strong>- seed id for which the measurement was taken<br> <strong>ITERATION </strong>- iteration id during whch the measurement was taken<br> <strong>CANDIDATE </strong>- unique id of the evaludated candidate SML model<br> <strong>SYNTHESISTIME </strong>- synthesis time of the evaludated candidate SML model<br> <strong>O1_SCENARIOS </strong>- objective value for o1<br> <strong>O2_FRAGMENTSRATIO </strong>- objective value for o2<br> <strong>O3_ENVFRAGMENTSRATIO </strong>- objective value for o3<br> <strong>C1_REALIZABILITY </strong>- constraint value for c1<br> <strong>C2_REACHABILITY </strong>- constraint value for c1</p> <p><strong>SMLITERATIONSTATISTICS </strong>contains aggregated statistical data for each iteration and consists of the following columns:<br> <strong>ALGORITHM </strong>- one of 'Random', 'NSGA2', 'tabu-75-intensify'<br> <strong>SEED </strong>- seed id for which the data was aggregated<br> <strong>ITERATION </strong>- unique id of this aggregated iteration data<br> <strong>ITERATIONSUCCESSRATE </strong>- achieved success rate in this iteration<br> <strong>ACCUMULATEDSUCCESSRATE </strong>- achieved aggregated success reate until this iteration<br> <strong>ACCUMULATEDHYPERVOLUMEINDICATOR </strong>- achieved hypervolume until this iteration<br> <strong>NUMITERATIONPARETOEQUIVALENTCANDIDATES </strong>- number of pareto-equivalent candidate SML models in this iteration<br> <strong>NUMACCUMULATEDPARETOEQUIVALENTCANDIDATES </strong>- number of pareto-equivalent candidate SML models until this iteration<br> <strong>NUMITERATIONPARETODOMINANTCANDIDATES </strong>- number of pareto-dominant candidate SML models in this iteration<br> <strong>NUMACCUMULATEDPARETODOMINANTCANDIDATES </strong>- number of pareto-dominant candidate SML models until this iteration<br> <strong>ITERATIONSYNTHESISTIME </strong>- total synthesis time of this iteration<br> <strong>ACCUMULATEDSYNTHESISTIME </strong>- accumulated total synthesis time until this iteration</p> <p><strong>SMLSEEDSTATISTICS</strong> contains aggregated statistical data for each seed and consists of the following columns:<br> <strong>ALGORITHM </strong>- one of 'Random', 'NSGA2', 'tabu-75-intensify'<br> <strong>SEED </strong>- unique id of this aggregated seed data<br> <strong>SUCCESSRATE</strong>- achieved success rate in this seed<br> <strong>HYPERVOLUMEINDICATOR </strong>- achieved hypervolume in this seed<br> <strong>NUMPARETOEQUIVALENTCANDIDATES </strong>- number of pareto-equivalent candidate SML models in this seed<br> <strong>NUMPARETODOMINANTCANDIDATES </strong>- number of pareto-dominant candidate SML models in this seed<br> <strong>TOTALSYNTHESISTIME </strong>- total synthesis time of this seed</p> <p> </p> <p><strong>Please note</strong>: the database files contain data for algorithms 'tabu-50-intensify' and 'tabu-25-intensify' representing evaluation runs with different Tabu search configurations. However, these still need to be analyzed and <strong>experiment-results.pdf</strong> refers to '<strong>tabu-75-intensify</strong>' only.</p>
When and How to Transfer Knowledge in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments of the paper below:</p> <p>Ruan, G., Minku, L.L., Menzel, S., Sendhoff, B., Yao, X., "When and How to Transfer Knowledge in Dynamic Multi-objective Optimization," 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 2019, pp. 2034-2041. </p> <p> </p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving another related problem. This paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective benchmark problems, we show that transfer learning fails on problems with fixed Pareto optimal solution sets and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always adequate. Therefore, transfer learning should be avoided when dealing with problems for which transfer learning fails and other kernel functions should be used when the Gaussian kernel is inadequate. This paper proposes novel strategies and kernel functions that can be used in such cases. Experimental studies have demonstrated the superiority of our proposed techniques to state-of-the-art methods, on a number of dynamic bi- and tri-objective test problems.</p>
Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments from the paper below:</p> <p>Ruan, G., Minku, L., Menzel, S., Sendhoff, B., Yao., “Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization” <em>2020 IEEE Congress on Evolutionary Computation</em></p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the ‘inner’ optimization problem introduced by transfer learning is very time-consuming. In order to enhance the efficiency, two alternatives are computationally investigated on a number of dynamic bi- and tri-objective test problems. Experimental results have shown that the greatly enhanced efficiency does not result in much degeneration on the performance of transfer learning. Considering the high computational cost of transfer learning, it is likely that the original purpose of using transfer learning in DMO might be negated. In other words, the computation time saved in optimization is eaten up by computationally expensive transfer learning. As a result, there is less gain than expected in the overall computational efficiency. To verify this, experiments have been conducted, regarding using computational cost of transfer learning to optimize randomly generated solutions. The results have demonstrated that the convergence and diversity of final solutions generated from the random solutions are significantly better than those generated from transferred solutions under the same total computational budget.</p>
Sample Research Object for Zenodo
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque sodales, dolor imperdiet viverra gravida, turpis est aliquet nisi, vel fermentum augue nisi ut mi. Curabitur placerat egestas scelerisque. Nunc sit amet justo non eros rhoncus egestas a semper dui. Sed at ipsum nec elit sodales rhoncus. Fusce vel ante id mi consectetur tincidunt. Proin lobortis dui vel nunc lacinia pellentesque. Sed pretium justo quis dolor ornare, in egestas turpis eleifend. Sed tellus tellus, fermentum cursus ligula vitae, tincidunt consectetur velit. Vivamus a magna varius, pulvinar purus quis, molestie odio. Nam auctor semper nisi at vehicula. Vestibulum et dui quis metus fringilla tempus. Integer vitae purus nisi. Curabitur a elit at eros mattis lobortis et at lectus.
Object bounding box annotations for the GTEA Gaze+ dataset
<p>Object bounding box annotations for the GTEA Gaze+ dataset of the works <em>Learning to recognize daily actions using gaze</em> (Fathi et al., 2012) and <em>Delving into Egocentric Actions </em>(Li et al., 2015). The dataset contains folders for each of the subjects, within each of them folders for actions, and, within each action folder, a folder for each video. The video folder has a name composed of <name of the original video>_<start frame>_<end frame>. Within this folder, a json file for some frames can be found.</p> <p>The json contains two keys: <em>filename</em> and <em>objects</em>. <em>filename</em> refers to the path to the image and <em>objects</em> to a dictionary of objects. The keys of the dictionary are the objects present in the image. Each of the objects values is a list containing bounding box coordinates. Each coordinate list is composed of the ymin, xmin, ymax and ymax values.</p>
Supplementary video of the paper "Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach"
<p>Supplementary video of the paper "Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach" showing the experimental results.</p>
AnDy Data - Human Human Object Co-Manipulation
<p>This dataset contains measurements for two manipulation experiments. The first is a human dyad executing a shared co-manipulation task. And the second is a human single-handedly executing the same task in the same experimental setup.</p> <p>Both experiments are described in detail in the provided .PDF file. The participants, and the conditions in which they executed the experiments are described in the accompanying .XLS files.</p> <p>With the exception of proprietary data, all files are in .CSV format. The collected data is comprised of: Kinematic data of the subjects' arm; raw electromyography (EMG) signals from the subjects' arm; as well as the maximum value of contraction for each measured muscle in their arm. Additionally, there is data regarding the efficiency in which the dyad executes the task (number of wall touches).</p> <p>The Kinematic, and the EMG data, were collected with proprietary software, so the original files in the proprietary format are also made available.</p>
Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses
<p>Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.</p>
Benefit-cost analysis of adaptation objectives to coastal flooding at the global scale
<p>This dataset presents results of benefit-cost analyses of different adaptation objectives using grey infrastructure to coastal flooding at the global scale. </p> <p>The four adaptation objectives are: (1) ‘Protection constant’, which keeps protection levels in the future the same as current protection levels; (2) ‘Absolute risk constant’, which calculates future protection standards when the absolute value for expected annual damage is kept the same as current; (3) ‘Relative risk constant’, which calculates future protection standards when expected annual damage as a percentage of GDP is kept the same as current; and (4) ‘Optimize’, which calculates future protection standards by maximizing net present value.</p> <p>The results for each adaptation objective include future protection standards, change in risk relative to GDP, benefit-cost ratio and net present value for various RCP-SSP combinations for sub-national regions.</p>
Markov Automata with Multiple Objectives: Supplemental Material
<p>This data set contains benchmark files for multi-objective model checking of Markov Automata with <a href="https://www.stormchecker.org/">Storm</a>.</p>
Acquisition of object-robbing and object/food-bartering behaviors: A culturally maintained token economy in free-ranging long-tailed macaques
<p>The token exchange paradigm shows that monkeys and great apes are able to use objects as symbolic tools to request specific food rewards. Such studies provide insights into the cognitive underpinnings of economic behavior in non-human primates. However, the ecological validity of these lab-based experimental situations tends to be limited. Our field research aims to address the need for a more ecologically valid primate model of trading systems in humans. Around the Uluwatu Temple in Bali, Indonesia, a large free-ranging population of long-tailed macaques spontaneously and routinely engage in token-mediated bartering interactions with humans. These interactions occur in two phases: after stealing inedible and more or less valuable objects from humans, the macaques appear to use them as tokens, by returning them to humans in exchange for food. Our field observational and experimental data showed (1) age differences in robbing/bartering success, indicative of experiential learning, and (2) clear behavioral associations between value-based token possession and quantity or quality of food rewards rejected and accepted by subadult and adult monkeys, suggestive of robbing/bartering payoff maximization and economic decision-making. This population-specific, prevalent, cross-generational, learned and socially influenced practice may be the first example of a culturally maintained token economy in free-ranging animals.</p>
Data from: Habitat specialization of birds in the Czech Republic: comparison of objective measures with expert opinion
CAPSULE: Expert-based classification of bird species as habitat specialists and as generalists agrees with objective measures of species' habitat requirements based on large-scale monitoring data. AIMS: To compare habitat specialization of 137 common bird species breeding in the Czech Republic using three different measures and to test their relationships to species' abundance and habitat associations. METHODS: Data on bird abundance and surveyed habitats were collected through a standardized monitoring scheme of common breeding species in the Czech Republic. From these data we calculated a quantitative species specialization index (SSI). Canonical correspondence analysis (CCA) was applied to calculate species' habitat niche breadth and the level of association of each species to the main habitats. A panel of 11 local bird experts classified each species as habitat generalist or habitat specialist. RESULTS: Species classified as habitat specialists by expert opinion showed higher habitat specialization according to the SSI, as well as according to CCA-based habitat niche breadth. These species were also more closely associated with one of the main habitat types. These relationships were significant even after controlling for abundance. CONCLUSIONS: As expert opinion accords with the level of species' habitat specialization expressed using two quantitative objective measures, we suggest that these characteristics reflect real interspecific variation in the breadth of habitat requirements in birds. Interspecific differences in habitat specialization are not caused solely by the variability in abundance among species.
Data from: Multi-objective optimization for plant germplasm collection conservation of genetic resources based on molecular variability
Germplasm collections play a significant role among strategies for conservation of diversity. It is common to select a core collection to represent the genetic diversity of a germplasm collection, in order to minimize the cost of conservation, while ensuring the maximization of genetic variation. We aimed to solve two main problems: (1) to select a set of individuals, from an in situ data set, that is genetically complementary to an existing germplasm collection, and (2) to define a core collection for a germplasm collection. We proposed a new multi-objective optimization (MOO) approach based on principles of systematic conservation planning (SCP) incorporating heterozygosity information; therefore, optimization takes genotypic diversity and variability patterns into account as well. As a case study, we used Dipteryx alata microsatellite loci information from two sources, an ex situ germplasm collection located at the Agronomy School of the Federal University of Goiás (UFG-AS), and an in situ data set composed of 642 sampled individual trees. We were able to identify within a population of several individuals, the exact accessions/samples that should be chosen in order to preserve the species diversity. We found that material from nine in situ individual trees are enough to complement the UFG-AS germplasm collection as it is, and that it is possible to define a core collection of 20 individual trees representing all studied genetic diversity. Moreover, we defined a method (a protocol) to deal with large amounts of accessions in the context of MOO. The proposed approach can be used to help constructing collections with maximal allelic richness and can also be extended to the in situ conservation. As far as we know, this is the first time that principles of SCP and the MOO approach are applied to the problem of complementing a germplasm collection and of finding a core collection for a germplasm collection.
Data from: Objective choice of phylogeographic models
Phylogeography seeks to discover the evolutionary processes that have given rise to organismal and genetic diversity. This requires explicit hypotheses (i.e., models) to be evaluated with genetic data in order to identify those hypotheses that best explain the data. In recent years, advancements in the model-based tools used to estimate phylogeographic parameters of interest such as gene flow, divergence time, and relationships among groups have been made. However, given the complexity of these models, available methods can typically only compare a handful of possible hypotheses, requiring researchers to specify in advance the small set of models to consider. Without formal quantitative approaches to model selection, researchers must rely on their intuition to formulate the model space to be explored.We explore the adequacy of intuitive choices made by researchers during the process of data analysis by reanalyzing 20 empirical phylogeographic datasets using PHRAPL, an objective tool for phylogeographic model selection. We show that the best models for most datasets include both gene flow and population divergence parameters, and that species tree methods (which do not consider gene flow) tend to be overly simplistic for many phylogeographic systems. Objective approaches to phylogeographic model selection offer an important complement to researcher intuition.
Bi-objective optimization for last-train coordination planning with dwell time adjustment strategy
<p><span><span><span><span>In the design of last-train coordination plan, adjusting (extending/reducing) dwell time can balance economic and service goals for operation company and late-night passengers, especially when transfer-passenger flow is uncertain. For this purpose, we first develop</span></span><span><span> a </span></span><span><span>bi-objective</span></span> <span><span>scenario-based</span></span> <span><span>stochastic programming model for</span></span> <span><span>last-train coordination planning problem combined with</span></span><span><span> the </span></span><span><span>dwell time adjustment strategy. Then, we</span></span> <span><span>develop a two-phase approach, wherein the first phase we adopt the varepsilon-constraint method to reformulate the original model to a modified single-objective one. This is followed by the second phase using the branch-and-bound algorithm implemented by</span></span><span><span> the </span></span><span><span>CPLEX solver to obtain</span></span> <span><span>Pareto-optimal solutions (frontier). Finally, we demonstrate the advantage of the proposed model through comparison with the corresponding model without dwell time strategy and the max-min robust model over a randomly generated small-scale network. Moreover, we also illustrate the application of the proposed model by a real-world case study on the large-scale Beijing subway network.</span></span></span></span></p>
geddes collection object
3d scan of Jim Geddes sculpture from Geddes Collection, London. Source: Objaverse 1.0 / Sketchfab
Lead Object
Photogrammetry model of a lead object. Source: Objaverse 1.0 / Sketchfab
Etruscan bone object
Etruscan bone object from the Necropoli del Poggione, Asciano, Tuscany. Bone object, circular shape, perforated, with decorative engraved bands, function is unknown. Orientalizing period, late 7th-early 6th century BCE. Tomb A, Necropolis of Poggione, Castelnuovo Berardenga. Displayed at the Civic Museum of Archaeology and Sacred Art at Palazzo Corboli, Asciano. Mangani 1988-1989. Processed in Reality Capture from 300 images. GDH ID No. C30_108808 This project was done under the authority of the Soprintendenza Archeologia, belle arti e paesaggio per le province di Siena Grosseto e Arezzo in collaboration with Global Digital Heritage and the Municipality of Asciano. We thank all the staff from Palazzo Corboli for making the collection accessible and for providing an excellent work environment. Source: Objaverse 1.0 / Sketchfab
Towards Compliance Checking between Business Process Models and Lawful States of Objects
<p>Dataset from publication http://ceur-ws.org/Vol-1230/paper-03.pdf </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.