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39 results for “resource heterogeneity”
Resource heterogeneity leads to unjust effort distribution in climate change mitigation
<p>Climate change mitigation is a shared global challenge that involves the collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together toward a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer - sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two "generous clusters'" and the richest into a "greedy cluster''. Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences.</p> <p>Vicens J, Bueno-Guerra N, Gutiérrez-Roig M, Gracia-Lázaro C, Gómez-Gardeñes J, Perelló J, et al. (2018) Resource heterogeneity leads to unjust effort distribution in climate change mitigation. PLoS ONE 13(10): e0204369. https://doi.org/10.1371/journal.pone.0204369</p>
Resources of IncRML: Incremental Knowledge Graph Construction from Heterogeneous Data Sources
<h2>IncRML resources</h2> <p>This Zenodo dataset contains all the resources of the paper 'IncRML: Incremental Knowledge Graph Construction from Heterogeneous Data Sources' submitted to the Semantic Web Journal's Special Issue on Knowledge Graph Construction. This resource aims to make the paper experiments fully reproducible through our <a href="https://github.com/kg-construct/exectool" target="_blank" rel="noopener">experiment tool</a> written in Python which was already used before in the <a href="https://doi.org/10.5281/zenodo.7837289" target="_blank" rel="noopener">Knowledge Graph Construction Challenge by the ESWC 2023 Workshop on Knowledge Graph Construction</a>. The exact Java JAR file of the RMLMapper (rmlmapper.jar) is also provided in this dataset which was used to execute the experiments. This JAR file was executed with Java OpenJDK 11.0.20.1 on Ubuntu 22.04.1 LTS (Linux 5.15.0-53-generic). Each experiment was executed 5 times and the median values are reported together with the standard deviation of the measurements.</p> <h2>Datasets</h2> <p>We provide both dataset dumps of the GTFS-Madrid-Benchmark and of real-life use cases from Open Data in Belgium.<br>GTFS-Madrid-Benchmark dumps are used to analyze the impact on execution time and resources, while the real-life use cases aim to verify the approach on different types of datasets since the GTFS-Madrid-Benchmark is a single type of dataset which does not advertise changes at all.</p> <h3>Benchmarks</h3> <ul> <li>GTFS-Madrid-Benchmark: change types with fixed data size and amount of changes: additions-only, modifications-only, deletions-only (11 versions)</li> <li>GTFS-Madrid-Benchmark: amount of changes with fixed data size: 0%, 25%, 50%, 75%, and 100% changes (11 versions)</li> <li>GTFS-Madrid-Benchmark: data size with fixed amount of changes: scales 1, 10, 100 (11 versions)</li> </ul> <h3>Real-world datasets</h3> <ul> <li>Traffic control center Vlaams Verkeerscentrum (Belgium): traffic board messages data (1 day, 28760 versions)</li> <li>Meteorological institute KMI (Belgium): weather sensor data (1 day, 144 versions)</li> <li>Public transport agency NMBS (Belgium): train schedule data (1 week, 7 versions)</li> <li>Public transport agency De Lijn (Belgium): busses schedule data (1 week, 7 versions)</li> <li>Bike-sharing company BlueBike (Belgium): bike-sharing availability data (1 day, 1440 versions)</li> <li>Bike-sharing company JCDecaux (EU): bike-sharing availability data (1 day, 1440 versions)</li> <li>OpenStreetMap (World): geographical map data (1 day, 1440 versions)</li> </ul> <h3>Ingestion</h3> <p>Real-world datasets LDES output was converted into SPARQL UPDATE queries and executed against Virtuoso to have an estimate for non-LDES clients how incremental generation impacted ingestion into triplestores.</p> <h2>Remarks</h2> <ol> <li>The first version of each dataset is always used as a baseline. All next versions are applied as an update on the existing version. The reported results are only focusing on the updates since these are the actual incremental generation.</li> <li>GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz datasets are not uploaded as GTFS-Madrid-Benchmark scale 100 because both share the same parameters (50% changes, scale 100). Please use GTFS-Scale-100-{ALL, CHANGE}.tar.xz for GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz</li> <li>All datasets are compressed with XZ and provided as a TAR archive, be aware that you need sufficient space to decompress these archives! 2 TB of free space is advised to decompress all benchmarks and use cases. The expected output is provided as a ZIP file in each TAR archive, decompressing these requires even more space (4 TB).</li> </ol> <h2>Reproducing</h2> <p>By using our <a href="https://github.com/kg-construct/exectool" target="_blank" rel="noopener">experiment tool</a>, you can easily reproduce the experiments as followed:</p> <ol> <li>Download one of the TAR.XZ archives and unpack them.</li> <li>Clone the GitHub repository of our experiment tool and install the Python dependencies with '<em>pip install -r requirements.txt'.</em></li> <li>Download the rmlmapper.jar JAR file from this Zenodo dataset and place it inside the experiment tool root folder.</li> <li>Execute the tool by running: '<em>./exectool --root=/path/to/the/root/of/the/tarxz/archive --runs=5 run</em>'. The argument '<em>--runs=5</em>' is used to perform the experiment 5 times.</li> <li>Once executed, you can generate the statistics by running: '<em>./exectool --root=/path/to/the/root/of/the/tarxz/archive stats</em>'.</li> </ol> <h2>Testcases</h2> <p>Testcases to verify the integration of RML and LDES with IncRML, see <a href="https://doi.org/10.5281/zenodo.10171394">https://doi.org/10.5281/zenodo.10171394</a></p>
Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
<p><span>The viability of wild bee populations and the pollination services that they provide are driven by the availability of food resources during their activity period and within the surroundings of their nesting sites. Changes in climate and land use influence the availability of these resources and are major threats to declining bee populations. Because wild bees may be vulnerable to interactions between these threats, spatially explicit models of population dynamics that capture how bee populations jointly respond to land use at a landscape scale and weather are needed. Here, we developed a spatially and temporally explicit theoretical model of wild bee populations aiming for a middle ground between the existing mapping of visitation rates using foraging equations and more refined agent-based modelling. The model is developed for <em>Bombus</em> sp. and captures within-season colony dynamics. The model describes mechanistically foraging at the colony level and temporal population dynamics for an average colony at the landscape level. Stages in population dynamics are temperature-dependent triggered with a theoretical generalized seasonal progression, which can be informed by growing degree days (GDD). The purpose of the LandscapePhenoBee model is to evaluate the impact of systematic changes and within-season variability in resources on bee population sizes and crop visitation rates. In a simulation study, we used the model to evaluate the impact of the shortage of food resources in the landscape arising from extreme drought events in different types of landscapes (ranging from different proportions of semi-natural habitats and early and late flowering crops) on bumblebee populations.</span></p>
Companion for "Design, Implementation and Performance Analysis of a CFD task-based Application for Heterogeneous CPU/GPU Resources"
<p>This is the companion data for the VECPAR2018 submission paper entitled: Design, Implementation and Performance Analysis of a CFD task-based Application for Heterogeneous CPU/GPU Resources by Lucas Leandro Nesi, Lucas Mello Schnorr, and Philippe Olivier Alexandre Navaux. All the data, source code, and images generation scripts used in the paper are present here.</p>
Code from: Spatial resource heterogeneity stabilizes local and regional predator-prey dynamics in ecologically-realistic networks
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Genomic identification of direct seeding and evolutionary lineages by combining heterogeneous genomic resources
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Spatial heterogeneity in resources alters selective dynamics in Drosophila melanogaster
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Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
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Data from: Determining the optimal movement strategies in environments with heterogeneously distributed resources and toxicants
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Foraging in nature: contrasting responses to resource heterogeneity at small and large spatial scales.
<p>A key problem faced by foragers is how to forage when resources are distributed heterogeneously in space. This heterogeneity and associated trade-offs may change with spatial scale. Furthermore, foragers may also have to optimize acquiring multiple resources. Such complexity of decision-making while foraging is poorly understood. We studied the butterfly <i>Ypthima huebneri</i> to examine how foraging decisions of adults are influenced by spatial scale and multiple resources. We predicted that, at a small-spatial scale, the time spent foraging in a patch should be proportional to resources in the patch, but at large-spatial scales, due to limitations arising from large travel costs, this relationship should turn negative. We also predicted that both adult and larval resources should jointly affect foraging butterflies. To test these predictions, we laid eleven plots, and sub-divided them into patches. We mapped nectar and larval resources and measured butterfly behavior in these patches and plots. We found that adult foraging behavior showed contrasting relationships with adult resource density at small versus large-spatial scales. At the smaller-spatial scale, butterflies spent more time feeding in resource-rich patches, whereas at the large-scale, butterflies spent more time feeding in resource-poor plots. Furthermore, both adult and larval resources appeared to affect foraging decisions, suggesting that individuals may optimize search costs for different resources. Overall, our findings suggest that the variation in foraging behavior seen in foragers might result from animals responding to complex ecological conditions, such as, resource heterogeneity at multiple spatial scales and the challenges of tracking multiple resources.</p>
The impact of market integration on the digital divide: An analysis based on the heterogeneous effect of innovation resource synergy
<p>Resource market reforms have provided opportunities for reducing the digital divide (DD). In this study, we analyzed the theoretical mechanism of the impact of market integration (MI) on DD and the intermediary role of innovation resource (IR) synergy. Data from 271 cities in China were collected, and the DEA–Malmquist index and Thiel index were utilized to quantify DD among these cities during 2011–2019. The intermediary effect and SDM models were adopted for an empirical analysis. The results showed that (1) MI had a negative effect on DD in addition to significant spatial spillover effects. MI widened DD in the local city but narrowed the same in the adjacent cities. (2) IR synergy exerted positive intermediary effects, which were heterogeneous. Through human resource synergy, MI narrowed DD, through knowledge synergy, MI widened DD, and through the R&D capital synergy, MI did not significantly influence DD. (3) There was no significant spatial spillover effect of the intermediary role of IR synergy.</p>
Companion data of Detection, Evaluation and Mitigation of Resource Affinity and Communication Contention Problems in a Task-Based Runtime over Heterogeneous Clusters
<p>This is the companion data repository for the paper entitled <strong>Detection, Evaluation, and Mitigation of Resource Affinity and Communication Contention Problems in a Task-Based Runtime over Heterogeneous Clusters</strong> by Lucas Leandro Nesi and Lucas Mello Schnorr. The manuscript has been accepted for publication in the <a href="http://wscad.sbc.org.br/current/index.html">WSCAD 2020</a>.</p>
HADES: An NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures. Study dataset
<p>The publication and research associated with this dataset are currently under review in the Journal of Network and Computer Applications.</p> <p>In that research, we present HADES, an NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures. HADES is an OSM (Open Source MANO) extension that allows the configuration of virtual network functions and their subsequent resource allocation and deployment at the edge, minimizing energy consumption and ensuring the quality of service.</p> <p>This dataset contains the following:</p> <ul> <li>1 CSV file with execution time results of the iTAREA module for (60) different problem sizes and (3) execution environments.</li> <li>1 CSV file with energy consumption results of HADES deployments and other 5 assignment policies: First-fit, Random-fit, Fastest-fit, Best-fit, and kube-scheduler (the Kubernetes’ assignment policy).</li> </ul> <p>This work is supported by the European Union's H2020 research and innovation programme under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15 and IRIS PID2021-122812OB-I00 (MCI/AEI).</p>
Data from: Virus community structuring is shaped by habitat heterogeneity and resource utilisation strategies
<p><span>After decades of disconcerted research, we still recognise large gaps in the understanding of mechanisms that govern disease dynamics in complex biological communities. To determine how spatial structuring of plant communities caused by anthropic disturbance affects resource utilisation traits of viruses, we combine high-throughput, network, and metacommunity approaches. We find that the disturbance gradient corresponded to network modules and habitat specificity exhibited by a majority of viruses. Communities were connected through key hub species of either generalist viruses or high potential host reservoirs. Spatial dependencies were evident in regression models of species richness and correlations between metacommunity structure and both host range and transmission mode at finer spatial resolutions. We propose that virus community assembly is influenced by variation in niche opportunities. Distinctions in virus community composition caused by resource compartmentalisation can be used to track ecological traits important in forecasting transmission risk.</span></p>
Data from: Resource quantity and heterogeneity drive successional plant diversity in managed and unmanaged boreal forests
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Data from: Virus community structuring is shaped by habitat heterogeneity and resource utilisation strategies
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Foraging in nature: contrasting responses to resource heterogeneity at small and large spatial scales.
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Data from: Resource heterogeneity and the evolution of public-goods cooperation
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The impact of market integration on the digital divide: An analysis based on the heterogeneous effect of innovation resource synergy
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Data from: Mallard resource selection trade‐offs in a heterogeneous environment during autumn and winter
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.