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3,118 results for “resources”
Resource Gradient Study Management at the Kellogg Biological Station, Hickory Corners, MI (2000 to 2020)
Dataset Abstract The log of the resource gradient study agronomic management. A “browsable” interface to the aglog is available at https://aglog.kbs.msu.edu original data source http://lter.kbs.msu.edu/datasets/110
Long-term fish abundance data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/346/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/356/3. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Fig. 2 in Intraspecific food resource partitioning in Brazilian silverside Atherinella brasiliensis (Atheriniformes: Atherinopsidae) in a tropical estuary, Brazil
Fig. 2. Box-plot of spatial and temporal variation of number of individuals (CPUE average ±SE) and Biomass (±SE) of A. brasiliensis collected Mamanguape river estuary in Brazil. Bold lines indicate medians, hinges indicate the 25th and 75th percentiles, whiskers indicate the largest and smallest observation within a distance of 1.5 the box size. White bar= Wet season and Dark Gray bar= Dry season.
Fig. 5 in Intraspecific food resource partitioning in Brazilian silverside Atherinella brasiliensis (Atheriniformes: Atherinopsidae) in a tropical estuary, Brazil
Fig. 5. Principal coordinate analysis ordination (PCO) coded by habitat (a) and size classes (b) for Atherinella brasiliensis in the Mamanguape River estuary, Brazil. Symbols Habitat: Mud flat (Full Black Triangle); Tidal Creek 1 (Gray Square); Tidal Creek 2 (Black Circle); Symbols size classes: Small juveniles (Open Black Square); Juveniles (Light Gray Circle) and Adults (Dark Gray Circle).
Fig. 7 in Intraspecific food resource partitioning in Brazilian silverside Atherinella brasiliensis (Atheriniformes: Atherinopsidae) in a tropical estuary, Brazil
Fig. 7. Feeding strategy for Atherinella brasiliensis in Mamanguape river estuary: A= Mudflat; B= Tidal Creek 1 and C= Tidal Creek 2. Food items: Cyc, Cyclopoida; Cal, Calanoida; Dec, Decapoda; Decl, Decapoda larvae; Hym, Hymnoptera; Egf, Fish eggs; Cer, Ceratopogonidae larvae; Ost, Ostracoda; Gas, Gastropoda; Pol, Polychaeta; Esc, Scale. (TL1= small juveniles; TL2= juveniles and TL3= adult).
Fig. 5 in A new and improved electric fish finder with resources for printed circuit board fabrication
Fig. 5. Design schematic for electric fish finder enclosure. Grey arrows show distances from edges to the center of drill holes/ objects. Grey dotted circles represent drill holes. The sealing washer marked with an asterisk has no waterproofing function and is used instead to trap the exposed terminal of a wire from the channel ground against the internal metal surface of the enclosure (see CHGND in Fig. 2).
Fig. 4 in Food resource partitioning among species of Astyanax (Characiformes: Characidae) in the Lower Iguaçu River and tributaries, Brazil
Fig. 4. Relative frequency (%) of the diet overlap index of Astyanax species pairs in the Low Iguaçu River and tributaries in Brazil. Ab=A. bifasciatus; Ad=A. dissimilis; Ag=A. gymnodontus; Al=A. lacustris; Am=A. minor. Low <0.39; intermediate =0.40-0.59; high> 0.60.
Fig.1 in Food resource partitioning among species of Astyanax (Characiformes: Characidae) in the Lower Iguaçu River and tributaries, Brazil
Fig.1. Study area showing the sampling sites (numbers 1 to 25) in the Lower Iguaçu River and tributaries in Brazil.
LRRo: A Lip Reading Data Set for the Under-resourced Romanian Language
<p>Two distinct collections are presented in this repository:</p> <p>(i) wild LRRo data is designed for an Internet in-the-wild, ad-hoc scenario, coming with more than 35 different speakers, 1.1k words, a vocabulary of 21 words, and more than 20 hours;</p> <p>(ii) lab LRRo data, addresses a lab controlled scenario for more accurate data, coming with 19 different speakers, 6.4k words, a vocabulary of 48 words, and more than 5 hours.</p>
Data of "Accurate photonic temporal mode analysis with reduced resources"
<p>Data published in "<em>Accurate photonic temporal mode analysis with reduced resources</em>".</p> <p>Phys. Rev. A <strong>101</strong>, 013801</p>
Network analysis reflects the trophic relationship between microbial colonizers and deadwood resources - supporting information
<p>Supporting tables S2 - S5 of "Network analysis reflects the trophic relationship between microbial colonizers and deadwood resources".</p> <p>The file Table_Legends_S2-S5.txt contains all legends, as given below:</p> <p>Table S2: Module-associated trees and OTUs, their relative abundances and identities for the fungal sapwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p> <p>Table S3: Module-associated trees and OTUs, their relative abundances and identities for the fungal heartwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p> <p>Table S4: Module-associated trees and OTUs, their relative abundances and identities for the prokaryotic sapwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p> <p>Table S5: Module-associated trees and OTUs, their relative abundances and identities for the prokaryotic heartwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p>
Preference and familiarity mediate spatial responses of a large herbivore to experimental manipulation of resource availability: datasets
<p>Publicly available dataset for:</p> <p>N. Ranc, P.R. Moorcroft, K.W. Hansen, F. Ossi, T. Sforna, E. Ferraro, A. Brugnoli & F. Cagnacci. 2020. Preference and familiarity mediate spatial responses of a large herbivore to experimental manipulation of resource availability. Scientific Reports. https://doi.org/10.1038/s41598-020-68046-7</p>
Accounting for New Types of Resource Consumption in a Federated Cloud
<p>As infrastructures and cloud services evolve, resource consumption is more flexible and users are often allowed to reserve resources without actual consumption. Relevant standardization bodies have developed new types of accounting record specifications, and new or updated tools are required to keep track of resource usage. The reaction to this is the development of a new accounting tool – GOAT.</p> <p>GOAT – GO Accounting Tool – is a service running in the background and waiting for a connection from a compatible client. The client connects to a cloud management framework, extracts computing data about projects, servers, networks, and storages, filters them accordingly, and sends them to a server for further processing. Multiple clients can use the server at once. When the server receives accounting data, it is transformed into the configured format and writes them to the destination file. The consumer collects data into a central accounting database where it is processed to generate statistical summaries.<br> For now, the GOAT project supports two cloud computing platforms on the client side – OpenNebula and Openstack. Thanks to its use of standard accounting record formats it can work with different consumers. The ones used in the real world are APEL and Prometheus.</p> <p>This Demonstration shows how the GOAT client extracts accounting data from a cloud management platform, sends them to the GOAT server where they are transformed, and how Prometheus and Grafana process them and present various views of resource usage.</p>
Dataset for Freedom to Choose between Public Resources Promotes Cooperation
<p>This dataset contains Matlab codes and Matlab data for "Freedom to choose between Public Resources promotes Cooperation". For a description of the dataset see the text file, "Description", in the dataset.</p>
Resource allocation decision-making in dementia care with and without budget constraints: a qualitative analysis
<p>Vignettes and COREQ checklist for Applied Partnership Award project: Resource Allocation, Priority-Setting and Consensus in Dementia Care. See Keogh F, Pierse T, O'Shea E <em>et al.</em> Resource allocation decision-making in dementia care with and without budget constraints: a qualitative analysis [version 1; peer review: awaiting peer review]. <em>HRB Open Res</em> 2020, <strong>3</strong>:69 (<a href="https://doi.org/10.12688/hrbopenres.13147.1">https://doi.org/10.12688/hrbopenres.13147.1</a>)</p>
Mateo-Ramírez et al Species_Chapter 25_Alboran Sea-Ecosystems and Marine Resources
<p>Lists of species include in conservation agreements or relevant by their singularity present in MPA and KBA of Alboran Sea and adjacent areas. </p> <p>This dataset is part of the Chapter 25 Marine Protected Areas and Key Biodiversity Areas of the Alboran Sea and Adjacent Areas. In J. C. Baéz et al. (eds.), Alboran Sea - Ecosystems and Marine Resources, https://doi.org/10.1007/978-3-030-65516-7_25</p>
Data from: Resource addition drives taxonomic divergence and phylogenetic convergence of plant communities
1. Anthropogenic environmental changes are known to affect the Earth's ecosystems. However, how these changes influence assembly trajectories of the impacted communities remains a largely open question. 2. In this study, we investigated the effect of elevated nitrogen (N) deposition and increased precipitation on plant taxonomic and phylogenetic β-diversity in a 9-year field experiment in the temperate semi-arid steppe of Inner Mongolia, China. 3. We found that both N and water addition significantly increased taxonomic β-diversity, whereas N, not water, addition significantly increased phylogenetic β-diversity. After the differences in local species diversity were controlled using null models, the standard effect size of taxonomic β-diversity still increased with both N and water addition, while water, not N, addition, significantly reduced the standard effect size of phylogenetic β-diversity. The increased phylogenetic convergence observed in the water addition treatment was associated with the colonization of different, but phylogenetically closely related, species into different replicate plots of the treatment. Species colonization in this treatment was found to be trait-based, with leaf nitrogen concentration being the key functional trait. 4. Synthesis. Our analyses demonstrate that anthropogenic environmental changes may affect the assembly trajectories of plant communities at both taxonomic and phylogenetic scales. Our results also suggest that while stochastic processes may cause communities to diverge in species composition, deterministic process could still drive communities to converge in phylogenetic community structure.
South African higher education data 1 - Data resources
<p>GIS-based map visualisation of the data resources providing open data on South African higher education data. Generated as part of research conducted for the 'Use of open data in the governance of South African higher education' research project, in the IDRC/WWWF 'Exploring Emerging Impacts of Open Data in the South' initiative.</p> <p> </p>
Autonomic Provisioning and Application Mapping on Spot Cloud Resources
<p>1. Attached files: </p> <p>500_100_fmincon_0.95_1.mat <br /> Experiment with 500 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>500_100_heuristic_0.95_1.mat<br /> Experiment with 500 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_70_fmincon_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, exact algorithm.</p> <p>2000_70_heuristic_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.9_1.mat<br /> Experiment with 2000 users, 100ms max response time, 90% availability, exact algorithm.</p> <p>2000_100_heuristic_0.9_1.mat <br /> Experiment with 2000 users, 100ms max response time, 90% availability, our algorithm.</p> <p>2000_100_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>2000_100_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, exact algorithm.</p> <p>2000_100_heuristic_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, our algorithm.</p> <p>2000_300_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, exact algorithm.</p> <p>2000_300_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, our algorithm.</p> <p>10000_100_fmincon_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>10000_100_heuristic_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2. Data format:</p> <p>MATLAB data format, can be load from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Model-driven Application Refactoring to Minimize Deployment Costs in Preemptible Cloud Resources
<p>1. Attached files: </p> <p>This archive contains 1440 MATLAB files, each one containing the results of a single experiment.<br /> The name of each file follows the following format:</p> <p> A_B_C_null_D_0.95_E_F.mat</p> <p>Where the fields A, B, C, D, E, and F are described as follows.</p> <p>A: number of users.<br /> Considered values are: 2500, 5000, 10000, 20000, 40000.</p> <p>B: variation in the profile of the requests.<br /> Considered values are: ref, var1, var2, var3, var4, var5.<br /> ref -> reference experiment, no users are halved or doubled<br /> var1 -> users in class 1 halved, other users doubled <br /> var2 -> users in class 2 halved, other users doubled <br /> var3 -> users in class 3 halved, other users doubled <br /> var4 -> users in class 4 halved, other users doubled <br /> var5 -> users in class 5 halved, other users doubled </p> <p>C: variation of the replaceability set of the application server.<br /> Considered values are: ref, k1, k2, k3, k4, k5.<br /> ref -> reference experiment, no rates are halved or doubled<br /> k1 -> only possible substitution has rate k1 halved and other rates doubled<br /> k2 -> only possible substitution has rate k2 halved and other rates doubled <br /> k3 -> only possible substitution has rate k3 halved and other rates doubled <br /> k4 -> only possible substitution has rate k4 halved and other rates doubled <br /> k5 -> only possible substitution has rate k5 halved and other rates doubled </p> <p>D: variation of the design constraints.<br /> Considered values are: none, 4, 34, 234.<br /> none -> no components can be replicated<br /> 4 -> only the application server can be replicated<br /> 34 -> only the application server and the database server can be replicated<br /> 234 -> all the components can be replicated</p> <p>E: Optimization algorithm.<br /> Considered values are: norefactoring, replacement, reassignment, full<br /> norefactoring -> experiment with no refactorings<br /> replacement -> experiment with only replacement refactoring<br /> reassignment -> experiment with only reassignment refactoring<br /> full -> experiment with replacement and reassignment refactorings</p> <p>F: Experiment seed.<br /> Considered values are from 1 to 20</p> <p> </p> <p>2. Data format:</p> <p>MATLAB data format, can be loaded from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</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.