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ShareScore release 0.9.0
Dataset results
72 results for “online resources”
Roads dataset of the Kunbaja online resource
<p>Information layer "roads" of the Kunbaja online resource model built in QGIS as dataset in the ESRI shapefile format</p>
Roads layer screenshot of the Kunbaja online resource
<p>Information layer "roads" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Plot layer screenshot of the Kunbaja online resource
<p>Secondary information layer "female plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Hydro features dataset of the Kunbaja online resource
<p>Information layer "hydro-features" of the Kunbaja online resource model built in QGIS as dataset in the ESRI shapefile format</p>
Magyar plots layer screenshot of the Kunbaja online resource
<p>Secondary information layer "magyar plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Data from: An R package and online resource for macroevolutionary studies using the ray-finned fish tree of life
1. Comprehensive, time-scaled phylogenies provide a critical resource for many questions in ecology, evolution, and biodiversity. Methodological advances have increased the breadth of taxonomic coverage in phylogenetic data; however, accessing and reusing these data remain challenging. 2. We introduce the Fish Tree of Life website and associated R package fishtree to provide convenient access to sequences, phylogenies, fossil calibrations, and diversification rate estimates for the most diverse group of vertebrate organisms, the ray-finned fishes. The Fish Tree of Life website presents subsets and visual summaries of phylogenetic and comparative data, and is complemented by the R package, which provides flexible programmatic access to the same underlying data source for advanced users wishing to extend or reanalyze the data. 3. We demonstrate functionality with an overview of the website, and show three examples of advanced usage through the R package. First, we test for the presence of long branch attraction artifacts across the fish tree of life. The second example examines the effects of habitat on diversification rate in the pufferfishes. The final example demonstrates how a community phylogenetic analysis could be conducted with the package. 4. This resource makes a large comparative vertebrate dataset easily accessible via the website, while the R package enables the rapid reuse and reproducibility of research results via its ability to easily integrate with other R packages and software for molecular biology and comparative methods.
QGIS dataset of the Kunbaja online resource
<p>QGIS dataset of the Kunbaja online resource model built in QGIS as dataset in the .qgz file format</p>
Demonstrating a Bayesian Online Learning forEnergy-Aware Resource Orchestration in vRANs
<p>Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration problems in energy-constrained vRANs. Specifically, we demonstrate two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, converge an order of magnitude faster than other machine learning methods-and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the advantages of our approach in a testbed comprised of fully-fledged LTE stacks and a power meter, and implemented our approach into O-RAN's non-real-time RAN Intelligent Controller (RIC).</p>
Figures 2 and 3 in HighRes for Springer book chapter "Online Infrastructures For Open Educational Resources"
<p>Figure 2 and Figure 3 in high resolution for the book chapter:</p> <p>Marín, V. I., & Villar-Onrubia, D. (in press, 2022). Online Infrastructures For Open Educational Resources. In I. Jung & O. Zawacki-Richter (Eds.), <em>Handbook of Open, Distance and Digital Education</em> (Global Perspectives and Internationalization). Springer. <a href="https://doi.org/10.1007/978-981-19-0351-9_18-1">https://doi.org/10.1007/978-981-19-0351-9_18-1</a></p> <p>----</p> <p>Details for the figures:</p> <p>Fig. 2 Examples of national and regional digital infrastructures in Europe. (Note: The original figure of the Europe map was created by Commons user Alexrk2, CC BY-SA 3.0, shared via Wikimedia Commons)</p> <p>Figure 3. Examples of national and regional digital infrastructures in South America. (Note: The original figure of the South America map was created by TUBS, CC BY-SA 3.0, shared via Wikimedia Commons)</p>
Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms: Virtual Machine ID Dataset
<p>The csv files in this dataset contain the virtual machine IDs used in [1] which correspond to the virtual machine traces in the Azure Public Dataset [2]. The file named "vmtable_lifetime_VMoL_1003.csv" holds the IDs used in Section 5 [1] and the file named "vmtable_lifetime_VMoL_55.csv" holds the IDs used in Section 6 [1]. The first column in both files refers to the ID labels in [1], while the second, third, and fourth columns refer to the Virtual Machine IDs, the Subscription IDs, and the Deployment IDs, respectively.</p> <p> </p> <p>[1] Joshua Comden, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. 2019. Online Optimization in<br> Cloud Resource Provisioning: Predictions, Regrets, and Algorithms. Proc. ACM Meas. Anal. Comput. Syst. 3, 1,<br> Article 179 (March 2019).</p> <p>[2] Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. 2017. Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. In Proceedings of SOSP’17. ACM, New York, NY, USA, 15 pages. https://doi.org/10.1145/3132747.3132772 Dataset access: https://github.com/Azure/AzurePublicDataset (August 2018)</p>
Online Resources Chapter 3 - Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate
<p>Online Resources to Chapter 3 "Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate" of PhD thesis from Ciska Overbeek, "Peat formation on a former landfill - Production and decomposition of aquatic pioneer vegetation". </p> <p>Published by Overbeek et al in 2019 in Wetlands 39(1): 113-125. https://doi.org/10.1007/s13157-018-1081-y. </p>
Appendix for "How Programmers Find Online Learning Resources"
<p>This artifact contains the the documents needed to replicate our user study as well as the collected data to validate our observations presented in the paper "How Programmers Find Online Learning Resources" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard.</p>
Plot attribute table of the Kunbaja online resource
<p>Plot attribute table of the QGIS database of the Kunbaja online resource model as dataset in the .xlsx file format</p>
Strengths to Grow - Preteen: An Online Parenting Resource
ClinicalTrials.gov study NCT05582057. IPD Sharing: NO. Countries: 1. Publications: 6.
Strengths to Grow: An Online Parenting Resource
ClinicalTrials.gov study NCT05535881. IPD Sharing: NO. Countries: 1. Publications: 6.
MyMenoPlan: Online Resource for Improving Women's Menopause Knowledge and Informed Decision-making
ClinicalTrials.gov study NCT05299983. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: An R package and online resource for macroevolutionary studies using the ray-finned fish tree of life
Open the record for dataset details and reuse information.
Online Resource 2 - Radial displacement at the tunnel wall of a tunnel excavated with a single shield TBM at the state of equilibrium (comparison between different calculation methods)
<p>The radial displacement at the tunnel wall at the state of equilibrium calculated with the various ConVergence-ConFinement (CV-CF) methods is compared with the results obtained with a 3D numerical model of a tunnel excavation. A sensibility analysis is performed in order to compare the performance of the CV-CF approaches. The choice of the values of the mechanical parameters of the ground and of the lining is carried out in an attempt to cover the large range of situations encountered within single shield TBM. The total set of results from the sensibility analysis is shown in this work. Results obtained from some empirical formula proposed by the authors are also included.</p>
Online Resource 1 – Maximal hoop stress developed in the lining of a tunnel excavated with a single shield TBM at the state of equilibrium (comparison between different calculation methods)
<p>The maximal hoop stress developed in the lining of a tunnel at the state of equilibrium calculated with the various ConVergence-ConFinement (CV-CF) methods is compared with the results obtained with a 3D numerical model of a tunnel excavation. A sensibility analysis is performed in order to compare the performance of the CV-CF approaches. The choice of the values of the mechanical parameters of the ground and of the lining is carried out in an attempt to cover the large range of situations encountered within single shield TBM. The total set of results from the sensibility analysis is shown in this work. Results obtained from some empirical formula proposed by the authors are also included. <strong>A version 2 of the document with some minor corrections has been published. </strong></p>
CEDAR, an online resource for the reporting and exploration of complexome profiling data
<p>Complexome profiling is an emerging ‘omics’ approach that systematically interrogates the composition of protein complexes (the complexome) of a sample, by combining biochemical separation of native protein complexes with mass-spectrometry based quantitation proteomics. The resulting fractionation profiles hold comprehensive information on the abundance and composition of the complexome, and have a high potential for reuse by experimental and computational researchers. However, the lack of a central resource that provides access to these data, reported with adequate descriptions and an analysis tool, has limited their reuse. Therefore, we established the ComplexomE profiling DAta Resource (CEDAR, www3.cmbi.umcn.nl/cedar/), an openly accessible database for depositing and exploring <a title="Learn more about mass spectrometry from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/mass-spectrometry">mass spectrometry</a> data from complexome profiling studies. Compatibility and reusability of the data is ensured by a standardized data and reporting format containing the “minimum information required for a complexome profiling experiment” (MIACE). The data can be accessed through a user-friendly web interface, as well as programmatically using the REST API portal. Additionally, all complexome profiles available on CEDAR can be inspected directly on the website with the profile viewer tool that allows the detection of correlated profiles and inference of potential complexes. In conclusion, CEDAR is a unique, growing and invaluable resource for the study of protein complex composition and dynamics across biological systems.</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.