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1,456 results for “parallelism”
Heterosis counteracts hybrid breakdown to forestall speciation by parallel natural selection
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Data from: Quantifying (non)parallelism of microbial community change using multivariate vector analysis
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Data from: MicroRNA gene regulation in extremely young and parallel adaptive radiations of crater lake cichlid fish
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Data from: Recurrent selection explains parallel evolution of genomic regions of high relative but low absolute differentiation in a ring species
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Data from: Parallel speciation or long-distance dispersal? Lessons from seaweeds (Fucus) in the Baltic Sea
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Data from: Within-host competition between Borrelia afzelii ospC strains in wild hosts as revealed by massively parallel amplicon sequencing
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A phylogeny of Antirrhinum reveals parallel evolution of alpine morphology
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Parallel and non-parallel phenotypic responses to environmental variation across Lesser Antillean anoles
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Problem instances for scheduling jobs with time windows on unrelated parallel machines
<p>The following dataset contains problem instances for the unrelated machine scheduling problem with job release dates and deadlines, which are used in the article "Tadumadze, G., Emde, S. & Diefenbach, H. Exact and heuristic algorithms for scheduling jobs with time windows on unrelated parallel machines. <em>OR Spectrum</em> <strong>42, </strong>461–497 (2020). <a href="https://doi.org/10.1007/s00291-020-00586-w">https://doi.org/10.1007/s00291-020-00586-w</a>".</p> <p>The problem instances are stored in table “instances”, where the columns of the table can be interpreted as follows:</p> <p>Problem_ID: <Autonumber></p> <p>n: <number of jobs>;</p> <p>m: <number of machines>;</p> <p>w: <vector with n elements: the j-th element corresponds to the weight of job j>;</p> <p>r: <vector with n elements: the j-th element corresponds to the release date of job j>;</p> <p>d: <vector with n elements: the j-th element corresponds to the deadline of job j>;</p> <p>p: <n*m matrix: each entry in j-th column and i-th row corresponds to the processing time of job j on machine i>;</p> <p>The first 80 entries (Problem_ID between 1-80), contain discrete Berth-allocation problem instances, provided by “Jean-François Cordeau, Gilbert Laporte, Pasquale Legato, Luigi Moccia, (2005) Models and Tabu Search Heuristics for the Berth-Allocation Problem. Transportation Science 39(4):526-538. https://doi.org/10.1287/trsc.1050.0120” and additionally contain machine availability times, which are stored in the following columns:</p> <p>s: <vector with m elements: the i-th element corresponds to the start availability time of machine i>;</p> <p>e: <vector with m elements: the i-th element corresponds to the end availability time of machine i>;</p> <p>The following 270 entries (Problem_ID between 81-270) contain newly generated random problem instances with the instance generation scheme proposed by “Nicholas G. Hall, Marc E. Posner, (2001) Generating Experimental Data for Computational Testing with Machine Scheduling Applications. Operations Research 49(6):854-865. https://doi.org/10.1287/opre.49.6.854.10014”. The first 10 instances (Problem_ID between 81-90) are used for parameter tuning tests and the next 180 (Problem_ID between 91-270) instances for computational performance comparison.</p> <p>The last 30 entries (Problem_ID between 271-300) contain integrated truck and workforce scheduling problem instances with fixed workforce at each door, provided by “Giorgi Tadumadze, Nils Boysen, Simon Emde, Felix Weidinger (2019) Integrated truck and workforce scheduling to accelerate the unloading of trucks. European Journal of Operational Research 278(1):343-362. https://doi.org/10.1016/j.ejor.2019.04.024”.</p> <p>The detailed computational results for each instance, approach and objective function are reported in tables which are named with the following convention: "results_<approach>_<objective value>”.</p>
According to this the mutual affinities of the species of the simpleX group might be expressed as follows t (the Ethiopian species are marked with an asterisk):— each other at base; in 4 p2 is half in row. To this latter I find no parallel in any specimen of ferrum-equinum (all races) I have seen, and in 4 skulls only, out of 33, there is a more or less distinct remnant of the interspace between the canine and p4. Of _R7z. deckeni I have seen one skull only; the dentition is as in many specimens of Ph. augur: c and p4 separated, p2 external. f I give the diagram the form of a genealogical tree, only because it is convenient to in On some Bats of the Genus Rhinolophus, with Remarks on their Mutual Affinities, and Descriptions of Twenty-six new Forms.
According to this the mutual affinities of the species of the simpleX group might be expressed as follows t (the Ethiopian species are marked with an asterisk):— each other at base; in 4 p2 is half in row. To this latter I find no parallel in any specimen of ferrum-equinum (all races) I have seen, and in 4 skulls only, out of 33, there is a more or less distinct remnant of the interspace between the canine and p4. Of _R7z. deckeni I have seen one skull only; the dentition is as in many specimens of Ph. augur: c and p4 separated, p2 external. f I give the diagram the form of a genealogical tree, only because it is convenient to
CRC tumor profiled in Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling
<p>Plain text files from mgatk output of CRC tumor sample originally described in "Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling" (https://doi.org/10.1038/s41587-020-0645-6). </p>
Selected data for Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling
<p>TF1 cell line (Figure 3) and CRC cancer (Figure 4) data from the "Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling" work. </p>
Application of spectral library prediction for parallel reaction monitoring of viral peptides_PRM_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
Performance Curves for better Performance Predictions of Parallel Applications in Multicore Environments
<p><br> Model-based performance prediction for parallel applications on architectural models suffers from significant inaccuracies. <br> A major reason is that current model-based performance prediction approaches consider CPU speed as a single metric for multicore performance. </p> <p>Thus, in this paper, we investigate performance-influencing factors for multicore environments, execute extensive experiments to determine their impact on the performance, and extract performance curves for characteristic behaviours.</p> <p>As a result, we present a set of performance curves to software engineers which enables them to increase the performance prediction power in an easy to use manner. <br> Further, we evaluate the approach using 13 SPEC Benchmarks and could show that our approach reduces the prediction error by up to 60% and increases the accuracy by up to 98% for certain scenarios. </p>
Female and male song exhibit both parallel and divergent patterns of cultural evolution: a long-term study of song structure and diversity in tropical wrens
<p>Animal culture changes over time through processes that include drift, immigration, selection, and innovation. Cultural change has been particularly well-studied for animal vocalizations, especially for the vocalizations of male animals in the temperate zone. Here we examine cultural change in the vocalizations of tropical Rufous-and-white Wrens (<i>Thryophilus rufalbus</i>), quantifying temporal variation in song structure, song type diversity, and population-level distribution of song types in both males and females. We use data from 10 microsatellite loci to quantify patterns of immigration and neutral genetic differentiation over time, to investigate whether cultural diversity changes with rates of immigration. Based on 11 years of data, we show that the spectro-temporal features of several widely-used persistent song types maintain a relatively high level of consistency for both males and females, whereas the distribution and frequency of particular song types change over time for both sexes. Males and females exhibit comparable levels of cultural diversity (i.e. the diversity of song types across the population), although females exhibit greater rates of cultural change over time. We found that female changes in cultural diversity increased when immigration is high, whereas male cultural diversity did not change with immigration. Our study is the first long-term study to explore cultural evolution for both male and female birds and suggests that cultural patterns exhibit notable differences between the sexes.</p>
Solitary magnetic structures at quasi-parallel collisionless shocks: Formation
<p>PIC simulation data in figure 4.</p>
WarpX Accelerated Nodes Parallel Computing Paper
<p>This dataset contains the inputs, outputs, job submission scripts, and executables<br> used to create the Figures in "Porting WarpX to GPU-accelerated platforms" by A. Myers<br> et. al, submitted to Parallel Computing as part of the ECP Special Issue on Transitioning<br> to Accelerated nodes.</p> <p>These results were obtained using the October, 2020 release tags of WarpX and AMReX,<br> available on Github here:</p> <p> https://github.com/ECP-WarpX/WarpX</p> <p>and here:</p> <p> https://github.com/AMReX-Codes/amrex</p> <p>The following module files were loaded on Summit:</p> <p> 1) hsi/5.0.2.p5 2) xalt/1.2.0 3) lsf-tools/2.0 4) darshan-runtime/3.1.7<br> 5) DefApps 6) cuda/10.1.243 7) gcc/6.4.0 8) spectrum-mpi/10.3.1.2-20200121</p> <p>To use nsight-compute for the roofline plots, we also loaded:</p> <p> nsight-compute/2020.1.2</p> <p>Manifest:</p> <p>BinScan: contains material used to make Figure 1. To generate the figure, use the<br> Jupyter notebook called "bin_size.ipynb".</p> <p>StrongScaling: contains material used to make Figure 5. To generate the figure, use the<br> Jupyter notebook called "strong_scaling.ipynb".</p> <p>WeakScalingCPU: contains material used to make Figure 4. To generate the figure, use the<br> Jupyter notebook called "weak_scaling.ipynb".</p> <p>WeakScalingGPU: contains material used to make Figure 5. To generate the figure, use the<br> Jupyter notebook called "weak_scaling.ipynb".</p> <p>Roofline: contains material used to make the roofline plots (Figures 2 and 3). This<br> includes output generated using nsight-compute with WarpX and python scripts for<br> processing and plotting these output files. These scripts and methodology originally<br> come from Charlene Yang at NERSC. The file "script.sh" was used to generate the<br> profiler output</p>
Parallel CUHRE performance data for DES sigma_miscent_y1_scalarintegrand
<p>This is detailed performance data from the parallel CUHRE algorithm, integrating the sigma_miscent_y1_scalarintegrand integrand from DES.</p> <p> </p> <p>This is from an early version of the algorithm.</p>
Supplementary material 1 from: Gorin VA, Scherz MD, Korost DV, Poyarkov NA (2021) Consequences of parallel miniaturisation in Microhylinae (Anura, Microhylidae), with the description of a new genus of diminutive South East Asian frogs. Zoosystematics and Evolution 97(1): 21-54. https://doi.org/10.3897/zse.97.57968
Table S1
Supplementary material 9 from: Gorin VA, Scherz MD, Korost DV, Poyarkov NA (2021) Consequences of parallel miniaturisation in Microhylinae (Anura, Microhylidae), with the description of a new genus of diminutive South East Asian frogs. Zoosystematics and Evolution 97(1): 21-54. https://doi.org/10.3897/zse.97.57968
Figure S4
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.