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261 results for “Performance Analysis”
PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Survey at citizen level and data relative to regional performance of the Cohesion Policy and institutional quality
<p>1. Orignal PERCEIVE survey data (STATA file)</p> <p>2. description of survey questions, descriptive results (word file)</p> <p>3. EU Deliverable document with descriptive analysis of survey questions</p> <p> </p> <p>***please cite the following when using the microdata:</p> <p>Bauhr, M., & Charron, N. (2020). The EU as a savior and a saint? Corruption and public support for redistribution. <em>Journal of European Public Policy</em>, <em>27</em>(4), 509-527.</p> <p>https://www.tandfonline.com/doi/full/10.1080/13501763.2019.1578816</p>
Data for "Training data composition affects performance of protein structure analysis algorithms" by A. Derry, K. A. Carpenter, & R. B. Altman
<p><strong>Description</strong></p> <p>This repository contains all data used in "Training data composition affects performance of protein structure analysis algorithms", published in the Pacific Symposium on Biocomputing 2022 by A. Derry, K. A. Carpenter, & R. B. Altman. </p> <p>The data consists of the following files:</p> <ul> <li>ema_zenodo_data.tar.gz: train, validation, and test splits for Estimation of Model Accuracy task, in LMDB format</li> <li>design_zenodo_data.tar.gz: train, validation, and test splits for Protein Sequence Design task, in JSON format</li> <li>enz_cat_res_zenodo_data.tar.gz: train, validation, and test splits for Catalytic Residue and Enzyme Prediction task, in TF record format</li> </ul> <p>Details on dataset construction can be found in our paper and dataloaders can be found in our <a href="https://github.com/awfderry/ml-structure-bias">Github repo</a>.</p> <p><strong>Reference</strong></p> <p>A. Derry*, K. A. Carpenter*, & R. B. Altman, "Training data composition affects performance of protein structure analysis algorithms", 2021.</p> <p><strong>Dataset References</strong></p> <p>Datasets used were derived from the following works:</p> <p>Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019). Critical assessment of methods of protein structure prediction (CASP)—Round XIII. In <em>Proteins: Structure, Function and Bioinformatics</em> (Vol. 87, Issue 12, pp. 1011–1020). https://doi.org/10.1002/prot.25823</p> <p>Ingraham, J., Garg, V. K., Barzilay, R., & Jaakkola, T. (2019). <em>Generative Models for Graph-Based Protein Design</em>. https://openreview.net/pdf?id=SJgxrLLKOE</p> <p>Furnham, N., Holliday, G. L., de Beer, T. A. P., Jacobsen, J. O. B., Pearson, W. R., & Thornton, J. M. (2014). The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes. <em>Nucleic Acids Research</em>, <em>42 </em>(Database issue), D485–D489.</p>
Recording and analysing physical control variables used in clarinet playing: A Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT)
<p>Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.</p>
Performance analysis & optimization of inverted inorganic CsGeI3 perovskite cells with carbon/copper charge transport materials using SCAPS‑1D
<p>Hybrid perovskite solar cells (PSC) have achieved efficiencies (PCE) of more than 25%. However, the organic compound is causing structural degradation due to heat and moisture. This has led to the exploration of inorganic perovskites. Inorganic-PSC such as cesium has seen a breakthrough by achieving highly stable PSC with PCE exceeding 15%. In this work, the inorganic non-toxic PSC of cesium germanium tri-iodide (CsGeI<sub>3</sub>) is numerically modeled in SCAPS-1D with two carbon-based and two copper-based charge transport layers(CTL). This study introduces in-depth modelling and analysis of CsGeI<sub>3</sub> through continuity and Poisson equations. Cu-CTL are selected to increase the electric conductivity of the cell, while carbon-CTL is used to increase the thermal conductivity. Four structures are designed and presented. A systematic approach is adopted to obtain the optimized design parameters for maximum performance. From the results it is observed that the C<sub>60</sub>/CsGeI<sub>3</sub>/CuSCN structure has the highest performance, with open-circuit voltage of 1.0169V, short-circuit current of 19.653 mA/Cm<sup>2</sup>, fill factor of 88.13% and PCE of 17.61%. Moreover, the effect of quantum efficiency, electric field, interface recombination, interface defects, layer thickness, defect density, doping concentration, working temperature and reflection coating on the cell performance are studied in detail.</p>
Deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT"- BN network and Bellman shortest path analysis_Module 3_Annex 6
<p>This file will introduce the BN network and Bellman shortest path analysis Module 3 Annex 6 in deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT".</p>
Adopted Ranking Methodology for Emerging National Importance Technical Institutions: A Correlation Analysis and Dominance of Research Performance Indicators
<p>The article presents the research performance of newly established technical institutes in India. It gives an overview of NTU and NIRF ranking methodologies. This research would help institutions to check their performance and identify ways to strengthen their rank by producing more output with high-impact research work. The article provides appropriate input to the technical institutions under the study to set out practical and pedagogical approaches, and cross-institutional as well as international collaboration to achieve their research goals. The article gives rank to the technical institutes under the study based on their research performance using the NTU ranking methodology.</p>
Bee Tracker – an open-source machine-learning based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees
Open the record for dataset details and reuse information.
Performance analysis & optimization of inverted inorganic CsGeI3 perovskite cells with carbon/copper charge transport materials using SCAPS‑1D
Open the record for dataset details and reuse information.
High Performance Liquid Chromatography (HPLC) pigment analysis from rosette bottle samples at various depths from CalCOFI-CCE Augmented cruises in the California Current System, 2002 to 2023 (ongoing).
High Performance Liquid Chromatography (HPLC) samples are collected from rosette bottles (from three to eight different depths in the photic zone) at stations located within the CCE region on CalCOFI cruises (since 2002, ongoing). The HPLC method is used to measure concentrations of chlorophylls and carotenoids in samples of particulate matter, which includes filtering and freezing the filter while at sea. The taxon-specific phyto-pigments are extracted back onshore. Concentrations of chlorophyll a are used as a proxy for phytoplankton biomass and concentrations of other taxon-specific pigments are used to determine contributions of phytoplankton taxa to total phytoplankton biomass.
Analysis of performance indicators for German Library Statistics in 2019
<p>Analyse der Berechenbarkeit von Kennzahlen der ISO 11620:2014 auf Basis der Deutschen Bibliotheksstatistik des Berichtsjahres 2019.</p> <p>Analysis of the calculability of performance indicators of ISO 11620:2014 on the basis of the German Library Statistics of the reporting year 2019.</p>
Exascale Potholes for HPC: Execution Performance and Variability Analysis of the Flagship Application Code HemeLB
<p>Performance measurement and analysis of parallel applications is often challenging, despite many excellent commercial and open-source tools being available. Currently envisaged exascale computer systems exacerbate matters by requiring extremely high scalability to effectively exploit millions of processor cores. Unfortunately, significant application execution performance variability arising from increasingly complex interactions between hardware and system software makes this situation much more difficult for application developers and performance analysts alike.<br> </p> <p>This work considers the performance assessment of the HemeLB exascale flagship application code from the EU HPC Centre of Excellence (CoE) for Computational Biomedicine (CompBioMed) running on the SuperMUC-NG Tier-0 leadership system, using the methodology of the Performance Optimisation and Productivity (POP) CoE. Although 80% scaling efficiency is maintained to over 100,000 MPI processes, disappointing initial performance with more processes and corresponding poor strong scaling was identified to originate from the same few compute nodes in multiple runs, which later system diagnostic checks found had faulty DIMMs and lacklustre performance. Excluding these compute nodes from subsequent runs improved performance of executions with over 300,000 MPI processes by a factor of five, resulting in 190x speed-up compared to 864 MPI processes. While communication efficiency remains very good up to the largest scale, parallel efficiency is primarily limited by load balance found to be largely due to core-to-core and run-to-run variability from excessive stalls for memory accesses, that affect many HPC systems with Intel Xeon Scalable processors. The POP methodology for this performance diagnosis is demonstrated via a detailed exposition with widely deployed `standard' measurement and analysis tools.</p>
Scripts, inputs and outputs of Module 3 BN analysis and impact assessment regarding Deliverable D6.3 "Performance and Impact assessment" in the IP4MaaS project
<p>All the materials, inputs, models, and scripts that are used in the performance assessment toolbox (IP4MaaS project, Deliverable D6.3 Performance and Impact Assessment) are available in the attached folder. </p> <p>The attached folder contains the following:</p> <p>Module 3_ BN analysis_Graphs and weights (the inputs, weights, graphs, and scripts of Bayesian Network analysis per each IP4MaaS demo site).</p> <p>The scripts have been designed for easy adoption in other projects with similar end goals. While the scripts operate on the codified representation of the traveller profiles, functionalities, and service providers, there is no restriction on the type of codification used. The only factor that the scripts assume is the order of the variables introduced for codification. That is the traveller profile variable (wherever applicable), followed by the Functionality variable, followed by the Service Provider variable. As long as the order is maintained, the scripts self-analyze the number of variables representing the traveller profiles, functionalities, and service providers, and perform calculations (USI, Effectiveness) and analysis (Regression and BN), Hence, in a similar framework, the scripts may be used for performing data analysis on a large data set with no restriction on the number of functionalities, service providers or subsets of traveller profiles. Documented Scripts can be found in a downloadable ZIP file here. </p>
Targeted DNA-seq analysis was performed on sorted population of CD45+/CD34+ HSPCs from control or FLI-1 modified mRNA treated mPB after co-culture with vascular niche cells
<p>Human mPB HSPCs were harvested isolated and transduced with either control or FLI-1 modified mRNA. HSPCs were introduced into co-culture with vascular niche ECs. Cultures were harvested and CD45+/CD34+ HSPCs were resoerted and processed for trageted DNA-seq analysis. Contains raw FASTQ sequencing files, unfiltered VCFs, and curated results in an excel.</p>
Performance Measurement Dataset of the HPC Benchmarks FASTEST, Kripke, and RELeARN for Cost-Effective Modeling Analysis with Extra-P
<p>Performance Measurement Dataset of the HPC Benchmarks FASTEST, Kripke, RELeARN for Scalability Studies with Extra-P. This data was used to analyze cost-effective modeling approaches presented in the IPDPS 2020 paper "Learning Cost-Effective Sampling Strategies for Empirical Performance Modeling".</p>
Supplemental Information on the Weighted Gene Co-expression Network Analysis performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"
<p>Supplemental Information on the Weighted Gene Co-expression Network Analysis (WGNCA) performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"</p> <p>The results are sorted by the three species analysed: the two algae <em><span>Zygnema circumcarinatum</span></em><span> SAG 698-1b (<em>Zygnema</em>) and <em>Mesotaenium endlicherianum </em></span><span>SAG 12.97 (<em>Mesotaenium</em>); and the bryophyte <em>Physcomitrium patens</em></span><span><em> </em>strain Gransden 2004 (<em>Physcomitrium</em>).</span></p>
Data for Development of LOS Analysis Procedures and Performance Measurement Systems for Parking (UTEP Year 4 Project)
<p>These Excel files consists of the data in the tables and figures in the final report. They appear in Chapters 3 to 6 in the final report. Each file is for one chapter. Each table or figure is listed as an Excel worksheet. The name of the worksheet is the figure or table number in the final report.</p>
Input data for Differential NicheNet analysis performed in the liver atlas paper Guilliams et al., Cell 2022
<p>Input data for Differential NicheNet analysis performed in the liver atlas paper Guilliams et al., Cell 2022</p> <p>See https://github.com/saeyslab/NicheNet_LiverCellAtlas and https://www.sciencedirect.com/science/article/pii/S0092867421014811</p>
Precalculated Results of Throughput Analysis in "Comprehensive Benchmarking of High-Performance Vector Field Representations"
<p>This repository contains results calculated by our throughput analysis. These results should be exactly and deterministically reproducible using the aforementioned software, but precomputed results are provided for the benefit of the reader, in case certain software is not available. Included in this artifact are the assembly files generated by the compilers, the throughput analysis of these aforementioned assembly files, and a collection of tables which summarize the results.</p> <p>These results were generated using <em>clang</em> version 14.0.6, <em>gcc</em> version 11.2.0, and <em>llvm-mca</em> version 14.0.6.</p>
Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software.
<p><strong>If you use the data or the analysis pipeline, please refer to:</strong></p> <p>Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00006">https://doi.org/10.3389/fnins.2018.00006</a></p> <p><strong>and/or</strong></p> <p>Andersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00261">https://doi.org/10.3389/fnins.2018.00261</a></p> <p><strong>IMPORTANT</strong><br> Version 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p> <p><br> </p> <p>Dataset with tactile expectations to be analysed with pipelines for either <a href="https://mne.tools/stable/index.html">MNE-Python</a> or <a href="http://www.fieldtriptoolbox.org/">FieldTrip</a>, aiming to follow the MEG-BIDS structure</p> <p><br> <strong>Unzipping the data</strong></p> <p>Data is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called "analyses", which has a sub-directory called "omission_frontiers_BIDS-FieldTrip", which has a sub-directory called "data".<br> Thus, as an example, in my case, I should have the path: /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/data</p> <p><strong>Path:</strong><br> on a Linux system the path would be /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a macOS system the path would be /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a Windows system the path would be C:\Users\your_name\analyses\omission_frontiers_BIDS-FieldTrip\data</p> <p><strong>Steps for unzipping:</strong></p> <p>1. Set up the folder above according to your operating system, following the examples above and substitute "your_name" for your user name.<br> 2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/</p> <p>Now you are ready to run the analyses.</p> <p><br> <strong>The MEG data</strong></p> <p>Raw fif files are contained in the data folder, ordered by subject (n=20)<br> There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recording</p> <p><strong>Processed MRI data </strong></p> <p>For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects' identity</p> <p>For Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG data<br> For MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.</p> <p><br> <strong>Metadata</strong></p> <p>Each subject has a number of tsv-files:<br> *channel.tsv contain information about the channels in that recording<br> *events.tsv contain information about the events in that recording<br> removed_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)<br> ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)<br> *scans_tsv contain information about the scans conducted</p> <p><br> <strong>Scripts </strong></p> <p>Please see Github for the updated scripts at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p>
Figure 1. Steps to perform antiSMASH analysis for bacteria
<p><strong><span>Figure 1.</span></strong><span> Steps to perform antiSMASH analysis for bacteria: [A] An overview of the bacterial version of antiSMASH shows a data input window where you can either upload the genome sequence in FASTA format or get the genome sequence if it has already been deposited in NCBI (https://antismash.secondarymetabolites.org/ assessed on Feb. 7, 2023, at 16:44:21 h).</span><span> [B] </span><span>Analysis output: The user has the option of receiving an email notification for the analysed genome. Alternatively, users can either bookmark job links or copy and save a job link to view it further. In both conditions, data can be viewed for a month as per convenience.</span><span> [C] The genome</span><span> of<strong> </strong><em>Actinopolyspora mortivallis</em> DSM 44261 (NZ_KB913024.1) indicates that different colour codes correspond to B) regions of the identified secondary metabolite (Regions 1–25). [D] Graphical representation of ectoine gene clusters (region 1 and region 15) taking part in various cellular processes.</span></p>
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.