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6 results for “Employment structure”
Data and code: Climate policy accelerates structural changes in energy employment
<p>The file contains code to create the figures used in main text and supplementary information of the paper <strong>Climate policy accelerates structural changes in energy employment</strong>.</p> <p>To run the RMD file and see the resulting figures, press Knit on R studio (requires the package knitr), or else see the attached HTML file, already created through such a process.</p>
Structure and dynamics of plasma irregularities over the equatorial ionospheric region: A study using spaced receiver technique employing geostationary satellites' radio signals-Data set
<p>The study investigates the characteristic features of the ionospheric irregularities using spaced receiver technique. In the spaced receiver technique, we have used a trio of receivers separated by 40 and 100 m from each other. These receivers monitor scintillations patterns of the L1 signals transmitted by the geostationary satellites. The cross-correlation of the signals and the power spectral analysis yields the measure of characteristic features of the irregularities. The data folder contains the S4 index, drift velocity of the irregularities, powerspectral slopes and size of the irregularities observed on four days. The folder also contains the gnuscript used for plotting. </p> <p> </p> <p> </p>
Neural Networks for Structure-Informed Prediction of Formation Energy (employed in SIPFENN)
<p>pySIPFENN Documentation: <a href="https://pysipfenn.org">pysipfenn.org</a></p> <p>pySIPFENN GitHub: <a href="https://github.com/PhasesResearchLab/pySIPFENN">git.pysipfenn.org</a></p> <p>Original SIPFENN Paper: <a href="https://doi.org/10.1016/j.commatsci.2022.111254">10.1016/j.commatsci.2022.111254</a></p> <p> </p> <p>Network Changelog:</p> <p>V 0.10 - All models moved to the open ONNX format for improved interchangeability; NN30 neural network similar to NN20 but accepting the new KS2022 feature vector; Python code migrated to public GitHub repository.</p> <p>V 0.9 - Python code updated to the release version; paper published</p> <p>V 0.8 - Python code (beta) to run models included</p> <p>V 0.7 - Original upload of development models </p> <p> </p> <p>Selected works with SIPFENN alongside DFT and experiments:</p> <p>- <a href="https://doi.org/10.1016/j.actamat.2021.117448">10.1016/j.actamat.2021.117448</a></p> <p>- <a href="https://doi.org/10.1038/s41598-021-03578-0">10.1038/s41598-021-03578-0</a></p> <p> </p> <p>SIPFENN Abstract (original publication, 2021):</p> <p>In recent years, numerous studies have employed machine learning (ML) techniques to enable orders of magnitude faster high-throughput materials discovery by augmentation of existing methods or as standalone tools. In this paper, we introduce a new neural network-based tool for the prediction of formation energies based on elemental and structural features of Voronoi-tessellated materials. We provide a self-contained overview of the ML techniques used. Of particular importance is the connection between the ML and the true material-property relationship, how to improve the generalization accuracy by reducing overfitting, and how new data can be incorporated into the model to tune it to a specific material system.<br> <br> In the course of this work, over 30 novel neural network architectures were designed and tested. This lead to three final models optimized for (1) highest test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in the discovery of new materials, and (3) performance at a low computational cost. On a test set of 21,800 compounds randomly selected from OQMD, they achieve mean average error (MAE) of 28, 40, and 42 meV/atom respectively. The second model provides better predictions on materials far from ones reported in OQMD, while the third reduces the computational cost by a factor of 8.<br> <br> We collect our results in a new open-source tool called SIPFENN (Structure-Informed Prediction of Formation Energy using Neural Networks). SIPFENN not only improves the accuracy beyond existing models but also ships in a ready-to-use form with pre-trained neural networks and a user interface. </p> <p> </p> <p>Contacts:</p> <p>- Adam Krajewski: ak@psu.edu</p> <p>- Prof. Zi-Kui Liu: zxl15@psu.edu</p>
Do Go Chasing Waterfalls: Enoyl Reductase (FabI) in Complex with Inhibitors Stabilizes the Tetrameric Structure and Opens Water Channels - trajectories employed in Markov State Models and water analyses
<p>The following trajectories were employed in the generation of MSM models and water analyses:</p> <p>SaFabI_60us_align.zip</p> <p>EcFabI_60us_align.zip</p> <p>waters_SaFabI.tar.gz</p> <p>waters_EcFabI.tar.gz</p>
Exploring the Chemical Space of Glycosylation in Noncovalent Protein Complexes: an Expedition along Different Structural Levels of Human Chorionic Gonadotropin Employing Mass Spectrometry
<p><strong>Supplementary files for "Exploring the Chemical Space of Glycosylation in Noncovalent Protein Complexes: an Expedition along Different Structural Levels of Human Chorionic Gonadotropin Employing Mass Spectrometry"</strong></p> <p><strong>Introduction</strong></p> <p>This data repository contains all previously unpublished raw data files for the manuscript “Exploring the Chemical Space of Glycosylation in Noncovalent Protein Complexes: an Expedition along Different Structural Levels of Human Chorionic Gonadotropin Employing Mass Spectrometry” by Maximilian Lebede<sup>||</sup>, Fiammetta Di Marco<sup>||</sup>, Wolfgang Esser-Skala, René Hennig, Therese Wohlschlager, Christian G. Huber.</p> <p><strong>Files</strong></p> <p>This repository contains 9 files:</p> <ul> <li><strong>Dimer Raw Files.zip</strong> folder containing 4 files of native-MS data (*.raw, Thermo RAW file format) of two batches of the drug product Ovitrelle® at native dimer level. </li> <li><strong>H11M9 Ovitrelle BA056714 Glycopeptide R1 230920_07.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>H11M9 Ovitrelle BA056714 Glycopeptide R2 230920_08.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>H11M9 Ovitrelle BA056714 Glycopeptide R3 230920_09.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>H11M9 Ovitrelle BA059433 Glycopeptide R1 240920_15.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>H11M9 Ovitrelle BA059433 Glycopeptide R2 240920_16.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>H11M9 Ovitrelle BA059433 Glycopeptide R3 240920_17.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>MoFi Settings.zip</strong> folder containing 12 files of MoFi settings (*.xml) to annotate deconvoluted spectra of hCG subunits and dimer of two Ovitrelle® batches, untreated and after desialylation. A typical MoFi setting file is build from protein sequence (*.FASTA), monosaccharide and frequent modification atomic composition (*.csv), glycan or glycoform library (*.csv) and deconvoluted spectrum in centroid (*.csv). Files are named as following: Settings_Ovitrelle_Batch number (BA056714 or BA059433)_Structural level (Alpha, Beta or Dimer)_Enzymatic treatement (Untreated or Sialidase).</li> <li><strong>Subunit Raw Files.zip</strong> folder containing 8 files of HPLC-MS data (*.raw, Thermo RAW file format) of two batches of the drug product Ovitrelle® at intact subunit level. </li> </ul> <p>Raw files are named as following: Instrument, Drug product (Ovitrelle), Batch number (BA056714 or BA059433), Structural level (Dimer, Subunits or Glycopeptides), Enzymatic treatment (untreated, Sialidase, PNGase F or PNGase F + Sialidase) and date. Glycopeptide data includes 3 replicates (R1-3).</p> <p><strong>License</strong></p> <p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a> .</p> <p> </p>
THE ROLE OF THE DIGITAL ECONOMY ON THE LEVEL AND STRUCTURE OF EMPLOYMENT OF THE POPULATION
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.