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2,031 results for “Transformation”
Dataset for publication: High-Frequency Current Transformer Design and Implementation Considerations for Wideband Partial Discharge Applications
<p>Data set belonging to the IEEE Trans. Instr. Meas. paper with DOI: <a href="https://doi.org/10.1109/TIM.2021.3052002">10.1109/TIM.2021.3052002</a></p> <p>The authors preprint is available from Zendodo with DOI: <a href="https://doi.org/10.5281/zenodo.4772470">10.5281/zenodo.4772470</a> </p> <p>Klüss, Joni; Elg, Alf-Peter; Wingqvist Claes</p> <p><em>High-Frequency Current Transformer Design and Implementation Considerations for Wideband Partial Discharge Applications</em></p>
Uncertainty-aware molecular dynamics from Bayesian active learning: Phase Transformations and Thermal Transport in SiC
<p>Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomic level processes. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present an efficient Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. As an application, we train a model for silicon carbide (SiC), a wide-gap semiconductor with complex polymorphic structure and diverse technological applications in power electronics, nuclear physics and astronomy. We show that the high pressure phase transformation is accurately captured by the autonomous active learning workflow. The trained force field shows excellent agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow is readily generalized to a wide range of systems, accelerates computational understanding and design.</p>
Data set for FSE 2022 Submission Program Merge Conflict Resolution via Neural Transformers
<p>Data set for FSE 2022 Submission Program Merge Conflict Resolution via Neural Transformers</p>
Transformed raw data and R scripts for the paper Problem reports and team maturity in agile automotive software development published at CHASE2022
<p>Transformed raw data and R scripts for the paper Problem reports and team maturity in agile automotive software development published at CHASE2022</p> <p>15th International Conference on Cooperative and Human Aspects of Software Engineering, May 21–22, 2022, Pittsburgh, PA, USA}´</p>
Phosphorus supply increases nitrogen transformation rates and retention in soil: a global meta-analysis
<p>Interactions between nitrogen (N) and phosphorus (P) are important for plant growth and ecosystem carbon (C) sequestration. While effects of N supply on P dynamics have been much studied, much less is known about the opposite (P-effect on N). We conducted a meta-analysis by compiling a total of 1734 individual experimental observations from 116 peer-reviewed publications to assess P-addition effects on soil N dynamics. Globally, P additions increased the soil total N (TN) pool, potentially as a result of enhanced plant and microbial immobilization and reduced N losses, with a stronger effect detected under longer duration of P addition (≥ 5 years). A coupled increase in soil organic C with TN signifies the fundamental role of exogenous P supply in enhancing soil C sequestration. Phosphorus addition accelerated some of the soil N cycling processes including gross N mineralization, gross nitrification, and denitrification, with the effect sizes varying among ecosystem types and increasing with P addition rates. Our results indicate the fundamental role of P in affecting soil N pools and processes, and highlight the efficacy of P supply in sequestering soil C and mitigating global C emission.</p>
Convolutions are competitive with transformers for protein sequence pretraining
<p>- Pretrained models of protein sequences. See https://github.com/microsoft/protein-sequence-models for instructions on how to load models.</p> <p>- IDR datasets used for evaluation. </p> <p>- March 2020 version of UniRef50 with splits used for training. </p>
Supplementary Material | Dividing Apples and Pears: Towards a Taxonomy for Agile Transformation
<p><em>Agile Transformation (AT), the process of adopting agile methods and practices in organizational settings, has received grappling attention in research due to its extensive emergence in practice. Although the complexity of ATs is well known and its use cases widespread, research has not yet developed a comprehensive classification of AT. This lack limits the comparability of existing studies and our possibility to draw theoretical generalizations from their results. In this paper, we fill this gap by presenting a taxonomy for AT based on a systematic literature review. We abstracted the taxonomy in an analysis of 92 articles, including empirical and theoretical papers as well as experience reports. We contribute to the existing literature by providing a taxonomy that presents an analytical theory, offering a characterization of ATs which helps researchers and practitioners analyze ATs, identify how they differ, and provide insight into combinations of agile characteristics.</em></p>
Multilingual Transformations Data
<p>Data for "Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models" (Findings of ACL 2022).</p>
Atomic structures derived from high-temperature and -pressure transformations from C60 fullerenes
<p>These are XYZ files containing the atomic structures depicted in the high-temperature and high-pressure C<sub>60</sub> phase diagram (figure 7) of this publication:</p> <blockquote> <p>Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C<sub>60</sub><br> Heikki Muhli, Xi Chen, Albert P. Bartók, Patricia Hernández-León, Gábor Csányi, Tapio Ala-Nissila, and Miguel A. Caro<br> Phys. Rev. B 104, 054106 (2021)</p> <p><a href="https://doi.org/10.1103/PhysRevB.104.054106">https://doi.org/10.1103/PhysRevB.104.054106</a></p> </blockquote> <p>The structures were generated with a general-purpose Gaussian approximation potential (GAP) for carbon whose training database included a large number of C<sub>60</sub> structures. Refer to the publication listed above for the details of the simulation and an analysis of the structures.</p>
Automating Code-Related Tasks Through Transformers: The Impact of Pre-training
<p>Datasets used in the work "Automating Code-Related Tasks Through Transformers: The Impact of Pre-training".</p>
Explaining Non-Entailment by Model Transformation for the Description Logic EL - IJCKG22 - Resources
<p><strong>resources.zip</strong> contains the experiment data and results, and a README file explaining how to rerun the experiment.</p>
Tidal triggering of seismic swarm associated with hydrothermal circulation at Blanco Ridge Transform Fault Zone in northeast Pacific
<p>Supplementary Information</p> <p>for</p> <p>Tidal triggering of seismic swarm associated with hydrothermal circulation at Blanco Ridge Transform Fault Zone in northeast Pacific</p>
Reversible transformations between the non-porous phases of a flexible coordination network enabled by transient porosity
<p>Raw data archive for the manuscript titled "Reversible transformations between the non-porous phases of a flexible coordination network enabled by transient porosity"</p>
Detecting anomalies in system logs with a compact convolutional transformer - Data
<p><strong>Detecting anomalies in system logs with a compact convolutional transformer - Data</strong></p> <p>Preprocessed data and a pre-trained model for the Larisch, Vitay, Hamker (2022) publication.</p> <p>The <em>data</em> directory contains the Blue Gene/L data set, after tokenization, shuffling, and splitting in a training and test set (BGL_masked_Xtrain.npy and BGL_masked_Xtest.npy, respectively) and the corresponding labels.<br> Additionally, the BGL_masked_Xtest_uniq.npy and BGL_masked_Ytest_uniq.npy contains the test data, where samples from the training set are removed.</p> <p>The <em>model</em> directory contains a pre-trained compact convolutional transformer (CCT) model. The CCT is trained on the proposed BGL training data and uses a 4x4 convolutional kernel.</p>
Incremental Model Transformations with Triple Graph Grammars for Multi-version Models and Multi-version Pattern Matching Evaluation Data
<p>Java abstract syntax graphs for two software development projects in non-recreating multi-version model encoding.</p>
Dataset related to Research Article: Understanding the vicious cycle of myopic foresight and constrained technology deployment in transforming the European energy system
<p>Dataset related to research article:</p> <p><strong>Understanding the vicious cycle of myopic foresight and constrained technology deployment in transforming the European energy system</strong></p> <p>by Jacob Mannhardt, Paolo Gabrielli, Giovanni Sansavini (sansavig@ethz.ch)<br>Reliability and Risk Engineering Lab, Institute of Energy and Process Engineering, ETH Zurich</p> <p>All rights lie with the authors. All input data, source code, and result files needed to reproduce results and study. <br>Refer to README.docx for further information on content.</p>
FIGURE 6. A. transforming 9.5 in The lionfishes: Comparative development of Pterois volitans, Dendrochirus barberi, and D. hemprichi (Scorpaeniformes: Scorpaenidae: Pteroinae) and discrimination of their early life stages from non-pteroine scorpaenid genera in the Western North Atlantic
FIGURE 6. A. transforming 9.5 mm standard length (SL) Dendrochirus "bellus"; B. 11.5 mm SL D. zebra from Kojima (2014) used by permission of author and publisher.
A Transformer-based Approach for Augmenting Software Engineering Chatbots Datasets
<p>The results, datasets, and scripts used in "A Transformer-based Approach for Augmenting Software Engineering Chatbots Datasets" paper.</p>
FTIR-Plastics: a Fourier Transform Infrared Spectroscopy dataset for the six most prevalent industrial plastic polymers.
<p><span><span>Two datasets are presented: FTIR-Plastics-C4 and FTIR-Plastics-C8, comprising 6,000 spectra obtained through Fourier Transform Infrared Spectroscopy (FTIR) applied to the six most used synthetic polymers: Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS). The key feature of the datasets lies in the FTIR analysis, which reports the percentage transmittance as the intensity measure as a function of the wavelength of an Infrared light source, expressed as wavenumber (with units in cm</span></span><sup><span><span>-1</span></span></sup><span><span>). FTIR analysis was performed using a Jasco FTIR PRO 4x spectrophotometer with a wavenumber resolution setting of 8 cm</span></span><sup><span><span>-1</span></span></sup><span><span> for FTIR-Plastics-C8 and 4 cm</span></span><sup><span><span>-1</span></span></sup><span><span> for FTIR-Plastics-C4, both employing a configuration of 32 scans and a range from 4000 to 400 cm</span></span><sup><span><span>-1</span></span></sup><span><span>. The datasets are presented in CSV (comma-separated values) format, including the following information (per each column):</span></span></p> <ul> <li> <p><span><span><strong>IDE</strong></span></span><span><span>: unique identifier of the sample.</span></span></p> </li> <li> <p><span><span><strong>Polymer: </strong></span></span><span><span>type of synthetic polymer (PET, HDPE, PVC, LDPE, PP, or PS).</span></span></p> </li> <li> <p><span><span><strong>Technique: </strong></span></span><span><span>Type of technique used (FTIR).</span></span></p> </li> <li> <p><span><span><strong>Sample: </strong></span></span><span><span>polymer sample number.</span></span></p> </li> <li> <p><span><span><strong>BR</strong></span></span><span><span>: scanning configuration (32).</span></span></p> </li> <li> <p><span><span><strong>RST</strong></span></span><span><span>: resolution configuration (8 or 4 cm</span></span><sup><span><span>-1</span></span></sup><span><span>).</span></span></p> </li> <li> <p><span><span><strong>Data (x) y Data(y): </strong></span></span><span><span>1884 pairs of columns for FTIR-Plastics-C8 and 3751 pairs of columns for FTIR-Plastics-C4, representing values on the "x" axis (wavenumber) and the "y" axis values associated with molecular vibration intensities, indicating the transmittance (%), which differentiates each polymer.</span></span></p> </li> </ul> <p><span><span>Additionally, the files generated by the Jasco spectrophotometer for each polymer are provided, which were standardized by adding a header with the following structure:</span></span></p> <ul> <li> <p><span><span>TITLE SAMPLE NAME: referring to the name of the analyzed polymer.</span></span></p> </li> <li> <p><span><span>DATA TYPE: specifying the characterization technique.</span></span></p> </li> <li> <p><span><span>MEASUREMENT INFORMATION: equipment used for data collection.</span></span></p> </li> <li> <p><span><span>MODEL NAME: name of the equipment used.</span></span></p> </li> <li> <p><span><span>SERIAL No: serial number assigned to the equipment used.</span></span></p> </li> <li> <p><span><span>ACCESSORY: complementary device integrated into the equipment.</span></span></p> </li> <li> <p><span><span>LIGHT SOURCE: standardized light source related to the DLATGS detector.</span></span></p> </li> <li> <p><span><span>RESOLUTION: parameters are used to distinguish the wavenumber in the analyzed materials.</span></span></p> </li> <li> <p><span><span>XUNIT/HORIZONTAL AXIS: referring to the unit’s title assigned on the x-axis.</span></span></p> </li> <li> <p><span><span>YUNITS/VERTICAL AXIS: referring to the unit’s title designated on the y-axis.</span></span></p> </li> <li> <p><span><span>FIRSTX: initial value set for the x-axis.</span></span></p> </li> <li> <p><span><span>FIRSTY: initial value set for the y-axis.</span></span></p> </li> <li> <p><span><span>LASTX: final value set for the x-axis.</span></span></p> </li> <li> <p><span><span>LASTY: final value set for the y-axis.</span></span></p> </li> <li> <p><span><span>NPOINTS: total data points in the file.</span></span></p> </li> </ul> <p><span><span>Data collection was carried out meticulously, following specific steps to ensure the accuracy and reliability of the results. The calibration certificates issued by the supplier (calibration_certificate.pdf) corresponding to the equipment used in the experiments and data collection that give rise to these databases are attached.</span></span></p>
Effects of European emissions trading on the transformation of primary steelmaking: Assessment of economic and climate impacts in a case study from Germany
<p>Supplementary Information 1: This supporting information provides information on the modeling of material and energy flows for conventional and low-carbon steelmaking in an integrated steel mill. To this end, the production activities (unit processes) are provided. Also, information on the assessment of economic and climate impacts is included.</p> <p>Supplementary Information 2: This supporting information provides information on the main mechanisms of the European emissions trading system. Thereby, a focus is laid on regulations for the free allocation of EU allowances toward companies from the sectors with a high risk of carbon leakage. Additionally, the assessment and optimization models for designing favorable transformation pathways of integrated steel mills are provided. Also, the main assumptions and complementary results are provided.</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.