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1,184 results for “conversion”
North Temperate Lakes LTER Zooplankton conversion formulas length to biomass
Formulas for calculating zooplankton biomass based on measured length for species encountered in NTL's northern lakes. Formulas are either based on literature reports or measurements in particular research lakes. The mass unit in the formula is micrograms.
Large Spin-to-Charge Conversion at Room Temperature in Extended Epitaxial Sb2Te3 Topological Insulator Chemically Grown on Silicon (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Large Spin-to-Charge Conversion at Room Temperature in Extended Epitaxial Sb<sub>2</sub>Te<sub>3</sub> Topological Insulator Chemically Grown on Silicon</em>" by <a href="https://doi.org/10.1002/adfm.202109361">E. Longo et al., <em>Adv. Funct. Mater.</em> 2021, 2109361</a></p>
Half-kilowatt high energy third harmonic conversion to 50 J @ 10 Hz at 343 nm [dataset]
<p>Dataset relevant to the publication "Half-kilowatt high energy third harmonic conversion to 50 J @ 10 Hz at 343 nm" in HPLSE</p>
Gate tunability of highly efficient spin-to-charge conversion by spin Hall effect in graphene proximitized with WSe2
<p>Data associated with "Gate tunability of highly efficient spin-to-charge conversion by spin Hall effect in graphene proximitized with WSe<sub>2</sub>" </p> <p>Publication: <a href="https://arxiv.org/abs/2006.09227">https://arxiv.org/abs/2006.09227</a> and <a href="https://aip.scitation.org/doi/10.1063/5.0006101">https://aip.scitation.org/doi/10.1063/5.0006101</a></p> <p><br> </p>
Techno-economic sustainability analysis methodology for conversion routes of renewable feedstock resources to bio-based products – case studies
<p>The dataset provides a set of sustainability principles, criteria and indicators for the evaluation of the conversion routes stage of a bio-based product. The selected case studies on the employment of alternative feedstocks and production of the bio-based products are implemented in order to evaluate the proposed methodology. Mass and energy balances for all case studies, estimated techno-economic metrics, cost of externalities and risk assessment results are provided</p>
Voice Conversion Challenge 2020 database v1.0
<pre>Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform. In 2016, we have launched the Voice Conversion Challenge (VCC) 2016 [1][2] at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems. In 2018, we have launched the second edition of VCC, the VCC 2018 [3]. In the second edition, we revised three aspects of the challenge. First, we educed the amount of speech data used for the construction of participant's VC systems to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, we introduced a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. We then evaluate both parallel and non-parallel voice conversion systems via the same large-scale crowdsourcing listening test. Third, we also attempted to bridge the gap between the ASV and VC communities. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, we also asses spoofing performance of the VC systems based on anti-spoofing scores. In 2020, we launched the third edition of VCC, the VCC 2020 [4][5]. In this third edition, we constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. The dataset for intra-lingual VC consists of a smaller parallel corpus and a larger nonparallel corpus, where both of them are of the same language. The dataset for cross-lingual VC consists of a corpus of the source speakers speaking in the source language and another corpus of the target speakers speaking in the target language. As a more challenging task than the previous ones, we focused on cross-lingual VC, in which the speaker identity is transformed between two speakers uttering different languages, which requires handling completely nonparallel training over different languages. This repository contains the training and evaluation data released to participants, target speaker’s speech data in English for reference purpose, and the transcriptions for evaluation data. For more details about the challenge and the listening test results please refer to [4] and README file. </pre> <pre>[1] Tomoki Toda, Ling-Hui Chen, Daisuke Saito, Fernando Villavicencio, Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "The Voice Conversion Challenge 2016" in Proc. of Interspeech, San Francisco. [2] Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "Analysis of the Voice Conversion Challenge 2016 Evaluation Results" in Proc. of Interspeech 2016. [3] Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhenhua Ling, "The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods", Proc Speaker Odyssey 2018, June 2018. [4] Yi Zhao, Wen-Chin Huang, Xiaohai Tian, Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Zhenhua Ling, and Tomoki Toda. "Voice conversion challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion" Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14.</pre>
East Asian calendar conversion database
<p>SQL dump (MySQL 5.x) from a database for converting East Asian (here: Chinese, Japanese, Korean) calendars. The data is also available for download at http://authority.dila.edu.tw/docs/open_content/download.php, where separate datasets for Chinese, Korean, and Japanese calendars are available. An interface for querying the data is here: http://authority.dila.edu.tw/time/.</p> <p>Using the Julian Day as common standard it allows mapping of East Asian calendar dates to the Julian, proleptic Gregorian, and Gregorian calendar. It is the currently largest and most detailed open access dataset for this purpose. The database was compiled between 2008 and 2011 at the Dharma Drum Institute of Liberal Arts, Jinshan, Taiwan. The data for the Japanese calendar is based on material provided by Takashi Suga.</p> <p>A publication describing the dataset is Marcus BINGENHEIMER, Jen-Jou HUNG, Simon WILES, Boyong ZHANG. “Modeling East Asian Calendars in an Open Source Authority Database.”<em> International Journal of Humanities and Arts Computing</em> Vol. 10-2, pp. 127-144. DOI: 10.3366/ijhac.2016.0164.</p>
Voice Conversion Challenge 2020 Listening Test Data
<pre>Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform. In 2016, we have launched the Voice Conversion Challenge (VCC) 2016 [1][2] at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems. In 2018, we have launched the second edition of VCC, the VCC 2018 [3]. In the second edition, we revised three aspects of the challenge. First, we educed the amount of speech data used for the construction of participant's VC systems to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, we introduced a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. We then evaluate both parallel and non-parallel voice conversion systems via the same large-scale crowdsourcing listening test. Third, we also attempted to bridge the gap between the ASV and VC communities. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, we also asses spoofing performance of the VC systems based on anti-spoofing scores. In 2020, we launched the third edition of VCC, the VCC 2020 [4][5]. In this third edition, we constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. The dataset for intra-lingual VC consists of a smaller parallel corpus and a larger nonparallel corpus, where both of them are of the same language. The dataset for cross-lingual VC consists of a corpus of the source speakers speaking in the source language and another corpus of the target speakers speaking in the target language. As a more challenging task than the previous ones, we focused on cross-lingual VC, in which the speaker identity is transformed between two speakers uttering different languages, which requires handling completely nonparallel training over different languages. As for listening test, we subcontracted the crowd-sourced perceptual evaluation with English and Japanese listeners to Lionbridge TechnologiesInc. and Koto Ltd., respectively. Given the extremely large costs required for the perceptual evaluation, we selected 5 utterances (E30001, E30002, E30003,E30004, E30005) only from each speaker of each team. To evaluate the speaker similarity of the cross-lingual task, we used audio in both the English language and in the target speaker’s L2language as reference. For each source-target speaker pair, we selected three English recordings and two L2 language recordings as the natural reference for the converted five utterances. </pre> <p>This data repository includes the audio files used for the crowd-sourced perceptual evaluation and raw listening test scores. </p> <pre>[1] Tomoki Toda, Ling-Hui Chen, Daisuke Saito, Fernando Villavicencio, Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "The Voice Conversion Challenge 2016" in Proc. of Interspeech, San Francisco. [2] Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "Analysis of the Voice Conversion Challenge 2016 Evaluation Results" in Proc. of Interspeech 2016. [3] Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhenhua Ling, "The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods", Proc Speaker Odyssey 2018, June 2018. [4] Yi Zhao, Wen-Chin Huang, Xiaohai Tian, Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Zhenhua Ling, and Tomoki Toda. "Voice conversion challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion" Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14. [5] Rohan Kumar Das, Tomi Kinnunen, Wen-Chin Huang, Zhenhua Ling, Junichi Yamagishi, Yi Zhao, Xiaohai Tian, and Tomoki Toda. "Predictions of subjective ratings and spoofing assessments of voice conversion challenge 2020 submissions." Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 99-120, DOI: 10.21437/VCC_BC.2020-15. </pre>
Terahertz Spin-to-Charge Conversion by Interfacial Skew Scattering in Metallic Bilayers
<p>Data of the publication "Terahertz Spin-to-Charge Conversion by Interfacial Skew Scattering in Metallic Bilayers" published in Advanced Materials, 33, 2006281 (2021). THz waveforms for a subset and RMS data - corrected for pump incoupling and THz outcoupling - for various F and N metallic bilayers and interface modifications as well as the calculated spin Hall angles for different interfacial impurities are provided.</p>
The Online Conversation Threads Repository (Slashdot, Barrapunto, Wikipedia talk)
<p>This repository contains datasets with online conversation threads collected and analyzed by different researchers. Currently, you can find datsets from different news aggregators (Slashdot, Barrapunto) and the English Wikipedia talk pages.</p> <p>- Slashdot conversations (Aug 2005 - Aug 2006) Online conversations generated at Slashdot during a year. Posts and comments published between August 26th, 2005 and August 31th, 2006. For each discussion thread: sub-domains, title, topics and hierarchical relations between comments. For each comment: user, date, score and textual content. This dataset is different from the Slashdot Zoo social network (it is not a signed network of users) contained in the SNAP repository and represents the full version of the dataset used in the CAW 2.0 - Content Analysis for the WEB 2.0 workshop for the WWW 2009 conference that can be found in several repositories such as Konect Barrapunto conversations (Jan 2005 - Dec 2008)</p> <p>- Online conversations generated at Barrapunto (Spanish clone of Slashdot) during three years. For each discussion thread: sub-domains, title, topics and hierarchical relations between comments. For each comment: user, date, score and textual content Wikipedia (2001 - Mar 2010)</p> <p>- Data from articles discussions (talk) pages of the English Wikipedia as of March 2010. It contains comments on about 870,000 articles (i.e. all articles which had a corresponding talk page with at least one comment), in total about 9.4 million comments. The oldest comments date back to as early as 2001.</p> <p> </p>
Multifunctional Catalyst Combination for the Direct Conversion of CO2 to Propane
<p>Supplementary Material: CO2 hydrogenation thermodynamics, kinetic model for CO2 hydrogenation, catalyst regeneration data, chemical and textural characterisaton (EDS, N2 absorption, PXRD), spectroscopic characterisation (EXAFS, FTIR, Raman), imaging (HAADF-STEM)</p>
Supp. Data for the article From raw microalgae to bioplastics: conversion of Chlorella vulgaris starch granules into thermoplastic starch
<p>Supplementaty Data (Videos) for the article From raw microalgae to bioplastics: conversion of Chlorella vulgaris starch granules into thermoplastic starch</p>
On CNF Conversion for SAT and SMT Enumeration: Benchmarks, Results and Plots
<p>Experimental results for the paper:<br><br><a title="Arxiv Link" href="https://arxiv.org/abs/2303.14971" target="_blank" rel="noopener">On CNF encoding for SAT and SMT enumeration</a>, Gabriele Masina, Giuseppe Spallitta and Roberto Sebastiani. ArXiv, 2024.</p> <p>Content:</p> <ul> <li><code>aig-bench.zip</code> <code>iscas85-bench.zip</code> <code>syn-bool-bench.zip</code> contain the inputs and results for the Boolean benchmarks. Each zip contains: <ul> <li><code>data/</code> that contains the input data</li> <li><code>results-<tool>/</code> for each tested tool.</li> </ul> </li> <li><code>syn-lra-bench.zip</code> <code>wmi-bench.zip</code> contain the inputs and results for the Boolean benchmarks. Each zip contains: <ul> <li><code>data/</code> that contains the input data</li> <li><code>results-<tool>/</code> for each tested tool.</li> </ul> </li> <li><code>plot-d4</code>, <code>plot-msat</code>, <code>plot-tabularallsat</code>, <code>plot-tabularallsmt</code> contain the plots for enumeration with different tools. </li> <li><code>plot-msat-sat</code> contains the plot for plain satisfiability using MathSAT.</li> </ul> <p>Results are stored in JSON files, where the field <code>"mode"</code> indicates the CNF transformation used to preprocess the input:</p> <ul> <li><code>LAB</code> for Tseitin CNF</li> <li><code>LABELNEG_POL</code> for Plaisted&Greenbaum CNF, using negative labels for subformulas occurring negatively only.</li> <li><code>NNF_MUTEX_POL</code> for NNF+Plaisted&Greenbaum CNF+mutex clauses, as described in the paper</li> </ul> <p>The source code used to run the experiments is available at <a href="https://doi.org/10.5281/zenodo.14033422" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14033422</a>.</p> <p> </p>
Kelvin (color temperature) to RGB conversion table.
<p>This CSV provides the corresponding RGB values for a specific color temperature (measured in Kelvin). It can be used to determine the appropriate RGB color values for a given color temperature. This dataset provides an approximation following the <a href="https://cie.co.at/datatable/cie-1964-colour-matching-functions-10-degree-observer">CIE 1964 colour-matching functions</a> that is intended for low to mid quality output media (such as LED lighting, consumer screens and consumer grade VR headsets). </p>
Chicken Immunoglobulin Gene Conversion Full Dataset
<p>Full dataset containing PacBio sequencing data and gene conversion information output from Brepconvert, for the immunoglobulin heavy and light chain of six 3 week old Rhode Island Red chickens studied as part of the publication Diversification of Antibodies by Gene Conversion in the Domestic Chicken (Gallus gallus domesticus). </p>
Data set for the journal article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO2-to-CO conversion"
<p>In the article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO<sub>2</sub>-to-CO conversion" we demonstrated that Fe impurities in a hybrid CoPc@MWCNT catalyst lead to its performance deterioration during long-term CO<sub>2</sub> electrolysis. Here we present the dataset the work was based on. The data are divided into four groups:<br> (i) Current transients and gas chromatography data for short-term electrolysis at different potentials (in an excel file we give the numbers of chromatograms for each potential; current transients are given as an origin file with datasets and plots inside)<br> (ii) Current transients and gas chromatography data for long-term electrolysis at different potentials and with different catalysts (in respective excel files we give the numbers of chromatograms; figure numbers are given in the folder names)<br> (iii) Electron microscopy images and EDX datasets (the images and datasets are collected in the folders with respective figure numbers used in the paper)<br> (iv) Calibration curves for ICP-MS</p>
Microwave-to-optical conversion with a gallium phosphide photonic crystal cavity
<p>Electrically actuated optomechanical resonators provide a route to quantum-coherent, bidirectional conversion of microwave and optical photons. Such devices could enable optical interconnection of quantum computers based on qubits operating at microwave frequencies. Here we present a platform for microwave-to-optical conversion comprising a photonic crystal cavity made of single-crystal, piezoelectric gallium phosphide integrated on prefabricated niobium circuits on an intrinsic silicon substrate. The devices exploit spatially extended, sideband-resolved mechanical breathing modes at ~3.2 GHz, with vacuum optomechanical coupling rates of up to g<sub>0</sub>/2π ≈ 300 kHz. The mechanical modes are driven by integrated microwave electrodes via the inverse piezoelectric effect. We estimate that the system could achieve an electromechanical coupling rate to a superconducting transmon qubit of ~200 kHz. Our work represents a decisive step towards integration of piezoelectro-optomechanical interfaces with superconducting quantum processors.</p>
Learning to Give a Complete Argument with a Conversational Agent: An Experimental Study in Two Domains of Argumentation
<p>This data is collected to find out how having a conversation with our agent affects argumentation. To model the arguments, we used Toulmin's model of argument. Based on the model, a good argument contains 3 different parts: 1. Claim, 2. Warrant, 3. Evidence. Based on Toulmin's model, these three components are the core components of arguments. This dataset has been collected during a between-subject experiment in which the treatment groups first talked to our agent in Task 1 and received feedback on faulty structural arguments and then did Task 2 and 3 which were answering a question on the same and completely different topic in comparison to Task 1.</p>
Bio-oil production from biogenic wastes, the hydrothermal conversion step - Data
<p>Food wastes are an abundant resource that can be effectively valorised by hydrothermal liquefaction to produce bio-fuels. The objective of the European project Waste2Road is to demonstrate the complete value chain from waste collection to engine tests. The principle of hydrothermal liquefaction is well known but there are still many factors that make the science very empirical. Most experiments in the literature are performed on batch reactors. Comparison of results from batch reactors with experiments with continuous reactors are rare in the literature.</p> <p>This dataset presents fully documented experiments, performed in this project, on food wastes, with different compositions, conditions and solvents. The data set is extended with data from the literature. This data set also includes bio-oil and aqueous phase analysis by gas chromatography coupled with mass spectrometry.</p>
Structural conversion of the spidroin C-terminal domain during assembly of spider silk fibers
<p>GENERAL INFORMATION<br>- Dataset title: Structural conversion of the spidroin C-terminal domain during assembly of spider silk fibers<br>- Description: The dataset contains raw data associated with the publication with the same name, accepted for publication in Nature Communications.<br>- Authors: Danilo Hirabae De Oliveira, Vasantha Gowda, Tobias Sparrman, Linnea Gustafsson, Rodrigo Sanches Pires, Christian Riekel, Andreas Barth, Christofer Lendel, My Hedhammar </p> <p>ORGANIZATION<br>The folder contains zip-files for each figure in the publication. Each zip-file contains data and a .txt file describing the content, the methods for data acquisition and analysis, and the file types.</p> <p><br>DATA COLLECTION<br>Data collection and analysis is described in the paper and in the .txt files included in each zip-file.</p>
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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
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International Brain Laboratory public data
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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.