cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4727353
title: Model-based evaluation of decentralised electricity markets at different phases of the German energy transition - Dataset

description: Input and output data of the modelling work for the paper "Model-based evaluation of decentralised electricity markets at different phases of the German energy transition". <strong>Input:</strong> Demand as hourly profile in MWh Variable RES-E as hourly profile in MWh Power plant fleet as capacities in MW NTCs as capacities in MW <strong>Output:</strong> CO2 emissions as annual data in Mt Variable electricity generation costs per generation as annual data in Euro/MWh Electricity generation as annual data in TWh RES-E curtailment as annual data in TWh Storage losses as annual data in TWh Self-supply rate as annual data % Grid congestion as annual data in TWh Grid expansion as annual data in km The sources are described in the corresponding paper under the following link: (coming soon)
description: {"references": ["K\u00fchnbach, Matthias, Felix Guthoff, Anke Bekk, and Ludger Eltrop. 2020. \"Development of Scenarios for a Multi-Model System Analysis Based on the Example of a Cellular Energy System.\" Energies 13 (4) (773): 1\u201323. https://doi.org/10.3390/en13040773.", "Repenning, Julia, Hauke Hermann, Lukas Emele, Wolfram J\u00f6r\u00df, Ruth Blanck, Charlotte Loreck, Hannes B\u00f6ttcher, Sylvie Ludig, G\u00fcnter Dehoust, and Matthes, Felix Chr. et al. 2015. \"Klimaschutzszenario 2050: 2. Modellierungsrunde.\" https://www.oeko.de/oekodoc/2451/2015-608-de.pdf.", "Rippel, Kerstin Maria, Thomas Wiede, Mario Meinecke, and Regina K\u00f6nig. 2019. \"Netzentwicklungsplan Strom 2030: Zahlen - Daten - Fakten.\".", "Ritter, David, Roland Meyer, Matthias Koch, Markus Haller, Dierk Bauknecht, and Christoph Heinemann. 2019. \"Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System.\" Energies 12(16) (3098). https://doi.org/10.3390/en12163098. Accessed August 28, 2019."]}
DOI: 10.5281/zenodo.3257495
title: Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System - Dataset

description: Input and output data of the modelling work for the paper Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System Considered scenario years: 2030, 2040 and 2050 The profiles are based on the historical year 2016. Two scenarios are included: lower connectivity and high connectivity The data cover the ENTSO-E member countries except Iceland and Cyprus and is given in country-specific resolution. <strong>Input:</strong> Demand as hourly profile in MWh Variable RES-E as hourly profile in MWh Power plant fleet as capacities in MW NTCs as capacities in MW <strong>Output:</strong> CO2 emissions as annual data in Mt Variable electricity generation costs as annual data in MEur Variable electricity generation costs per generation as annual data in Euro/MWh Electricity generation as annual data in TWh Electricity export as annual data in TWh Electricity import as annual data in TWh Transit flows as annual data in TWh The sources are described in the corresponding paper under the following link: https://www.mdpi.com/1996-1073/12/16/3098
description: {"references": ["Repenning, J.; Hermann, H.; Emele, L.; J\u00f6r\u00df, W.; Blanck, R.; Loreck, C.; B\u00f6ttcher, H.; Ludig, S.; Dehoust, G.; Matthes, Felix Chr. et al. Klimaschutzszenario 2050. 2. Modellierungsrunde, Berlin, 2015. Available online: https://www.oeko.de/oekodoc/2451/2015-608-de.pdf", "Andersky, T.; Sanchis, G.; Betraoui, B. e-Highway 2050 - Database per country. Excel-Sheet, 2016", "ENTSO-E. Transparency Platform. Available online: https://transparency.entsoe.eu/", "ENTSO-E. TYNDP 2018 Scenario Report. Data set, Brussels, 2018. Available online: https://tyndp.entsoe.eu/maps-data/"]}
DOI: 10.5281/zenodo.3257494
title: Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System - Dataset

description: Input and output data of the modelling work for the paper Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System Considered scenario years: 2030, 2040 and 2050 The profiles are based on the historical year 2016. Two scenarios are included: lower connectivity and high connectivity The data cover the ENTSO-E member countries except Iceland and Cyprus and is given in country-specific resolution. <strong>Input:</strong> Demand as hourly profile in MWh Variable RES-E as hourly profile in MWh Power plant fleet as capacities in MW NTCs as capacities in MW <strong>Output:</strong> CO2 emissions as annual data in Mt Variable electricity generation costs as annual data in MEur Variable electricity generation costs per generation as annual data in Euro/MWh Electricity generation as annual data in TWh Electricity export as annual data in TWh Electricity import as annual data in TWh Transit flows as annual data in TWh The sources are described in the corresponding paper under the following link: https://www.mdpi.com/1996-1073/12/16/3098
description: {"references": ["Repenning, J.; Hermann, H.; Emele, L.; J\u00f6r\u00df, W.; Blanck, R.; Loreck, C.; B\u00f6ttcher, H.; Ludig, S.; Dehoust, G.; Matthes, Felix Chr. et al. Klimaschutzszenario 2050. 2. Modellierungsrunde, Berlin, 2015. Available online: https://www.oeko.de/oekodoc/2451/2015-608-de.pdf", "Andersky, T.; Sanchis, G.; Betraoui, B. e-Highway 2050 - Database per country. Excel-Sheet, 2016", "ENTSO-E. Transparency Platform. Available online: https://transparency.entsoe.eu/", "ENTSO-E. TYNDP 2018 Scenario Report. Data set, Brussels, 2018. Available online: https://tyndp.entsoe.eu/maps-data/"]}
DOI: 10.6084/m9.figshare.14509521
title: Data on the paper titled that “Soil nitrogen transformations respond diversely to multiple levels of nitrogen addition in a Tibetan alpine steppe”

description: The dataset provides soil nitrogen (N) transformation rates, physiochemical and microbial properties along eight levels of N addition gradient in a Tibetan alpine steppe. Soil N transformation rates include gross rates of N mineralization, gross nitrification, and microbial immobilization. Soil physiochemical properties include soil pH, content of dissolved organic N, dissolved organic carbon and NH<sub>4</sub><sup>+</sup>-N. Soil microbial properties include microbial biomass carbon content, β-1,4-N-acetylglucosaminidase and leucine aminopeptidase activities, the gene abundances of ammonia-oxidizing archaea and bacteria. The dataset is obtained with soil samples taken in July-2019.<br><br>
DOI: 10.6084/m9.figshare.14509521.v2
title: Data on the paper titled that “Soil nitrogen transformations respond diversely to multiple levels of nitrogen addition in a Tibetan alpine steppe”

description: The dataset provides soil nitrogen (N) transformation rates, physiochemical and microbial properties along eight levels of N addition gradient in a Tibetan alpine steppe. Soil N transformation rates include gross rates of N mineralization, gross nitrification, and microbial immobilization. Soil physiochemical properties include soil pH, content of dissolved organic N, dissolved organic carbon and NH<sub>4</sub><sup>+</sup>-N. Soil microbial properties include microbial biomass carbon content, β-1,4-N-acetylglucosaminidase and leucine aminopeptidase activities, the gene abundances of ammonia-oxidizing archaea and bacteria. The dataset is obtained with soil samples taken in July-2019.<br><br>
DOI: 10.6084/m9.figshare.14502477
title: Table S1 - ECTyper database v1.0.0 allele sources

description: ECTyper v1.0.0 database allele sources
DOI: 10.6084/m9.figshare.14510394
title: Table S4 - Guelph Reference and Diagnostic Services dataset metadata and serotyping results

description: Table S4 - Guelph Reference and Diagnostic Services dataset metadata and serotyping results
DOI: 10.6084/m9.figshare.14510394.v1
title: Table S4 - Guelph Reference and Diagnostic Services dataset metadata and serotyping results

description: Table S4 - Guelph Reference and Diagnostic Services dataset metadata and serotyping results
DOI: 10.6084/m9.figshare.14502486
title: Table S3 - ECTyper v1.0.0 allele database metadata

description: Table S3 - ECTyper v1.0.0 allele database metadata
DOI: 10.5517/cc8x07d
title: CCDC 265243: Experimental Crystal Structure Determination

description: Related Article: M.Westerhausen,T.Rotter,S.Weinrich,M.Ossberger,P.Mayer,H.Piotrowski|2009|CSD Communication|||
DOI: 10.5281/zenodo.4727405
title: Gas Supply and Demand: Forecasts and Historical Data

description: The data set includes the following data: -The yearly gas supply and demand forecasts by countries and balancing zones (where available) provided by ENTSO-G in ENTSOs TYNDP 2018 Final Scenario Report, ENTSOG TYNDP 2018 Final Scenario Report Demand. Data is formatted in the plan4res file format. - The historical physical flow data that constitutes yearly distribution for EU supply sources, i.e., RU, NO, indigenous production, etc., and demand of countries and balancing zones (where forecast available) provided in ENTSO-G Transparency Platform. Data is formatted in the plan4res file format. - A list of geographical description of data and a list including the description of historical data of time-series The related documentation is included in plan4res deliverable D4.5
description: {"references": ["ENTSO-G Transparency Platform. Available online: https://transparency.entsog.eu/ (accessed on 30.04.2020).", "ENTSO-G Ten Year Network Development Plans. Available online: https://www.entsog.eu/tyndp# (accessed on 30.04.2020).", "ENTSO-G and ENTSO-E, ENTSOs TYNDP 2018 Final Scenario Report. Available online: https://www.entsog.eu/sites/default/files/entsog-migration/publications/TYNDP/2018/entsos_tyndp_2018_Final_Scenario_Report.pdf (accessed on 30.04.2020)."]}
DOI: 10.5281/zenodo.3778892
title: Gas Supply and Demand: Forecasts and Historical Data

description: The data set includes the following data: -The yearly gas supply and demand forecasts by countries and balancing zones (where available) provided by ENTSO-G in ENTSOs TYNDP 2018 Final Scenario Report, ENTSOG TYNDP 2018 Final Scenario Report Demand. Data is formatted in the plan4res file format. - The historical physical flow data that constitutes yearly distribution for EU supply sources, i.e., RU, NO, indigenous production, etc., and demand of countries and balancing zones (where forecast available) provided in ENTSO-G Transparency Platform. Data is formatted in the plan4res file format. - A list of geographical description of data and a list including the description of historical data of time-series The related documentation is included in plan4res deliverable D4.5
description: {"references": ["ENTSO-G Transparency Platform. Available online: https://transparency.entsog.eu/ (accessed on 30.04.2020).", "ENTSO-G Ten Year Network Development Plans. Available online: https://www.entsog.eu/tyndp# (accessed on 30.04.2020).", "ENTSO-G and ENTSO-E, ENTSOs TYNDP 2018 Final Scenario Report. Available online: https://www.entsog.eu/sites/default/files/entsog-migration/publications/TYNDP/2018/entsos_tyndp_2018_Final_Scenario_Report.pdf (accessed on 30.04.2020)."]}
DOI: 10.6084/m9.figshare.11619867.v1
title: A Fused Gaussian Process Model for Very Large Spatial Data*

description: With the development of new remote sensing technology, large or even massive spatial datasets covering the globe become available. Statistical analysis of such data is challenging. This article proposes a semiparametric approach to model large or massive spatial datasets. In particular, a Gaussian process with additive components is proposed, with its covariance structure consisting of two components: one component is flexible without assuming a specific parametric covariance function but is able to achieve dimension reduction; the other is parametric and simultaneously induces sparsity. The inference algorithm for parameter estimation and spatial prediction is devised. The resulting spatial prediction methodology that we call fused Gaussian process (FGP), is applied to simulated data and a massive satellite dataset. The results demonstrate the computational and inferential benefits of FGP over competing methods and show that FGP is robust against model misspecification and captures spatial nonstationarity. The supplemental materials are available online.
DOI: 10.6084/m9.figshare.11932950.v1
title: Probabilistic Detection and Estimation of Conic Sections from Noisy Data

description: Inferring unknown conic sections on the basis of noisy data is a challenging problem with applications in computer vision. A major limitation of the currently available methods for conic sections is that estimation methods rely on the underlying shape of the conics (being known to be ellipse, parabola or hyperbola). A general purpose Bayesian hierarchical model is proposed for conic sections and corresponding estimation method based on noisy data is shown to work even when the specific nature of the conic section is unknown. The model, thus, provides probabilistic detection of the underlying conic section and inference about the associated parameters of the conic section. Through extensive simulation studies where the true conics may not be known, the methodology is demonstrated to have practical and methodological advantages relative to many existing techniques. In addition, the proposed method provides probabilistic measures of uncertainty of the estimated parameters. Furthermore, we observe high fidelity to the true conics even in challenging situations, such as data arising from partial conics in arbitrarily rotated and non-standard form, and where a visual inspection is unable to correctly identify the type of conic section underlying the data.
DOI: 10.6084/m9.figshare.11114498.v1
title: Classification with the matrix-variate-<i>t</i> distribution

description: Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal or repeated measures. This paper develops an Expectation-Maximization algorithm for discriminant analysis and classification with matrix-variate <i>t</i>-distributions. The methodology shows promise on simulated datasets or when applied to the forensic matching of fractured surfaces or the classification of functional Magnetic Resonance, satellite or hand gestures images.
DOI: 10.6084/m9.figshare.14510490
title: Dataset used in "Selective inhibitory control in middle childhood"

description: Dataset used in the research titled Selective inhibitory control in middle childhood (143 children) <br>
DOI: 10.6084/m9.figshare.14510490.v1
title: Dataset used in "Selective inhibitory control in middle childhood"

description: Dataset used in the research titled Selective inhibitory control in middle childhood (143 children) <br>
DOI: 10.6084/m9.figshare.11973558.v1
title: Bayesian variational inference for exponential random graph models

description: Deriving Bayesian inference for exponential random graph models (ERGMs) is a challenging “doubly intractable” problem as the normalizing constants of the likelihood and posterior density are both intractable. Markov chain Monte Carlo (MCMC) methods which yield Bayesian inference for ERGMs, such as the exchange algorithm, are asymptotically exact but computationally intensive, as a network has to be drawn from the likelihood at every step using, for instance, a “tie no tie” sampler. In this article, we develop a variety of variational methods for Gaussian approximation of the posterior density and model selection. These include nonconjugate variational message passing based on an adjusted pseudolikelihood and stochastic variational inference. To overcome the computational hurdle of drawing a network from the likelihood at each iteration, we propose stochastic gradient ascent with biased but consistent gradient estimates computed using adaptive self-normalized importance sampling. These methods provide attractive fast alternatives to MCMC for posterior approximation. We illustrate the variational methods using real networks and compare their accuracy with results obtained via MCMC and Laplace approximation.
DOI: 10.6084/m9.figshare.12141069.v1
title: A Tree-based Semi-Varying Coefficient Model for the COM-Poisson Distribution

description: We propose a tree-based semi-varying coefficient model for the Conway-Maxwell-Poisson (CMP or COM-Poisson) distribution which is a two-parameter generalization of the Poisson distribution and is flexible enough to capture both under-dispersion and over-dispersion in count data. The advantage of tree-based methods is their scalability to high-dimensional data. We develop CMPMOB, an estimation procedure for a semi-varying coefficient model, using model-based recursive partitioning (MOB). The proposed framework is broader than the existing MOB framework as it allows node-invariant effects to be included in the model. To simplify the computational burden of the exhaustive search employed in the original MOB algorithm, a new split point estimation procedure is proposed by borrowing tools from change point estimation methodology. The proposed method uses only the estimated score functions without fitting models for each split point and, therefore, is computationally simpler. Since the tree-based methods only provide a piece-wise constant approximation to the underlying smooth function, we propose the CMPBoost semi-varying coefficient model which uses the gradient boosting procedure for estimation. The usefulness of the proposed methods are illustrated using simulation studies and a real example from a bike sharing system in Washington, DC.
DOI: 10.5281/zenodo.4727509
title: 39Ar input data

description: 39Ar input function in the atmosphere and age correction curve for 39Ar dating.
DOI: 10.5281/zenodo.4727508
title: 39Ar input data

description: 39Ar input function in the atmosphere and age correction curve for 39Ar dating.
DOI: 10.5258/soton/d1701
title: Dataset for Non-Isothermal Phase-Field Simulation of Laser-Written In-Plane SiGe Heterostructures for Photonic Applications

description: DATASET for Non-Isothermal Phase-Field Simulation of Laser-Written In-Plane SiGe Heterostructures for Photonic Applications in Communications Physics.
DOI: 10.6084/m9.figshare.14502480
title: Table S2 - High similarity antigen allele pairs in ECTyper database v1.0.0

description: Table S2 - High similarity antigen allele pairs in ECTyper database v1.0.0
DOI: 10.6084/m9.figshare.14502480.v1
title: Table S2 - High similarity antigen allele pairs in ECTyper database v1.0.0

description: Table S2 - High similarity antigen allele pairs in ECTyper database v1.0.0
DOI: 10.6084/m9.figshare.6088073.v1
title: Identifying demand shocks from production data

description: Standard productivity estimates contain a mixture of cost efficiency and demand conditions. I propose a method to identify the distribution of the demand shock using production data. Identification does not depend on functional form restrictions. It is also robust to dynamic demand considerations and flexible labor. In the parametric case the ratio of intermediate inputs to the wage bill (input ratio) contains information about the magnitude of the demand shock. The method is tested using data from Spain that contains information on prices and demand conditions. Finally, we generate Monte Carlo simulations to evaluate the method's performance and sensitivity.
DOI: 10.6084/m9.figshare.5813793.v2
title: Shape constrained kernel-weighted least squares: Estimating production functions for Chilean manufacturing industries

description: In this paper we examine a novel way of imposing shape constraints on a local polynomial kernel estimator. The proposed approach is referred to as Shape Constrained Kernel-weighted Least Squares (SCKLS). We prove uniform consistency of the SCKLS estimator with monotonicity and convexity/concavity constraints and establish its convergence rate. In addition, we propose a test to validate whether shape constraints are correctly specified. The competitiveness of SCKLS is shown in a comprehensive simulation study. Finally, we analyze Chilean manufacturing data using the SCKLS estimator and quantify production in the plastics and wood industries. The results show that exporting firms have significantly higher productivity.
DOI: 10.17863/cam.68835
title: Research data supporting "Extending motifs in lithiocuprate chemistry: unexpected structural diversity in thiocyanate complexes"

description: Supporting data for the publication "Extending motifs in lithiocuprate chemistry: unexpected structural diversity in thiocyanate complexes". Single crystal X-ray diffraction structures and NMR spectroscopic data for complexes 8-11 in the paper. All four complexes were made at Cambridge in 2015 and were characterized in house in 2015. NMR spectra for compounds 15 and 16. These were made and characterized in Rennes in 2015.
description: This work was supported by the EPSRC [grant number EP/J500380/1] and Ministère de l'Enseignement Supérieur et de la Recherche scientifique Algérien.
DOI: 10.17863/cam.68836
title: Research data supporting "Controlling nanowire growth by light"

description: Data of graphs published in Nano Letters, article "Controlling nanowire growth by light". This record supports publication and is available at http://dx.doi.org/10.1021/acs.nanolett.5b02953
description: This work was supported by the EPSRC [grant numbers EP/G060649/1, EP/L027151/1, EP/G037221/1, EPSRC NanoDTC] and the ERC [grant numbers LINASS 320503 and InsituNANO 279342].
DOI: 10.5281/zenodo.4675217
title: The Austronesian Game Taxonomy

description: AustroGames is an R package and data on games played by ethnolinguistic groups in Oceania. It coincides with a publication by Leisterer-Peoples, et al. (2021).
description: Release created with: https://github.com/sarahpeoples/AustroGames . Current owner: https://github.com/ccp-eva/AustroGames
DOI: 10.5281/zenodo.4675779
title: The Austronesian Game Taxonomy

description: AustroGames is an R package and data on games played by ethnolinguistic groups in Oceania. It coincides with a publication by Leisterer-Peoples, et al. (2021).
description: Release created with: https://github.com/sarahpeoples/AustroGames . Current owner: https://github.com/ccp-eva/AustroGames
DOI: 10.17863/cam.68837
title: Data for Generalized circuit model for coupled plasmonic systems

description: The experimental data taken in the NanoPhotonics Group at the Cavendish Laboratory (University of Cambridge) and the Molecular and Nanoscale Physics Group (University of Leeds) was collected between 01/05/2014 to 01/02/2015. The data provided for the analytical circuit model was obtained in the NanoPhotonics Group at the Cavendish Laboratory (University of Cambridge) between 01/09/2015 to 20/07/2015. Additional simulations performed at the Donostia International Physics Center (DIPC) and Centro de Fisica de Materiales are provided. Dataset for accepted journal article "Generalized circuit model for coupled plasmonic systems"
description: "This work was supported by the ERC [grant number LINASS 320503] and EPSRC [grant numbers [EP/G060649/1, EP/I012060/1, EP/L027151/1 and EP/K028510/1]."
DOI: 10.17632/m4yf4rzxbn.1
title: Data for: Volatility-of-Volatility Risk in Asset Pricing

description: VOV data. 1) VOV: For each day, we estimate VOV by calculating the realized bipower variance from a series of five-minute model-free implied market variance within the day, using the high-frequency S&P 500 index option data. 2) ΔVOV: The innovations in market volatility-of-volatility (ΔVOV) is computed as the ARMA(1,1) model residual of the market volatility-of-volatility (VOV). To calculate implied volatility, we use the tick-by-tick quote data for the S&P 500 index (SPX) options from CBOE’s Market Data Report (MDR) tapes over the time period from January 1996 to December 2015. Citation: Chen, Te-Feng, Tarun Chordia, San-Lin Chung, and Ji-Chai Lin. 2021. VOV Risk in Asset Pricing. Review of Asset Pricing Studies. (https://www.dropbox.com/s/es8sew1zpu9xciu/VOV-data-RAPS.xlsx?dl=0)