cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5s2m5xhfst.1
title: G3 dataset

description: ในปัจจุบัณมีการซื้อขายแลกเปลี่ยนสินทรัพย์มากมายจากสินทรัพย์หนึ่งสู่อีกสินทรัพย์หนึ่ง โดยที่มีนักลงทุนมากมายที่พยายามสร้างกำไรจากส่วนต่างของการแลกเปลี่ยนสินทรัพย์ต่างๆ โดยบิทคอยน์ก็เป็นสินทรัพย์ที่มีความผันผวนสูงทำให้นักลงทุนเข้ามาเก็กำไรกันมากในสินทรัพย์นี้ โดยวิเคราะห์จากปัจจัยทางด้านเทคนิค ปัจจัยพื้นฐาน รวมไปถึงผลกระทบจากตลาดทางสินทรัพย์อื่นๆ โดยในปัจจุบัณได้มีการนำระบบคอมพิวเตอร์หรือปัญญาประดิษฐ์มาศึกษาหาความสัมพันธ์ของสินทรัพย์ผ่านปัจจัยทางด้านเทคนิค ปัจจัยพื้นฐานรวมไปถึงผลกระทบจากตลาดทางสินทรัพย์อื่นๆ โดยในการศึกษาครั้งนี้จะทำการหาความสัมพันธ์ของสินทรัพย์แต่ละชนิดกับราคาบิทคอยน์ ได้แก่ดัชนีหุ้นอุตสาหกรรม (Dow Jones Index), ดัชนีหุ้นเทคโนโลยี (Nasdaq Index), XAU, ราคาน้ำมันดิบและพันธบัตรสหรัฐอเมริกาโดยการหาค่าสัมประสิทธิ์สหสัมพันธ์ เพื่อนำข้อมูลมาใช้ในการสร้างโครงข่ายประสาทเทียม
DOI: 10.5281/zenodo.6517595
title: The rehydration attributes and quality characteristics of 'Quick-cooking' dehydrated beans: Implications of glass transition on storage stability

description: The data set was used to generate the figures.
DOI: 10.5281/zenodo.6517593
title: The rehydration attributes and quality characteristics of 'Quick-cooking' dehydrated beans: Implications of glass transition on storage stability

description: The data set was used to generate the figures.
DOI: 10.5203/kw1p-gx07
title: marine-indicators-for-regional-monitoring-in-the-inuvialuit-settlement-region1
DOI: 10.17632/wpn4gsct8z.1
title: Proinflammatory cytokine sepsis levels 

description: Dataset contains the output of a systematic review of studies with available pro-inflammatory cytokine levels  (TNFα IL-1β and IFNγ)
DOI: 10.17028/rd.lboro.19308299
title: Measured indoor temperature, weather, infiltration, and ventilation in synthetically occupied test houses: summer 2021, UK

description: This dataset is maintained by Ben Roberts (b.m.roberts@lboro.ac.uk), Building Energy Research Group (BERG), School of Architecture, Building and Civil Engineering, Loughborough University.<br>---<br>The IMPROVE project (IMproving the PRedictions of OVErheating) comprised a measurement phase and a modelling phase. The measurement phase is the subject of this dataset and was carried out from April 2021 to September 2021 (inclusive). Two matched pair houses, one synthetically occupied and one unoccupied were instrumented with sensors to measure: indoor dry bulb temperature, indoor operative temperature, indoor surface temperature, operation of synthetic occupancy (window, door, and curtain operation; internal heat gains), the whole house infiltration rate, single room ventilation rate, and the weather conditions.<br>The dataset is made publicly available here. This dataset includes:<br><br>- README file (.txt).<br>- Guidance document (.pdf).<br>- Indoor temperature, internal heat gain operation, and weather data (.csv).<br>Other information on the houses' geometry and construction can be found here: https://doi.org/10.17028/rd.lboro.8094575.<br><br>---<br>This work has been carried out as part of the IMPROVE project (IMproving the PREdictions of OVErheating). IMPROVE is a consortium of two universities: Loughborough University and London South Bank University - and two industry stakeholders: Hilson Moran Partnership Ltd. and Inkling Partnership LLP.
DOI: 10.17028/rd.lboro.19308299.v1
title: Measured indoor temperature, weather, infiltration, and ventilation in synthetically occupied test houses: summer 2021, UK

description: This dataset is maintained by Ben Roberts (b.m.roberts@lboro.ac.uk), Building Energy Research Group (BERG), School of Architecture, Building and Civil Engineering, Loughborough University.<br>---<br>The IMPROVE project (IMproving the PRedictions of OVErheating) comprised a measurement phase and a modelling phase. The measurement phase is the subject of this dataset and was carried out from April 2021 to September 2021 (inclusive). Two matched pair houses, one synthetically occupied and one unoccupied were instrumented with sensors to measure: indoor dry bulb temperature, indoor operative temperature, indoor surface temperature, operation of synthetic occupancy (window, door, and curtain operation; internal heat gains), the whole house infiltration rate, single room ventilation rate, and the weather conditions.<br>The dataset is made publicly available here. This dataset includes:<br><br>- README file (.txt).<br>- Guidance document (.pdf).<br>- Indoor temperature, internal heat gain operation, and weather data (.csv).<br>Other information on the houses' geometry and construction can be found here: https://doi.org/10.17028/rd.lboro.8094575.<br><br>---<br>This work has been carried out as part of the IMPROVE project (IMproving the PREdictions of OVErheating). IMPROVE is a consortium of two universities: Loughborough University and London South Bank University - and two industry stakeholders: Hilson Moran Partnership Ltd. and Inkling Partnership LLP.
DOI: 10.17632/6tc5jcxv5x.6
title: No. 24: Medios hegemónicos, conglomerados económicos y gobiernos.

description: La Revista Textos y Contextos, publicación científica de la Facultad de Comunicación Social de la Universidad Central del Ecuador, dedica su edición 24 al tema “Medios hegemónicos, conglomerados económicos y gobiernos”.

Desde su emergencia, el rol de los medios de comunicación se ha vinculado a valores democráticos como la libertad de expresión, el debate informado, el disenso y el derecho a una información veraz y de calidad, sin los cuales peligran las aspiraciones de participación política de una ciudadanía con capacidad para reconocer, exigir y ejercer sus derechos. Sin embargo, con la mercantilización de la información y el monopolio sobre las industrias culturales los medios se convierten en dispositivos de manipulación y control social desde una razón instrumental que ha predominado por encima de los bienes que producen.
DOI: 10.17632/wx25nrt7kk.1
title: COMPUTATION OF PLASTIC LIMIT USING MATHEMATICAL MODEL  

description: This paper presents a mathematical model that uses a British cone to predict the soil moisture-penetration relationship. This relationship is nonlinear for non-bentonite bearing soil and can be modelled using a sigmoidal mathematical function. The developed mathematical model gives a unique opportunity for computing and predicting the fall cone penetration of the soil even at a very low moisture content, at which, due to practical limitations, it is impossible to determine the soil penetration. The mathematical model was found to fit the moisture-penetration data reasonably well. Based on the data presented in this paper, both the liquid limit and plastic limit can be determined from the extended liquid limit flow curve by determining the plastic limit at a 1.2 mm penetration value. The computed plastic limit at a 1.2 mm penetration value was found to be in close agreement with the measured plastic limit.
DOI: 10.17632/8j4x6dj2f7.1
title: Guo_Kowalczyk_Lee_Data_Software_Package

description: Software and supporting materials for STAR Protocols manuscript "Label and quantify mRNA molecules in live cell experiments using SunRISER and dNEMO".
Authors: Yue Guo, Gabriel Kowalczyk, & Robin E.C. Lee. Additional information on operating dNEMO can be found at https://github.com/recleelab.
DOI: 10.17632/4by7d9mpmr.1
title: Triage Dermoscopic Images 

description: Diagnostic performance of medical providers in differentiating between malignant melanoma and benign nevi using the MClass-D test set from the International Skin Imaging Collaboration.
DOI: 10.17632/zsbxdmhtj9.1
title: Monte Carlo exercises with Logit peer effects with missing outcomes

description: This dataset has all the codes that are used to create the Monte Carlo exercises described in: Madeira, C. (2022), "Partial identification of non-linear peer effects models with missing data", Swiss Journal of Economics and Statistics, forthcoming. The codes include a README file that explains each program and the estimator options.
DOI: 10.5203/51bw-ge22
title: nlmphase1
DOI: 10.5203/vcmd-b290
title: nlmphase2
DOI: 10.6084/m9.figshare.19780276
title: A Deep Learning Framework for analyzing Cloud Characteristics of Aggregated Convection Using Cloud-Resolving Model Simulations

description: The zip files include the codes and the trained CNN models for analyzing and visualizing the classification of (non) aggregation cases from CRM simulations. The analyzing codes and training logs, and the graphs can be found in jupyter notebook (.ipynb) files. The trained models can be found in the "saved_model" folder. Ones who are interested in the original model output data may contact the authors
DOI: 10.5203/amdw-3d02
title: nlmfinalreport
DOI: 10.5281/zenodo.6557786
title: largeEELproject_computational_repository

description: Data and code repository for the thesis project: High-throughput spatial transcriptome<br> profling with EEL-FISH: Enhancing sensitivity, spatial coverage, and computational<br> bottlenecks
DOI: 10.5281/zenodo.6555485
title: largeEELproject_computational_repository

description: Data and code repository for the thesis project: High-throughput spatial transcriptome<br> profling with EEL-FISH: Enhancing sensitivity, spatial coverage, and computational<br> bottlenecks
DOI: 10.5517/ccdc.csd.cc214n73
title: CCDC 1881304: Experimental Crystal Structure Determination

description: Related Article: Wei Yao, Chaoqun Ma, Huili Ma, Lishun Fu, Song Lu, Anqi Lv, Suzhi Cai, Xiaochun Hang, Manjeet Singh, Huifang Shi, Zhongfu An, Wei Huang|2020|Giant|1|100007|doi:10.1016/j.giant.2020.100007
DOI: 10.5517/ccdc.csd.cc214n84
title: CCDC 1881305: Experimental Crystal Structure Determination

description: Related Article: Wei Yao, Chaoqun Ma, Huili Ma, Lishun Fu, Song Lu, Anqi Lv, Suzhi Cai, Xiaochun Hang, Manjeet Singh, Huifang Shi, Zhongfu An, Wei Huang|2020|Giant|1|100007|doi:10.1016/j.giant.2020.100007
DOI: 10.5281/zenodo.5521310
title: Late blight resistance in potato conferred by Rpi-Smira2/R8

description: The data are related to Figure 7 of the publication Blatnik et al. (2022) Late blight resistance conferred by <em>Rpi-Smira2/R8</em> in potato genotypes<em> in vitro</em> depends on the genetic background, published in Plants 11: 1319 (https://doi.org/10.3390/plants11101319) The data represent late blight (<em>Phytophtora infestans</em>) disease scores of progeny <em>R8</em> genotypes and parental cultivars inoculated with four <em>P. infestans </em>isolates <em>in vitro</em>. The disease scores were evaluated daily for an eight day period post inoculation according to the late blight disease rating scale (see publication and info sheet of the data).
DOI: 10.5281/zenodo.5521309
title: Late blight resistance in potato conferred by Rpi-Smira2/R8

description: The data are related to Figure 7 of the publication Blatnik et al. (2022) Late blight resistance conferred by <em>Rpi-Smira2/R8</em> in potato genotypes<em> in vitro</em> depends on the genetic background, published in Plants 11: 1319 (https://doi.org/10.3390/plants11101319) The data represent late blight (<em>Phytophtora infestans</em>) disease scores of progeny <em>R8</em> genotypes and parental cultivars inoculated with four <em>P. infestans </em>isolates <em>in vitro</em>. The disease scores were evaluated daily for an eight day period post inoculation according to the late blight disease rating scale (see publication and info sheet of the data).
DOI: 10.6084/m9.figshare.19615710
title: People with disabilities Dataset in spanish

description: The data set is contemplating variables of the Theory of Planned Behavior and the Entrepreneurial Event Model. Also is considering other variables such as Perceived Collective Efficacy and resilience included in other models
DOI: 10.6084/m9.figshare.19615710.v1
title: People with disabilities Dataset

description: The data set is contemplating variables of the Theory of Planned Behavior and the Entrepreneurial Event Model. Also is considering other variables such as Perceived Collective Efficacy and resilience included in other models
DOI: 10.6084/m9.figshare.19780462
title: Tables S1 and S2, Dataset S1

description: <strong>The diversity of quinoa morphological traits and seed metabolic composition</strong> Iman Tabatabaei, Saleh Alseekh, Mohammad Shahid, Ewa Leniak, Mateusz Wagner, Henda Mahmoudi, Sumitha Thushar, Alisdair R. Fernie, Kevin M. Murphy, Sandra M. Schmöckel, Mark Tester, Bernd Mueller-Roeber, Aleksandra Skirycz, Salma Balazadeh
DOI: 10.6084/m9.figshare.19780476
title: Supplementary figures from Hydrological implications of large-scale afforestation in tropical biomes for climate change mitigation

description: Rising interest in large-scale afforestation and reforestation as a strategy for climate change mitigation has recently motivated research efforts aiming at the identification of areas suitable for the plantation of trees. An often-overlooked aspect of agroforestry projects for carbon sequestration is their impact on water resources. It is often unclear to what extent the establishment of forest vegetation would be limited by water availability, whether it would engender competition with other local water uses or induce water scarcity. Here we use global water models to study the hydrologic constraints and impacts of afforestation in tropical biomes. We find that 36% of total suitable and available afforestation areas are in areas where the rain alone can meet just up to the 40% of total plant water requirement. Planting trees will substantially increase water scarcity and possible dispossession (green water grab) especially in dryland regions of Africa and Oceania. Moreover, the combination of tree restoration and irrigation expansion to rainfed agricultural areas is expected to further exacerbate water scarcity, with about half of the global suitable areas for tree restoration experiencing water scarcity at least 7 months per year. Thus, the unavailability of water can overall limit climate change adaptation strategies.This article is part of the theme issue ‘Ecological complexity and the biosphere: the next 30 years’.
DOI: 10.6084/m9.figshare.19780476.v1
title: Supplementary figures from Hydrological implications of large-scale afforestation in tropical biomes for climate change mitigation

description: Rising interest in large-scale afforestation and reforestation as a strategy for climate change mitigation has recently motivated research efforts aiming at the identification of areas suitable for the plantation of trees. An often-overlooked aspect of agroforestry projects for carbon sequestration is their impact on water resources. It is often unclear to what extent the establishment of forest vegetation would be limited by water availability, whether it would engender competition with other local water uses or induce water scarcity. Here we use global water models to study the hydrologic constraints and impacts of afforestation in tropical biomes. We find that 36% of total suitable and available afforestation areas are in areas where the rain alone can meet just up to the 40% of total plant water requirement. Planting trees will substantially increase water scarcity and possible dispossession (green water grab) especially in dryland regions of Africa and Oceania. Moreover, the combination of tree restoration and irrigation expansion to rainfed agricultural areas is expected to further exacerbate water scarcity, with about half of the global suitable areas for tree restoration experiencing water scarcity at least 7 months per year. Thus, the unavailability of water can overall limit climate change adaptation strategies.This article is part of the theme issue ‘Ecological complexity and the biosphere: the next 30 years’.
DOI: 10.6084/m9.figshare.13480302
title: Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors

description: In the past decade, many Bayesian shrinkage models have been developed for linear regression problems where the number of covariates, <i>p</i>, is large. Computation of the intractable posterior is often done with three-block Gibbs samplers (3BG), based on representing the shrinkage priors as scale mixtures of Normal distributions. An alternative computing tool is a state of the art Hamiltonian Monte Carlo (HMC) method, which can be easily implemented in the Stan software. However, we found both existing methods to be inefficient and often impractical for large <i>p</i> problems. Following the general idea of Rajaratnam et al., we propose two-block Gibbs samplers (2BG) for three commonly used shrinkage models, namely, the Bayesian group lasso, the Bayesian sparse group lasso, and the Bayesian fused lasso models. We demonstrate with simulated and real data examples that the Markov chains underlying 2BG’s converge much faster than that of 3BG’s, and no worse than that of HMC. At the same time, the computing costs of 2BG’s per iteration are as low as that of 3BG’s, and can be several orders of magnitude lower than that of HMC. As a result, the newly proposed 2BG is the only practical computing solution to do Bayesian shrinkage analysis for datasets with large <i>p</i>. Further, we provide theoretical justifications for the superior performance of 2BG’s. We establish geometric ergodicity of Markov chains associated with the 2BG for each of the three Bayesian shrinkage models. We also prove, for most cases of the Bayesian group lasso and the Bayesian sparse group lasso model, the Markov operators for the 2BG chains are trace-class. Whereas for all cases of all three Bayesian shrinkage models, the Markov operator for the 3BG chains is not even Hilbert–Schmidt. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13480302.v3
title: Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors

description: In the past decade, many Bayesian shrinkage models have been developed for linear regression problems where the number of covariates, <i>p</i>, is large. Computation of the intractable posterior is often done with three-block Gibbs samplers (3BG), based on representing the shrinkage priors as scale mixtures of Normal distributions. An alternative computing tool is a state of the art Hamiltonian Monte Carlo (HMC) method, which can be easily implemented in the Stan software. However, we found both existing methods to be inefficient and often impractical for large <i>p</i> problems. Following the general idea of Rajaratnam et al., we propose two-block Gibbs samplers (2BG) for three commonly used shrinkage models, namely, the Bayesian group lasso, the Bayesian sparse group lasso, and the Bayesian fused lasso models. We demonstrate with simulated and real data examples that the Markov chains underlying 2BG’s converge much faster than that of 3BG’s, and no worse than that of HMC. At the same time, the computing costs of 2BG’s per iteration are as low as that of 3BG’s, and can be several orders of magnitude lower than that of HMC. As a result, the newly proposed 2BG is the only practical computing solution to do Bayesian shrinkage analysis for datasets with large <i>p</i>. Further, we provide theoretical justifications for the superior performance of 2BG’s. We establish geometric ergodicity of Markov chains associated with the 2BG for each of the three Bayesian shrinkage models. We also prove, for most cases of the Bayesian group lasso and the Bayesian sparse group lasso model, the Markov operators for the 2BG chains are trace-class. Whereas for all cases of all three Bayesian shrinkage models, the Markov operator for the 3BG chains is not even Hilbert–Schmidt. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13480302.v2
title: Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors

description: In the past decade, many Bayesian shrinkage models have been developed for linear regression problems where the number of covariates, <i>p</i>, is large. Computation of the intractable posterior is often done with three-block Gibbs samplers (3BG), based on representing the shrinkage priors as scale mixtures of Normal distributions. An alternative computing tool is a state of the art Hamiltonian Monte Carlo (HMC) method, which can be easily implemented in the Stan software. However, we found both existing methods to be inefficient and often impractical for large <i>p</i> problems. Following the general idea of Rajaratnam et al., we propose two-block Gibbs samplers (2BG) for three commonly used shrinkage models, namely, the Bayesian group lasso, the Bayesian sparse group lasso, and the Bayesian fused lasso models. We demonstrate with simulated and real data examples that the Markov chains underlying 2BG’s converge much faster than that of 3BG’s, and no worse than that of HMC. At the same time, the computing costs of 2BG’s per iteration are as low as that of 3BG’s, and can be several orders of magnitude lower than that of HMC. As a result, the newly proposed 2BG is the only practical computing solution to do Bayesian shrinkage analysis for datasets with large <i>p</i>. Further, we provide theoretical justifications for the superior performance of 2BG’s. We establish geometric ergodicity of Markov chains associated with the 2BG for each of the three Bayesian shrinkage models. We also prove, for most cases of the Bayesian group lasso and the Bayesian sparse group lasso model, the Markov operators for the 2BG chains are trace-class. Whereas for all cases of all three Bayesian shrinkage models, the Markov operator for the 3BG chains is not even Hilbert–Schmidt. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.6557732
title: Seasonal hindcast of temperature and precipitation at a local scale by using TeWA approach

description: <strong>Methodology</strong> Data set of simulated time-series of temperature and precipitation for the 1982-2020 period. Our statistical seasonal prediction model have two main components: a) the ocean-atmosphere coupling represented by correlations between surface variables with delayed teleconnections and b) the self-predictability of the residual anomalies by trends or cycles (quasi-oscillations). The approach has three stages approach with two main predictor components, as mentioned above. The first two stages consist of separate predictions, one per each component, and the third stage is a combination of both predictions (Fig. 2): Teleconnection-based approach (Redolat et al. 2019, 2020) and a self-predictability by using Wavelet-ARIMA models (Conejo et al. 2005; Joo and Kim 2015). Therefore, the total method is a Teleconnection+Wavelet+ ARIMA (TeWA) approach. <strong>References</strong> Conejo, A.J., M.A. Plazas, R. Espinola, A.B. Molina, 2005: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans. Power Syst., 20, 1035-1042, https://doi.org/10.1109/TPWRS.2005.846054. Joo, T., S. Kim, 2015: Time series forecasting based on wavelet filtering. Expert Syst. Appl. 42, 3868-3874. https://doi.org/10.1016/j.eswa.2015.01.026 Redolat, D., R. Monjo, C. Paradinas, J. Pórtoles, E. Gaitán, C. Prado-López, and J. Ribalaygua, 2020: Local decadal prediction according to statistical/dynamical approaches. Int. J. Climatol., 40: 5671–5687. https://doi.org/10.1002/joc.6543. Redolat, D.; R. Monjo, J.A. Lopez-Bustins, and J. Martin-Vide, 2019: Upper-Level Mediterranean Oscillation index and seasonal variability of rainfall and temperature. Theor. Appl. Climatol., 135: 1059–1077. https://doi.org/10.1007/s00704-018-2424-6.
DOI: 10.5281/zenodo.6557558
title: Arquivos complementares - Bioinformática na Caracterização Evolutiva de Clusters de Genes do Metabolismo Especializado em Coffea spp.

description: Arquivos complementares - Bioinformática na Caracterização Evolutiva de Clusters de Genes do Metabolismo Especializado em Coffea spp.
DOI: 10.5281/zenodo.6557557
title: Arquivos complementares - Bioinformática na Caracterização Evolutiva de Clusters de Genes do Metabolismo Especializado em Coffea spp.

description: Arquivos complementares - Bioinformática na Caracterização Evolutiva de Clusters de Genes do Metabolismo Especializado em Coffea spp.