cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc1zlcpm
title: CCDC 1834911: Experimental Crystal Structure Determination

description: Related Article: Mizuho Sugiuchi, Yukatsu Shichibu, Katsuaki Konishi|2018|Angew.Chem.,Int.Ed.|57|7855|doi:10.1002/anie.201804087
description: Related Article: Mitsuhiro Iwasaki, Yukatsu Shichibu, Katsuaki Konishi|2019|Angew.Chem.,Int.Ed.|58|2443|doi:10.1002/anie.201814359
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zlcqn
title: CCDC 1834912: Experimental Crystal Structure Determination

description: Related Article: Mizuho Sugiuchi, Yukatsu Shichibu, Katsuaki Konishi|2018|Angew.Chem.,Int.Ed.|57|7855|doi:10.1002/anie.201804087
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zwztn
title: CCDC 1844153: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Mark I. Ogden, David H. Brown|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.21227/h2zw9d
title: The normalized wavelet package transform energy in each band for the grounding current of distribution network
DOI: 10.5281/zenodo.1247392
title: Sotorrent Dataset

description: Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of code snippets and free-form text on a wide variety of topics. Like other software artifacts, questions and answers on SO evolve over time, for example when bugs in code snippets are fixed, code is updated to work with a more recent library version, or text surrounding a code snippet is edited for clarity. To be able to analyze how content on SO evolves, we built <em>SOTorrent</em>, an open dataset based on the official SO data dump. <em>SOTorrent </em>provides access to the version history of SO content at the level of whole posts and individual text or code blocks. It connects SO posts to other platforms by aggregating URLs from text blocks and by collecting references from GitHub files to SO posts. Our vision is that researchers will use <em>SOTorrent </em>to investigate and understand the evolution of SO posts and their relation to other platforms such as GitHub.
description: The dataset is based on the official Stack Overflow data dump released 2018-03-13 (https://archive.org/details/stackexchange) and the Google BigQuery GitHub data set queried 2018-05-04 (https://cloud.google.com/bigquery/public-data/github). Please read the license files (LICENSE.md) before using the dataset.

In the previous release of version 2018-03-28, column PostHistoryTypeId of Table PostVersion was missing.
DOI: 10.5258/soton/d0001
title: Dataset for "Plasmonic absorption properties of bimetallic metamaterials"

description: This file contains the dataset for "Plasmonic absorption properties of bimetallic metamaterials" by E. Atmatzakis, N. Papasimakis, N. I. Zheludev.File list:
- fig2a_exp.txt: four column text file with the experimental spectra of Fig. 2a. The first column corresponds to the wavelength (in nm), the second to transmittance, the third to reflectance and the fourth to absorption.
- fig2a_num.txt: four column text file with the computational spectra of Fig. 2a. The first column corresponds to the wavelength (in nm), the second to transmittance, the third to reflectance and the fourth to absorption.
- fig2b.bmp: bitmap containing the colormap of Fig. 2b.
- fig2c.bmp: bitmap containing the colormap of Fig. 2c.
- fig2d_exp.txt: two column file with the experimental points of Fig. 2d. The first column corresponds to Ni sector angles (in degrees) and the second to Q-factors.
- fig2d_num.txt: two column file with the numerical curve of Fig. 2d. The first column corresponds to Ni sector angles (in degrees) and the second to Q-factors.
- fig2e_exp.txt: two column file with the experimental points of Fig. 2e. The first column corresponds to Ni sector angles (in degrees) and the second to resonance wavelength.
- fig2e_num.txt: two column file with the numerical curve of Fig. 2e. The first column corresponds to Ni sector angles (in degrees) and the second to resonance wavelength.
- fig3a.png: bitmap containing the colormap of Fig. 3a.
- fig3b.png: bitmap containing the colormap of Fig. 3b.
- fig3c.png: bitmap containing the colormap of Fig. 3c.
- fig3d.txt: three column file with the curves of Fig. 3d. The first column correspond to wavelength (in nm), the second to the blue curve (Au), and the third to the red one (Ni).
- fig3d_inset_i.bmp: bitmap containing the colormap of inset i to Fig. 3d.
- fig3d_inset_ii.bmp: bitmap containing the colormap of inset ii to Fig. 3d.
- fig4a.txt: three column file with the curves of Fig. 4a. The first column correspond to polarization angle (in degrees), the second to the blue curve (Au), and the third to the red one (Ni).
- fig4b.txt: three column file with the curves of Fig. 4b. The first column correspond to polarization angle (in degrees), the second to the blue curve (Au), and the third to the red one (Ni).
- fig4c_i_top.bmp: bitmap containing the colormap of Fig. 4c (i), top.
- fig4c_ii_top.bmp: bitmap containing the colormap of Fig. 4c (ii), top.
- fig4c_iii_top.bmp: bitmap containing the colormap of Fig. 4c (iii), top.
- fig4c_i_bottom.bmp: bitmap containing the colormap of Fig. 4c (i), bottom.
- fig4c_ii_bottom.bmp: bitmap containing the colormap of Fig. 4c (ii), bottom.
- fig4c_iii_bottom.bmp: bitmap containing the colormap of Fig. 4c (iii), bottom.For enquiries contact:
Dr Nikitas Papasimakis
n.papasimakis@soton.ac.uk
DOI: 10.5517/ccdc.csd.cc1zx5z1
title: CCDC 1844344: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Scott G. Stewart, Charles Heath|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zx6lp
title: CCDC 1844363: Experimental Crystal Structure Determination

description: Related Article: Shuai Tian|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zx6nr
title: CCDC 1844365: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Scott G. Stewart, Charles Heath|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zx9kr
title: CCDC 1844455: Experimental Crystal Structure Determination

description: Related Article: Bauer, Carsten, Luu, Van Tong, Eggimann, Fabian, Bross, Patrick, Gertsch, Werner, Hu, Cheng, Ramstein, Philippe, Bourgailh, Julien, Glaenzel, Albrecht, Dix, Ina, Guenat, Christian, Soldermann, Nicolas, Litherland, Katherine, Desrayaud, Sandrine, Hengy, Jean-Claude, Pearson, David, Blanz, Joachim, Burkhart, Christoph|2018|Helv.Chim.Acta|101|e1800044|doi:10.1002/hlca.201800044
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zx9ls
title: CCDC 1844456: Experimental Crystal Structure Determination

description: Related Article: Bauer, Carsten, Luu, Van Tong, Eggimann, Fabian, Bross, Patrick, Gertsch, Werner, Hu, Cheng, Ramstein, Philippe, Bourgailh, Julien, Glaenzel, Albrecht, Dix, Ina, Guenat, Christian, Soldermann, Nicolas, Litherland, Katherine, Desrayaud, Sandrine, Hengy, Jean-Claude, Pearson, David, Blanz, Joachim, Burkhart, Christoph|2018|Helv.Chim.Acta|101|e1800044|doi:10.1002/hlca.201800044
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6073/pasta/f1fb37d04e08ffcb62eca754d7950d68
title: Tree Rings, Huron Mountain Club

description: These tree ring data are from the Huron Mountain Club, Michigan as part of Alex Dye's West Virginia University PhD dissertation.  His dissertation abstract is as follows: This dissertation is organized into three individual standalone manuscripts supporting the overarching theme of reconstructing annual aboveground biomass growth in temperate forests of eastern North America using dendrochronological applications. Each manuscript is organized with the intent of submission to a peer-reviewed journal. The first manuscript validated the technique I use throughout my dissertation by comparing tree-ring derived estimates of annual aboveground productivity with estimates from co-located or nearby permanent remeasurement plots at Howland, Maine, Harvard Forest, Massachusetts, and Fernow, West Virginia. The second manuscript investigated the size-related distribution of biomass growth at 16 eastern U.S. forest sites and compared results with United States Forest Service inventory plot data. The goal of this manuscript was to determine where, structurally, biomass was allocated in forests and whether these quantities changed over time and between forests. The third manuscript was inspired by the results of my second chapter. Here, I investigated whether the degree of asymmetry, or the slope of the linear regression between tree diameter and growth, is a useful indicator of total forest productivity. Previous studies linking asymmetry and productivity have been inconclusive, and this chapter evaluates consistency or lack of consistency across the same 16-site eastern U.S. forest network.
DOI: 10.5517/ccdc.csd.cc1zx9mt
title: CCDC 1844457: Experimental Crystal Structure Determination

description: Related Article: Bauer, Carsten, Luu, Van Tong, Eggimann, Fabian, Bross, Patrick, Gertsch, Werner, Hu, Cheng, Ramstein, Philippe, Bourgailh, Julien, Glaenzel, Albrecht, Dix, Ina, Guenat, Christian, Soldermann, Nicolas, Litherland, Katherine, Desrayaud, Sandrine, Hengy, Jean-Claude, Pearson, David, Blanz, Joachim, Burkhart, Christoph|2018|Helv.Chim.Acta|101|e1800044|doi:10.1002/hlca.201800044
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zx9sz
title: CCDC 1844462: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Scott G. Stewart, Charles Heath|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zx9v1
title: CCDC 1844464: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Scott G. Stewart, Charles Heath|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxcbl
title: CCDC 1844510: Experimental Crystal Structure Determination

description: Related Article: Mark E Light, Thomas A Mayer and Terence L Threlfall|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxccm
title: CCDC 1844511: Experimental Crystal Structure Determination

description: Related Article: Mark E Light, Thomas A Mayer and Terence L Threlfall|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15482/usda.adc/1438034
title: USDA Dietary Guidelines Sentiment Analysis
DOI: 10.5517/ccdc.csd.cc1zxcdn
title: CCDC 1844512: Experimental Crystal Structure Determination

description: Related Article: Mark E Light, Thomas A Mayer and Terence L Threlfall|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxcfp
title: CCDC 1844513: Experimental Crystal Structure Determination

description: Related Article: Mark E Light, Thomas A Mayer and Terence L Threlfall|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxcnx
title: CCDC 1844520: Experimental Crystal Structure Determination

description: Related Article: Mark E Light, Brenda J Farrow and Terence L Threlfall|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxcpy
title: CCDC 1844521: Experimental Crystal Structure Determination

description: Related Article: Mark E Light, Brenda J Farrow and Terence L Threlfall|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxcqz
title: CCDC 1844522: Experimental Crystal Structure Determination

description: Related Article: Mark E Light, Brenda J Farrow and Terence L Threlfall|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zxcs1
title: CCDC 1844524: Experimental Crystal Structure Determination

description: Related Article: Mark E Light, Brenda J Farrow and Terence L Threlfall|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.7483/opendata.opera.bvc1.ui85
title: Emulsion data for track multiplicity
DOI: 10.7483/opendata.opera.hjc7.3dcc
title: Electronic detector data for multiplicity studies
DOI: 10.7483/opendata.opera.ns3i.ll21
title: Electronic detector data for tau neutrino appearance studies
DOI: 10.14457/tu.the.2017.210
title: Signal processing for electronic nose

description: Ripeness identification of fruits with short lifetime is important to benefit both the cultivators and consumers. Recently electronic noses (E-Noses) have become popular for fruit quality checkup for its sturdiness and repetitive usability without fatigue dissimilar to human experts. The primary components of an E-Nose are a data acquisition device, a sensor panel and a classification algorithm. Most sensors which are used for E-Noses are expensive. In addition a sensor panel with large number of sensors increases design complexity. Thus to find a minimal set of sensors with maximum relevant data classification efficiency is of vital importance. To analyze the classification efficiencies of different classification methods fruits, such as banana, mango, sapodilla, and pineapple are chosen. Two novel methods for finding a minimal set of sensors are proposed in this thesis. One is a principal component loading and mutual information based approach, and the other is a threshold based approach. With these methods minimal set of sensors are found which show more than 90% classification accuracy while classifying each of the four fruit types at three ripeness states. Once a sensor panel is designed and a data acquisition device chosen, a simple, fast, efficient classification method is required for classifying data of relevant training classes, and to reject any irrelevant data. At present to classify E- Nose data, k-nearest neighbor (k-NN), support vector machine (SVM) and multilayer perceptron neural network (MLPNN) classification algorithms are often applied. Due to open ended hyperplane based classification boundaries, these algorithms falsely classify extraneous odor data. For reducing false classification error and thereby improve correct rejection performance classification algorithms with hyperspheric boundary such as generalized regression neural network (GRNN) and radial basis function neural network (RBFNN) should be used. Simulation results show that GRNN has better ability to overcome false classification problem compared to RBFNN. For large number of neurons requirement, designing a GRNN is complex and expensive. A simple hyperspheric classification method based on minimum, maximum, and mean (MMM) values of the training data is also proposed in this thesis. It is observed that the MMM algorithm is simpler, faster, and have higher accuravy for classifying data of training classes and correctly rejecting data of irrelevant classes.
DOI: 10.14457/tu.the.2017.211
title: Distributed scheduling of electric vehicles in a residential area

description: Smart grid technology provides a platform for management of electricity consumption and provision.  The management of electricity consumption known as Demand Side Management (DSM) has received a lot of interests from researchers. As the penetration of Electric Vehicles (EVs)  increase in the coming future, this becomes a challenge for the electric grid as uncoordinated charging of EVs may cause power outages or even damage the distribution transformers due to overloading of distribution transformers. Therefore, this provides an opportunity for better strategies for management of charging of EVs in order to prevent outages to the electric grid, which can come with a greater risk and expense to energy providers. In this thesis, we perform a simulation study of the distributed scheduling algorithm, proposed by Chen et al, 2014, in order to coordinate charging of EVs in a residential area. We call the algorithm, Aggregate Power Sharing (APS). We are interested in APS since it provides a distributed EV charging scheduling, requiring minimal message exchanges between a distribution transformer and Energy Management Controller (EMC) in each household and among the EMCs themselves. APS allows EMCs to not violate a pre-specified target aggregate consumption profile.  To study APS, we compare it with a well-known Water-Filling scheduling algorithm and without scheduling.  To be realistic, we consider a real typical load profile from an area in Pattaya, Thailand. This area is a pilot smart grid project, planned by the Provincial Electricity Authority (PEA). Our simulation results show that APS provides better fairness than water-filling because each EV is essentially charged at the same rate regardless of their independent state of charge of the EV batteries. In addition to the study of APS proposed in Chen et al, 2014, we make an improvement to APS by revising the probabilistic strategy to get closer to the target aggregate profile. This is validated by simulation.