cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7669541
title: Metrics As Scores Dataset: Metrics and Domains From the Qualitas.class Corpus

description: This dataset was created by extracting software metrics data from the Qualitas.class corpus (Terra et al. 2013; Tempero et al. 2010). Therefore, the principal quantity type is Metric, and the context is given by a system’s Domain (e.g., "Game", "Middleware", etc.). Some metrics were obtained on program-level, while others are package- or method-level metrics. Most of the metrics in the corpus are of discrete/integral nature. The corpus holds 23 types of pre-computed software metrics for a total of 111 systems which are spread across eleven different domains. This dataset has the following discrete <strong>Features</strong> (Metrics): CA: Afferent Coupling CE: Efferent Coupling DIT: Depth of Inheritance Tree MLOC: Method Lines of Code NBD: Nested Block Depth NOC: Number of Classes NOF: Number of Attributes NOI: Number of Interfaces NOM: Number of Methods NOP: Number of Packages NORM: Number of Overridden Methods NSC: Number of Children NSF: Number of Static Attributes NSM: Number of Static Methods PAR: Number of Parameters TLOC: Total Lines of Code VG: McCabe Cyclomatic Complexity WMC: Weighted Methods per Class The following features are continuous: LCOM: Lack of Cohesion in Methods RMA: Abstractness RMD: Normalized Distance RMI: Instability SIX: Specialization Index It has a total of 11 <strong>Groups</strong> (Domains): 3D; Graphics; Media, Databases, Diagrams; Visualiz., Games, IDE, Middleware, Parsers; Generators, Progr. Language, SDK, Testing, and Tool.
description: {"references": ["H\u00f6nel, Sebastian, Morgan Ericsson, Welf L\u00f6we, and Anna Wingkvist. 2022. \"Contextual Operationalization of Metrics as Scores: Is My Metric Value Good?\" In 22nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2022, Guangzhou, China, December 5-9, 2022, 333\u201343. IEEE. https://doi.org/10.1109/QRS57517.2022.00042.", "Terra, Ricardo, Luis Fernando Miranda, Marco T\u00falio Valente, and Roberto da Silva Bigonha. 2013. \"Qualitas.class Corpus: A Compiled Version of the Qualitas Corpus.\" ACM SIGSOFT Softw. Eng. Notes 38 (5): 1\u20134. https://doi.org/10.1145/2507288.2507314.", "Tempero, Ewan D., Craig Anslow, Jens Dietrich, Ted Han, Jing Li, Markus Lumpe, Hayden Melton, and James Noble. 2010. \"The Qualitas Corpus: A Curated Collection of Java Code for Empirical Studies.\" In 17th Asia Pacific Software Engineering Conference, APSEC 2010, Sydney, Australia, November 30 - December 3, 2010, edited by Jun Han and Tran Dan Thu, 336\u201345. IEEE Computer Society. https://doi.org/10.1109/APSEC.2010.46."]}
DOI: 10.6084/m9.figshare.22141448
title: npj Clim - Figshare.xlsx

description: Dataset with GHG estimations for wild and domestic herbivores in the Serengeti and Loliondo area. Five different estimations approaches are applied.
DOI: 10.5281/zenodo.7669645
title: Metrics As Scores Dataset: The Iris Flower Data Set

description: The Iris flower data set or Fisher’s Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher. The dataset was introduced in his 1936 paper "The Use of Multiple Measurements in Taxonomic Problems" (Fisher 1936) as an example of linear discriminant analysis. This dataset has the following Features: <em>Petal.Length</em>: Length of the petal <em>Petal.Width</em>: Width of the petal <em>Sepal.Length</em>: Length of the sepal <em>Sepal.Width</em>: Width of the sepal It has a total of 3 <strong>Groups</strong>: <em>setosa</em>, <em>versicolor</em>, and <em>virginica</em>.
description: {"references": ["H\u00f6nel, Sebastian, Morgan Ericsson, Welf L\u00f6we, and Anna Wingkvist. 2022. \"Contextual Operationalization of Metrics as Scores: Is My Metric Value Good?\" In 22nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2022, Guangzhou, China, December 5-9, 2022, 333\u201343. IEEE. https://doi.org/10.1109/QRS57517.2022.00042.", "Fisher, R. A. 1936. \"The Use of Multiple Measurements in Taxonomic Problems.\" Annals of Eugenics 7 (2): 179\u201388. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x."]}
DOI: 10.5281/zenodo.7669646
title: Metrics As Scores Dataset: The Iris Flower Data Set

description: The Iris flower data set or Fisher’s Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher. The dataset was introduced in his 1936 paper "The Use of Multiple Measurements in Taxonomic Problems" (Fisher 1936) as an example of linear discriminant analysis. This dataset has the following Features: <em>Petal.Length</em>: Length of the petal <em>Petal.Width</em>: Width of the petal <em>Sepal.Length</em>: Length of the sepal <em>Sepal.Width</em>: Width of the sepal It has a total of 3 <strong>Groups</strong>: <em>setosa</em>, <em>versicolor</em>, and <em>virginica</em>.
description: {"references": ["H\u00f6nel, Sebastian, Morgan Ericsson, Welf L\u00f6we, and Anna Wingkvist. 2022. \"Contextual Operationalization of Metrics as Scores: Is My Metric Value Good?\" In 22nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2022, Guangzhou, China, December 5-9, 2022, 333\u201343. IEEE. https://doi.org/10.1109/QRS57517.2022.00042.", "Fisher, R. A. 1936. \"The Use of Multiple Measurements in Taxonomic Problems.\" Annals of Eugenics 7 (2): 179\u201388. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x."]}
DOI: 10.5281/zenodo.7669664
title: Metrics As Scores Dataset: The Iris Flower Data Set

description: The Iris flower data set or Fisher’s Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher. The dataset was introduced in his 1936 paper "The Use of Multiple Measurements in Taxonomic Problems" (Fisher 1936) as an example of linear discriminant analysis. This dataset has the following Features: <em>Petal.Length</em>: Length of the petal <em>Petal.Width</em>: Width of the petal <em>Sepal.Length</em>: Length of the sepal <em>Sepal.Width</em>: Width of the sepal It has a total of 3 <strong>Groups</strong>: <em>setosa</em>, <em>versicolor</em>, and <em>virginica</em>.
description: {"references": ["H\u00f6nel, Sebastian, Morgan Ericsson, Welf L\u00f6we, and Anna Wingkvist. 2022. \"Contextual Operationalization of Metrics as Scores: Is My Metric Value Good?\" In 22nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2022, Guangzhou, China, December 5-9, 2022, 333\u201343. IEEE. https://doi.org/10.1109/QRS57517.2022.00042.", "Fisher, R. A. 1936. \"The Use of Multiple Measurements in Taxonomic Problems.\" Annals of Eugenics 7 (2): 179\u201388. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x."]}
DOI: 10.6084/m9.figshare.22132952
title: Limited holding space reduces growth and behavioural performance in juvenile European lobsters

description: <br> I provide here the general dataset (in txt and xlsx format) and the R codes used for the statistical analysis presented in the article.
DOI: 10.6084/m9.figshare.22132952.v1
title: Limited holding space reduces growth and behavioural performance in juvenile European lobsters

description: <br> I provide here the general dataset (in txt and xlsx format) and the R codes used for the statistical analysis presented in the article.
DOI: 10.5517/ccdc.csd.cc2cks80
title: CCDC 2191832: Experimental Crystal Structure Determination

description: Related Article: Sanjukta Pahar, Gargi Kundu, Christy P. George, Rajesh G. Gonnade, Sakya S. Sen|2023|Organometallics|42|276|doi:10.1021/acs.organomet.2c00630
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.cc2cktqh
title: CCDC 2191877: Experimental Crystal Structure Determination

description: Related Article: Sanjukta Pahar, Gargi Kundu, Christy P. George, Rajesh G. Gonnade, Sakya S. Sen|2023|Organometallics|42|276|doi:10.1021/acs.organomet.2c00630
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.cc2ckv82
title: CCDC 2191894: Experimental Crystal Structure Determination

description: Related Article: Sanjukta Pahar, Gargi Kundu, Christy P. George, Rajesh G. Gonnade, Sakya S. Sen|2023|Organometallics|42|276|doi:10.1021/acs.organomet.2c00630
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.cc2cktvm
title: CCDC 2191881: Experimental Crystal Structure Determination

description: Related Article: Sanjukta Pahar, Gargi Kundu, Christy P. George, Rajesh G. Gonnade, Sakya S. Sen|2023|Organometallics|42|276|doi:10.1021/acs.organomet.2c00630
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.cc2cl0vv
title: CCDC 2192067: Experimental Crystal Structure Determination

description: Related Article: Sanjukta Pahar, Gargi Kundu, Christy P. George, Rajesh G. Gonnade, Sakya S. Sen|2023|Organometallics|42|276|doi:10.1021/acs.organomet.2c00630
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.cc2cl3hl
title: CCDC 2192149: Experimental Crystal Structure Determination

description: Related Article: Sanjukta Pahar, Gargi Kundu, Christy P. George, Rajesh G. Gonnade, Sakya S. Sen|2023|Organometallics|42|276|doi:10.1021/acs.organomet.2c00630
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.cc2cl1vw
title: CCDC 2192098: Experimental Crystal Structure Determination

description: Related Article: Sanjukta Pahar, Gargi Kundu, Christy P. George, Rajesh G. Gonnade, Sakya S. Sen|2023|Organometallics|42|276|doi:10.1021/acs.organomet.2c00630
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.cc2cl1mn
title: CCDC 2192091: Experimental Crystal Structure Determination

description: Related Article: Sanjukta Pahar, Gargi Kundu, Christy P. George, Rajesh G. Gonnade, Sakya S. Sen|2023|Organometallics|42|276|doi:10.1021/acs.organomet.2c00630
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.cc2dj177
title: CCDC 2219948: Experimental Crystal Structure Determination

description: Related Article: Zhi-Hao Chen, Olafs Daugulis, Maurice Brookhart|2023|Organometallics|42|235|doi:10.1021/acs.organomet.2c00561
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: 10.5281/zenodo.7669653
title: Dataset related to article "OCULAR PHENOTYPE AND ELECTRORETINOGRAM ABNORMALITIES IN LAFORA DISEASE AND CORRELATION WITH DISEASE STAGE"

description: The dataset contains clinical data of a single Eye-Lafora patient involved in the study described at title followed at Fondazione Besta
DOI: 10.5281/zenodo.7669652
title: Dataset related to article "OCULAR PHENOTYPE AND ELECTRORETINOGRAM ABNORMALITIES IN LAFORA DISEASE AND CORRELATION WITH DISEASE STAGE"

description: The dataset contains clinical data of a single Eye-Lafora patient involved in the study described at title followed at Fondazione Besta
DOI: 10.5281/zenodo.7660541
title: Ptychotomography datasets of an ultrafine eutectic Ti-Fe-based alloy processed by additive manufacturing

description: The development of metals tailored to the metallurgical conditions of laser-based additive manufacturing is crucial to advance the maturity of these materials for their use in structural applications. While efforts in this regard are being carried out around the globe, the use of high strength eutectic alloys have, so far, received minor attention, although previous works showed that rapid solidification techniques can result in ultrafine microstructures with excellent mechanical performance, albeit for small sample sizes. In the present work, a eutectic Ti-32.5Fe alloy has been produced by laser powder bed fusion aiming at exploiting rapid solidification and the capability to produce bulk ultrafine microstructures provided by this processing technique. The uploaded datasets are a raw dataset of the 3D microstructure of the eutectic Ti-32.5Fe alloy acquired by ptychotomography at the beamline ID16A of the ESRF in Grenoble, France and a corresponding binary dataset for the bright Ti-Fe phase. The datasets have a dimension of 1173 x 1182 x 1356 px³ with a voxel-size of (10 nm)³. raw dataset: "RAW_10nm_ptycho_1173x1182x1356.tif" binary dataset of the Ti-Fe phase: "TIFE_BINARY_10nm_ptycho_1173x1182x1356.tif" more details can be found in J. Gussone, K. Bugelnig and P. Barriobero-Vila et al. / Applied Materials Today 20 (2020) 100767. https://doi.org/10.1016/j.apmt.2020.100767<br>
DOI: 10.5281/zenodo.7669038
title: Dataset for Phys. Rev. Applied (2023) - Accelerating the heat diffusion: Fast thermal relaxation of a microcantilever

description: <strong>"Fig_step.fig"</strong>: Matlab figures including all the data used to plot figure 3 of the article. <strong>"Fig_N1.fig "</strong>: Matlab figures including the data used to plot figure 4 for N=1. <strong>"Fig_N2.fig "</strong>: Matlab figures including the data used to plot figure 4 for N=2. <strong>"Fig_N3.fig "</strong>: Matlab figures including the data used to plot figure 4 for N=3. <strong>"Fig_N4.fig "</strong>: Matlab figures including the data used to plot figure 4 for N=4. <strong>"gamman.m "/ "gammansym.m "</strong>: Matlab scripts to compute coefficients gamma_n of the polynomial function F(t). <strong>"DisplayData.m "</strong>: Matlab script to display the data of the .mat files 'Data_N1.mat', 'Data_N2.mat', 'Data_N3.mat', and 'Data_N4.mat'. <strong>"Fig7a.fig" / "Fig7b.fig"</strong>: Matlab figures including the data used to plot figure 7 of the article
DOI: 10.5281/zenodo.7669037
title: Dataset for Phys. Rev. Applied (2023) - Accelerating the heat diffusion: Fast thermal relaxation of a microcantilever

description: <strong>"Fig_step.fig"</strong>: Matlab figures including all the data used to plot figure 3 of the article. <strong>"Fig_N1.fig "</strong>: Matlab figures including the data used to plot figure 4 for N=1. <strong>"Fig_N2.fig "</strong>: Matlab figures including the data used to plot figure 4 for N=2. <strong>"Fig_N3.fig "</strong>: Matlab figures including the data used to plot figure 4 for N=3. <strong>"Fig_N4.fig "</strong>: Matlab figures including the data used to plot figure 4 for N=4. <strong>"gamman.m "/ "gammansym.m "</strong>: Matlab scripts to compute coefficients gamma_n of the polynomial function F(t). <strong>"DisplayData.m "</strong>: Matlab script to display the data of the .mat files 'Data_N1.mat', 'Data_N2.mat', 'Data_N3.mat', and 'Data_N4.mat'. <strong>"Fig7a.fig" / "Fig7b.fig"</strong>: Matlab figures including the data used to plot figure 7 of the article
DOI: 10.5281/zenodo.7398824
title: Nabro volcano event catalogue from Lapins et al., 2021, JGR Solid Earth

description: Catalogue of seismic events from Nabro volcano (Sep 2011 - Oct 2012). Data format is a csv file. Events were detected by U-GPD phase arrival picking model. See following paper for details on event detection and location procedure: <em>A Little Data Goes A Long Way Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning</em> by Lapins et al., 2021, https://doi.org/10.1029/2021JB021910). Original seismic waveforms are from the Nabro Urgency Array (Hammond et al., 2011; https://doi.org/10.7914/SN/4H_2011), which is publicly available through IRIS Data Services (http://service.iris.edu/fdsnws/dataselect/1/). See Hammond et al. (2011) for further details on waveform data access and availability. Full code to reproduce our U-GPD transfer learning model, perform model training, run the U-GPD model over continuous sections of data and use model picks to locate events in NonLinLoc (Lomax et al., 2000) are available at https://github.com/sachalapins/U-GPD, with the release (v1.0.0) associated with this study also archived and available through Zenodo (Lapins, 2021; https://doi.org/10.5281/zenodo.4558121). Dataset column key: time = Origin time of seismic event (UTC) lat = Hypocentre latitude in decimal degrees lon = Hypocentre longitude in decimal degrees depth = Hypocentre depth in km rms = RMS error for phase arrival picks and hypocentre (sec) erh = Estimate of horizontal Gaussian error (km) erz = Estimate of vertical Gaussian error (km) azgap = Azimuthal gap (maximum angle separating two adjacent seismic stations, measured from earthquake epicentre) cluster = HDBSCAN cluster number (see Chapter 6 of Lapins, 2021 doctoral thesis: <em>Detecting and characterising seismicity associated with volcanic and magmatic processes through deep learning and the continuous wavelet transform</em>. Persistent URL: https://hdl.handle.net/1983/ea90148c-a1b2-47ae-afad-5dd0a8b5ebbd) nab*_p_time = P-wave arrival time for station NAB* (UTC) nab*_p_prob = Maximum detection 'probability' around P-wave phase arrival from U-GPD model (between 0 and 1) nab*_s_time = S-wave arrival time for station NAB* (UTC) nab*_s_prob = Maximum detection 'probability' around S-wave phase arrival from U-GPD model (between 0 and 1)
description: {"references": ["Lapins et al., 2021. A Little Data Goes A Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning"]}
DOI: 10.5281/zenodo.7669361
title: Dataset related to the paper "The vibe of musical and social reward: Listening to beat-based music acts as a surrogate for socioemotional support during the Covid-19 pandemic"

description: The set includes data related to the paper "The vibe of musical and social reward: Listening to beat-based music acts as a surrogate for socioemotional support during the Covid-19 pandemic"
DOI: 10.5281/zenodo.7669360
title: Dataset related to the paper "The vibe of musical and social reward: Listening to beat-based music acts as a surrogate for socioemotional support during the Covid-19 pandemic"

description: The set includes data related to the paper "The vibe of musical and social reward: Listening to beat-based music acts as a surrogate for socioemotional support during the Covid-19 pandemic"
DOI: 10.5281/zenodo.7669717
title: Nabro volcano event catalogue from Lapins et al., 2021, JGR Solid Earth

description: Catalogue of seismic events from Nabro volcano (Sep 2011 - Oct 2012). Data format is a csv file. Events were detected by U-GPD phase arrival picking model. See following paper for details on event detection and location procedure: <em>A Little Data Goes A Long Way Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning</em> by Lapins et al., 2021, https://doi.org/10.1029/2021JB021910). Original seismic waveforms are from the Nabro Urgency Array (Hammond et al., 2011; https://doi.org/10.7914/SN/4H_2011), which is publicly available through IRIS Data Services (http://service.iris.edu/fdsnws/dataselect/1/). See Hammond et al. (2011) for further details on waveform data access and availability. Full code to reproduce our U-GPD transfer learning model, perform model training, run the U-GPD model over continuous sections of data and use model picks to locate events in NonLinLoc (Lomax et al., 2000) are available at https://github.com/sachalapins/U-GPD, with the release (v1.0.0) associated with this study also archived and available through Zenodo (Lapins, 2021; https://doi.org/10.5281/zenodo.4558121). Dataset column key: time = Origin time of seismic event (UTC) lat = Hypocentre latitude in decimal degrees lon = Hypocentre longitude in decimal degrees depth = Hypocentre depth in km rms = RMS error for phase arrival picks and hypocentre (sec) erh = Estimate of horizontal Gaussian error (km) erz = Estimate of vertical Gaussian error (km) azgap = Azimuthal gap (maximum angle separating two adjacent seismic stations, measured from earthquake epicentre) cluster = HDBSCAN cluster number (see Chapter 6 of Lapins, 2021 doctoral thesis: <em>Detecting and characterising seismicity associated with volcanic and magmatic processes through deep learning and the continuous wavelet transform</em>. Persistent URL: https://hdl.handle.net/1983/ea90148c-a1b2-47ae-afad-5dd0a8b5ebbd) nab*_p_time = P-wave arrival time for station NAB* (UTC) nab*_p_prob = Maximum detection 'probability' around P-wave phase arrival from U-GPD model (between 0 and 1) nab*_s_time = S-wave arrival time for station NAB* (UTC) nab*_s_prob = Maximum detection 'probability' around S-wave phase arrival from U-GPD model (between 0 and 1) Station csv column key: Network = Seismic network name Station = Seismic station name Latitude = Latitude in decimal degrees Longitude = Longitude in decimal degrees Elevation_asl_km = Station elevation in km above sea level
description: {"references": ["Lapins et al., 2021. A Little Data Goes A Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning"]}