cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3894650
title: The BrukerAPI test data set (Bruker ParaVision v6.0.1)

description: This data set contains data from 45 experiments performed using the Bruker ParaVision v6.0.1 software. The purpose of this data set is to test the unified application programming interface to the Bruker data format - the <strong>BrukerAPI</strong>. See the software repository for more information. The data was measured using the following pulse sequences: FLASH.ppg FLASHAngio.ppg IgFLASH.ppg MGE.ppg MSME.ppg RARE.ppg FAIR_RARE.ppg RAREVTR.ppg RAREst.ppg MDEFT.ppg FISP.ppg FLOWMAP.ppg DtiStandard.ppg EPI.ppg FAIR_EPI.ppg CASL_EPI.ppg DtiEpi.ppg T1_EPI.ppg T2_EPI.ppg T2S_EPI.ppg SPIRAL.ppg DtiSpiral.ppg UTE.ppg UTE3D.ppg ZTE.ppg CSI.ppg FieldMap.ppg SINGLEPULSE.ppg NSPECT.ppg EPSI.ppg PRESS.ppg STEAM.ppg ISIS.ppg CPMG.ppg RfProfile.ppg The data was measured by the NMR group at the Institute of Scientific Instruments of the CAS, v. v. i. Change Log: Version 1.0.0 Initial release Version 1.1.0 Compressed NumPy arrays were added to each data set to enable data reading tests
DOI: 10.5517/ccdc.csd.cc24v135
title: CCDC 1991226: Experimental Crystal Structure Determination

description: Related Article: David Elder, Paul Taylor, Robert O. Gould|1992|Thesis|||
DOI: 10.5517/ccdc.csd.cc24v179
title: CCDC 1991230: Experimental Crystal Structure Determination

description: Related Article: David Elder, Paul Taylor, Robert O. Gould|1992|Thesis|||
DOI: 10.25387/g3.12994292
title: Supplemental Material for Earl et al., 2020

description: <b>Table S1. </b><b><i>P{EP}</i>mutants used in the screen.</b>The columns indicate the insertion name, Flybase transposon insertion code (FBti) and the Bloomington Drosophila Stock Center stock number and genotype (if available).<b>Table S2. Full Screen Results.</b>The columns indicate the chromosome carrying the insertion, the <i>P{EP}</i>line, the gene affected, Raw Data Counts for <i>Rh4</i>and <i>Rh3</i>expression in the Gain of Function (GOF) and Loss of Function (LOF) screens, the calculated <i>Rh3</i>% for GOF (GOF Rh3%) and LOF (LOF Rh3%) and the calculated Difference between the two screens (Diff = GOF – LOF) (Rh3%), Logistic Regression Testing results of GOF, LOF and Diff are shown that each list WaldChiSq (Z-value for Diff Comparison), <i>p</i>-value, and FDR (False Discovery Rate). The table is sorted by increasing FDR for Diff, GOF and LOF. FDR &lt; 0.05 is shaded in yellow. FDR &lt; 0.10 is shaded in gray.<b>Table S3. Interaction Network Details.</b>The table provides data on the Interaction Network described in <b>Figure </b><b>3</b>. The columns indicate the genes within the network by gene symbol, gene name, Flybase gene code (FBID Key), whether they are candidates identified in the screen, the total number of connections (all genetic and physical interactions) and the number of unique connections to that gene.
DOI: 10.25387/g3.12994292.v1
title: Supplemental Material for Earl et al., 2020

description: <b>Table S1. </b><b><i>P{EP}</i>mutants used in the screen.</b>The columns indicate the insertion name, Flybase transposon insertion code (FBti) and the Bloomington Drosophila Stock Center stock number and genotype (if available).<b>Table S2. Full Screen Results.</b>The columns indicate the chromosome carrying the insertion, the <i>P{EP}</i>line, the gene affected, Raw Data Counts for <i>Rh4</i>and <i>Rh3</i>expression in the Gain of Function (GOF) and Loss of Function (LOF) screens, the calculated <i>Rh3</i>% for GOF (GOF Rh3%) and LOF (LOF Rh3%) and the calculated Difference between the two screens (Diff = GOF – LOF) (Rh3%), Logistic Regression Testing results of GOF, LOF and Diff are shown that each list WaldChiSq (Z-value for Diff Comparison), <i>p</i>-value, and FDR (False Discovery Rate). The table is sorted by increasing FDR for Diff, GOF and LOF. FDR &lt; 0.05 is shaded in yellow. FDR &lt; 0.10 is shaded in gray.<b>Table S3. Interaction Network Details.</b>The table provides data on the Interaction Network described in <b>Figure </b><b>3</b>. The columns indicate the genes within the network by gene symbol, gene name, Flybase gene code (FBID Key), whether they are candidates identified in the screen, the total number of connections (all genetic and physical interactions) and the number of unique connections to that gene.
DOI: 10.21253/dmu.12651785
title: Slide presentation for "Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform"

description: This research item contains the PDF file of the slide presentation given at the IEEE World Congress on Computational Intelligence (WCCI) 2020 for the article:<br><br>J. Florez-Lozano, F. Caraffini, C. Parra and M. Gongora, "Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform," <em>2020 IEEE Congress on Evolutionary Computation (CEC)</em>, Glasgow, United Kingdom, 2020, pp. 1-8, doi: 10.1109/CEC48606.2020.9185686.<br><br>Links to the published manuscript its preprint are avalable below.
DOI: 10.21253/dmu.12651785.v1
title: Slide presentation for "Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform"

description: This research item contains the PDF file of the slide presentation given at the IEEE World Congress on Computational Intelligence (WCCI) 2020 for the article:<br><br>J. Florez-Lozano, F. Caraffini, C. Parra and M. Gongora, "Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform," <em>2020 IEEE Congress on Evolutionary Computation (CEC)</em>, Glasgow, United Kingdom, 2020, pp. 1-8, doi: 10.1109/CEC48606.2020.9185686.<br><br>Links to the published manuscript its preprint are avalable below.
DOI: 10.5281/zenodo.4011346
title: SOAP Simulation Results for iROC Project

description: SOAP Report Data Output for iROC_Optical_with_transit.orb<br>
DOI: 10.5281/zenodo.4011345
title: SOAP Simulation Results for iROC Project

description: SOAP Report Data Output for iROC_Optical_with_transit.orb<br>
DOI: 10.5281/zenodo.4048286
title: The BrukerAPI test data set (Bruker ParaVision v5.1)

description: This data set contains data from 32 experiments performed using the Bruker ParaVision v5.1 software. The purpose of this data set is to test the unified application programming interface to the Bruker data format - the <strong>BrukerAPI</strong>. See the software repository for more information. The data was measured using the following pulse sequences: FLASH.ppg MGE.ppg MSME.ppg RARE.ppg FAIR_RARE.ppg RAREVTR.ppg RAREst.ppg MDEFT.ppg FISP.ppg FLOWMAP.ppg DtiStandard.ppg EPI.ppg FAIR_EPI.ppg DtiEpi.ppg T1_EPI.ppg T2_EPI.ppg T2S_EPI.ppg SPIRAL.ppg DtiSpiral.ppg UTE.ppg UTE3D.ppg ZTE.ppg CSI.ppg FieldMap.ppg NSPECT.ppg PRESS.ppg STEAM.ppg igFLASH.ppg The data was measured by the NMR group at the Institute of Scientific Instruments of the Czech Academy of Sciences. Change Log: Version 1.0.0 Initial release Version 1.1.0 Compressed NumPy arrays were added to each data set to enable data reading tests
DOI: 10.5281/zenodo.3899267
title: The BrukerAPI test data set (Bruker ParaVision v5.1)

description: This data set contains data from 32 experiments performed using the Bruker ParaVision v5.1 software. The purpose of this data set is to test the unified application programming interface to the Bruker data format - the <strong>BrukerAPI</strong>. See the software repository for more information. The data was measured using the following pulse sequences: FLASH.ppg MGE.ppg MSME.ppg RARE.ppg FAIR_RARE.ppg RAREVTR.ppg RAREst.ppg MDEFT.ppg FISP.ppg FLOWMAP.ppg DtiStandard.ppg EPI.ppg FAIR_EPI.ppg DtiEpi.ppg T1_EPI.ppg T2_EPI.ppg T2S_EPI.ppg SPIRAL.ppg DtiSpiral.ppg UTE.ppg UTE3D.ppg ZTE.ppg CSI.ppg FieldMap.ppg NSPECT.ppg PRESS.ppg STEAM.ppg igFLASH.ppg The data was measured by the NMR group at the Institute of Scientific Instruments of the Czech Academy of Sciences. Change Log: Version 1.0.0 Initial release Version 1.1.0 Compressed NumPy arrays were added to each data set to enable data reading tests
DOI: 10.5281/zenodo.3731872
title: Qualisign: Software Metrics and GoF Design Patterns of the Maven Central Repository

description: This dataset contains software metric and design pattern data for around 100,000 projects from the Maven Central repository. The data was collected and analyzed as part of my master's thesis "Mining Software Repositories for the Effects of Design Patterns on Software Quality" (https://www.overleaf.com/read/vnfhydqxmpvx, https://zenodo.org/record/4048275). The included qualisign.* files all contain the same data in different formats:<br> - qualisign.sql: standard SQL format (exported using "pg_dump --inserts ..."),<br> - qualisign.psql: PostgreSQL plain format (exported using "pg_dump -Fp ..."),<br> - qualisign.csql: PostgreSQL custom format (exported using "pg_dump -Fc ..."). create-tables.sql has to be executed before importing one of the qualisign.* files. Once qualisign.*sql has been imported, create-views.sql can be executed to preprocess the data, thereby creating materialized views that are more appropriate for data analysis purposes. --- Software metrics were calculated using CKJM extended:<br> http://gromit.iiar.pwr.wroc.pl/p_inf/ckjm/ Included software metrics are (21 total):<br> - AMC: Average Method Complexity<br> - CA: Afferent Coupling<br> - CAM: Cohesion Among Methods<br> - CBM: Coupling Between Methods<br> - CBO: Coupling Between Objects<br> - CC: Cyclomatic Complexity<br> - CE: Efferent Coupling<br> - DAM: Data Access Metric<br> - DIT: Depth of Inheritance Tree<br> - IC: Inheritance Coupling<br> - LCOM: Lack of Cohesion of Methods (Chidamber and Kemerer)<br> - LCOM3: Lack of Cohesion of Methods (Constantine and Graham)<br> - LOC: Lines of Code<br> - MFA: Measure of Functional Abstraction<br> - MOA: Measure of Aggregation<br> - NOC: Number of Children<br> - NOM: Number of Methods<br> - NOP: Number of Polymorphic Methods<br> - NPM: Number of Public Methods<br> - RFC: Response for Class<br> - WMC: Weighted Methods per Class In the qualisign.* data, these metrics are only available on the class level. create-views.sql additionally provides averages of these metrics on the package and project levels. --- Design patterns were detected using SSA:<br> https://users.encs.concordia.ca/~nikolaos/pattern_detection.html Included design patterns are (15 total):<br> - Adapter<br> - Bridge<br> - Chain of Responsibility<br> - Command<br> - Composite<br> - Decorator<br> - Factory Method<br> - Observer<br> - Prototype<br> - Proxy<br> - Singleton<br> - State<br> - Strategy<br> - Template Method<br> - Visitor --- The code to generate the dataset is available at:<br> https://github.com/jaichberg/qualisign The code to perform quality analysis on the dataset is available at:<br> https://github.com/jaichberg/qualisign-analysis
DOI: 10.5281/zenodo.3731871
title: Qualisign: Software Metrics and GoF Design Patterns of the Maven Central Repository

description: This dataset contains software metric and design pattern data for around 100,000 projects from the Maven Central repository. The data was collected and analyzed as part of my master's thesis "Mining Software Repositories for the Effects of Design Patterns on Software Quality" (https://www.overleaf.com/read/vnfhydqxmpvx, https://zenodo.org/record/4048275). The included qualisign.* files all contain the same data in different formats:<br> - qualisign.sql: standard SQL format (exported using "pg_dump --inserts ..."),<br> - qualisign.psql: PostgreSQL plain format (exported using "pg_dump -Fp ..."),<br> - qualisign.csql: PostgreSQL custom format (exported using "pg_dump -Fc ..."). create-tables.sql has to be executed before importing one of the qualisign.* files. Once qualisign.*sql has been imported, create-views.sql can be executed to preprocess the data, thereby creating materialized views that are more appropriate for data analysis purposes. --- Software metrics were calculated using CKJM extended:<br> http://gromit.iiar.pwr.wroc.pl/p_inf/ckjm/ Included software metrics are (21 total):<br> - AMC: Average Method Complexity<br> - CA: Afferent Coupling<br> - CAM: Cohesion Among Methods<br> - CBM: Coupling Between Methods<br> - CBO: Coupling Between Objects<br> - CC: Cyclomatic Complexity<br> - CE: Efferent Coupling<br> - DAM: Data Access Metric<br> - DIT: Depth of Inheritance Tree<br> - IC: Inheritance Coupling<br> - LCOM: Lack of Cohesion of Methods (Chidamber and Kemerer)<br> - LCOM3: Lack of Cohesion of Methods (Constantine and Graham)<br> - LOC: Lines of Code<br> - MFA: Measure of Functional Abstraction<br> - MOA: Measure of Aggregation<br> - NOC: Number of Children<br> - NOM: Number of Methods<br> - NOP: Number of Polymorphic Methods<br> - NPM: Number of Public Methods<br> - RFC: Response for Class<br> - WMC: Weighted Methods per Class In the qualisign.* data, these metrics are only available on the class level. create-views.sql additionally provides averages of these metrics on the package and project levels. --- Design patterns were detected using SSA:<br> https://users.encs.concordia.ca/~nikolaos/pattern_detection.html Included design patterns are (15 total):<br> - Adapter<br> - Bridge<br> - Chain of Responsibility<br> - Command<br> - Composite<br> - Decorator<br> - Factory Method<br> - Observer<br> - Prototype<br> - Proxy<br> - Singleton<br> - State<br> - Strategy<br> - Template Method<br> - Visitor --- The code to generate the dataset is available at:<br> https://github.com/jaichberg/qualisign The code to perform quality analysis on the dataset is available at:<br> https://github.com/jaichberg/qualisign-analysis
DOI: 10.5281/zenodo.3957430
title: Monthly CO2 emissions projections from 2015-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity

description: Monthly CO2 emissions projections 2015-2025, modified by country-specific impacts of COVID-19 lockdown in 2020-2023, with 4 different projections for the period 2024-2025. This repository holds the netcdf files for CO2 emissions from ground-level and aviation sources from the MESSAGE_GLOBIOM scenario SSP2-4.5, from the Scenario4MIPs database (https://esgf-node.llnl.gov/search/input4mips/), modified by the country and sector activity levels associated with lockdown for 2020. Sector activity level in 2020 is based on data up until June, and a fixed estimate is used thereafter. This is the monthly equivalent of https://zenodo.org/record/3951601#.XxYBsihKhPY for this time period. Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) http://constrain-eu.org/ see https://github.com/Priestley-Centre/COVID19_emissions for more details.
description: Note that the data and normalisation process used in the aviation data is subtly updated since the version 4 found in related databases, hence that is referred to as v4.5. It still does not cover the whole month of July, due to publication date.
DOI: 10.5281/zenodo.4048255
title: LoED: The LoRaWAN at the Edge Dataset

description: This repository contains dataset from nine LoRaWAN gateways collected in an urban environment. The dataset contains raw payload information, along with other metadata from the gateway. The dataset can be cited at: <strong>Dataset: LoED: The LoRaWAN at the Edge Dataset</strong><br> Laksh Bhatia, Michael Breza, Ramona Marfievici, Julie A. McCann<br> <strong>(under submission)</strong> DATA '20 Proceedings of the 3rd Workshop on Data Acquisition To Analysis, 2020 <strong>Files in the folder</strong> <strong>README.md</strong><br> A readme file detailing the gateway location, model and statistics about the data collected at each of the gateways. <strong>loed_dataset.zip</strong><br> A zip file containing all data files. Data files are in <strong>dd_mm_yyyy.csv</strong> format with one file for every day of the collection campaign <strong>LoRaDatasetParser.ipynb</strong><br> A jupyter notebook to generate statistics of the dataset <strong>LoRaDatasetParser.html</strong><br> HTML rendering of the LoRaDatasetParser.ipynb notebook <strong>LoRaDatasetParser.py</strong><br> A python file to generate basic statistics from the dataset
DOI: 10.5281/zenodo.4048321
title: LoED: The LoRaWAN at the Edge Dataset

description: This repository contains dataset from nine LoRaWAN gateways collected in an urban environment. The dataset contains raw payload information, along with other metadata from the gateway. The dataset can be cited at: <strong>Dataset: LoED: The LoRaWAN at the Edge Dataset</strong><br> Laksh Bhatia, Michael Breza, Ramona Marfievici, Julie A. McCann<br> <strong>(under submission)</strong> DATA '20 Proceedings of the 3rd Workshop on Data Acquisition To Analysis, 2020 <strong>Files in the folder</strong> <strong>README.md</strong><br> A readme file detailing the gateway location, model and statistics about the data collected at each of the gateways. <strong>loed_dataset.zip</strong><br> A zip file containing all data files. Data files are in <strong>dd_mm_yyyy.csv</strong> format with one file for every day of the collection campaign <strong>LoRaDatasetParser.ipynb</strong><br> A jupyter notebook to generate statistics of the dataset <strong>LoRaDatasetParser.html</strong><br> HTML rendering of the LoRaDatasetParser.ipynb notebook <strong>LoRaDatasetParser.py</strong><br> A python file to generate basic statistics from the dataset
DOI: 10.5281/zenodo.4048191
title: Pure POPC Membrane with 650mM NaCl simulations using Drude Polarizable Force Field and OpenMM

description: 500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 144 POPC lipids, 77 NaCl, and 6400 SWM4 water molecules. The simulation have been performed using OpenMM 7.4.1 Frames are saved in every 100 ps. Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset. <strong>Please note that</strong> the trajectories might need to be reimaged.
DOI: 10.6084/m9.figshare.12121386
title: A hybrid data model for dynamic GIS : application to marine geomorphological dynamics

description: <br>Abstract : <br><br>The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.<br><br>Data Description : <br><br> The data set used in our research is a set of bathymetric surveys recorded over three years from 2009 to 2011 as Digital Terrain Models (DTM) with 2m grid spacing. The first survey was carried out in February 2009 by the French hydrographic office, the second one was recorded on August-September 2010 and the third in July 2011, both by the “Institut Universitaire Européen de la Mer”.<br>
DOI: 10.5517/ccdc.csd.cc1ytjw5
title: CCDC 1812008: Experimental Crystal Structure Determination

description: Related Article: Wei Wu, Yan Wang, Jing Guo, Liu Cai, Yuan Chen, Yanmin Huang, Yungui Peng|2020|Chem.Commun.|56|11235|doi:10.1039/D0CC03201H
DOI: 10.5517/ccdc.csd.cc240rtr
title: CCDC 1966944: Experimental Crystal Structure Determination

description: Related Article: Dongming Lu, Jia-Hong Wu, Jianke Pan, Xue Chen, Xiaoyu Ren, Tianli Wang|2020|Chem.Commun.|56|11231|doi:10.1039/D0CC04820H
DOI: 10.5517/ccdc.csd.cc2488g5
title: CCDC 1974156: Experimental Crystal Structure Determination

description: Related Article: Fen Xu, Yuan-Yuan Song, Wen-Jing Zhu, Chun-Sen Liu, Ya-Zhou Lu, Miao Du|2020|Chem.Commun.|56|11227|doi:10.1039/D0CC04885B
DOI: 10.5517/ccdc.csd.cc2488h6
title: CCDC 1974157: Experimental Crystal Structure Determination

description: Related Article: Fen Xu, Yuan-Yuan Song, Wen-Jing Zhu, Chun-Sen Liu, Ya-Zhou Lu, Miao Du|2020|Chem.Commun.|56|11227|doi:10.1039/D0CC04885B
DOI: 10.5517/ccdc.csd.cc2488j7
title: CCDC 1974158: Experimental Crystal Structure Determination

description: Related Article: Fen Xu, Yuan-Yuan Song, Wen-Jing Zhu, Chun-Sen Liu, Ya-Zhou Lu, Miao Du|2020|Chem.Commun.|56|11227|doi:10.1039/D0CC04885B
DOI: 10.5517/ccdc.csd.cc2488k8
title: CCDC 1974159: Experimental Crystal Structure Determination

description: Related Article: Fen Xu, Yuan-Yuan Song, Wen-Jing Zhu, Chun-Sen Liu, Ya-Zhou Lu, Miao Du|2020|Chem.Commun.|56|11227|doi:10.1039/D0CC04885B
DOI: 10.5517/ccdc.csd.cc2488l9
title: CCDC 1974160: Experimental Crystal Structure Determination

description: Related Article: Fen Xu, Yuan-Yuan Song, Wen-Jing Zhu, Chun-Sen Liu, Ya-Zhou Lu, Miao Du|2020|Chem.Commun.|56|11227|doi:10.1039/D0CC04885B
DOI: 10.5517/ccdc.csd.cc248rbj
title: CCDC 1974617: Experimental Crystal Structure Determination

description: Related Article: Wei Wu, Yan Wang, Jing Guo, Liu Cai, Yuan Chen, Yanmin Huang, Yungui Peng|2020|Chem.Commun.|56|11235|doi:10.1039/D0CC03201H
DOI: 10.17632/vxrmfch8h9.1
title: Radiocarbon and U-series age data of deep-sea corals

description: There are 3 dataset in this contribution. Table S1 contains radiocarbon data of the Galápagos deep-sea coral together with data from the low latitude Atlantic intermediate waters. Table S2 contains U-Th age data of the deep-sea corals from the low latitude Atlantic. Table S3 contains the test of the coral radiocarbon age reproducibility between AMS in UCI and Bristol
DOI: 10.17632/vb6bsj669y.1
title: H_Cr_fo_data

description: Supplemental datasets
DOI: 10.17632/3mmh7vnfh5.1
title: Geochemical behaviors of deep red-bed sandstones bleaching induced by methane migration in petroliferous basins

description: Sample list, petrology and mineralogy statistics, EPMA, XRD, XRF, carbon and oxygen isotopes results are summarized in the given database