cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4058431
title: Data for Assessing the renewable energy policy paradox: a scenario analysis for the Italian electricity market

description: This page contains the datasets and codes used to generate the figures for the article Assessing the renewable energy policy paradox: a scenario analysis for the Italian electricity market. Below you will find two datasets (.dta) and four codes (.do) files. Please note that the .do files contain the original paths to where the datasets were saved, you should change them to where they are saved in your computer. The file <em>clustering_prer_ok.do</em> uses dataset <em>yearlyvars_raw.dta</em> to generate three additional datasets <em>clusters.dta</em>, <em>yearly_scenarios.dta</em> and <em>xwalk.dta</em> <em>clusters.dta</em> is used in <em>Figs_2-5-6_ok.do</em> <em>yearly_scenarios.dta</em> is used in <em>Figs_3_ok.do</em> Dataset <em>hprice_raw.dta</em> is used together with the generated <em>xwalk.dta</em> in <em>Fig_4_ok.do</em>.
DOI: 10.6084/m9.figshare.13019585.v1
title: R0235_20181018a_videos

description: Rat 0235, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13019585
title: R0235_20181018a_videos

description: Rat 0235, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.5517/ccdc.csd.cc250qpm
title: CCDC 1996700: Experimental Crystal Structure Determination

description: Related Article: Ting Li, Yumei Mao, Yue Qi, Hongmei Zeng, Guohong Zou, Zhien Lin|2020|CrystEngComm|22|6096|doi:10.1039/D0CE01182G
DOI: 10.5517/ccdc.csd.cc250qqn
title: CCDC 1996701: Experimental Crystal Structure Determination

description: Related Article: Ting Li, Yumei Mao, Yue Qi, Hongmei Zeng, Guohong Zou, Zhien Lin|2020|CrystEngComm|22|6096|doi:10.1039/D0CE01182G
DOI: 10.5517/ccdc.csd.cc251byj
title: CCDC 1997297: Experimental Crystal Structure Determination

description: Related Article: Jinwei Chen, Yu Li, Jinzhong Gu, Marina V. Kirillova, Alexander M. Kirillov|2020|New J.Chem.|44|16082|doi:10.1039/D0NJ03628E
DOI: 10.5517/ccdc.csd.cc2556wg
title: CCDC 2001015: Experimental Crystal Structure Determination

description: Related Article: Nikolay S. Zimnitskiy, Alexey Yu. Barkov, Maria V. Ulitko, Igor B. Kutyashev, Vladislav Yu. Korotaev, Vyacheslav Ya. Sosnovskikh|2020|New J.Chem.|44|16185|doi:10.1039/D0NJ02817G
DOI: 10.5517/ccdc.csd.cc2556xh
title: CCDC 2001016: Experimental Crystal Structure Determination

description: Related Article: Nikolay S. Zimnitskiy, Alexey Yu. Barkov, Maria V. Ulitko, Igor B. Kutyashev, Vladislav Yu. Korotaev, Vyacheslav Ya. Sosnovskikh|2020|New J.Chem.|44|16185|doi:10.1039/D0NJ02817G
DOI: 10.5517/ccdc.csd.cc2556yj
title: CCDC 2001017: Experimental Crystal Structure Determination

description: Related Article: Nikolay S. Zimnitskiy, Alexey Yu. Barkov, Maria V. Ulitko, Igor B. Kutyashev, Vladislav Yu. Korotaev, Vyacheslav Ya. Sosnovskikh|2020|New J.Chem.|44|16185|doi:10.1039/D0NJ02817G
DOI: 10.5517/ccdc.csd.cc25l6pp
title: CCDC 2013502: Experimental Crystal Structure Determination

description: Related Article: Daniel Omar González-Ábrego, Gloria Sánchez-Cabrera, Francisco J. Zuno-Cruz, Jose A. Rodriguez, José G. Alvarado-Rodríguez, Noemí Andrade-López, Jorge A. López, Crispin Cristobal, Gerardo González-García|2021|Inorg.Chim.Acta|514|120000|doi:10.1016/j.ica.2020.120000
DOI: 10.5517/ccdc.csd.cc25l6qq
title: CCDC 2013503: Experimental Crystal Structure Determination

description: Related Article: Daniel Omar González-Ábrego, Gloria Sánchez-Cabrera, Francisco J. Zuno-Cruz, Jose A. Rodriguez, José G. Alvarado-Rodríguez, Noemí Andrade-López, Jorge A. López, Crispin Cristobal, Gerardo González-García|2021|Inorg.Chim.Acta|514|120000|doi:10.1016/j.ica.2020.120000
DOI: 10.5517/ccdc.csd.cc251c0m
title: CCDC 1997299: Experimental Crystal Structure Determination

description: Related Article: Jinwei Chen, Yu Li, Jinzhong Gu, Marina V. Kirillova, Alexander M. Kirillov|2020|New J.Chem.|44|16082|doi:10.1039/D0NJ03628E
DOI: 10.5517/ccdc.csd.cc251c1n
title: CCDC 1997300: Experimental Crystal Structure Determination

description: Related Article: Jinwei Chen, Yu Li, Jinzhong Gu, Marina V. Kirillova, Alexander M. Kirillov|2020|New J.Chem.|44|16082|doi:10.1039/D0NJ03628E
DOI: 10.5517/ccdc.csd.cc251bxh
title: CCDC 1997296: Experimental Crystal Structure Determination

description: Related Article: Jinwei Chen, Yu Li, Jinzhong Gu, Marina V. Kirillova, Alexander M. Kirillov|2020|New J.Chem.|44|16082|doi:10.1039/D0NJ03628E
DOI: 10.5517/ccdc.csd.cc251bzk
title: CCDC 1997298: Experimental Crystal Structure Determination

description: Related Article: Jinwei Chen, Yu Li, Jinzhong Gu, Marina V. Kirillova, Alexander M. Kirillov|2020|New J.Chem.|44|16082|doi:10.1039/D0NJ03628E
DOI: 10.5281/zenodo.4058441
title: D-Glucosamine hydrochloride

description: D-Glucosamine hydrochloride
DOI: 10.5281/zenodo.4058440
title: D-Glucosamine hydrochloride

description: D-Glucosamine hydrochloride
DOI: 10.6084/m9.figshare.10298696
title: MIX-seq data

description: Data accompanying the manuscript describing MIX-Seq, a method for transcriptional profiling of mixtures of cancer cell lines treated with small molecule and genetic perturbations (McFarland and Paolella et al., Nat Commun, 2020). <br>Data consists of single-cell RNA-sequencing (UMI count matrices), and associated drug sensitivity and genomic features of the cancer cell lines.<br>See README file for more information on dataset contents.
DOI: 10.6084/m9.figshare.10298696.v2
title: MIX-seq data

description: Data accompanying the manuscript describing MIX-Seq, a method for transcriptional profiling of mixtures of cancer cell lines treated with small molecule and genetic perturbations. <br>Data consists of single-cell RNA-sequencing (UMI count matrices), and associated drug sensitivity and genomic features of the cancer cell lines.<br>See README file for more information on dataset contents.
DOI: 10.5519/qd.v67mhfst
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3799 records)
DOI: 10.5281/zenodo.4058449
title: Representation model of wind velocity fluctuations and saltation sand transport in aeolian sand flow

description: The data of the figures in the article.
DOI: 10.5281/zenodo.4058448
title: Representation model of wind velocity fluctuations and saltation sand transport in aeolian sand flow

description: The data of the figures in the article.
DOI: 10.5281/zenodo.4045329
title: Appendices to 'The genetic Position of Anindilyakwa' paper

description: This is the dataset that is referred to in the paper 'The genetic Position of Anindilyakwa'. It contains all the cognate words that have been uncovered between the North Australian languages Anindilyakwa, Wubuy and Ngandi.
DOI: 10.5281/zenodo.3459074
title: Appendices to 'The genetic Position of Anindilyakwa' paper

description: This is the dataset that is referred to in the paper 'The genetic Position of Anindilyakwa'. It contains all the cognate words that have been uncovered between the North Australian languages Anindilyakwa, Wubuy and Ngandi.
DOI: 10.17632/vvgcgs65x3.4
title: Data for: Aeolian additions of Podzolic soils on the high-altitude mountains in central Taiwan

description: The data used in the article.
DOI: 10.18117/pmt1-f254
title: Supporting Data: Effect of various pore fluids on the strength of granite
DOI: 10.7284/138107
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise KM2004 using a Hawaii UHDAS instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138116
title: Anemometer data collected during research cruise KM2004 using a RM Young 85106 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138110
title: Barometer data collected during research cruise KM2004 using a Vaisala PTB220 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138111
title: Fluorometer data collected during research cruise KM2004 using a WET Labs WETStar instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138117
title: Anemometer data collected during research cruise KM2004 using a RM Young 85106 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138108
title: Gravimeter data collected during research cruise KM2004 using a Bell BGM-3 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138109
title: Gyrocompass data collected during research cruise KM2004 using a Sperry NAVIGAT X MK 1 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138105
title: INS (Inertial Navigation System) data collected during research cruise KM2004 using a Applanix POS/MV-320 V4 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138106
title: Meteorological Station data collected during research cruise KM2004 using a Hawaii MET-System instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138112
title: Meteorological Station data collected during research cruise KM2004 using a Hawaii MET-System instrument system onboard the platform RV Kilo Moana

description: Processed data created by vessel technician during a Field_expedition
DOI: 10.7284/138113
title: Multibeam Echosounder data collected during research cruise KM2004 using a Kongsberg EM122 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138114
title: Multibeam Echosounder data collected during research cruise KM2004 using a Kongsberg EM710 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138115
title: pCO2 (Partial Pressure of Carbon Dioxide) data collected during research cruise KM2004 using a General Oceanics 8050 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138102
title: Speedlog data collected during research cruise KM2004 using a Yokogawa EML500FA2 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138103
title: SSV (Sound Speed Velocimeter) data collected during research cruise KM2004 using a AML SV&P instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138104
title: TSG (Thermosalinograph) data collected during research cruise KM2004 using a Sea-Bird SBE-45 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138240
title: Winch data collected during research cruise OC1901A using a Dynacon Deep Tow instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138241
title: Winch data collected during research cruise OC1901A using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138243
title: Winch data collected during research cruise OC1901X using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138244
title: Winch data collected during research cruise OC1904A using a Dynacon Deep Tow instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138245
title: Winch data collected during research cruise OC1904A using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138246
title: Winch data collected during research cruise OC1904B using a Dynacon Deep Tow instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138247
title: Winch data collected during research cruise OC1904B using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138249
title: Winch data collected during research cruise OC1905A using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138253
title: Winch data collected during research cruise OC1905C using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138256
title: Winch data collected during research cruise OC1907A using a Dynacon Deep Tow instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138257
title: Winch data collected during research cruise OC1907A using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138261
title: Winch data collected during research cruise OC1907X using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138262
title: Winch data collected during research cruise OC1908B using a Dynacon Deep Tow instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138263
title: Winch data collected during research cruise OC1908B using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138264
title: Winch data collected during research cruise OC1908C using a Dynacon Deep Tow instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138265
title: Winch data collected during research cruise OC1908C using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138269
title: Winch data collected during research cruise OC1910A using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138273
title: Winch data collected during research cruise OC1911A using a Markey Markey DESH-5/COM-15 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.13019111
title: JVASP-52840 to JVASP-54948

description: JARVIS-DFT
DOI: 10.7284/133099
title: HDSS (Hydrographic Doppler Sonar System) data collected during research cruise RR1319 using a SIO HDSS instrument system onboard the platform RV Roger Revelle

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/126138
title: HDSS (Hydrographic Doppler Sonar System) data collected during research cruise RR1614 using a SIO HDSS instrument system onboard the platform RV Roger Revelle

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/135811
title: HDSS (Hydrographic Doppler Sonar System) data collected during research cruise RR1901 using a SIO HDSS instrument system onboard the platform RV Roger Revelle

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/135828
title: HDSS (Hydrographic Doppler Sonar System) data collected during research cruise RR1902 using a SIO HDSS instrument system onboard the platform RV Roger Revelle

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/135845
title: HDSS (Hydrographic Doppler Sonar System) data collected during research cruise RR1903 using a SIO HDSS instrument system onboard the platform RV Roger Revelle

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.13019108
title: JVASP-54949 to JVASP-56688

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13019102
title: JVASP-56889 to JVASP-57689

description: JARVIS-DFT
DOI: 10.21227/ge6y-rz90
title: Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning

description: Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has beenconducted by random forest. Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinationsof the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organswas proven to be the most significant parameter of the TCM diagnosis and treatment.Conclusions: The results indicate that the machine learning methodsare worthy ofbeingadoptedto study the dominant diseases of TCM forexploringthe crucial rules of the diagnosis and treatment.
DOI: 10.21227/bws0-nm98
title: Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning

description: Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has beenconducted by random forest. Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinationsof the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organswas proven to be the most significant parameter of the TCM diagnosis and treatment.Conclusions: The results indicate that the machine learning methodsare worthy ofbeingadoptedto study the dominant diseases of TCM forexploringthe crucial rules of the diagnosis and treatment.
DOI: 10.6084/m9.figshare.13012244
title: 123

description: abcd
DOI: 10.6084/m9.figshare.13012244.v2
title: 123

description: abcd
DOI: 10.6084/m9.figshare.13012244.v1
title: SL-0002-L.csv

description: abcd
DOI: 10.6084/m9.figshare.13019102.v1
title: JVASP-56889 to JVASP-57689

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13019108.v1
title: JVASP-54949 to JVASP-56688

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13019111.v1
title: JVASP-52840 to JVASP-54948

description: JARVIS-DFT
DOI: 10.25919/5f73452a7958e
title: Parkes observations for project P1063 semester 2020APRS_15

description: We propose to use the Parkes UWL receiver for monitoring a small sub-set of the FAST-discovered pulsars, with special properties. The Parkes UWL system provides a unique capability for studying the pulsars over wide bandwidths. The observation proposed will provide significant insight into the special emission properties of these new discoveries and facilitate science.
DOI: 10.25345/c58b5b
title: MassIVE MSV000086208 - Chemical composition of material extractives influences microbial growth and dynamics on wetted wood materials
title: The impact of material chemical composition on microbial growth on building materials remains relatively poorly understood. We investigate the influence of the chemical composition of material extractives on microbial growth and community dynamics on 30 different wood species that were naturally inoculated, wetted, and held at high humidity for several weeks. Microbial growth was assessed by visual assessment and molecular sequencing. Unwetted material powders and microbial swab samples were analyzed using reverse phase liquid chromatography with tandem mass spectrometry. Different wood species demonstrated varying susceptibility to microbial growth after 3 weeks and visible coverage and fungal qPCR concentrations were correlated (R2 = 0.55). Aspergillaceae was most abundant across all samples; Meruliaceae was more prevalent on 8 materials with the highest visible microbial growth. A larger and more diverse set of compounds was detected from the wood shavings compared to the microbial swabs, indicating a complex and heterogeneous chemical composition within wood types. Several individual compounds putatively identified in wood samples showed statistically significant, near-monotonic associations with microbial growth, including C11H16O4, C18H34O4, and C6H15NO. A pilot experiment confirmed the inhibitory effects of dosing a sample of wood materials with varying concentrations of liquid C6H15NO (assuming it presented as Diethylethanolamine).