cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18434/m32080
title: Bayesian Hierarchical Models for Service Life Prediction of Polymers Data and Code

description: The data and code that accompanies the book chapter Bayesian Hierarchical Models for Service Life Prediction of Polymers.
DOI: 10.21227/gnx0-es60
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Recent advances in computer vision and artificial intelligence have allowed researchers to develop environment recognition systems for lower-limb exoskeletons and prostheses. However, insufficient and private training databases have impeded the widespread development and dissemination of image classification algorithms for environment recognition. To address these shortcomings, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in both scale and diversity, ExoNet comprises over 5.6 million images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone camera system throughout the summer, fall, and winter seasons. Approximately 940,000 images in ExoNet were human-annotated using a 12-class hierarchical classification architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform for training, developing, and comparing image classification algorithms for next-generation environment recognition systems. Beyond the control of lower-limb exoskeletons and prostheses, applications of ExoNet extend to humanoid and autonomous legged robotics.
DOI: 10.5281/zenodo.3748119
title: Dataset for Intervention strategies against COVID-19 and their estimated impact on Swedish healthcare capacity

description: Dataset for individual-based modeling of COVID-19 spread in Sweden.
DOI: 10.5281/zenodo.3748120
title: Dataset for Intervention strategies against COVID-19 and their estimated impact on Swedish healthcare capacity

description: Dataset for individual-based modeling of COVID-19 spread in Sweden.
DOI: 10.5439/1608931
title: ARM: KASACR2CFRSPCCWRHICROSS

description: kasacr
DOI: 10.5439/1608932
title: ARM: KASACR2CFRSPCCWRHIXPOL

description: kasacr
DOI: 10.17632/zd8nj4t5c4.3
title: Data for: A Network Analytic Method for Measuring Patent Thickets: A Case of FCEV Technology

description: Fuel cell electric vehicle patents at the USPTO and EPO in 1998-2017
DOI: 10.5519/qd.a85kuvx0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.3747829
title: CTRPv2

description: CTRPv2 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://orcestra.ca/10.5281/zenodo.3747829
DOI: 10.5281/zenodo.3747828
title: CTRPv2

description: CTRPv2 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://orcestra.ca/10.5281/zenodo.3747829
DOI: 10.5281/zenodo.3747988
title: Emiliania huxleyi (UNC1419) reference transcriptome

description: Reference transcriptome and associated annotations for <em>Emiliania huxleyi </em>(UNC1419). A culture was grown into late exponential phase for filtration. Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol except for an initial bead beating step and two instead of one chloroform steps to separate proteins and DNA. RNA libraries were created with the KAPA Stranded mRNA-Seq kit for Illumina platforms. The library was sequenced on an Illumina MiSeq (300 bp, paired-end reads) and an Illumina HiSeq 2500 with one lane in high output mode (100 bp, paired-end reads) and another lane in rapid run mode (150 bp, paired-end reads). Raw reads were trimmed for quality with Trimmomatic v0.36 then assembled <em>de novo </em>with Trinity v2.5.1 with the default parameters for paired-reads and a minimum contig length of 90 bp. Contigs were clustered based on 99% similarity using CD-HIT-EST v4.7 and then protein sequences were predicted with GeneMark S-T. Protein sequences were annotated by best-homology (lowest E-value) with the KEGG (Release 86.0), UniProt (Release 2018_03), and PhyloDB (v1.076) databases via BLASTP v2.7.1 (E-value ≤ 10<sup>-5</sup>) and with Pfam 31.0 via HMMER v3.1b2 (Dataset S2). KEGG Ortholog (KO) annotations were assigned from the top hit with a KO annotation from the top 10 hits (https://github.com/ctberthiaume/keggannot). Provided here are predicted proteins as nucleotides and peptides. Raw reads are deposited in SRA (SRP234650).
DOI: 10.5281/zenodo.3747989
title: Emiliania huxleyi (UNC1419) reference transcriptome

description: Reference transcriptome and associated annotations for <em>Emiliania huxleyi </em>(UNC1419). A culture was grown into late exponential phase for filtration. Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol except for an initial bead beating step and two instead of one chloroform steps to separate proteins and DNA. RNA libraries were created with the KAPA Stranded mRNA-Seq kit for Illumina platforms. The library was sequenced on an Illumina MiSeq (300 bp, paired-end reads) and an Illumina HiSeq 2500 with one lane in high output mode (100 bp, paired-end reads) and another lane in rapid run mode (150 bp, paired-end reads). Raw reads were trimmed for quality with Trimmomatic v0.36 then assembled <em>de novo </em>with Trinity v2.5.1 with the default parameters for paired-reads and a minimum contig length of 90 bp. Contigs were clustered based on 99% similarity using CD-HIT-EST v4.7 and then protein sequences were predicted with GeneMark S-T. Protein sequences were annotated by best-homology (lowest E-value) with the KEGG (Release 86.0), UniProt (Release 2018_03), and PhyloDB (v1.076) databases via BLASTP v2.7.1 (E-value ≤ 10<sup>-5</sup>) and with Pfam 31.0 via HMMER v3.1b2 (Dataset S2). KEGG Ortholog (KO) annotations were assigned from the top hit with a KO annotation from the top 10 hits (https://github.com/ctberthiaume/keggannot). Provided here are predicted proteins as nucleotides and peptides. Raw reads are deposited in SRA (SRP234650).
DOI: 10.5281/zenodo.3747740
title: Alles eine Frage des Alters? Zum Lernen durch plötzliche Erkenntnis bei Alt und Jung- Daten

description: Diese Dokumente enthalten die demographischen- und ausgewerteten Fragebogendaten sowie die Rohdaten der Probanden zur Arbeit "Alles eine Frage des Alters? Zum Lernen durch plötzliche Erkenntnis bei Alt und Jung." in anonymisierter Weise.
DOI: 10.5281/zenodo.3747741
title: Alles eine Frage des Alters? Zum Lernen durch plötzliche Erkenntnis bei Alt und Jung- Daten

description: Diese Dokumente enthalten die demographischen- und ausgewerteten Fragebogendaten sowie die Rohdaten der Probanden zur Arbeit "Alles eine Frage des Alters? Zum Lernen durch plötzliche Erkenntnis bei Alt und Jung." in anonymisierter Weise.
DOI: 10.17632/32mndhpsf3.1
title: Data for: Preparation of Li (tri-(4-carboxyphenyl) amine) doped polypyrrole as cathode material of lithium ion batteries and its electrochemical performances

description: energy storage materials and its application as the lithtium battery
DOI: 10.6084/m9.figshare.3761562.v1448
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.3748049
title: Evolution of COVID-19 by country until 28th May

description: This dataset is a three dimensional dataset in wich we analyze the evolution of some data related with COVID-19 along the time. We analyse how a type of data behave along the time in the different countries. In each csv, we have kind of varibale (Cases, recovered, deaths) by country and date (from 03/30 to 05/28). So we have 5 time series by country: one for each kind of data. The csv contais the information related with a kind of data, and are described by the other two dimensions: country and date. We obtained this dataset scrapping Worldometers.
description: This dataset its updated until 2020/05/04. However, the dataset is up to date in https://github.com/AdrianArnaiz/scrap_uoc, where its updated automatically every day.
description: {"references": ["Worldometers. Covid-19 coronavirus pandemic. https://www.worldometers.info/coronavirus/, 2020.", "Ensheng Dong, Hongru Du, and Lauren Gardner. An interactive web-based dashboard to track covid-19 in real time. The Lancet infectious diseases, 2020.", "Richard Lawson. Web scraping with Python. Packt Publishing Ltd, 2015", "Max Roser, Hannah Ritchie, and Esteban Ortiz-Ospina. Coronavirus disease (covid-19)\u2013statistics and research. Our World in Data, 2020.", "Laia Subirats Mate and Mireia Calvo Gonzalez. Web scraping. Technical report, UOC, Barcelona, (sf). PID00256970."]}
DOI: 10.5281/zenodo.3748050
title: Evolution of COVID-19 by country

description: This dataset is a three dimensional dataset in wich we analyze the evolution of some data related with COVID-19 along the time. We analyse how a type of data behave along the time in the different countries. In each csv, we have kind of varibale (Cases, recovered, deaths) by country and date (from 03/30 to 04/10). Howevwe, t<strong>he data its up to date in https://github.com/AdrianArnaiz/scrap_uoc (updated automatically every day).</strong> So we have 5 time series by country: one for each kind of data. The csv contais the information related with a kind of data, and are described by the other two dimensions: country and date. We obtained this dataset scrapping Worldometers.
description: This dataset its updated until 2020/04/10. However, the dataset is up to date in https://github.com/AdrianArnaiz/scrap_uoc, where its updated automatically every day.
description: {"references": ["Worldometers. Covid-19 coronavirus pandemic. https://www.worldometers.info/coronavirus/, 2020.", "Ensheng Dong, Hongru Du, and Lauren Gardner. An interactive web-based dashboard to track covid-19 in real time. The Lancet infectious diseases, 2020.", "Richard Lawson. Web scraping with Python. Packt Publishing Ltd, 2015", "Max Roser, Hannah Ritchie, and Esteban Ortiz-Ospina. Coronavirus disease (covid-19)\u2013statistics and research. Our World in Data, 2020.", "Laia Subirats Mate and Mireia Calvo Gonzalez. Web scraping. Technical report, UOC, Barcelona, (sf). PID00256970."]}
DOI: 10.17632/32mndhpsf3.2
title: Data for: Preparation of Li (tri-(4-carboxyphenyl) amine) doped polypyrrole as cathode material of lithium ion batteries and its electrochemical performances

description: energy storage materials and its application as the lithtium battery
DOI: 10.17632/c25nh9nfxp.1
title: Data for: Development of a new code for stopping power and CSDA range calculation of incident charged particles, Part A: electron and positron

description: these data present the stopping power and range of electron and positron in selected matters
DOI: 10.25345/c56d9x
title: MassIVE MSV000085273 - test_Fritz_2020_SWATH_TripleTOF5600 with DIA mzXML
title: Test description for test dataset. Test description for test dataset. Test description for test dataset. Test description for test dataset. Test description for test dataset. Test description for test dataset.
DOI: 10.5281/zenodo.3748053
title: Habitatges en règim de lloguer a l'àrea de Barcelona

description: <strong>Els següents camps del dataset s’han obtingut com a resultat del scraping de pàgines d’ofertes a habitaclia.com. </strong> <strong>Cada registre del dataset correspon a una oferta en el site. La majoria dels camps s’han obtingut a partir del markup de la pàgina, fent ús principalment de les diferents seccions i de diferents tractaments de cadena. Alguns dels camps s’obtenen directament de elements “input” amb un identificador ben definit, utilitzant també el markup, a mode de fallback, per si no estiguessin disponibles en alguna oferta en particular. </strong> <strong>Finalment, algunes dades s’obtenen directament a partir de la pròpia URL de cada oferta.</strong> <strong>Camps:</strong> <strong>Id: Identificador de l'habitatge</strong> <strong>URL: URL de la oferta obtinguda</strong> <strong>Tipus Oferta: Indica si es tracta d'un habitatge en Venda o en Lloguer. En el nostre CSV trobem només 'lloguer', que és el principal objectiu de la pràctica. Però val a dir que l’scraper pot obtenir informació també per ofertes de compra.</strong> <strong>Tipus immoble: Pis, Casa, Àtic o Dúplex</strong> <strong>Municipi: per exemple, 'Barcelona'</strong> <strong>Província: pe exemple, 'Barcelona'</strong> <strong>Zona: Barri o zona dins d'un municipi on es situa l'habitatge. Per exemple, 'Dreta de l'Eixample'</strong> <strong>Preu: Preu, en euros, de l'habitatge en el moment d'obtenir les dades</strong> <strong>Data: Data en què es va obtenir la informació de l'habitatge</strong> <strong>Superfície: Superfície, en metres quadrats, de l'habitatge</strong> <strong>#Habitacions: Nombre d'habitacions de què l'habitatge disposa</strong> <strong>#Banys: Nombre de banys de què consta la vivenda</strong> <strong>Antiguitat: Any de construcció de la vivenda</strong> <strong>Planta: Número de planta (1ª, 2ª, ...)</strong> <strong>Parking: booleà indicant si la vivenda disposa o no de pàrking</strong> <strong>Calefacció: booleà indicant si la vivenda disposa o no de calefacció</strong> <strong>Aire acondicionat: booleà indicant si la vivenda disposa o no de aire acondicionat</strong> <strong>Moblat: booleà indicant si la vivenda disposa o no de mobles</strong> <strong>Ascensor: booleà indicant si la vivenda disposa o no d'ascensor</strong> <strong>Jardí: booleà indicant si la vivenda disposa o no de jardí</strong> <strong>Eficiència energètica: Classificació de la vivenda segons la seva eficiència energètica (A..G),</strong> <strong>Classe emissions: Classificació de la vivenda segons les emissions (A..G)</strong> <strong>Cal tenir en compte que no tots els habitatges disposen de tots els camps d'informació. Per aquells camps dels quals no es disposa d'alguna dada el valor reportat és un valor buit o ''.</strong>
DOI: 10.5281/zenodo.3748054
title: Habitatges en règim de lloguer a l'àrea de Barcelona

description: <strong>Els següents camps del dataset s’han obtingut com a resultat del scraping de pàgines d’ofertes a habitaclia.com. </strong> <strong>Cada registre del dataset correspon a una oferta en el site. La majoria dels camps s’han obtingut a partir del markup de la pàgina, fent ús principalment de les diferents seccions i de diferents tractaments de cadena. Alguns dels camps s’obtenen directament de elements “input” amb un identificador ben definit, utilitzant també el markup, a mode de fallback, per si no estiguessin disponibles en alguna oferta en particular. </strong> <strong>Finalment, algunes dades s’obtenen directament a partir de la pròpia URL de cada oferta.</strong> <strong>Camps:</strong> <strong>Id: Identificador de l'habitatge</strong> <strong>URL: URL de la oferta obtinguda</strong> <strong>Tipus Oferta: Indica si es tracta d'un habitatge en Venda o en Lloguer. En el nostre CSV trobem només 'lloguer', que és el principal objectiu de la pràctica. Però val a dir que l’scraper pot obtenir informació també per ofertes de compra.</strong> <strong>Tipus immoble: Pis, Casa, Àtic o Dúplex</strong> <strong>Municipi: per exemple, 'Barcelona'</strong> <strong>Província: pe exemple, 'Barcelona'</strong> <strong>Zona: Barri o zona dins d'un municipi on es situa l'habitatge. Per exemple, 'Dreta de l'Eixample'</strong> <strong>Preu: Preu, en euros, de l'habitatge en el moment d'obtenir les dades</strong> <strong>Data: Data en què es va obtenir la informació de l'habitatge</strong> <strong>Superfície: Superfície, en metres quadrats, de l'habitatge</strong> <strong>#Habitacions: Nombre d'habitacions de què l'habitatge disposa</strong> <strong>#Banys: Nombre de banys de què consta la vivenda</strong> <strong>Antiguitat: Any de construcció de la vivenda</strong> <strong>Planta: Número de planta (1ª, 2ª, ...)</strong> <strong>Parking: booleà indicant si la vivenda disposa o no de pàrking</strong> <strong>Calefacció: booleà indicant si la vivenda disposa o no de calefacció</strong> <strong>Aire acondicionat: booleà indicant si la vivenda disposa o no de aire acondicionat</strong> <strong>Moblat: booleà indicant si la vivenda disposa o no de mobles</strong> <strong>Ascensor: booleà indicant si la vivenda disposa o no d'ascensor</strong> <strong>Jardí: booleà indicant si la vivenda disposa o no de jardí</strong> <strong>Eficiència energètica: Classificació de la vivenda segons la seva eficiència energètica (A..G),</strong> <strong>Classe emissions: Classificació de la vivenda segons les emissions (A..G)</strong> <strong>Cal tenir en compte que no tots els habitatges disposen de tots els camps d'informació. Per aquells camps dels quals no es disposa d'alguna dada el valor reportat és un valor buit o ''.</strong>
DOI: 10.14272/gcoxaoyaiwzfrc-uhfffaoysa-n.1
title: 2-(2-$l^{1}-oxidanyl-1,1,3,3-tetramethylisoindol-5-yl)ethynyl-trimethylsilane
title: InChI=1S/C17H24NOSi/c1-16(2)14-9-8-13(10-11-20(5,6)7)12-15(14)17(3,4)18(16)19/h8-9,12H,1-7H3

description: This dataset contains experimental data from a sample (2-(2-lambda1-oxidanyl-1,1,3,3-tetramethylisoindol-5-yl)ethynyl-trimethylsilane, InChI=1S/C17H24NOSi/c1-16(2)14-9-8-13(10-11-20(5,6)7)12-15(14)17(3,4)18(16)19/h8-9,12H,1-7H3). The datasets from the following experiments are available: mass spectrometry (MS)
DOI: 10.14272/gcoxaoyaiwzfrc-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (2-(2-lambda1-oxidanyl-1,1,3,3-tetramethylisoindol-5-yl)ethynyl-trimethylsilane)
title: InChI=1S/C17H24NOSi/c1-16(2)14-9-8-13(10-11-20(5,6)7)12-15(14)17(3,4)18(16)19/h8-9,12H,1-7H3

description: dataset for mass spectrometry (MS) EI (m/z, 70 eV, 80 °C): 287/286 (19/100) [M+], 272 (29) [M+ – CH3 + H], 271 (96) [M+ – CH3], 256 (55) [M+ – 2 CH3], 241 (63) [M+ – 3 CH3].HRMS–EI (m/z): [M]+ calcd for C17H24NO28Si, 286.1622; found, 286.1622.
DOI: 10.25345/c5b392
title: MassIVE MSV000085272 - Hawaiian Coral Metabolome Project
title: This data comes from coral samples taken during four years in Hawaii. We identified a class of lipids which predicts differences in coral bleaching phenotype across time. This data is unpublished yet and any use have to be informed to their corresponding PIs.
DOI: 10.14272/alurcexfhypefu-uhfffaoysa-l.1
title: C57H46Cu2F6I2NP3
title: InChI=1S/C21H22NP.2C18H12F3P.2Cu.2HI/c1-21(2,3)17-14-15-22-20(16-17)23(18-10-6-4-7-11-18)19-12-8-5-9-13-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h4-16H,1-3H3;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: This dataset contains experimental data from a sample (InChI=1S/C21H22NP.2C18H12F3P.2Cu.2HI/c1-21(2,3)17-14-15-22-20(16-17)23(18-10-6-4-7-11-18)19-12-8-5-9-13-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h4-16H,1-3H3;2*1-12H;;;2*1H/q;;;2*+1;;/p-2). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) 31P nuclear magnetic resonance spectroscopy (31P NMR) 19F nuclear magnetic resonance spectroscopy (19F NMR) infrared absorption spectroscopy (IR) mass spectrometry (MS) single crystal X-ray diffraction (single-crystal X-ray diffraction)
DOI: 10.14272/alurcexfhypefu-uhfffaoysa-l/chmo0000159
title: single crystal X-ray diffraction (single-crystal X-ray diffraction) ()
title: InChI=1S/C21H22NP.2C18H12F3P.2Cu.2HI/c1-21(2,3)17-14-15-22-20(16-17)23(18-10-6-4-7-11-18)19-12-8-5-9-13-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h4-16H,1-3H3;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for single crystal X-ray diffraction (single-crystal X-ray diffraction)
DOI: 10.14272/alurcexfhypefu-uhfffaoysa-l/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C21H22NP.2C18H12F3P.2Cu.2HI/c1-21(2,3)17-14-15-22-20(16-17)23(18-10-6-4-7-11-18)19-12-8-5-9-13-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h4-16H,1-3H3;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for mass spectrometry (MS) MS (FAB, 3-NBA) m/z [%] = 1393 (1) [M+Cu]+, 1077 (3) [Cu3I2LP]+, 887 (7) [Cu2ILP]+, 884 (2) [Cu2IP2]+, 761 (32) [Cu3I2L]+, 698 (25) [CuLP]+, 695 (6) [CuP2]+, 571 (74) [Cu2IL]+, 382 (100) [CuL]+, 320 (22) [L+H]+, 317 (14) [P+H]+.
DOI: 10.14272/alurcexfhypefu-uhfffaoysa-l/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C21H22NP.2C18H12F3P.2Cu.2HI/c1-21(2,3)17-14-15-22-20(16-17)23(18-10-6-4-7-11-18)19-12-8-5-9-13-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h4-16H,1-3H3;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, DMSO-d6) δ [ppm] = 8.50 (bs, 1H, HPyr), 7.62 (bs, 1H, HPyr), 7.57–7.00 (m, 35H), 1.11 (s, 9H, HtertBu). Impurities: 1% H-grease, 1% phosphine oxide of the ligands according to the 31P-NMR.
DOI: 10.14272/alurcexfhypefu-uhfffaoysa-l/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C21H22NP.2C18H12F3P.2Cu.2HI/c1-21(2,3)17-14-15-22-20(16-17)23(18-10-6-4-7-11-18)19-12-8-5-9-13-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h4-16H,1-3H3;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (101 MHz, DMSO-d6) δ [ppm] = 164.4 (s), 162.0 (s), 136.0 (dd, J = 15.5 Hz, J = 8.3 Hz), 133.3 (bd, J = 13.0 Hz), 130.0 (s), 129.0 (d, J = 27.9 Hz), 128.5 (d, J = 7.7 Hz), 115.9 (dd, J = 21.2 Hz, J = 9.3 Hz), 34.9 (s, CtertBu), 29.7 (s, CtertBu), resonances could not be assigned due to low resolution even at 8192 ns (47C). Impurities: 1% H-grease, 1% phosphine oxide of the ligands according to the 31P-NMR.
DOI: 10.14272/alurcexfhypefu-uhfffaoysa-l/chmo0000597
title: 19F nuclear magnetic resonance spectroscopy (19F NMR) ()
title: InChI=1S/C21H22NP.2C18H12F3P.2Cu.2HI/c1-21(2,3)17-14-15-22-20(16-17)23(18-10-6-4-7-11-18)19-12-8-5-9-13-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h4-16H,1-3H3;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for 19F nuclear magnetic resonance spectroscopy (19F NMR) 19F NMR (376 MHz, DMSO-d6) δ [ppm] = −114.82 (s, 6F, F(F-Ph)3P). Impurities: 1% phosphine oxide of the ligands according to the 31P-NMR.
DOI: 10.14272/alurcexfhypefu-uhfffaoysa-l/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C21H22NP.2C18H12F3P.2Cu.2HI/c1-21(2,3)17-14-15-22-20(16-17)23(18-10-6-4-7-11-18)19-12-8-5-9-13-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h4-16H,1-3H3;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for infrared absorption spectroscopy (IR) IR (ATR) 𝜈̃ [cm–1] = 2962 (vw), 1586 (m), 1493 (m), 1434 (w), 1393 (w), 1225 (m), 1158 (m), 1092 (w), 1013 (w), 824 (m), 742 (w), 693 (w), 634 (w), 520 (m), 467 (w), 432 (m).
DOI: 10.14272/alurcexfhypefu-uhfffaoysa-l/chmo0000739
title: 31P nuclear magnetic resonance spectroscopy (31P NMR) ()
title: InChI=1S/C21H22NP.2C18H12F3P.2Cu.2HI/c1-21(2,3)17-14-15-22-20(16-17)23(18-10-6-4-7-11-18)19-12-8-5-9-13-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h4-16H,1-3H3;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for 31P nuclear magnetic resonance spectroscopy (31P NMR) 31P NMR (162 MHz, DMSO-d6) δ [ppm] = −4.76 (bs, 1P, PtertBuPyrPhos), −14.47 (bs, 2P, P(F-Ph)3P). Impurities: 1% phosphine oxide of the ligands.
DOI: 10.14272/zeqlggmcoxyggy-uhfffaoysa-n.1
title: 5-iodo-2-$l^{1}-oxidanyl-1,1,3,3-tetramethylisoindole
title: InChI=1S/C12H15INO/c1-11(2)9-6-5-8(13)7-10(9)12(3,4)14(11)15/h5-7H,1-4H3

description: This dataset contains experimental data from a sample (5-iodo-2-lambda1-oxidanyl-1,1,3,3-tetramethylisoindole, InChI=1S/C12H15INO/c1-11(2)9-6-5-8(13)7-10(9)12(3,4)14(11)15/h5-7H,1-4H3). The datasets from the following experiments are available: mass spectrometry (MS)