cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4900669
title: Priming in aquatic ecosystems database

description: Priming-Meta-analysis This database has been created by Lucia Fernandes Sanches in 2020 and is associated to a paper submitted to JGR: Biogeosciences called "Exploring the drivers controlling the priming effect and its magnitude in aquatic systems" by Lúcia Fernandes Sanches, Bertrand Guenet, Nicholas dos Anjos Cristiano Marino and Francisco de Assis Esteves. Requests have to be send to sanches1983(@)gmail.com and bertrand.guenet(@)ens.fr
DOI: 10.3886/e114567v1-88120
title: Replication data for: Adaptation to Climate Change: Evidence from US Agriculture

description: Understanding the potential impacts of climate change on economic outcomes requires knowing how agents might adapt to a changing climate. We exploit large variation in recent temperature and precipitation trends to identify adaptation to climate change in US agriculture, and use this information to generate new estimates of the potential impact of future climate change on agricultural outcomes. Longer run adaptations appear to have mitigated less than half--and more likely none--of the large negative short-run impacts of extreme heat on productivity. Limited recent adaptation implies substantial losses under future climate change in the absence of countervailing investments.
DOI: 10.5281/zenodo.4836397
title: Supplementary Data: Thermal WIMPs and the Scale of New Physics: Global Fits of Dirac Dark Matter Effective Field Theories

description: <strong>Supplementary Data</strong><br> <em>Thermal WIMPs and the Scale of New Physics:<br> Global Fits of Dirac Dark Matter Effective Field Theories.</em> The files in this record contain supplementary data including the samples and plotting scripts used for the results in Athron. P et al. "Thermal WIMPs and the Scale of New Physics: Global Fits of Dirac Dark Matter Effective Field Theories." Samples have been created using GAMBIT and figures can be reproduced with pippi.<br>
DOI: 10.5281/zenodo.4836396
title: Supplementary Data: Thermal WIMPs and the Scale of New Physics: Global Fits of Dirac Dark Matter Effective Field Theories

description: <strong>Supplementary Data</strong><br> <em>Thermal WIMPs and the Scale of New Physics:<br> Global Fits of Dirac Dark Matter Effective Field Theories.</em> The files in this record contain supplementary data including the samples and plotting scripts used for the results in Athron. P et al. "Thermal WIMPs and the Scale of New Physics: Global Fits of Dirac Dark Matter Effective Field Theories." Samples have been created using GAMBIT and figures can be reproduced with pippi.<br>
DOI: 10.3886/e141981
title: Developing Resilience from Attachment Anxiety/Avoidance: The Roles Self-Compassion and Self-Efficacy Play

description: While many studies have focused on resilience development, few have explored resilience development in adolescents with insecure attachment (attachment anxiety/attachment avoidance). In this study, we addressed how self-compassion and self-efficacy mediated insecure attachment’s influence on resilience. We also examined the extent to which the two mediators could mitigate insecure attachment’s influence on resilience. <br>
DOI: 10.3886/e141981v1
title: Developing Resilience from Attachment Anxiety/Avoidance: The Roles Self-Compassion and Self-Efficacy Play

description: While many studies have focused on resilience development, few have explored resilience development in adolescents with insecure attachment (attachment anxiety/attachment avoidance). In this study, we addressed how self-compassion and self-efficacy mediated insecure attachment’s influence on resilience. We also examined the extent to which the two mediators could mitigate insecure attachment’s influence on resilience. <br>
DOI: 10.25739/rm4j-3580
title: Carolyn_Lawrence_Dill_GOMAP_Coffee_CGH_v1.0_June_2021.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in the Coffea canephora genome (v1.0) from the Coffee Genome Hub (CGH) with a median of 10 annotations per gene model with 98.81% coverage. It was created in April of 2021.
DOI: 10.15468/9t66zp
title: Nomenclatural notes on Ferula tunshanica (= F. licentiana var. tunshanica) (Apiaceae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Li, Jin-Ping, Yu, Xiao-Ming Peng And Wen-Bin (2010): Nomenclatural notes on Ferula tunshanica (= F. licentiana var. tunshanica) (Apiaceae). Phytotaxa 13: 59-60, DOI: 10.11646/phytotaxa.13.1.6, URL: http://biotaxa.org/Phytotaxa/article/view/phytotaxa.13.1.6
DOI: 10.6084/m9.figshare.14538162.v1
title: Litterfall chemistry is modulated by wet-dry seasonality and leaf phenology of dominant species in the tropics

description: Data from a study conducted in riparian ecosystems of Cerrado biome (Brazil). Chemical composition and plant species composition of leaf litterfall entering three tropical streams. Data was collected monthly over two years (24 sampling occasions).
DOI: 10.6084/m9.figshare.14538162
title: Litterfall chemistry is modulated by wet-dry seasonality and leaf phenology of dominant species in the tropics

description: Data from a study conducted in riparian ecosystems of Cerrado biome (Brazil). Chemical composition and plant species composition of leaf litterfall entering three tropical streams. Data was collected monthly over two years (24 sampling occasions).
DOI: 10.5066/p92h86mz
title: South America Province Boundaries, 1999 (prv6ag)

description: South America is part of Region 6 (Central and South America) for the World Energy Assessment. South America was divided into 107 geologic provinces as background for prioritization and assessment of undiscovered oil and gas resources. The boundaries of geologic provinces are required for the assessment as oil and gas. Data must be allocated to a geographic entity so that decisions can be made as to which provinces are priority for the assessment. Many sources of geologic information were used to define the province boundaries in South America, and several versions of the map were reviewed. Of the 107 geologic provinces defined in South America, about 40 have had some oil and gas production to date.
DOI: 10.15468/gd5y8j
title: Taxonomy and evolution of the Convolvulus sabatius complex (Convolvulaceae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Carine, Mark A., Robba, Lavinia (2010): Taxonomy and evolution of the Convolvulus sabatius complex (Convolvulaceae). Phytotaxa 14: 1-21, DOI: 10.11646/phytotaxa.14.1.1, URL: http://biotaxa.org/Phytotaxa/article/view/phytotaxa.14.1.1
DOI: 10.15468/nxa7fd
title: Nova Espécie Do Gênero Galissus Dupont (Coleoptera, Cerambycidae) E Chave Para Identificação Das Espécies

description: This dataset contains the digitized treatments in Plazi based on the original journal article Martins, Ubirajara R., Galileo, Maria Helena M., Campos, Rolando Ramírez (2012): Nova Espécie Do Gênero Galissus Dupont (Coleoptera, Cerambycidae) E Chave Para Identificação Das Espécies. Papéis Avulsos de Zoologia 52 (17): 197-200, DOI: 10.1590/S0031-10492012001700001
DOI: 10.7303/syn25834831
title: Newborn screening cards
DOI: 10.15468/bssf32
title: Begonia shenzhenensis, a new species of Begoniaceae from Guangdong, China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Tian, Dai-Ke, Chen, Bin, Xiao, Yan, Zheng, Min-Min, Ge, Bin-Jie (2021): Begonia shenzhenensis, a new species of Begoniaceae from Guangdong, China. PhytoKeys 178: 171-177, DOI: http://dx.doi.org/10.3897/phytokeys.178.66462, URL: http://dx.doi.org/10.3897/phytokeys.178.66462
DOI: 10.15468/twqs4b
title: Smelowskia sunhangii (Brassicaceae), a new species from China, with a re-evaluation of the S. tibetica complex

description: This dataset contains the digitized treatments in Plazi based on the original journal article Zhang, Xin-jian, Chen, Jun-Tong, Al-Shehbaz, Ihsan A., Liu, Qun, Li, Li-Juan, Liu, Peng-Ju, Huang, Xian-Han, Deng, Tao (2021): Smelowskia sunhangii (Brassicaceae), a new species from China, with a re-evaluation of the S. tibetica complex. PhytoKeys 178: 179-191, DOI: http://dx.doi.org/10.3897/phytokeys.178.62922, URL: http://dx.doi.org/10.3897/phytokeys.178.62922
DOI: 10.15468/8sy4jy
title: Six new species of Horniella Raffray from the Oriental region (Coleoptera, Staphylinidae, Pselaphinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Zhang, Wen-Xuan, Hu, Fang-Shuo, Yin, Zi-Wei (2021): Six new species of Horniella Raffray from the Oriental region (Coleoptera, Staphylinidae, Pselaphinae). ZooKeys 1042: 1-22, DOI: http://dx.doi.org/10.3897/zookeys.1042.66576, URL: http://dx.doi.org/10.3897/zookeys.1042.66576
DOI: 10.15468/vu2qsn
title: A new, unusually dark, typhlocybine leafhopper (Hemiptera, Cicadellidae, Typhlocybinae, Erythroneurini) from China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Song, Yuehua, Yuan, Zhouwei, Jiang, Jia (2021): A new, unusually dark, typhlocybine leafhopper (Hemiptera, Cicadellidae, Typhlocybinae, Erythroneurini) from China. ZooKeys 1042: 35-40, DOI: http://dx.doi.org/10.3897/zookeys.1042.63593, URL: http://dx.doi.org/10.3897/zookeys.1042.63593
DOI: 10.25500/edata.bham.00000656
title: Research data supporting the publication “Digital twins for managing railway maintenance and resilience”
DOI: 10.6084/m9.figshare.14736093
title: H3K27me3 P8_1.5dpi mm10

description: For UCSC
DOI: 10.6084/m9.figshare.14736093.v1
title: H3K27me3 P8_1.5dpi mm10

description: For UCSC
DOI: 10.3886/e113649v1-88160
title: Replication data for: The Effects of Micro-entrepreneurship Programs on Labor Market Performance: Experimental Evidence from Chile

description: We investigate the impact of a program providing asset transfers and business training to low income individuals in Chile, and asked whether a larger asset transfer would magnify the program's impact. We randomly assigned participation in a large scale, publicly run micro-entrepreneurship program and evaluated its effects over 45 months. The program improved business practices, employment, and labor income. In the short run, self-employment increased by 14.8/25.2 percentage points for a small/large asset transfer. In the long run, individuals assigned to a smaller transfer were 9 percentage points more likely to become wage workers, whereas those assigned to larger transfers tended to remain self-employed.
DOI: 10.6084/m9.figshare.14710974
title: Fig. 1

description: Script: plot_rf.txt
DOI: 10.6084/m9.figshare.14710974.v2
title: Fig. 1

description: Script: plot_rf.txt
DOI: 10.6084/m9.figshare.14710974.v1
title: Fig. 1

description: Script: plot_rf.txt
DOI: 10.6084/m9.figshare.14711022
title: Fig. 3

description: Script: dplot_h.py<br>
DOI: 10.6084/m9.figshare.14711022.v2
title: Fig. 3

description: Script: dplot_h.py<br>
DOI: 10.6084/m9.figshare.14711022.v1
title: Fig. 3

description: Script: dplot_h.py<br>
DOI: 10.25345/c56j8x
title: MassIVE MSV000087574 - An Integrated Omics Guided Approach to Lignification and Gravitational Responses in Plants
title: This data comes from research studying adaptations of key scientific model organisms at normal gravity and in microgravity. The research used Arabidopsis thaliana, aiming to identify systems-wide adaptations when plant lines are grown in 1g and microgravity environments. This work builds upon our recent progress in a) modulating lignin contents and vascular apparatus performance, via manipulating the arogenate dehydratase (ADT) multi-gene family, and b) enhancing carbon concentrating mechanisms (CCMs) to improve photosynthetic carbon assimilation and water use efficiency (WUE), alone and/or in combination with altered lignin levels. The knowledge gained will be essential for optimizing plants for spaceflight exploration and utilization (such as on Moon or Mars bases), as well as gaining new insights and knowledge into the various network interdependencies including, for example, photo-/gravi-tropisms, lignification, and WUE. Samples were digested with trypsin, labeled with 8-plex iTRAQ, then analyzed by LC-MS/MS. Data was searched with MS-GF+ using PNNL's DMS processing pipeline.
DOI: 10.6084/m9.figshare.14711151
title: Fig. 4

description: Script: dplot_h.py<br>If you comment the first two ax.plot: panel a<br>If you comment the last ax.plot: panel b<br>
DOI: 10.6084/m9.figshare.14711151.v2
title: Fig. 4

description: Script: dplot_h.py<br>If you comment the first two ax.plot: panel a<br>If you comment the last ax.plot: panel b<br>
DOI: 10.6084/m9.figshare.14711151.v1
title: Fig. 4

description: Script: dplot_h.py<br>If you comment the first two ax.plot: panel a<br>If you comment the last ax.plot: panel b<br>
DOI: 10.6084/m9.figshare.14711181.v1
title: Fig. 5

description: Script: dplot_h.py<br>If you comment the first and third ax.plot: panel a<br>If you comment the second ax.plot: panel b
DOI: 10.6084/m9.figshare.14711244.v1
title: Fig. 6

description: Script panel a: dplot_h_16.pyScript panel b: dplot_h_19.py
DOI: 10.6084/m9.figshare.14711301
title: Fig. 7

description: Script: dconv.py<br>
DOI: 10.6084/m9.figshare.14711301.v2
title: Fig. 7

description: Script: dconv.py<br>
DOI: 10.6084/m9.figshare.14711301.v1
title: Fig. 7

description: Script: dconv.py<br>
DOI: 10.6084/m9.figshare.14711349
title: Fig.8

description: Script panel a: dconv_k_m1.pyScript panel b: dconv_k_p2.py
DOI: 10.6084/m9.figshare.14711349.v2
title: Fig.8

description: Script panel a: dconv_k_m1.pyScript panel b: dconv_k_p2.py
DOI: 10.6084/m9.figshare.14711349.v1
title: Fig.8

description: Script panel a: dconv_k_m1.pyScript panel b: dconv_k_p2.py
DOI: 10.6084/m9.figshare.14711706.v1
title: Fig. 9

description: Script panel a: dconv_k_m2.pyScript panel b: dconv_k_p1.py
DOI: 10.6084/m9.figshare.14712219
title: Fig. 10

description: Script: plot_visc.txt<br>
DOI: 10.6084/m9.figshare.14712219.v2
title: Fig. 10

description: Script: plot_visc.txt<br>
DOI: 10.6084/m9.figshare.14712219.v1
title: Fig. 10

description: Script: plot_visc.txt<br>
DOI: 10.6073/pasta/fa74b6fd049af01379f50d9693552d55
title: Percentage of Carbon and Nitrogen of Soil Sediments from the Shark River Slough, Taylor Slough and Florida Bay within Everglades National Park (FCE LTER) from August 2008 to Present

description: These data represent the results of CHN Analysis from soil sampling from all 17 FCE LTER transect locations during from Year 2008 thru Year 2017. In 2017, the samples were collected before Hurricane Irma (2017a) and post Hurricane Irma (2017b). Surface soils from 0-10 cm been homogenized and analyzed from Sawgrass and mangrove sites and Florida Bay sites. Soils and sediments were analyzed for percentage by weight of total Carbon and Nitrogen with a PerkinElmer CHNS /O 2400 Series II Elemental Analyzer. These analyses are completed to document the differences in soil composition among transect sites, and to provide a baseline dataset against which long-term changes in the properties of the soils can be detected.
DOI: 10.6084/m9.figshare.14711706
title: Fig. 9

description: Script panel a: dconv_k_m2.pyScript panel b: dconv_k_p1.py
DOI: 10.6084/m9.figshare.14711706.v3
title: Fig. 9

description: Script panel a: dconv_k_m2.pyScript panel b: dconv_k_p1.py
DOI: 10.6084/m9.figshare.14711706.v2
title: Fig. 9

description: Script panel a: dconv_k_m2.pyScript panel b: dconv_k_p1.py
DOI: 10.6084/m9.figshare.14712474.v1
title: Fig. 11

description: Script: dplot_h.py<br>
DOI: 10.6084/m9.figshare.14712474.v3
title: Fig. 11

description: Script: dplot_h.py<br>Panel (a):Comment second ax.plotComment first plt.ylabelComment first plt.title<br>Panel (b):Comment first ax.plotComment second plt.ylabelComment second plt.title
DOI: 10.6084/m9.figshare.14712474
title: Fig. 11

description: Script: dplot_h.py<br>Panel (a):Comment second ax.plotComment first plt.ylabelComment first plt.title<br>Panel (b):Comment first ax.plotComment second plt.ylabelComment second plt.title
DOI: 10.6084/m9.figshare.14712474.v2
title: Fig. 11

description: Script: dplot_h.py<br>
DOI: 10.6084/m9.figshare.14713221
title: Fig. 12

description: Script: dconv_more.py
DOI: 10.6084/m9.figshare.14713221.v2
title: Fig. 12

description: Script: dconv_more.py
DOI: 10.6084/m9.figshare.14713221.v1
title: Fig. 12

description: Script: dconv_more.py
DOI: 10.6084/m9.figshare.14713842
title: Fig. 14

description: Script: dplot_h.txtChanging the value of the paramater v, you plot the five panels.v=-2, panel av=-1, panel bv=0, panel cv=1, panel dv=2, panel e<br>
DOI: 10.6084/m9.figshare.14713842.v2
title: Fig. 14

description: Script: dplot_h.txtChanging the value of the paramater v, you plot the five panels.v=-2, panel av=-1, panel bv=0, panel cv=1, panel dv=2, panel e<br>
DOI: 10.6084/m9.figshare.14713842.v1
title: Fig. 14

description: Script: dplot_h.txtChanging the value of the paramater v, you plot the five panels.v=-2, panel av=-1, panel bv=0, panel cv=1, panel dv=2, panel e<br>
DOI: 10.6084/m9.figshare.14713851
title: Fig. 15

description: Script: dplot_h.txtChanging the value of the paramater v, you plot the five panels.v=-2, panel av=-1, panel bv=0, panel cv=1, panel dv=2, panel e
DOI: 10.6084/m9.figshare.14713851.v2
title: Fig. 15

description: Script: dplot_h.txtChanging the value of the paramater v, you plot the five panels.v=-2, panel av=-1, panel bv=0, panel cv=1, panel dv=2, panel e
DOI: 10.6084/m9.figshare.14713851.v1
title: Fig. 15

description: Script: dplot_h.txtChanging the value of the paramater v, you plot the five panels.v=-2, panel av=-1, panel bv=0, panel cv=1, panel dv=2, panel e
DOI: 10.6084/m9.figshare.14711181
title: Fig. 5

description: Script: dplot_h.py<br>If you comment the first and third ax.plot: panel a<br>If you comment the second ax.plot: panel b
DOI: 10.6084/m9.figshare.14711181.v3
title: Fig. 5

description: Script: dplot_h.py<br>If you comment the first and third ax.plot: panel a<br>If you comment the second ax.plot: panel b
DOI: 10.6084/m9.figshare.14711181.v2
title: Fig. 5

description: Script: dplot_h.py<br>If you comment the first and third ax.plot: panel a<br>If you comment the second ax.plot: panel b
DOI: 10.6084/m9.figshare.14711244
title: Fig. 6

description: Script panel a: dplot_h_16.pyScript panel b: dplot_h_19.py
DOI: 10.6084/m9.figshare.14711244.v3
title: Fig. 6

description: Script panel a: dplot_h_16.pyScript panel b: dplot_h_19.py
DOI: 10.6084/m9.figshare.14711244.v2
title: Fig. 6

description: Script panel a: dplot_h_16.pyScript panel b: dplot_h_19.py
DOI: 10.18739/a2959c85n
title: Surface Mass Balance and Snow Depth on Sea Ice Working Group (SUMup) snow density subdataset, Greenland and Antartica, 1950-2018

description: This dataset includes snow density measurements from both hemispheres. This dataset is a community effort to distribute easy to use in-situ data to improve surface mass balance modeling and remote sensing efforts. This dataset is a compilation of work from many individual researchers. When using this dataset please cite both the individual researchers who provided the data as listed in the Citation field as well as this dataset. This dataset includes snow density measurements over both ice sheets, ice caps and snow on sea ice. It excludes seasonal snow on land measurements. The dataset contains over 2,200,000 point measurements of density at different depths. The measurement methods include different sized density cutters (generally from 100 - 1000 cc) used in snow pits, ice core sections, neutron-density methods, X-ray microfocus computer tomography, Gamma-ray attenuation, pycnometers, optical televiewer (OPTV) borehole logging, and Density-and-conductivity mixed permittivity (DECOMP).The majority of the observations (~72%) come from Greenland ice cores or snow pits. Antarctic data comprises ~28% of the snow density subdataset and is predominately from ice cores. The depth of the density measurements are recorded using two different methods, either the top and bottom depth or a midpoint depth. Fields of the data include the date taken, latitude, longitude, start depth, stop depth, midpoint depth, density, error, elevation, method, and associated citation.
DOI: 10.5281/zenodo.4902143
title: Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from InSAR (Interferometric Synthetic Aperture Radar) of Sentinel-1 A/B, ALOS2, and CSK satellites

description: These supplementary datasets represent the adopted InSAR data associated with the coseismic displacement fields of the 2019 Ridgecrest earthquake sequence.
DOI: 10.5281/zenodo.4902142
title: Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from InSAR (Interferometric Synthetic Aperture Radar) of Sentinel-1 A/B, ALOS2, and CSK satellites

description: These supplementary datasets represent the adopted InSAR data associated with the coseismic displacement fields of the 2019 Ridgecrest earthquake sequence.
DOI: 10.14272/ybjfsdxucgyjsk-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (2-bromo-3-methylquinoxaline)
title: InChI=1S/C9H7BrN2/c1-6-9(10)12-8-5-3-2-4-7(8)11-6/h2-5H,1H3

description: dataset for mass spectrometry (MS) MS (EI, 70 eV, 20 °C), m/z (%): 224/222 (32/31) [M]+, 144 (12), 143 (100), 105 (35), 102 (31), 75 (15).HRMS–EI (m/z): [M]+ calcd for C9H7N279Br 221.9787; found 221.9786.
DOI: 10.14272/ybjfsdxucgyjsk-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (2-bromo-3-methylquinoxaline)
title: InChI=1S/C9H7BrN2/c1-6-9(10)12-8-5-3-2-4-7(8)11-6/h2-5H,1H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3064, 3030, 2996, 2948, 2922, 2850, 1560, 1551, 1503, 1480, 1463, 1429, 1425, 1385, 1371, 1344, 1312, 1296, 1286, 1275, 1237, 1204, 1133, 1045, 1021, 1013, 1000, 955, 888, 867, 832, 793, 761, 697, 679, 636, 612, 595, 575, 545, 540, 528, 507, 499, 487, 479, 463, 424, 395, 384 cm–1.
DOI: 10.14272/ybjfsdxucgyjsk-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (2-bromo-3-methylquinoxaline)
title: InChI=1S/C9H7BrN2/c1-6-9(10)12-8-5-3-2-4-7(8)11-6/h2-5H,1H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (101 MHz, CDCl3, ppm) δ = 154.3, 141.8, 141.4, 141.1, 130.4, 130.1, 128.6, 128.3, 25.1.
DOI: 10.14272/ybjfsdxucgyjsk-uhfffaoysa-n.1
title: 2-bromo-3-methylquinoxaline
title: InChI=1S/C9H7BrN2/c1-6-9(10)12-8-5-3-2-4-7(8)11-6/h2-5H,1H3

description: This dataset contains experimental data from a sample (2-bromo-3-methylquinoxaline, InChI=1S/C9H7BrN2/c1-6-9(10)12-8-5-3-2-4-7(8)11-6/h2-5H,1H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.14272/ybjfsdxucgyjsk-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (2-bromo-3-methylquinoxaline)
title: InChI=1S/C9H7BrN2/c1-6-9(10)12-8-5-3-2-4-7(8)11-6/h2-5H,1H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3, ppm) δ = 8.05–7.94 (m, 2H), 7.79–7.66 (m, 2H), 2.86 (s, 3H).
DOI: 10.5517/ccdc.csd.cc21697t
title: CCDC 1882885: Experimental Crystal Structure Determination

description: Related Article: Bryan C. Paulus, Karl C. Nielsen, Christopher R. Tichnell, Monica C. Carey, James K. McCusker|2021|J.Am.Chem.Soc.|143|8086|doi:10.1021/jacs.1c02451
DOI: 10.5517/ccdc.csd.cc25nlm2
title: CCDC 2015794: Experimental Crystal Structure Determination

description: Related Article: Yinsong Wu, Xinwei He, Mengqing Xie, Ruxue Li, Yi Ning, Jiahui Duan, Enshen Zhang, Yongjia Shang|2021|J.Org.Chem.|86|7370|doi:10.1021/acs.joc.1c00259
DOI: 10.5517/ccdc.csd.cc267x9p
title: CCDC 2033392: Experimental Crystal Structure Determination

description: Related Article: Toshinobu Nakajo, Jun Kumagai, Shinpei Kusaka, Akihiro Hori, Yuh Hijikata, Jenny Pirillo, Yunsheng Ma, Ryotaro Matsuda|2021|J.Am.Chem.Soc.|143|8129|doi:10.1021/jacs.1c02430