cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19125815
title: January 2022 Covid-19 Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets containing at <b>least one vaccine-related word</b> (i.e., words that start with vaccin*, vacin*, or vax*) collected in <b>January, 2022</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.19125815.v1
title: January 2022 Covid-19 Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets containing at <b>least one vaccine-related word</b> (i.e., words that start with vaccin*, vacin*, or vax*) collected in <b>January, 2022</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.25345/c5jk4v
title: MassIVE MSV000088786 - Consequences of phosphorylation and acetylation of mitochondrial transcription factor A to DNA packaging, transcription initiation and processivity
title: This work involved in-vitro phosphorylation and acetylation of TFAM.
DOI: 10.25345/c5ds2x
title: MassIVE MSV000088787 - Consequences of phosphorylation and acetylation of mitochondrial transcription factor A to DNA packaging, transcription initiation and processivity
title: In-vitro phosphorylation of TFAM by hPKAc and nonenzymatic acetylation by acetyl-CoA
DOI: 10.3886/e161502
title: COVID-19 vaccination and people with disabilities

description: People with disabilities face greater risk of adverse health outcomes and death from COVID-19 infection. As vaccination efforts continue across the United States, it is critical that accurate and reliable information about the COVID-19 vaccines reach this population. The purpose of this study was to investigate how people with disabilities view these vaccines as well as what barriers they face in getting vaccinated. In February of 2021, we launched an online survey looking into the vaccination status, trust in information sources on COVID-19 and vaccines, and attitudes and barriers surrounding vaccination efforts. In particular, we sought to examine differences among urban and rural respondents. We recruited participants from MTurk and invited them to complete the survey we had created using Qualtrics. We included a set of screening questions to filter for age, disability status, and to screen out bots. The following questions asked about health status, health literacy, vaccination status and intent, views of and barriers surrounding access to vaccines, trust in information sources, and other pandemic related topics. We processed and analyzed the results using a statistical software and produced the results in a research brief and manuscript.
description: Presence of Common Scales: WGSS 4-point Likert-type scale
description: Workers of Amazon Mechanical Turk (MTurk) that were screened for disability status
description: US residents aged 18+ Amazon Mechanical Turk (MTurk) workers. Smallest Geographic Unit: United States
description: web-based survey;
DOI: 10.3886/e161502v1
title: COVID-19 vaccination and people with disabilities

description: People with disabilities face greater risk of adverse health outcomes and death from COVID-19 infection. As vaccination efforts continue across the United States, it is critical that accurate and reliable information about the COVID-19 vaccines reach this population. The purpose of this study was to investigate how people with disabilities view these vaccines as well as what barriers they face in getting vaccinated. In February of 2021, we launched an online survey looking into the vaccination status, trust in information sources on COVID-19 and vaccines, and attitudes and barriers surrounding vaccination efforts. In particular, we sought to examine differences among urban and rural respondents. We recruited participants from MTurk and invited them to complete the survey we had created using Qualtrics. We included a set of screening questions to filter for age, disability status, and to screen out bots. The following questions asked about health status, health literacy, vaccination status and intent, views of and barriers surrounding access to vaccines, trust in information sources, and other pandemic related topics. We processed and analyzed the results using a statistical software and produced the results in a research brief and manuscript.
description: Presence of Common Scales: WGSS 4-point Likert-type scale
description: Workers of Amazon Mechanical Turk (MTurk) that were screened for disability status
description: US residents aged 18+ Amazon Mechanical Turk (MTurk) workers. Smallest Geographic Unit: United States
description: web-based survey;
DOI: 10.5281/zenodo.5975840
title: OFM pheromone autodetection paper - raw data

description: Raw data for the pheromone autodetection paper. Alicia Perez-Aparicio PhD thesis. Electrophysiological recording from Grapholita molesta male and female antennae and olfactory receptor neurons
DOI: 10.5281/zenodo.5975839
title: OFM pheromone autodetection paper - raw data

description: Raw data for the pheromone autodetection paper. Alicia Perez-Aparicio PhD thesis. Electrophysiological recording from Grapholita molesta male and female antennae and olfactory receptor neurons
DOI: 10.5517/ccdc.csd.cc28ywpt
title: CCDC 2114097: Experimental Crystal Structure Determination

description: Related Article: Bei-Bei Zhang, Shuo Peng, Feiyi Wang, Cuifen Lu, Junqi Nie, Zuxing Chen, Guichun Yang, Chao Ma|2022|Chemical Science|13|775|doi:10.1039/D1SC05629H
DOI: 10.5517/ccdc.csd.cc28yx38
title: CCDC 2114110: Experimental Crystal Structure Determination

description: Related Article: Bei-Bei Zhang, Shuo Peng, Feiyi Wang, Cuifen Lu, Junqi Nie, Zuxing Chen, Guichun Yang, Chao Ma|2022|Chemical Science|13|775|doi:10.1039/D1SC05629H
DOI: 10.5517/ccdc.csd.cc28z95r
title: CCDC 2114484: Experimental Crystal Structure Determination

description: Related Article: Henrik Beer, Alexander Linke, Jonas Bresien, Grzegorz Mlostoń, Magorzata Celeda, Alexander Villinger, Axel Schulz|2022|Inorg.Chem.|61|2031|doi:10.1021/acs.inorgchem.1c03207
DOI: 10.5517/ccdc.csd.cc28z96s
title: CCDC 2114485: Experimental Crystal Structure Determination

description: Related Article: Henrik Beer, Alexander Linke, Jonas Bresien, Grzegorz Mlostoń, Magorzata Celeda, Alexander Villinger, Axel Schulz|2022|Inorg.Chem.|61|2031|doi:10.1021/acs.inorgchem.1c03207
DOI: 10.5517/ccdc.csd.cc28zdsg
title: CCDC 2114596: Experimental Crystal Structure Determination

description: Related Article: Li-Zhen Yang, Ru-Xia Yang, Peng-Yin Zhu, Xiao-Yan Yao, Tian-Cai Yue, Bin-Bin Xiong, Yu-Ming Yu, Duo-Zhi Wang, Lu-Lu Wang|2022|J.Mol.Struct.|1256|132477|doi:10.1016/j.molstruc.2022.132477
DOI: 10.6084/m9.figshare.19125842
title: Globally-centered autocovariances in MCMC

description: Autocovariances are a fundamental quantity of interest in Markov chain Monte Carlo (MCMC) simulations with autocorrelation function (ACF) plots being an integral visualization tool for performance assessment. Unfortunately, for slow-mixing Markov chains, the empirical autocovariance can highly underestimate the truth. For multiple-chain MCMC sampling, we propose a globally-centered estimator of the autocovariance function (G-ACvF) that exhibits significant theoretical and empirical improvements. We show that the bias of the G-ACvF estimator is smaller than the bias of the current state-of-the-art. The impact of this improved estimator is evident in three critical output analysis applications: (1) ACF plots, (2) estimates of the Monte Carlo asymptotic covariance matrix, and (3) estimates of the effective sample size. Under weak conditions, we establish strong consistency of our improved asymptotic covariance estimator, and obtain its large-sample bias and variance. The performance of the new estimators is demonstrated through various examples.
DOI: 10.6084/m9.figshare.19125842.v1
title: Globally-centered autocovariances in MCMC

description: Autocovariances are a fundamental quantity of interest in Markov chain Monte Carlo (MCMC) simulations with autocorrelation function (ACF) plots being an integral visualization tool for performance assessment. Unfortunately, for slow-mixing Markov chains, the empirical autocovariance can highly underestimate the truth. For multiple-chain MCMC sampling, we propose a globally-centered estimator of the autocovariance function (G-ACvF) that exhibits significant theoretical and empirical improvements. We show that the bias of the G-ACvF estimator is smaller than the bias of the current state-of-the-art. The impact of this improved estimator is evident in three critical output analysis applications: (1) ACF plots, (2) estimates of the Monte Carlo asymptotic covariance matrix, and (3) estimates of the effective sample size. Under weak conditions, we establish strong consistency of our improved asymptotic covariance estimator, and obtain its large-sample bias and variance. The performance of the new estimators is demonstrated through various examples.
DOI: 10.5517/ccdc.csd.cc28zdth
title: CCDC 2114597: Experimental Crystal Structure Determination

description: Related Article: Li-Zhen Yang, Ru-Xia Yang, Peng-Yin Zhu, Xiao-Yan Yao, Tian-Cai Yue, Bin-Bin Xiong, Yu-Ming Yu, Duo-Zhi Wang, Lu-Lu Wang|2022|J.Mol.Struct.|1256|132477|doi:10.1016/j.molstruc.2022.132477
DOI: 10.5517/ccdc.csd.cc28zdvj
title: CCDC 2114598: Experimental Crystal Structure Determination

description: Related Article: Li-Zhen Yang, Ru-Xia Yang, Peng-Yin Zhu, Xiao-Yan Yao, Tian-Cai Yue, Bin-Bin Xiong, Yu-Ming Yu, Duo-Zhi Wang, Lu-Lu Wang|2022|J.Mol.Struct.|1256|132477|doi:10.1016/j.molstruc.2022.132477
DOI: 10.5517/ccdc.csd.cc28zdwk
title: CCDC 2114599: Experimental Crystal Structure Determination

description: Related Article: Li-Zhen Yang, Ru-Xia Yang, Peng-Yin Zhu, Xiao-Yan Yao, Tian-Cai Yue, Bin-Bin Xiong, Yu-Ming Yu, Duo-Zhi Wang, Lu-Lu Wang|2022|J.Mol.Struct.|1256|132477|doi:10.1016/j.molstruc.2022.132477
DOI: 10.5517/ccdc.csd.cc28zdxl
title: CCDC 2114600: Experimental Crystal Structure Determination

description: Related Article: Li-Zhen Yang, Ru-Xia Yang, Peng-Yin Zhu, Xiao-Yan Yao, Tian-Cai Yue, Bin-Bin Xiong, Yu-Ming Yu, Duo-Zhi Wang, Lu-Lu Wang|2022|J.Mol.Struct.|1256|132477|doi:10.1016/j.molstruc.2022.132477
DOI: 10.5517/ccdc.csd.cc28zkpj
title: CCDC 2114748: Experimental Crystal Structure Determination

description: Related Article: Xin-Jie Du, Lin-Lin Zhang, Li-Ting Li, Lei-Yu Zhan, Tian-Ran Li, Jiong-Peng Zhao, Fu-Chen Liu|2022|Inorg.Chem.|61|2265|doi:10.1021/acs.inorgchem.1c03636
DOI: 10.5517/ccdc.csd.cc28zkqk
title: CCDC 2114749: Experimental Crystal Structure Determination

description: Related Article: Xin-Jie Du, Lin-Lin Zhang, Li-Ting Li, Lei-Yu Zhan, Tian-Ran Li, Jiong-Peng Zhao, Fu-Chen Liu|2022|Inorg.Chem.|61|2265|doi:10.1021/acs.inorgchem.1c03636
DOI: 10.5517/ccdc.csd.cc28zkrl
title: CCDC 2114750: Experimental Crystal Structure Determination

description: Related Article: Xin-Jie Du, Lin-Lin Zhang, Li-Ting Li, Lei-Yu Zhan, Tian-Ran Li, Jiong-Peng Zhao, Fu-Chen Liu|2022|Inorg.Chem.|61|2265|doi:10.1021/acs.inorgchem.1c03636
DOI: 10.5281/zenodo.5975896
title: OF autodetection paper - R.code

description: Alicia Pérez-Aparicio PhD thesis. Pheromone autodetection paper. R code used for analysis of the data
DOI: 10.5281/zenodo.5975895
title: OF autodetection paper - R.code

description: Alicia Pérez-Aparicio PhD thesis. Pheromone autodetection paper. R code used for analysis of the data
DOI: 10.6073/pasta/001936c321503992deb743abe14b54ba
title: Phytoplankton primary production measurements from discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2019, ongoing)

description: An important part of the McMurdo Long Term Ecological Research (LTER) is monitoring of spatial and temporal patterns, and processes that control phytoplankton production in perennial ice-covered lakes. This dataset addresses this core area of research and quantifies carbon production at specific depths in McMurdo Dry Valley lakes.
DOI: 10.5281/zenodo.5975814
title: Large-scale dataset for the analysis of outdoor-to-indoor propagation for 5G mid-band operational networks

description: We present a comprehensive dataset of channel measurements, performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. The dataset includes measurements of channel power delay profiles from two 5G networks operating in Band n78, i.e., 3.3--3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy, by using the Rohde & Schwarz (R&S) network scanner TSMA6 for several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics towards the definition and refinement of empirical channel propagation models.
DOI: 10.14272/kpfaboobzklzgj-uhfffaoysa-n/chmo0000470.1
title: mass spectrometry (MS) (3-quinoxalin-2-yl-1H-quinoxalin-2-one)
title: InChI=1S/C16H10N4O/c21-16-15(19-12-7-3-4-8-13(12)20-16)14-9-17-10-5-1-2-6-11(10)18-14/h1-9H,(H,20,21)

description: dataset for mass spectrometry (MS) MS (EI, 70 eV, 180 °C), m/z (%): 275 (18) [M+H]+, 274 (100) [M]+, 247 (19), 246 (98), 156 (25), 145 (10), 144 (80), 129 (12), 118 (14), 103 (25), 102 (17), 90 (23), 76 (16).HRMS–EI (m/z): [M]+ calcd for C16H10O1N4 274.0849; found 274.0849.
DOI: 10.14272/kpfaboobzklzgj-uhfffaoysa-n/chmo0000595.1
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (3-quinoxalin-2-yl-1H-quinoxalin-2-one)
title: InChI=1S/C16H10N4O/c21-16-15(19-12-7-3-4-8-13(12)20-16)14-9-17-10-5-1-2-6-11(10)18-14/h1-9H,(H,20,21)

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, DMSO-d6 [39.51 ppm], ppm) δ = 115.6 (CH), 123.8 (CH), 128.9 (CH), 129.4 (CH), 129.6 (CH), 130.7 (CH), 131.1 (CH), 131.7 (CH), 132.3 (Cq), 132.8 (Cq), 141.4 (Cq), 141.5 (Cq), 146.2 (CH), 149.6 (Cq), 153.4 (Cq), 154.6 (Cq).
DOI: 10.14272/kpfaboobzklzgj-uhfffaoysa-n/chmo0000630.1
title: infrared absorption spectroscopy (IR) (3-quinoxalin-2-yl-1H-quinoxalin-2-one)
title: InChI=1S/C16H10N4O/c21-16-15(19-12-7-3-4-8-13(12)20-16)14-9-17-10-5-1-2-6-11(10)18-14/h1-9H,(H,20,21)

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3166, 3105, 3054, 3007, 2955, 2890, 2837, 1662, 1605, 1557, 1547, 1496, 1480, 1466, 1428, 1417, 1366, 1343, 1302, 1285, 1271, 1255, 1232, 1217, 1177, 1150, 1139, 1123, 1060, 1021, 965, 943, 919, 856, 847, 809, 790, 768, 745, 725, 679, 647, 616, 601, 582, 560, 531, 517, 482, 460, 415, 399 cm–1.
DOI: 10.14272/kpfaboobzklzgj-uhfffaoysa-n.2
title: 3-(2-quinoxalinyl)-1H-quinoxalin-2-one
title: InChI=1S/C16H10N4O/c21-16-15(19-12-7-3-4-8-13(12)20-16)14-9-17-10-5-1-2-6-11(10)18-14/h1-9H,(H,20,21)

description: This dataset contains experimental data from a sample (3-quinoxalin-2-yl-1H-quinoxalin-2-one, InChI=1S/C16H10N4O/c21-16-15(19-12-7-3-4-8-13(12)20-16)14-9-17-10-5-1-2-6-11(10)18-14/h1-9H,(H,20,21)). 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) distortionless enhancement with polarization transfer (DEPT) distortionless enhancement with polarization transfer (DEPT)
DOI: 10.14272/kpfaboobzklzgj-uhfffaoysa-n/chmo0000593.1
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (3-quinoxalin-2-yl-1H-quinoxalin-2-one)
title: InChI=1S/C16H10N4O/c21-16-15(19-12-7-3-4-8-13(12)20-16)14-9-17-10-5-1-2-6-11(10)18-14/h1-9H,(H,20,21)

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, DMSO-d6 [2.50 ppm], ppm) δ = 7.35–7.46 (m, 2H, HAr), 7.61–7.70 (m, 1H, HAr), 7.90–8.00 (m, 3H, HAr), 8.14–8.29 (m, 2H, HAr), 9.50 (s, 1H, HAr), 12.85 (bs, 1H, OH).
DOI: 10.14272/kpfaboobzklzgj-uhfffaoysa-n/chmo0000596.3
title: distortionless enhancement with polarization transfer (DEPT) (3-quinoxalin-2-yl-1H-quinoxalin-2-one)
title: InChI=1S/C16H10N4O/c21-16-15(19-12-7-3-4-8-13(12)20-16)14-9-17-10-5-1-2-6-11(10)18-14/h1-9H,(H,20,21)

description: dataset for distortionless enhancement with polarization transfer (DEPT)
DOI: 10.14272/kpfaboobzklzgj-uhfffaoysa-n/chmo0000596.2
title: distortionless enhancement with polarization transfer (DEPT) (3-quinoxalin-2-yl-1H-quinoxalin-2-one)
title: InChI=1S/C16H10N4O/c21-16-15(19-12-7-3-4-8-13(12)20-16)14-9-17-10-5-1-2-6-11(10)18-14/h1-9H,(H,20,21)

description: dataset for distortionless enhancement with polarization transfer (DEPT)
DOI: 10.6084/m9.figshare.19125680.v1
title: tree_internac_disp_delta.tree

description: Maximum likelihood tree used for plotting figure S5.
DOI: 10.25345/c58w0z
title: MassIVE MSV000088788 - Consequences of phosphorylation and acetylation of mitochondrial transcription factor A to DNA packaging, transcription initiation and processivity
title: TFAM in-vitro phosphorylation by hPKAc and nonenzymatic acetylation by acetyl-CoA
DOI: 10.6084/m9.figshare.19125680
title: test

description: test
DOI: 10.6084/m9.figshare.19125680.v3
title: test

description: test
DOI: 10.6084/m9.figshare.19125680.v2
title: tree_internac_disp_delta.tree

description: Maximum likelihood tree used for plotting figure S5.
DOI: 10.6084/m9.figshare.19125638
title: test

description: test
DOI: 10.6084/m9.figshare.19125638.v2
title: test

description: test
DOI: 10.6084/m9.figshare.19125638.v1
title: BEAST_files

description: Input files for the phylogeographic analyses in BEAST
DOI: 10.21942/uva.19125452
title: Tara Zohrevand - Material for analyses

description: Data: data.aliens1<br>R-code: Thesis<br><br><br>Methods analyses:<br><br>SD: SDT1, SDT2<br>Means: meanT1, meanT2Rational updating: KSdata<br><br>Trait anxiety analyses:<br>exploratory analysis for rational updating: KSdataTA<br>correlation with all methods: T1corrdataforJASP, T2corrdataforJASP<br><br>correlation between each method and sd: remaining files<br><br><br><br>
DOI: 10.21942/uva.19125452.v2
title: Tara Zohrevand - Material for analyses

description: Data: data.aliens1<br>R-code: Thesis<br><br><br>Methods analyses:<br><br>SD: SDT1, SDT2<br>Means: meanT1, meanT2Rational updating: KSdata<br><br>Trait anxiety analyses:<br>exploratory analysis for rational updating: KSdataTA<br>correlation with all methods: T1corrdataforJASP, T2corrdataforJASP<br><br>correlation between each method and sd: remaining files<br><br><br><br>
DOI: 10.21942/uva.19125452.v1
title: Tara Zohrevand - Material for analyses

description: Data: data.aliens1<br>R-code: Thesis<br><br><br>Methods analyses:<br><br>SD: SDT1, SDT2<br>Means: meanT1, meanT2Rational updating: KSdata<br><br>Trait anxiety analyses:<br>exploratory analysis for rational updating: KSdataTA<br>correlation with all methods: T1corrdataforJASP, T2corrdataforJASP<br><br>correlation between each method and sd: remaining files<br><br><br><br>
DOI: 10.6084/m9.figshare.19125617
title: test

description: test
DOI: 10.6084/m9.figshare.19125617.v2
title: test

description: test
DOI: 10.6084/m9.figshare.19125617.v1
title: data_tables

description: Tables with epidemiological data obtained from https://www.saude.rj.gov.br/informacao-sus/dados-sus/2020/11/covid-19 and modified to be used as input for the inhouse scripts
DOI: 10.34740/kaggle/dsv/3140335
title: US counties COVID 19 dataset
DOI: 10.21223/uwxeqb/dmvnou
title: 01 Overview econ data improved ambient ware potato stores -Potato flow ind stores.tab

description: :unav
DOI: 10.21223/uwxeqb/q939xv
title: 04 Overview econ data improved ambient ware potato stores - Costs group stores.tab

description: :unav
DOI: 10.21223/uwxeqb/mak5ni
title: 02 Overview econ data improved ambient ware potato stores - Costs ind stores.tab

description: :unav
DOI: 10.21223/uwxeqb/tv7u2s
title: 05 Survey management performance group ambient stores.tab

description: :unav
DOI: 10.21223/uwxeqb/7vyern
title: 07 Questionnaire survey management group ambient stores farmers.pdf

description: :unav
DOI: 10.21223/uwxeqb/aqlp1m
title: 06 DataDictionary_Economic performance improved ambient stores.tab

description: :unav
DOI: 10.21223/uwxeqb/jxkehz
title: 08 Questionnaire survey management group ambient stores traders.pdf

description: :unav
DOI: 10.21223/uwxeqb/yhvi4y
title: 03 Overview econ data improved ambient ware potato stores - Potato flow group stores.tab

description: :unav
DOI: 10.5517/ccdc.csd.cc28ztrv
title: CCDC 2114998: Experimental Crystal Structure Determination

description: Related Article: Ying Wu, Zong-Mei Wu, Shuai-Shuai Zhang, Li-Yun Liu, Fan Sun, Wei-Hua Jiao, Shu-Ping Wang, Hou-Wen Lin|2022|Org.Lett.|24|934|doi:10.1021/acs.orglett.1c04309
DOI: 10.5517/ccdc.csd.cc28ztsw
title: CCDC 2114999: Experimental Crystal Structure Determination

description: Related Article: Ying Wu, Zong-Mei Wu, Shuai-Shuai Zhang, Li-Yun Liu, Fan Sun, Wei-Hua Jiao, Shu-Ping Wang, Hou-Wen Lin|2022|Org.Lett.|24|934|doi:10.1021/acs.orglett.1c04309
DOI: 10.5517/ccdc.csd.cc28zttx
title: CCDC 2115000: Experimental Crystal Structure Determination

description: Related Article: Ying Wu, Zong-Mei Wu, Shuai-Shuai Zhang, Li-Yun Liu, Fan Sun, Wei-Hua Jiao, Shu-Ping Wang, Hou-Wen Lin|2022|Org.Lett.|24|934|doi:10.1021/acs.orglett.1c04309
DOI: 10.5517/ccdc.csd.cc2908sd
title: CCDC 2115433: Experimental Crystal Structure Determination

description: Related Article: Miao Wang, Chau Ming So|2022|Org.Lett.|24|681|doi:10.1021/acs.orglett.1c04138
DOI: 10.5517/ccdc.csd.cc2908tf
title: CCDC 2115434: Experimental Crystal Structure Determination

description: Related Article: Miao Wang, Chau Ming So|2022|Org.Lett.|24|681|doi:10.1021/acs.orglett.1c04138
DOI: 10.5517/ccdc.csd.cc28tr51
title: CCDC 2110113: Experimental Crystal Structure Determination

description: Related Article: Vinu V. Panikkattu, Abhijeet S. Sinha, Christer B. Aakeröy|2022|CrystEngComm|24|738|doi:10.1039/D1CE01583D
DOI: 10.5517/ccdc.csd.cc28tqkd
title: CCDC 2110094: Experimental Crystal Structure Determination

description: Related Article: Vinu V. Panikkattu, Abhijeet S. Sinha, Christer B. Aakeröy|2022|CrystEngComm|24|738|doi:10.1039/D1CE01583D
DOI: 10.5519/qd.xo0dfldk
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3229 records)