cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/qe4p-x157
title: FruitsGB: Top Indian Fruits with quality

description: INDIA is the second-largest fruit and vegetable exporter in the world after China. It ranked first in the production of Bananas, Papayas, and Mangoes. Public datasets of fruits are available but they are limited to general fruit classes and failed to classify the fruits according to the fruit quality. To overcome this problem, we have created a dataset named FruitsGB (Fruits Good/Bad) dataset.The main objectives to create this dataset were: 1) Target the top six Indian fruits which are exported or highly consumed. 2) Create a dataset for fruit classification with the quality of fruit. 3) Dataset consists of 12000 high-quality images of 12 different classes of fruits.We use mobile phones rear camera to take the images of fruits. The dataset consists of total 12000 images with 12 classes namely, Bad Apple, Good Apple, Bad Banana, Good Banana, Bad Guava, Good Guava, Bad Lime, Good Lime, Bad Orange, Good Orange, Bad Pomegranate, and Good Pomegranate. In the dataset, each class consists of 1000 images of size 256x256. The images had taken with different angles, with different backgrounds, and in different lighting conditions. As we considered the most consumed fruits this dataset is very useful for researchers.
DOI: 10.21227/x1z8-6q84
title: Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT

description: This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training. The model improves the existing techniques used for low-dose HRCT image inspection through an application of Stacked Autoencoders (SAEs) structures using the segmentation function for the area object detection model on Mask-RCNN. As a result, the proposed approach can quickly analyze X-ray images in detecting abnormalities in patients with lab-confirmed coronavirus even before clinical symptoms appear. In addition to detecting early abnormalities, area object detection model reveals a finding not seen in the latest cases of COVID-19. Most noteworthy, the study has shown that all COVID-19 patients exhibit an associated bilateral pleural effusion. The features are augmented to the model for the improvement of detection quality improvement and the shorten of the examination period.
DOI: 10.34619/pnrg-2a80
title: An integrated computational and experimental study for overproducing fatty acids in Escherichia coli [Datasets]

description: Data from the manuscript: Ranganathan S, Tee TW, Chowdhury A, et al. An integrated computational and experimental study for overproducing fatty acids in Escherichia coli. Metab Eng. 2012;14(6):687-704. doi:10.1016/j.ymben.2012.08.008
DOI: 10.25828/8y6d-1153
title: Cape Area Panel Study 2002-2009, Waves 1-5 Secure Data
DOI: 10.5281/zenodo.3964497
title: Data for "Automated Single Particle Reconstruction of Heterogeneous Inorganic Nanoparticles"

description: Raw ADF-STEM images for "Automated Single Particle Reconstruction of Heterogeneous Inorganic Nanoparticles".
DOI: 10.5281/zenodo.3964496
title: Data for "Automated Single Particle Reconstruction of Heterogeneous Inorganic Nanoparticles"

description: Raw ADF-STEM images for "Automated Single Particle Reconstruction of Heterogeneous Inorganic Nanoparticles".
DOI: 10.25828/9fw3-np87
title: Cape Town RSC Levy Firm Panel Data 2000-2006
DOI: 10.5517/ccdc.csd.cc1k2n8k
title: CCDC 1432518: Experimental Crystal Structure Determination

description: Related Article: Mark A. Wright, Tyler Wooldridge, Maria A. O’Connell, Joseph A. Wright|2020|Chem.Commun.|56|4300|doi:10.1039/D0CC01449D
DOI: 10.5517/ccdc.csd.cc1pxhhl
title: CCDC 1576520: Experimental Crystal Structure Determination

description: Related Article: Daniel O'Reilly, Robin S. Stein, Mihai Burai Patrascu, Sunit Kumar Jana, Jerry Kurian, Nicolas Moitessier, Masad J. Damha|2018|Chem.-Eur.J.|24|16432|doi:10.1002/chem.201803940
DOI: 10.5517/ccdc.csd.cc1pxpz8
title: CCDC 1576721: Experimental Crystal Structure Determination

description: Related Article: Daniel O'Reilly, Robin S. Stein, Mihai Burai Patrascu, Sunit Kumar Jana, Jerry Kurian, Nicolas Moitessier, Masad J. Damha|2018|Chem.-Eur.J.|24|16432|doi:10.1002/chem.201803940
DOI: 10.5517/ccdc.csd.cc1pxq0b
title: CCDC 1576722: Experimental Crystal Structure Determination

description: Related Article: Daniel O'Reilly, Robin S. Stein, Mihai Burai Patrascu, Sunit Kumar Jana, Jerry Kurian, Nicolas Moitessier, Masad J. Damha|2018|Chem.-Eur.J.|24|16432|doi:10.1002/chem.201803940
DOI: 10.5517/ccdc.csd.cc20j4l9
title: CCDC 1862560: Experimental Crystal Structure Determination

description: Related Article: Yashar Soltani, Ayan Dasgupta, Theodore A. Gazis, Darren M. C. Ould, Emma Richards, Ben Slater, Katarina Stefkova, Vladimar Y. Vladimirov, Lewis C. Wilkins, Darren Willcox and Rebecca L. Melen|2020|Cell Reports Physical Science|1|100016|doi:10.1016/j.xcrp.2020.100016
DOI: 10.5517/ccdc.csd.cc20j4mb
title: CCDC 1862561: Experimental Crystal Structure Determination

description: Related Article: Yashar Soltani, Ayan Dasgupta, Theodore A. Gazis, Darren M. C. Ould, Emma Richards, Ben Slater, Katarina Stefkova, Vladimar Y. Vladimirov, Lewis C. Wilkins, Darren Willcox and Rebecca L. Melen|2020|Cell Reports Physical Science|1|100016|doi:10.1016/j.xcrp.2020.100016
DOI: 10.5517/ccdc.csd.cc21nt4p
title: CCDC 1896832: Experimental Crystal Structure Determination

description: Related Article: Stefano Racioppi, Michal Andrzejewski, Valentina Colombo, Angelo Sironi, Piero Macchi|2020|Inorg.Chem.|59|2223|doi:10.1021/acs.inorgchem.9b02852
DOI: 10.5517/ccdc.csd.cc21nt5q
title: CCDC 1896833: Experimental Crystal Structure Determination

description: Related Article: Stefano Racioppi, Michal Andrzejewski, Valentina Colombo, Angelo Sironi, Piero Macchi|2020|Inorg.Chem.|59|2223|doi:10.1021/acs.inorgchem.9b02852
DOI: 10.5517/ccdc.csd.cc21nt6r
title: CCDC 1896834: Experimental Crystal Structure Determination

description: Related Article: Stefano Racioppi, Michal Andrzejewski, Valentina Colombo, Angelo Sironi, Piero Macchi|2020|Inorg.Chem.|59|2223|doi:10.1021/acs.inorgchem.9b02852
DOI: 10.5517/ccdc.csd.cc21nt7s
title: CCDC 1896835: Experimental Crystal Structure Determination

description: Related Article: Stefano Racioppi, Michal Andrzejewski, Valentina Colombo, Angelo Sironi, Piero Macchi|2020|Inorg.Chem.|59|2223|doi:10.1021/acs.inorgchem.9b02852
DOI: 10.5517/ccdc.csd.cc21nt8t
title: CCDC 1896836: Experimental Crystal Structure Determination

description: Related Article: Stefano Racioppi, Michal Andrzejewski, Valentina Colombo, Angelo Sironi, Piero Macchi|2020|Inorg.Chem.|59|2223|doi:10.1021/acs.inorgchem.9b02852
DOI: 10.5517/ccdc.csd.cc21nt9v
title: CCDC 1896837: Experimental Crystal Structure Determination

description: Related Article: Stefano Racioppi, Michal Andrzejewski, Valentina Colombo, Angelo Sironi, Piero Macchi|2020|Inorg.Chem.|59|2223|doi:10.1021/acs.inorgchem.9b02852
DOI: 10.5517/ccdc.csd.cc21ntbw
title: CCDC 1896838: Experimental Crystal Structure Determination

description: Related Article: Stefano Racioppi, Michal Andrzejewski, Valentina Colombo, Angelo Sironi, Piero Macchi|2020|Inorg.Chem.|59|2223|doi:10.1021/acs.inorgchem.9b02852
DOI: 10.5517/ccdc.csd.cc21ntcx
title: CCDC 1896839: Experimental Crystal Structure Determination

description: Related Article: Stefano Racioppi, Michal Andrzejewski, Valentina Colombo, Angelo Sironi, Piero Macchi|2020|Inorg.Chem.|59|2223|doi:10.1021/acs.inorgchem.9b02852
DOI: 10.5517/ccdc.csd.cc2293h0
title: CCDC 1915381: Experimental Crystal Structure Determination

description: Related Article: Yashar Soltani, Ayan Dasgupta, Theodore A. Gazis, Darren M. C. Ould, Emma Richards, Ben Slater, Katarina Stefkova, Vladimar Y. Vladimirov, Lewis C. Wilkins, Darren Willcox and Rebecca L. Melen|2020|Cell Reports Physical Science|1|100016|doi:10.1016/j.xcrp.2020.100016
DOI: 10.5517/ccdc.csd.cc2293j1
title: CCDC 1915382: Experimental Crystal Structure Determination

description: Related Article: Yashar Soltani, Ayan Dasgupta, Theodore A. Gazis, Darren M. C. Ould, Emma Richards, Ben Slater, Katarina Stefkova, Vladimar Y. Vladimirov, Lewis C. Wilkins, Darren Willcox and Rebecca L. Melen|2020|Cell Reports Physical Science|1|100016|doi:10.1016/j.xcrp.2020.100016
DOI: 10.25828/mdtp-9k07
title: Graduate Destination Survey 2012
DOI: 10.6073/pasta/aeea28f969ed79086b845e1f6ab89d18
title: Interagency Ecological Program San Francisco Estuary Spring Kodiak Trawl Survey 2002 - 2020

description: The Interagency Ecological Program San Francisco Estuary Spring Kodiak Trawl Survey was initiated by the California Department of Fish and Wildlife (CDFW) in 2002 to improve the detection rate of pre-spawning and mature Delta Smelt (hypomesus transpacificus). The survey began as the Spring Midwater Trawl Survey, an extension of the Fall Midwater Trawl Survey, in 1991 to assist with water management and export decisions in relation to the distribution of pre-spawning Delta Smelt. Net evaluations studies compared the relative catch efficiency of 3 types of nets and found that the Kodiak net was most effective for Sampling adult Delta Smelt. Objectives of SKT are to improve the ability to detect Delta Smelt, obtain maturity status for adult Delta Smelt, and provide results to scientist and managers on a near real-time basis to ais in resource management. SKT operates 6 monthly surveys December through May each lasting 4-5 days in length. Each survey, 40 fix sample locations are sampled using a standard 10 minute Kodiak trawl with a total length of 65 feet and a fully expanded mouth opening of 25 feet by 6 feet. The volume of water sampled is estimated using a General Oceanics flowmeter placed over the side of the boat. The net is constructed of green or black variable mesh ranging in dimension from 2 inches knoteted stretched mesh at the mouth and decreasing by ½ inch through a series of 5 panels to ¼ inch knotless stretched mesh at the cod-end. All fish sampled are identified to species and enumeration, Delta Smelt are staged in the field. At each station, environmental and water quality data are also collected, and include: temperature, electrical conductivity (normalized @ 25°C), water clarity, and tidal condition.
DOI: 10.25810/es52-xr23.27
title: Canadian Newspaper Coverage of Climate Change or Global Warming, 2000-2020 - June 2020

description:         The Media and Climate Change Observatory Data monitors 117 sources (across newspapers, radio and TV) in 53 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.25828/966a-gt44
title: University of Cape Town Student Admissions Data 2006-2014
DOI: 10.15139/s3/fuc5ew
title: Data for Placental microRNA Expression Associates with Birthweight through Control of Adipokines: Results from Two Independent Cohorts

description: MicroRNAs are non-coding RNAs that regulate gene expression post-transcriptionally. In the placenta, the master regulator of fetal growth and development, microRNAs shape the basic processes of trophoblast biology and specific microRNA have been associated with fetal growth. To comprehensively assess the role of microRNAs in placental function and fetal development, we have performed small RNA sequencing to profile placental microRNAs from two independent mother-infant cohorts: the Rhode Island Child Health Study (n=225) and the New Hampshire Birth Cohort Study (n=317). We modeled microRNA counts on infant birthweight percentile (BWP) in each cohort, while accounting for race, sex, parity and technical factors, using negative binomial generalized linear models.
DOI: 10.5281/zenodo.3964511
title: Network origin-destination data

description: The first sheet has the tonnage data for each of the 440 routes. Each of these routes is connecting some of the 72 segments, which are entered in Column B and C. Name of the routes/segments in the original format are provided in the second sheet. Column C has the names of the segments.
DOI: 10.5281/zenodo.3964510
title: Network origin-destination data

description: The first sheet has the tonnage data for each of the 440 routes. Each of these routes is connecting some of the 72 segments, which are entered in Column B and C. Name of the routes/segments in the original format are provided in the second sheet. Column C has the names of the segments.
DOI: 10.25810/fx6e-r462.15
title: European Newspaper Coverage of Climate Change or Global Warming, 2004-2020 - June 2020

description:         The Media and Climate Change Observatory Data monitors 117 sources (across newspapers, radio and TV) in 53 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.25828/cy36-bs17
title: University of Cape Town Student Admissions Data 2015-2019
DOI: 10.25810/rxtv-eb29.27
title: German Newspaper Coverage of Climate Change or Global Warming, 2004-2020 - June 2020

description:         The Media and Climate Change Observatory Data monitors 117 sources (across newspapers, radio and TV) in 53 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.25345/c5gj0c
title: MassIVE MSV000085841 - IDH1 Mutations Induce Organelle Defects 1 Via Dysregulated Phospholipids
title: Infiltrating gliomas are devastating and incurable tumors. Amongst all gliomas, those harboring a mutation in isocitrate dehydrogenase 1 mutation (IDH1mut) acquire a different tumor biology and clinical manifestation than those that are IDH1WT. Understanding the unique metabolic profile reprogrammed by IDH1 mutation has the potential to identify new molecular targets for glioma therapy. Herein, we discovered increased monounsaturated fatty acids (MUFA) and their phospholipids in ER, generated by IDH1 mutation, that were responsible for Golgi and ER dilation. We demonstrated a direct link between the IDH1 mutation and these organelle morphology via D-2HG-induced stearyl-CoA desaturase (SCD) overexpression, the rate-limiting enzyme in MUFA biosynthesis. Inhibition of IDH1 mutation or SCD silencing restored ER and Golgi morphology, while D-2HG and oleic acid induced morphological defects in these organelles. Moreover, addition of oleic acid, which tilted the balance towards elevated levels of MUFA, produced IDH1mut-specific cellular apoptosis. Collectively, these results suggest that IDH1mut-induced SCD overexpression can rearrange the distribution of lipids in the organelles of glioma cells, providing a new insight on the link between lipids metabolism and organelle morphology in these cells, with potential and unique therapeutic implications.
DOI: 10.6073/pasta/4a62fadddb15f561a51196f2d9dff68a
title: Interagency Ecological Program and US Fish and Wildlife Service: San Francisco Estuary Enhanced Delta Smelt Monitoring Program data, 2016-2020

description: The Enhanced Delta Smelt Monitoring Program (EDSM) was initiated by the U.S. Fish and Wildlife Service in 2016. EDSM focuses on providing real-time data to help managers respond to population patterns of Delta Smelt within the upper San Francisco Estuary, primarily by providing estimates of Delta Smelt distribution and abundance. The dataset can also be useful in evaluating habitat use and behavior patterns of this species and other fish species of interest. Sampling is done year round via Kodiak trawls and larval (“20-mm”) gear to sample Delta Smelt across most life stages. Sites are chosen via stratified random sampling. Over the course of a week, field crews sample between 18 and 41 random sites. A minimum of two tows are conducted at each site. All fish collected are identified (in the field when possible, in the lab for early life stages), measured, enumerated, and recorded. In addition to fish information, environmental data are collected for each sampling event. For more information: https://www.fws.gov/lodi/juvenile_fish_monitoring_program/jfmp_index.htm
DOI: 10.21227/9dzz-s281
title: Infrared Human Activity Recognition Dataset - Coventry-2018

description: Coventry-2018 is a human activity recognition dataset captured by three Panasonic® Grid-EYE (AMG8833) infrared sensors in March 2018. The Grid-EYE sensors represent a 60 field of view scene by an 8 × 8 array named frame. The data streams are synchronized to 10 frames per second and saved as *.csv recordings using the LabVIEW® software. Two layouts are considered in this dataset with different geometry sizes: 1) small layout; and 2) large layout. The three sensors are located 1.5m from the area of interest in the small geometry size, while the distance grows to 2.5m for the large geometry size as illustrated in the dataset image. The dataset consists of 15 gestures distinguished in two categories: 1) single-subject gestures; and 2) double-subject gestures. There exist eight single-subject activities, and seven double-subject activities. Each single-subject gesture is repeated 10 times by three participants, while for double-subject gestures, the three possible combinations of the three subjects are used and similarly, the gestures are repeated 10 times. It needs to be emphasized that the single-subject activities are conducted in both layouts (small layout and large layout), while the double-subject activities are conducted only in the large layout.
DOI: 10.5281/zenodo.3964558
title: SK inventory [dataset] gold objects version 1.0 28/07/2020

description: SK inventory [dataset] gold objects version 1.0 28/07/2020
DOI: 10.5281/zenodo.3964561
title: SK inventory [dataset] iron objects version 1.0 28/07/2020

description: SK inventory [dataset] iron objects version 1.0 28/07/2020
DOI: 10.5281/zenodo.3964394
title: Lidar-derived snow depth maps along the Chilean Extratropical Andes, winter 2018

description: All the files included here contain the data produced for the manuscript "<strong>Spatial distribution and scaling properties of lidar-derived snow depth in the extratropical Andes</strong>", submitted for possible publication in Water Resources Research. Lidar measurements for snow-covered conditions were conducted on September 4th, August 9th, and October 25th 2018 in Tascadero, Las Bayas and Valle Hermoso, respectively. Each data acquisition was conducted using a Riegl VZ6000 long range scanner on dates with and without snow, using an angular resolution of 0.01°. Lidar-derived maps are contained in the following files: SiteName_SD_TPI.txt (SiteName: Tascadero, Las Bayas, VH East or VH West). Where the file structure is as follows: X,Y,Z,SD,SLP,NOR,TPI04,TPI07,TPI15,TPI20,TPI30,TPI40,TPI50 where Z is bare earth elevation (m a.s.l.), SD is snow depth, SLP is slope (°), NOR is northness (°) whereby 180 = north facing and 0 = south facing, TPIXX is the Topographic Position Index (TPI) computed for a search distance of XX m, whereby positive differences are convex landforms, and negative is concave. The magnitude of TPI indicates the scale of the relative concavity/convexity. Variogram results are contained in files .rsav with the following nomenclature: SiteName_X_TypeOfVariogram.rsav Where X can be SD (snow depth) or Z (bare earth topography), and the type of variogram can be omnidirectional or directional.
DOI: 10.26300/vhnq-vt27
title: The effect of IDH inhibitors in AML patients
DOI: 10.5281/zenodo.3964393
title: Lidar-derived snow depth maps along the Chilean Extratropical Andes, winter 2018

description: All the files included here contain the data produced for the manuscript "<strong>Spatial distribution and scaling properties of lidar-derived snow depth in the extratropical Andes</strong>", submitted for possible publication in Water Resources Research. Lidar measurements for snow-covered conditions were conducted on September 4th, August 9th, and October 25th 2018 in Tascadero, Las Bayas and Valle Hermoso, respectively. Each data acquisition was conducted using a Riegl VZ6000 long range scanner on dates with and without snow, using an angular resolution of 0.01°. Lidar-derived maps are contained in the following files: SiteName_SD_TPI.txt (SiteName: Tascadero, Las Bayas, VH East or VH West). Where the file structure is as follows: X,Y,Z,SD,SLP,NOR,TPI04,TPI07,TPI15,TPI20,TPI30,TPI40,TPI50 where Z is bare earth elevation (m a.s.l.), SD is snow depth, SLP is slope (°), NOR is northness (°) whereby 180 = north facing and 0 = south facing, TPIXX is the Topographic Position Index (TPI) computed for a search distance of XX m, whereby positive differences are convex landforms, and negative is concave. The magnitude of TPI indicates the scale of the relative concavity/convexity. Variogram results are contained in files .rsav with the following nomenclature: SiteName_X_TypeOfVariogram.rsav Where X can be SD (snow depth) or Z (bare earth topography), and the type of variogram can be omnidirectional or directional.
DOI: 10.6073/pasta/e73196a56cbee37148761f83c94aacf2
title: NOAA Daily Surface Meteorologic Data at NCDC Miami International Airport Station (ID-085663)(FCE LTER), South Florida from January 1948 to Present

description: The National Climatic Data Center's (NOAA) daily mean, maximum, and minimum air temperatures and daily precipitation collected at Miami International Airport Station (Coop ID- 085663).
DOI: 10.5281/zenodo.3961917
title: Unsupervised-Hackathon

description: The record contains LHC simulations to be used in the hackathon related with the darkmachines unsupervised challenge project. The documentation about the files content in the tar file is included as well (dataset.pdf).
DOI: 10.5281/zenodo.3961916
title: Unsupervised-Hackathon

description: The record contains LHC simulations to be used in the hackathon related with the darkmachines unsupervised challenge project. The documentation about the files content in the tar file is included as well (dataset.pdf).
DOI: 10.5517/ccdc.csd.cc1mxtdr
title: CCDC 1517245: Experimental Crystal Structure Determination

description: Related Article: Jin-xin Zhu, Yaojia Lu, Jun Chen, Jianwei Chen, Huawei Zhang, Xiaoze Bao, Xinyi Ye, Hong Wang|2020|RSC Advances|10|24251|doi:10.1039/D0RA05244B
DOI: 10.5517/ccdc.csd.cc1npvnv
title: CCDC 1540348: Experimental Crystal Structure Determination

description: Related Article: Hong-bo Tan, Yin-feng Wang|2020|J.Mol.Struct.|1220|128751|doi:10.1016/j.molstruc.2020.128751
DOI: 10.5517/ccdc.csd.cc20lcss
title: CCDC 1864705: Experimental Crystal Structure Determination

description: Related Article: Min Fan, Dan Luo, Li-Yan Peng, Xing-De Wu, Xu Ji, Qin-Shi Zhao|2020|Fitoterapia|146|104672|doi:10.1016/j.fitote.2020.104672
DOI: 10.5517/ccdc.csd.cc20lctt
title: CCDC 1864706: Experimental Crystal Structure Determination

description: Related Article: Min Fan, Dan Luo, Li-Yan Peng, Xing-De Wu, Xu Ji, Qin-Shi Zhao|2020|Fitoterapia|146|104672|doi:10.1016/j.fitote.2020.104672
DOI: 10.5517/ccdc.csd.cc20lcvv
title: CCDC 1864707: Experimental Crystal Structure Determination

description: Related Article: Min Fan, Dan Luo, Li-Yan Peng, Xing-De Wu, Xu Ji, Qin-Shi Zhao|2020|Fitoterapia|146|104672|doi:10.1016/j.fitote.2020.104672
DOI: 10.5517/ccdc.csd.cc23457p
title: CCDC 1940421: Experimental Crystal Structure Determination

description: Related Article: Tuğba Aycan, Filiz Öztürk, Tuğrul Doruk, Serkan Demir, Melek Fidan, Hümeyra Paşaoğlu|2020|Spectrochim.Acta,Part A|241|118639|doi:10.1016/j.saa.2020.118639
DOI: 10.5517/ccdc.csd.cc23jhbh
title: CCDC 1952266: Experimental Crystal Structure Determination

description: Related Article: Ankit Kumar, Kandala Laxman, Mangalampalli Ravikanth|2019|Org.Lett.|21|8726|doi:10.1021/acs.orglett.9b03329