cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14252966
title: WSSV-IN-NS.fasta

description: The genome sequences of two White Spot Syndrome Virus (WSSV) strains from India (WSSV-IN-LS and WSSV-IN-NS) that were previously studied by Sivakumar et al 2018 could not be found in public databases. The raw Illumina read sequences corresponding to these WSSV strains were downloaded from the NCBI Short Read Archive (SRR3233836 - WSSV-IN-LS and SRR3233837 - WSSV-IN-NS) and adapter sequences and low-quality bases (including poly-G and low complexity sequences) were removed using fastp v0.20.0 (Chen et al 2018). For each sample, sequences were normalised using BBNorm, part of BBTools v38.03 (Bushnell) (parameters used: ecc=t, bits=16, prefilter) prior to de novo assembly with SPAdes v3.13.1 (Bankevich et al 2012) using the only-assembler flag and k-mers of 21, 33, 55, 77, 99 and 127. Assembled contigs were blasted against a reference WSSV genome sequence (WSSV-CN, GenBank Accession AF332093.3) using Blastn v2.9.0+ (Camacho et al 2009) to identify contigs representing WSSV.<br><br>This file contains the contigs that represent the WSSV-IN-NS genome sequences.<br><b>References</b>Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477.<br>Bushnell B. https://sourceforge.net/projects/bbmap/.Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. 2009. BLAST+: architecture and applications. BMC Bioinformatics 10:421.<br>Chen S, Zhou Y, Chen Y, Gu J. 2018. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884–i890.<br>Sivakumar, S., Vimal, S., Abdul Majeed, S., Santhosh Kumar, S., Taju, G., Madan, N., Rajkumar, T., Thamizhvanan, S., Shamsudheen, K.V., Scaria, V., Sivasubbu, S., and Sahul Hameed, A.S. 2018. A new strain of white spot syndrome virus affecting <i>Litopenaeus vannamei</i> in Indian shrimp farms. J Fish Dis. 41:1129–1146.
DOI: 10.5281/zenodo.4914625
title: Rural-Urban depression prevalence in Rwanda

description: Data set on depression prevalence among Rwandan grandmothers and their daughters. The new version includes all data in the free alternative software: JASP
DOI: 10.5281/zenodo.4723384
title: Rural-Urban depression prevalence in Rwanda

description: Data set on depression prevalence among Rwandan grandmothers and their daughters. The new version includes all data in the free alternative software: JASP
DOI: 10.6084/m9.figshare.14751306.v1
title: Predicted Coronavirus Nsp5 Protease Cleavage Sites in the Human Proteome: A Resource for SARS-CoV-2 Research - supplementary data, tables, and figures

description: <b>Additional File 1:</b> This includes Supplementary Tables S1-S15 <b> </b> <b>Additional File 2:</b> human_all_proteins_netcorona.txt (108MB) This is the raw data output by NetCorona following analysis of the “All Human Proteins” dataset <b>Additional File 3:</b> human_one_gene_netcorona.txt (46MB) This is the raw data output by NetCorona following analysis of the “One Protein Per Gene” dataset <b>Additional File 4:</b> dataset human_w_PDB_netcorona.txt (16MB) This is the raw data output by NetCorona following analysis of the “Proteins With PDB” dataset <b>Additional File 5: Figure S1</b> interaction scores vs max NetCorona score.pdf Nsp5-human protein interaction data from Samavarchi-Tehrani <i>et al.</i>, plotted against the maximum NetCorona score for human proteins from the “One Protein Per Gene” dataset. <b>Additional File 6:</b> matching predicted motif to PDB.xlsx Raw data displaying how predicted cleaved motifs were matched to a PDB file <b>Additional File 7:</b> <b>Figure S2</b> ASA vs NetCorona score.pdf Accessible surface area (ASA) of a predicted and known Nsp5 motifs plotted against NetCorona scores, with data published by Moustaqil <i>et al. </i>and Koudelka <i>et al. </i>highlighted, and the Nsp5 access score cut-off displayed.
DOI: 10.6084/m9.figshare.14751306
title: Predicted Coronavirus Nsp5 Protease Cleavage Sites in the Human Proteome: A Resource for SARS-CoV-2 Research - supplementary data, tables, and figures

description: <b>Additional File 1:</b> This includes Supplementary Tables S1-S15 <b> </b> <b>Additional File 2:</b> human_all_proteins_netcorona.txt (108MB) This is the raw data output by NetCorona following analysis of the “All Human Proteins” dataset <b>Additional File 3:</b> human_one_gene_netcorona.txt (46MB) This is the raw data output by NetCorona following analysis of the “One Protein Per Gene” dataset <b>Additional File 4:</b> dataset human_w_PDB_netcorona.txt (16MB) This is the raw data output by NetCorona following analysis of the “Proteins With PDB” dataset <b>Additional File 5: Figure S1</b> interaction scores vs max NetCorona score.pdf Nsp5-human protein interaction data from Samavarchi-Tehrani <i>et al.</i>, plotted against the maximum NetCorona score for human proteins from the “One Protein Per Gene” dataset. <b>Additional File 6:</b> matching predicted motif to PDB.xlsx Raw data displaying how predicted cleaved motifs were matched to a PDB file <b>Additional File 7:</b> <b>Figure S2</b> ASA vs NetCorona score.pdf Accessible surface area (ASA) of a predicted and known Nsp5 motifs plotted against NetCorona scores, with data published by Moustaqil <i>et al. </i>and Koudelka <i>et al. </i>highlighted, and the Nsp5 access score cut-off displayed.
DOI: 10.6073/pasta/f0a076ef1cdb35abafab8b0b61fde59f
title: Water flow velocity data, Shark River Slough (SRS) near Chekika tree island, Everglades National Park (FCE LTER) from January 2006 to Present

description: Water velocity data measured every 5 or 15 minutes in Shark River Slough beside Chekika tree island, Everglades National Park, using Sontek Agronaut water flow sampler.
DOI: 10.6084/m9.figshare.14751333
title: Supplemental data for: Circulating plasma MicroRNA in patients with active acromegaly

description: This is a supplementary table with individual characteristics of all study participants.<br>
DOI: 10.6084/m9.figshare.14751333.v1
title: Supplemental data for: Circulating plasma MicroRNA in patients with active acromegaly

description: This is a supplementary table with individual characteristics of all study participants.<br>
DOI: 10.25345/c5d25t
title: MassIVE MSV000087588 - DOM LCMS-Interlab Study 2021 Lab5, version 2
title: LCMS Interlab study of marine dissolved organic matter and algal extracts 2021, lab 5
DOI: 10.5517/ccdc.csd.cc1mbxb6
title: CCDC 1500038: Experimental Crystal Structure Determination

description: Related Article: Ryan M. Bennett, Wei Sun, Dharyl C. Wilson, Mark E. Light, David C. Harrowven|2021|Chem.Commun.|57|5694|doi:10.1039/D1CC02097H
DOI: 10.5517/ccdc.csd.cc24ysyv
title: CCDC 1994848: Experimental Crystal Structure Determination

description: Related Article: Liejin Zhou, Lu Liu|2021|Chem.Commun.|57|5690|doi:10.1039/D1CC02080C
DOI: 10.5517/ccdc.csd.cc25fjx2
title: CCDC 2009014: Experimental Crystal Structure Determination

description: Related Article: Chengzhou Jiang, Jiamin Wu, Jiabin Han, Kai Chen, Yang Qian, Zhengyu Zhang, Yaojia Jiang|2021|Chem.Commun.|57|5710|doi:10.1039/D1CC01254A
DOI: 10.5517/ccdc.csd.cc26k8tw
title: CCDC 2042398: Experimental Crystal Structure Determination

description: Related Article: Ryan M. Bennett, Wei Sun, Dharyl C. Wilson, Mark E. Light, David C. Harrowven|2021|Chem.Commun.|57|5694|doi:10.1039/D1CC02097H
DOI: 10.5517/ccdc.csd.cc26mn0j
title: CCDC 2044667: Experimental Crystal Structure Determination

description: Related Article: Milan Pramanik, Ashis Mathuri, Prasenjit Mal|2021|Chem.Commun.|57|5698|doi:10.1039/D1CC01257F
DOI: 10.5517/ccdc.csd.cc26mn2l
title: CCDC 2044669: Experimental Crystal Structure Determination

description: Related Article: Milan Pramanik, Ashis Mathuri, Prasenjit Mal|2021|Chem.Commun.|57|5698|doi:10.1039/D1CC01257F
DOI: 10.5517/ccdc.csd.cc273yr2
title: CCDC 2059384: Experimental Crystal Structure Determination

description: Related Article: Ryan M. Bennett, Wei Sun, Dharyl C. Wilson, Mark E. Light, David C. Harrowven|2021|Chem.Commun.|57|5694|doi:10.1039/D1CC02097H
DOI: 10.5517/ccdc.csd.cc274rlr
title: CCDC 2060154: Experimental Crystal Structure Determination

description: Related Article: Malte Fischer, Fabian Reiß, Christian Hering-Junghans|2021|Chem.Commun.|57|5626|doi:10.1039/D1CC01305J
DOI: 10.5517/ccdc.csd.cc274rms
title: CCDC 2060155: Experimental Crystal Structure Determination

description: Related Article: Malte Fischer, Fabian Reiß, Christian Hering-Junghans|2021|Chem.Commun.|57|5626|doi:10.1039/D1CC01305J
DOI: 10.5517/ccdc.csd.cc274rnt
title: CCDC 2060156: Experimental Crystal Structure Determination

description: Related Article: Malte Fischer, Fabian Reiß, Christian Hering-Junghans|2021|Chem.Commun.|57|5626|doi:10.1039/D1CC01305J
DOI: 10.5517/ccdc.csd.cc274rpv
title: CCDC 2060157: Experimental Crystal Structure Determination

description: Related Article: Malte Fischer, Fabian Reiß, Christian Hering-Junghans|2021|Chem.Commun.|57|5626|doi:10.1039/D1CC01305J
DOI: 10.5517/ccdc.csd.cc27h5sq
title: CCDC 2070173: Experimental Crystal Structure Determination

description: Related Article: Deeb Taher, Jessica R. Wilson, Grayson Ritch, Matthias Zeller, Nathaniel K. Szymczak|2021|Chem.Commun.|57|5718|doi:10.1039/D1CC01407B
DOI: 10.5517/ccdc.csd.cc27h5tr
title: CCDC 2070174: Experimental Crystal Structure Determination

description: Related Article: Deeb Taher, Jessica R. Wilson, Grayson Ritch, Matthias Zeller, Nathaniel K. Szymczak|2021|Chem.Commun.|57|5718|doi:10.1039/D1CC01407B
DOI: 10.5517/ccdc.csd.cc27sn90
title: CCDC 2079272: Experimental Crystal Structure Determination

description: Related Article: Beiqi Sun, Sihan Zheng, Fanyang Mo|2021|Chem.Commun.|57|5674|doi:10.1039/D1CC02134F
DOI: 10.5066/p93xdvsz
title: National Assessment of Oil and Gas Project - Brookian Coalbed Gas Composite Total Petroleum System, Northern Alaska Province (001) Assessment Units

description: The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
DOI: 10.6084/m9.figshare.13530587.v1
title: A data set with ground reaction forces of human balance in Parkinson's disease

description: This data set comprises data with ground reaction forces of subjects standing still for 60 s in different conditions where vision and the standing surface were manipulated. Twenty-five subjects with Parkinson disease were evaluated.
DOI: 10.6084/m9.figshare.14544405
title: Data for analysis in "A tipping point for open citation data"

description: Metadata used for the analysis in the article "A tipping point for open citation data" appearing in Quantitative Science Studies, 2021. Metadata were adapted from the NIH Open Citation Collection (NIH-OCC, https://doi.org/10.1371/journal.pbio.3000385) and the PubMed Knowledge Graph (PKG, https://doi.org/10.1038/s41597-020-0543-2).<br><br>The NIH-OCC, for which a more recent update is available than the PKG, was truncated at the max PubMed Identifier (PMID) found in the PKG, to ensure the comparison of the two covers the same range of articles.<br><br>nihocc_and_pkg.csv.gz: Gzipped citations from the NIH-OCC that were also found in the PKG (2 columns, citing PMID followed by referenced PMID)<br><br>nihocc_not_pkg.csv.gz: Gzipped citations from the NIH-OCC that were not found in the PKG (2 columns, citing PMID followed by referenced PMID)<br><br>pkg_not_nihocc.csv.gz: Gzipped citations from the PKG that were not found in the NIH-OCC (2 columns, citing PMID followed by referenced PMID)<br><br>The paper describing the PKG is provided under a CC-BY 4.0 license, and Creative Commons Public Domain Dedication applies to the metadata files associated with that article https://doi.org/10.1038/s41597-020-0543-2). The paper describing the NIH-OCC and the NIH-OCC itself are both public domain (https://icite.od.nih.gov).<br>
DOI: 10.18739/a2s756m24
title: Carbon and Nitrogen Percent Compositions of Vegetation in Arctic Alaska, 2017-2019

description: Sample weights and carbon and nitrogen percentages from vegetation collected by destructive biomass sampling across the general year-round range of the Alaskan Central Arctic Caribou Herd (CAH), June 2017 - September 2019. These data were collected as a part of the National Science Foundation funded project "Nutritional Landscapes of Arctic Caribou: Observations, Experiments, and Models Provide Process-Level Understanding of Forage Traits and Trajectories" and have been used to produce estimates of vegetation biomass and aboveground nutrient pools throughout the CAH's range. The goals for the project as a whole were to further develop our understanding of the drivers of forage quality and quantity across the Alaskan arctic, the CAH's use of their landscape through space and time, and the possible ways that forage quality will vary under likely climate change scenarios.
DOI: 10.25345/c58c0s
title: MassIVE MSV000087589 - Mouse Intestinal HMGCR complex
title: Mice lacking 3-hydroxy-3-methylglutaryl-coenzyme A reductase (Hmgcr) in intestinal villus and crypt epithelial cells were generated using a Villin-Cre transgene. Label free proteome profiling was measure for Wild type and KO mouse.
DOI: 10.5281/zenodo.4731169
title: Water-quality data imputation with a high percentage of missing values: a machine learning approach

description: The monitoring of surface-water quality followed by water-quality modeling and analysis is essential for generating effective strategies in water resource management. However, water-quality studies are limited by the lack of complete and reliable data sets on surface-water-quality variables. These deficiencies are particularly noticeable in developing countries. This work focuses on surface-water-quality data from Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. Data collected at six monitoring stations are publicly available at https://www.dinama.gub.uy/oan/datos-abiertos/calidad-agua/. The high temporal and spatial variability that characterizes water-quality variables and the high rate of missing values (between 50% and 70%) raises significant challenges. To deal with missing values, we applied several statistical and machine-learning imputation methods. The competing algorithms implemented belonged to both univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Huber Regressor (HR), Support Vector Regressor (SVR), and K-nearest neighbors Regressor (KNNR)). IDW outperformed the others, achieving a very good performance (NSE greater than 0.8) in most cases. In this dataset, we include the original and imputed values for the following variables: Water temperature (<em>T</em><em>w</em>) Dissolved oxygen (<em>DO</em>) Electrical conductivity (EC) pH Turbidity (<em>Turb</em>) Nitrite (<em>NO</em><em>2</em>-) Nitrate (<em>NO</em><em>3</em>-) Total Nitrogen (<em>TN</em>) Each variable is identified as [STATION] VARIABLE FULL NAME (VARIABLE SHORT NAME) [UNIT METRIC]. More details about the study area, the original datasets, and the methodology adopted can be found in our paper https://www.mdpi.com/2071-1050/13/11/6318. If you use this dataset in your work, please cite our paper:<br> Rodríguez, R.; Pastorini, M.; Etcheverry, L.; Chreties, C.; Fossati, M.; Castro, A.; Gorgoglione, A. Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach. <em>Sustainability</em> <strong>2021</strong>, <em>13</em>, 6318. https://doi.org/10.3390/su13116318
description: {"references": ["Rodr\u00edguez, R.; Pastorini, M.; Etcheverry, L.; Chreties, C.; Fossati, M.; Castro, A.; Gorgoglione, A. Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach. Sustainability 2021, 13, 6318. https://doi.org/10.3390/su13116318"]}
DOI: 10.5281/zenodo.4731168
title: Water-quality data imputation with a high percentage of missing values: a machine learning approach

description: The monitoring of surface-water quality followed by water-quality modeling and analysis is essential for generating effective strategies in water resource management. However, water-quality studies are limited by the lack of complete and reliable data sets on surface-water-quality variables. These deficiencies are particularly noticeable in developing countries. This work focuses on surface-water-quality data from Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. Data collected at six monitoring stations are publicly available at https://www.dinama.gub.uy/oan/datos-abiertos/calidad-agua/. The high temporal and spatial variability that characterizes water-quality variables and the high rate of missing values (between 50% and 70%) raises significant challenges. To deal with missing values, we applied several statistical and machine-learning imputation methods. The competing algorithms implemented belonged to both univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Huber Regressor (HR), Support Vector Regressor (SVR), and K-nearest neighbors Regressor (KNNR)). IDW outperformed the others, achieving a very good performance (NSE greater than 0.8) in most cases. In this dataset, we include the original and imputed values for the following variables: Water temperature (<em>T</em><em>w</em>) Dissolved oxygen (<em>DO</em>) Electrical conductivity (EC) pH Turbidity (<em>Turb</em>) Nitrite (<em>NO</em><em>2</em>-) Nitrate (<em>NO</em><em>3</em>-) Total Nitrogen (<em>TN</em>) Each variable is identified as [STATION] VARIABLE FULL NAME (VARIABLE SHORT NAME) [UNIT METRIC]. More details about the study area, the original datasets, and the methodology adopted can be found in our paper https://www.mdpi.com/2071-1050/13/11/6318. If you use this dataset in your work, please cite our paper:<br> Rodríguez, R.; Pastorini, M.; Etcheverry, L.; Chreties, C.; Fossati, M.; Castro, A.; Gorgoglione, A. Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach. <em>Sustainability</em> <strong>2021</strong>, <em>13</em>, 6318. https://doi.org/10.3390/su13116318
description: {"references": ["Rodr\u00edguez, R.; Pastorini, M.; Etcheverry, L.; Chreties, C.; Fossati, M.; Castro, A.; Gorgoglione, A. Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach. Sustainability 2021, 13, 6318. https://doi.org/10.3390/su13116318"]}
DOI: 10.6084/m9.figshare.14751486
title: clearclip.bam.bai

description: FXR1 bam index file from clearclip experiment
DOI: 10.6084/m9.figshare.14751486.v1
title: clearclip.bam.bai

description: FXR1 bam index file from clearclip experiment
DOI: 10.13011/m3-98qq-sc35
title: Estimation of micro-Hall-Petch coefficients for prismatic slip system in Mg-4Al as a function of grain boundary parameters
DOI: 10.5066/p9qz7ty2
title: National Assessment of Oil and Gas Project - Yukon Flats Assessment Area (002) Assessment Units

description: The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
DOI: 10.5281/zenodo.4914711
title: Dataset_coffee_15Channels and Dataset_coffee_RGB

description: The database has 640 images of cherry coffee fruits in different stages of ripening. This images has captured with a dimension of 224 X 224, and 15 channels (each channel represents a different wavelength). images:<br> Inmadure -&gt; 130<br> Semi-mature -&gt; 160<br> Mature -&gt; 160<br> Overripe -&gt; 112<br> Dry -&gt; 78 In addition, a conversion of the 15 channels of each image to RGB was performed.
DOI: 10.6084/m9.figshare.14707113
title: EABL_Metadata

description: Sykes and Balenger dataset with metadata
DOI: 10.6084/m9.figshare.14707113.v2
title: EABL_Metadata

description: Sykes and Balenger dataset with metadata
DOI: 10.6084/m9.figshare.14707113.v1
title: EABL_MasterData_2018_2020

description: Sykes and Balenger master dataset
DOI: 10.5517/ccdc.csd.cc267f74
title: CCDC 2032925: Experimental Crystal Structure Determination

description: Related Article: Matteo Briganti, Eva Lucaccini, Laura Chelazzi, Samuele Ciattini, Lorenzo Sorace, Roberta Sessoli, Federico Totti, Mauro Perfetti|2021|J.Am.Chem.Soc.|143|8108|doi:10.1021/jacs.1c02502
DOI: 10.5517/ccdc.csd.cc267f85
title: CCDC 2032926: Experimental Crystal Structure Determination

description: Related Article: Matteo Briganti, Eva Lucaccini, Laura Chelazzi, Samuele Ciattini, Lorenzo Sorace, Roberta Sessoli, Federico Totti, Mauro Perfetti|2021|J.Am.Chem.Soc.|143|8108|doi:10.1021/jacs.1c02502
DOI: 10.5517/ccdc.csd.cc267fb7
title: CCDC 2032928: Experimental Crystal Structure Determination

description: Related Article: Matteo Briganti, Eva Lucaccini, Laura Chelazzi, Samuele Ciattini, Lorenzo Sorace, Roberta Sessoli, Federico Totti, Mauro Perfetti|2021|J.Am.Chem.Soc.|143|8108|doi:10.1021/jacs.1c02502
DOI: 10.5517/ccdc.csd.cc267fc8
title: CCDC 2032929: Experimental Crystal Structure Determination

description: Related Article: Matteo Briganti, Eva Lucaccini, Laura Chelazzi, Samuele Ciattini, Lorenzo Sorace, Roberta Sessoli, Federico Totti, Mauro Perfetti|2021|J.Am.Chem.Soc.|143|8108|doi:10.1021/jacs.1c02502
DOI: 10.5517/ccdc.csd.cc267fkg
title: CCDC 2032935: Experimental Crystal Structure Determination

description: Related Article: Matteo Briganti, Eva Lucaccini, Laura Chelazzi, Samuele Ciattini, Lorenzo Sorace, Roberta Sessoli, Federico Totti, Mauro Perfetti|2021|J.Am.Chem.Soc.|143|8108|doi:10.1021/jacs.1c02502
DOI: 10.5517/ccdc.csd.cc267fnk
title: CCDC 2032938: Experimental Crystal Structure Determination

description: Related Article: Matteo Briganti, Eva Lucaccini, Laura Chelazzi, Samuele Ciattini, Lorenzo Sorace, Roberta Sessoli, Federico Totti, Mauro Perfetti|2021|J.Am.Chem.Soc.|143|8108|doi:10.1021/jacs.1c02502
DOI: 10.5517/ccdc.csd.cc267fxt
title: CCDC 2032946: Experimental Crystal Structure Determination

description: Related Article: Matteo Briganti, Eva Lucaccini, Laura Chelazzi, Samuele Ciattini, Lorenzo Sorace, Roberta Sessoli, Federico Totti, Mauro Perfetti|2021|J.Am.Chem.Soc.|143|8108|doi:10.1021/jacs.1c02502