cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35111/31qn-be41
title: ESPADA

description: <h3>Introduction</h3><br>
<p>ESPADA (Extended Syntactic Phrase Alignment DAtaset) consists of annotated parse trees and alignment on English sentential paraphrases extracted from machine translation evaluation corpora. It extends <a href="../../../LDC2018T09">SPADE (LDC2018T09)</a> by adding new annotated data for training/testing phrasal paraphrase detection and phrase representation models to SPADE's development and test sets.</p><br>
<p>Reference translations from machine translation evaluation corpora were used as sentential paraphrases. They were sourced from the following data sets released by LDC from the NIST (National Institute of Standards and Technology) open machine translation evaluation series (<a href="https://www.nist.gov/itl/iad/mig/open-machine-translation-evaluation">OpenMT</a>): <a href="../../../LDC2010T14">LDC2010T14</a>, <a href="../../../LDC2010T17">LDC2010T17</a>, <a href="../../../LDC2010T21">LDC2010T21</a>, <a href="../../../LDC2010T23">LDC2010T23</a>, and <a href="../../../LDC2013T03">LDC2013T03</a>.</p><br>
<h3>Data</h3><br>
<p>Reference translations were randomly extracted for annotation from NIST OpenMT corpora. Gold standard annotations of HPSG (head-driven phrase structure grammar) trees and phrase alignments were performed, resulting in 251,972 phrase alignments identified in 1,916 sentential paraphrases. Further information about the annotation process is contained in the documentation accompanying this release.</p><br>
<p>All annotation data is presented as UTF-8 XML.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2021T10.xml">sample (XML)</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2018, 2021 Yuki Arase, © 2005-2007, 2009-2011, 2013, 2018, 2021 Trustees of the University of Pennsylvania
DOI: 10.15468/ma3rq5
title: A new water mite species of the Hygrobates fluviatilis-complex from Russia (Acari Hydrachnidia, Hygrobatidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Tuzovskij, Petr V. (2021): A new water mite species of the Hygrobates fluviatilis-complex from Russia (Acari Hydrachnidia, Hygrobatidae). Zootaxa 4974 (1): 193-196, DOI: https://doi.org/10.11646/zootaxa.4974.1.11
DOI: 10.35111/cv5n-e349
title: BOLT Chinese SMS/Chat Parallel Training Data

description: <h3>Introduction</h3><br>
<p><span data-testid="comment-base-item-125611">BOLT Chinese SMS/Chat Parallel Training Data</span> was developed by LDC and consists of approximately 1.8 million tokens of Chinese SMS/Chat data collected for the DARPA BOLT program along with their corresponding English translations</p><br>
<p>The DARPA <a href="https://www.ldc.upenn.edu/collaborations/current-projects/bolt"> BOLT</a> (Broad Operational Language Translation) program developed machine translation and information retrieval for less formal genres, focusing particularly on user-generated content. LDC supported the BOLT program by collecting informal data sources -- discussion forums, text messaging and chat -- in Chinese, Egyptian Arabic and English. The collected data was translated and annotated for various tasks including word alignment, treebanking, propbanking and co-reference.</p><br>
<h3>Data</h3><br>
<p>The source date in this release was collected using two methods: new collection via LDC's collection platform, and donation of SMS or chat archives from BOLT collection participants. All data were reviewed manually to exclude any messages/conversations that were not in the target language or that had sensitive content, such as personal identifying information.</p><br>
<p>Data was manually selected for translation. Messages/conversations were arranged in chronological order, segmented into sentence units (all or portions of message threads depending on their length), and assigned to translation vendors. Translators followed LDC's BOLT translation guidelines.</p><br>
<p>Source and translation files are presented in UTF-8 encoded XML format.</p><br>
<h3>Sponsorship</h3><br>
<p>This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR0011-11-C-0145. The content does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2021T11.cmn.xml">Chinese sample (XML)</a> and <a href="desc/addenda/LDC2021T11.eng.xml">English sample (XML)</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2021 Trustees of the University of Pennsylvania
DOI: 10.15468/3ns8nu
title: A new species of Metaruncina (Gastropoda: Heterobranchia: Runcinida) from Vietnam with a discussion on the so-called rudimentary radula in runcinids

description: This dataset contains the digitized treatments in Plazi based on the original journal article Chernyshev, Alexei V. (2021): A new species of Metaruncina (Gastropoda: Heterobranchia: Runcinida) from Vietnam with a discussion on the so-called rudimentary radula in runcinids. Zootaxa 4974 (1): 197-200, DOI: https://doi.org/10.11646/zootaxa.4974.1.12
DOI: 10.5281/zenodo.4751759
title: Complex Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development

description: Longitudinal electrophysiological dataset from cortical organoids, recorded weekly using planar multi-electrode arrays over 10 months. Please cite DOI of this repository when using the data. Thanks! <strong>Data associated with the following publication:</strong> Cleber A. Trujillo, Richard Gao, Priscilla D. Negraes, Jing Gu, Justin Buchanan, Sebastian Preissl, Allen Wang, Wei Wu, Gabriel G. Haddad, Isaac A. Chaim, Alain Domissy, Matthieu Vandenberghe, Anna Devor, Gene W. Yeo, Bradley Voytek, Alysson R. Muotri, Complex Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development, Cell Stem Cell,<br> Volume 25, Issue 4,<br> 2019,<br> Pages 558-569.e7,<br> ISSN 1934-5909,<br> https://doi.org/10.1016/j.stem.2019.08.002.<br> (https://www.sciencedirect.com/science/article/pii/S1934590919303376) --- Instructions: /CTC_YYMMDD/LFP_Sp_YYMMDD.mat files contain preprocessed data for recording on a given date YY-MM-DD. Each .mat file contains the following variables: (explanation given in python and with 0-indexing) - LFP: broadband recording downsampled to 1000Hz; 12-element cell array, one for each well of the MEA. In each cell is a 2D matrix, [time x 64 channels]. - t_ds: downsampled timestamps at 1000Hz (just a continuously increasing vector, 1ms apart) - t_s: original timestamps at 12.5kHz (same idea as t_ds) - spikes: [12 wells x 64 channel] cell array; each cell contains a 1D array denoting multi-unit spike times in sample number (at 12.5kHz), use t_s to find the corresponding time in seconds. e.g., spikes[5, 32] contains the spiketrain from well 5 (of 12), channel 32 (of 64). Briefly, spikes were detected after bandpass filtering (300-3000Hz) and as threshold crossings adaptively set to 5.5 standard deviations. Note that the spikes were not sorted, and thus represent multi-unit activity. Refer to manuscript for details. - spike_cnt: [12 wells x 64 channel] total spike count at each well and channel. Just a count over the channels of the ‘spikes’ variable. - spike_shape: cell array, [12 wells x 64 channel] each cell contains a 2D array [101 samples x N spikes], containing 101-samples of the spike waveform for each of the N detected spike, centered at time 0 of spike, i.e., detected spike peak/trough. Empty arrays denote no spikes detected. - spike_avg: [12 wells x 64 channel x 101 timepoints] 3D array average spike waveform detected at that well and channel, 101-samples (at 12.5kHz), i.e., averaged over all N spikes in each element of the ‘spike_shape” variable. ———————— Note that wells 1-4 are empty, all data analyzed in the paper are from wells 5-12. To load the data: in MATLAB: just open the .mat file and do as you please. in python: from scipy import io data = io.loadmat(‘CTC_YYMMDD/LFP_Sp_YYMMDD.mat’, squeeze_me = True) LFP = data[‘LFP’]
DOI: 10.5281/zenodo.4751758
title: Complex Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development

description: Longitudinal electrophysiological dataset from cortical organoids, recorded weekly using planar multi-electrode arrays over 10 months. Please cite DOI of this repository when using the data. Thanks! <strong>Data associated with the following publication:</strong> Cleber A. Trujillo, Richard Gao, Priscilla D. Negraes, Jing Gu, Justin Buchanan, Sebastian Preissl, Allen Wang, Wei Wu, Gabriel G. Haddad, Isaac A. Chaim, Alain Domissy, Matthieu Vandenberghe, Anna Devor, Gene W. Yeo, Bradley Voytek, Alysson R. Muotri, Complex Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development, Cell Stem Cell,<br> Volume 25, Issue 4,<br> 2019,<br> Pages 558-569.e7,<br> ISSN 1934-5909,<br> https://doi.org/10.1016/j.stem.2019.08.002.<br> (https://www.sciencedirect.com/science/article/pii/S1934590919303376) --- Instructions: /CTC_YYMMDD/LFP_Sp_YYMMDD.mat files contain preprocessed data for recording on a given date YY-MM-DD. Each .mat file contains the following variables: (explanation given in python and with 0-indexing) - LFP: broadband recording downsampled to 1000Hz; 12-element cell array, one for each well of the MEA. In each cell is a 2D matrix, [time x 64 channels]. - t_ds: downsampled timestamps at 1000Hz (just a continuously increasing vector, 1ms apart) - t_s: original timestamps at 12.5kHz (same idea as t_ds) - spikes: [12 wells x 64 channel] cell array; each cell contains a 1D array denoting multi-unit spike times in sample number (at 12.5kHz), use t_s to find the corresponding time in seconds. e.g., spikes[5, 32] contains the spiketrain from well 5 (of 12), channel 32 (of 64). Briefly, spikes were detected after bandpass filtering (300-3000Hz) and as threshold crossings adaptively set to 5.5 standard deviations. Note that the spikes were not sorted, and thus represent multi-unit activity. Refer to manuscript for details. - spike_cnt: [12 wells x 64 channel] total spike count at each well and channel. Just a count over the channels of the ‘spikes’ variable. - spike_shape: cell array, [12 wells x 64 channel] each cell contains a 2D array [101 samples x N spikes], containing 101-samples of the spike waveform for each of the N detected spike, centered at time 0 of spike, i.e., detected spike peak/trough. Empty arrays denote no spikes detected. - spike_avg: [12 wells x 64 channel x 101 timepoints] 3D array average spike waveform detected at that well and channel, 101-samples (at 12.5kHz), i.e., averaged over all N spikes in each element of the ‘spike_shape” variable. ———————— Note that wells 1-4 are empty, all data analyzed in the paper are from wells 5-12. To load the data: in MATLAB: just open the .mat file and do as you please. in python: from scipy import io data = io.loadmat(‘CTC_YYMMDD/LFP_Sp_YYMMDD.mat’, squeeze_me = True) LFP = data[‘LFP’]
DOI: 10.6084/m9.figshare.14619180
title: DATA.docx

description: Land suitability evaluation technique is designed, combining seventeen thematic layers from five different categories climatic parameters, topographic features, soil characteristics, Euclidean distance and land elements
DOI: 10.6084/m9.figshare.14619180.v1
title: DATA.docx

description: Land suitability evaluation technique is designed, combining seventeen thematic layers from five different categories climatic parameters, topographic features, soil characteristics, Euclidean distance and land elements
DOI: 10.5281/zenodo.4773824
title: Dataset for the paper "Transport measurements on van der Waals heterostructures under pressure"

description: Dataset for the paper "Transport measurements on van der Waals heterostructures under pressure". The folder "KeroseneTest" contains data for fig. 3. The folder WALcompare contains data for fig. 4.
DOI: 10.5255/ukda-sn-854904
title: Classroom Survey on Dividing a Real Estate Asset, 2018

description: This data is a summary of classroom surveys conducted in the UK, Netherlands and Norway. Participants were presented with an experiment from game theory where a real estate asset was shared between a group. The outcome of the classroom survey was analysed using the predictions of the Shapeley value concept.
DOI: 10.6084/m9.figshare.14619243.v1
title: data_final.xlsx

description: Database - The Economic Knowledge of Czech High School Students: Analysis of the Economics Olympiad
DOI: 10.6084/m9.figshare.14619243
title: data_final.xlsx

description: Database - The Economic Knowledge of Czech High School Students: Analysis of the Economics Olympiad
DOI: 10.5517/ccdc.csd.cc267pms
title: CCDC 2033185: Experimental Crystal Structure Determination

description: Related Article: Berenika M. Szczęśniak-Sięga, Benita Wiatrak, Żaneta Czyżnikowska, Jan Janczak, Rafal J. Wiglusz, Jadwiga Maniewska|2020|Bioorg.Chem.|106|104476|doi:10.1016/j.bioorg.2020.104476
DOI: 10.5255/ukda-sn-854889
title: Epidemiology of Cohort Social Media, 2018-2019

description: Interactions on social media have the potential to help us to understand human behaviour, including the development of both good and poor mental health. However, to do the best science we need to know as much as possible about the people who are participating in our research. The CLOSER group of UK longitudinal cohorts include people who have contributed their data to research since birth. By inviting participants in these cohorts to also allow us to derive information from their social media feeds, we will be able to relate this information to gold-standard measures of the behaviours we are trying to understand and to world-class data on other aspects of life. To work out the best way to do this, our project will engage with participants in the Children of the '90s cohort to find out what is acceptable to them in terms of collecting and using their interactions on social media. We will use what we have learnt to develop software that collects and codes social media data in a way that protects the anonymity of participants by scoring Tweets without making the text available to researchers. We will share this software with other CLOSER cohorts to make it easy for them to invite participants to contribute their Twitter data in a safe and secure way. The high-resolution data collected in this way will help us to understand human behaviour and how mental health changes over time. Collecting these data in well known groups of people will also give scientists the information they need to improve the quality of all research using social media.
DOI: 10.6084/m9.figshare.14619273
title: ReadMe

description: Description of the variables included in this dataset
DOI: 10.34740/kaggle/dsv/2250203
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.15131/shef.data.14573676
title: Dataset for: Spinning Beta Silks Requires Both pH Activation and Extensional Stress

description: Data from the publication Spinning Beta Silks Requires Both pH Activation and Extensional Stress<br>Data that links silks’ processing and performance by spinning a native silk feedstock into a fiber that exceeds the natural mechanical properties controlling just two parameters; pH and extensional flow, is presented. This allows to link previous experimental and modeling hypothesis surrounding silk’s pH responsiveness directly to controlling multiscale hierarchical structure development during spinning through rate of work input.
DOI: 10.15131/shef.data.14573676.v1
title: Dataset for: Spinning Beta Silks Requires Both pH Activation and Extensional Stress

description: Data from the publication Spinning Beta Silks Requires Both pH Activation and Extensional Stress<br>Data that links silks’ processing and performance by spinning a native silk feedstock into a fiber that exceeds the natural mechanical properties controlling just two parameters; pH and extensional flow, is presented. This allows to link previous experimental and modeling hypothesis surrounding silk’s pH responsiveness directly to controlling multiscale hierarchical structure development during spinning through rate of work input.
DOI: 10.5281/zenodo.4773839
title: Tutorial ML Data

description: Tutorial dataset used for traditional machine learning training and artificial neural networks.
DOI: 10.5281/zenodo.4773838
title: Tutorial ML Data

description: Tutorial dataset used for traditional machine learning training and artificial neural networks.
DOI: 10.6084/m9.figshare.14077415
title: 37F Phylogeny

description: Foraminifera phylogeny within which are placed the 12,742 OTUs obtained after all filtering steps, for the foraminiferal 37F marker.<br>
DOI: 10.6084/m9.figshare.14077406
title: 37F Taxonomy

description: Taxonomy of the 12,742 OTUs obtained after all filtering steps, for the foraminiferal 37F marker.
DOI: 10.6084/m9.figshare.14077406.v1
title: 37F Taxonomy

description: Taxonomy of the 12,742 OTUs obtained after all filtering steps, for the foraminiferal 37F marker.
DOI: 10.6084/m9.figshare.14077418
title: V1V2 Phylogeny

description: Eukaryota (SILVA) phylogeny within which are placed the 43,091 OTUs obtained after all filtering steps, for the eukaryotic V1V2 marker.<br>
DOI: 10.6084/m9.figshare.14077418.v1
title: V1V2 Phylogeny

description: Eukaryota (SILVA) phylogeny within which are placed the 43,091 OTUs obtained after all filtering steps, for the eukaryotic V1V2 marker.<br>
DOI: 10.6084/m9.figshare.14077388
title: V1V2 Sequences

description: Sequences of the 43,091 OTUs obtained after all filtering steps, for the V1V2 marker.
DOI: 10.6084/m9.figshare.14077379.v1
title: V1V2 OTU table

description: OTUs-to-samples table expressed in terms of read counts in BIOM format, containing the 43,091 OTUs in 896 samples were obtained after all filtering steps, for the eukaryotic V1V2 marker<br>
DOI: 10.6084/m9.figshare.14077382
title: 37F Sequences

description: Sequences of the 12,742 OTUs obtained after all filtering steps, for the 37F marker.
DOI: 10.6084/m9.figshare.14077388.v1
title: V1V2 Sequences

description: Sequences of the 43,091 OTUs obtained after all filtering steps, for the V1V2 marker.
DOI: 10.6084/m9.figshare.14077403
title: V1V2 Taxonomy

description: Taxonomy of the 43,091 OTUs obtained after all filtering steps, for the eukaryotic V1V2 marker,<br>
DOI: 10.6084/m9.figshare.14077379
title: V1V2 OTU table

description: OTUs-to-samples table expressed in terms of read counts in BIOM format, containing the 43,091 OTUs in 896 samples were obtained after all filtering steps, for the eukaryotic V1V2 marker<br>
DOI: 10.6084/m9.figshare.14077370.v1
title: 37F OTU table

description: OTUs-to-samples table expressed in terms of read counts in BIOM format, containing the 12,742 OTUs in 1,003 samples were obtained after all filtering steps, for the foraminiferal 37F marker
DOI: 10.6084/m9.figshare.14077382.v1
title: 37F Sequences

description: Sequences of the 12,742 OTUs obtained after all filtering steps, for the 37F marker.
DOI: 10.6084/m9.figshare.14077403.v1
title: V1V2 Taxonomy

description: Taxonomy of the 43,091 OTUs obtained after all filtering steps, for the eukaryotic V1V2 marker,<br>
DOI: 10.6084/m9.figshare.14077370
title: 37F OTU table

description: OTUs-to-samples table expressed in terms of read counts in BIOM format, containing the 12,742 OTUs in 1,003 samples were obtained after all filtering steps, for the foraminiferal 37F marker
DOI: 10.5281/zenodo.4773888
title: Campylomormyrus tamandua SPAdes preassembly

description: .
DOI: 10.5281/zenodo.4773887
title: Campylomormyrus tamandua SPAdes preassembly

description: .
DOI: 10.5517/ccdc.csd.cc20x5ns
title: CCDC 1874125: Experimental Crystal Structure Determination

description: Related Article: Gabrielle A. Leith, Allison M. Rice, Brandon J. Yarbrough, Preecha Kittikhunnatham, Abhijai Mathur, Nicholas A. Morris, Megan J. Francis, Anna A. Berseneva, Poonam Dhull, Richard D. Adams, M. Victoria Bobo, Aaron A. Vannucci, Mark D. Smith, Sophya Garashchuk, Natalia B. Shustova|2021|Chemical Science|12|6600|doi:10.1039/D0SC06755E
DOI: 10.5517/ccdc.csd.cc20x5pt
title: CCDC 1874126: Experimental Crystal Structure Determination

description: Related Article: Gabrielle A. Leith, Allison M. Rice, Brandon J. Yarbrough, Preecha Kittikhunnatham, Abhijai Mathur, Nicholas A. Morris, Megan J. Francis, Anna A. Berseneva, Poonam Dhull, Richard D. Adams, M. Victoria Bobo, Aaron A. Vannucci, Mark D. Smith, Sophya Garashchuk, Natalia B. Shustova|2021|Chemical Science|12|6600|doi:10.1039/D0SC06755E
DOI: 10.5517/ccdc.csd.cc20x5qv
title: CCDC 1874127: Experimental Crystal Structure Determination

description: Related Article: Gabrielle A. Leith, Allison M. Rice, Brandon J. Yarbrough, Preecha Kittikhunnatham, Abhijai Mathur, Nicholas A. Morris, Megan J. Francis, Anna A. Berseneva, Poonam Dhull, Richard D. Adams, M. Victoria Bobo, Aaron A. Vannucci, Mark D. Smith, Sophya Garashchuk, Natalia B. Shustova|2021|Chemical Science|12|6600|doi:10.1039/D0SC06755E
DOI: 10.5517/ccdc.csd.cc26t0tw
title: CCDC 2049838: Experimental Crystal Structure Determination

description: Related Article: Weigao Hu, Qiangqiang Zhan, Hongwei Zhou, Shanshan Cao, Zhiyong Jiang|2021|Chemical Science|12|6543|doi:10.1039/D1SC01470F
DOI: 10.5517/ccdc.csd.cc26yfm7
title: CCDC 2054079: Experimental Crystal Structure Determination

description: Related Article: Max Schütz, Christian Gemel, Maximilian Muhr, Christian Jandl, Samia Kahlal, Jean-Yves Saillard, Roland A. Fischer|2021|Chemical Science|12|6588|doi:10.1039/D1SC00268F
DOI: 10.5517/ccdc.csd.cc26yfn8
title: CCDC 2054080: Experimental Crystal Structure Determination

description: Related Article: Max Schütz, Christian Gemel, Maximilian Muhr, Christian Jandl, Samia Kahlal, Jean-Yves Saillard, Roland A. Fischer|2021|Chemical Science|12|6588|doi:10.1039/D1SC00268F
DOI: 10.5517/ccdc.csd.cc26yfp9
title: CCDC 2054081: Experimental Crystal Structure Determination

description: Related Article: Max Schütz, Christian Gemel, Maximilian Muhr, Christian Jandl, Samia Kahlal, Jean-Yves Saillard, Roland A. Fischer|2021|Chemical Science|12|6588|doi:10.1039/D1SC00268F
DOI: 10.5517/ccdc.csd.cc26yfqb
title: CCDC 2054082: Experimental Crystal Structure Determination

description: Related Article: Max Schütz, Christian Gemel, Maximilian Muhr, Christian Jandl, Samia Kahlal, Jean-Yves Saillard, Roland A. Fischer|2021|Chemical Science|12|6588|doi:10.1039/D1SC00268F
DOI: 10.5517/ccdc.csd.cc271ys1
title: CCDC 2057463: Experimental Crystal Structure Determination

description: Related Article: Myojeong Kim, Euna You, Seongjin Park, Sungwoo Hong|2021|Chemical Science|12|6629|doi:10.1039/D1SC00776A
DOI: 10.5517/ccdc.csd.cc271z1b
title: CCDC 2057471: Experimental Crystal Structure Determination

description: Related Article: Myojeong Kim, Euna You, Seongjin Park, Sungwoo Hong|2021|Chemical Science|12|6629|doi:10.1039/D1SC00776A
DOI: 10.5517/ccdc.csd.cc272prs
title: CCDC 2058175: Experimental Crystal Structure Determination

description: Related Article: Andrey Yu. Rogachev, Zheng Zhou, Shuyang Liu, Zheng Wei, Tobias A. Schaub, Ramesh Jasti, Marina A. Petrukhina|2021|Chemical Science|12|6526|doi:10.1039/D1SC00713K
DOI: 10.5517/ccdc.csd.cc20x5mr
title: CCDC 1874124: Experimental Crystal Structure Determination

description: Related Article: Gabrielle A. Leith, Allison M. Rice, Brandon J. Yarbrough, Preecha Kittikhunnatham, Abhijai Mathur, Nicholas A. Morris, Megan J. Francis, Anna A. Berseneva, Poonam Dhull, Richard D. Adams, M. Victoria Bobo, Aaron A. Vannucci, Mark D. Smith, Sophya Garashchuk, Natalia B. Shustova|2021|Chemical Science|12|6600|doi:10.1039/D0SC06755E
DOI: 10.5517/ccdc.csd.cc20yk6r
title: CCDC 1875444: Experimental Crystal Structure Determination

description: Related Article: Gabrielle A. Leith, Allison M. Rice, Brandon J. Yarbrough, Preecha Kittikhunnatham, Abhijai Mathur, Nicholas A. Morris, Megan J. Francis, Anna A. Berseneva, Poonam Dhull, Richard D. Adams, M. Victoria Bobo, Aaron A. Vannucci, Mark D. Smith, Sophya Garashchuk, Natalia B. Shustova|2021|Chemical Science|12|6600|doi:10.1039/D0SC06755E