cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3700125
title: VAMDC extraction with identifier = 7974a04d-3adc-4d26-a1df-10c0e3be44ba

description: This is a dataset extracted from http://vald.astro.uu.se/atoms-12.07/tap/ VAMDC node.Query originating this dataset: query=select * where ( atomsymbol in ['h'] and radtranswavelength &lt;= 11634.49275862069 and radtranswavelength &gt;= 8827.596206896555 ); Data source version: 2020-01-24Data format: XSAMS 12.07Query uuid in VAMDC query store: 7974a04d-3adc-4d26-a1df-10c0e3be44ba
description: {"references": ["N.N. (2020).", "{Murphy}, P.~W. (1968). {Transition Probabilities in the Spectra of Ne I, Ar I, and Kr I}. Journal of the Optical Society of America (1917-1983).", "{Redfors}, A. (1991). {Oscillator strengths for Y III and Zr III in the IUE region}. aap."]}
DOI: 10.5281/zenodo.3700126
title: VAMDC extraction with identifier = 7974a04d-3adc-4d26-a1df-10c0e3be44ba

description: This is a dataset extracted from http://vald.astro.uu.se/atoms-12.07/tap/ VAMDC node.Query originating this dataset: query=select * where ( atomsymbol in ['h'] and radtranswavelength &lt;= 11634.49275862069 and radtranswavelength &gt;= 8827.596206896555 ); Data source version: 2020-01-24Data format: XSAMS 12.07Query uuid in VAMDC query store: 7974a04d-3adc-4d26-a1df-10c0e3be44ba
description: {"references": ["N.N. (2020).", "{Murphy}, P.~W. (1968). {Transition Probabilities in the Spectra of Ne I, Ar I, and Kr I}. Journal of the Optical Society of America (1917-1983).", "{Redfors}, A. (1991). {Oscillator strengths for Y III and Zr III in the IUE region}. aap."]}
DOI: 10.5281/zenodo.3700131
title: VAMDC extraction with identifier = 9202de74-a9b0-4e4d-9c1c-15a9673360bb

description: This is a dataset extracted from http://topbase.obspm.fr/12.07/vamdc/tap/ VAMDC node.Query originating this dataset: query=select * where ( atomsymbol in ['h'] and radtranswavelength &lt;= 12450.010000000002 and radtranswavelength &gt;= 2550.0100000000007 ); Data source version: 2017-01-30Data format: XSAMS 12.07Query uuid in VAMDC query store: 9202de74-a9b0-4e4d-9c1c-15a9673360bb
description: {"references": ["N.N. (2020).", "Seaton, M. J. (1995). . unpublished."]}
DOI: 10.5281/zenodo.3700130
title: VAMDC extraction with identifier = 9202de74-a9b0-4e4d-9c1c-15a9673360bb

description: This is a dataset extracted from http://topbase.obspm.fr/12.07/vamdc/tap/ VAMDC node.Query originating this dataset: query=select * where ( atomsymbol in ['h'] and radtranswavelength &lt;= 12450.010000000002 and radtranswavelength &gt;= 2550.0100000000007 ); Data source version: 2017-01-30Data format: XSAMS 12.07Query uuid in VAMDC query store: 9202de74-a9b0-4e4d-9c1c-15a9673360bb
description: {"references": ["N.N. (2020).", "Seaton, M. J. (1995). . unpublished."]}
DOI: 10.17632/x22tdxjmmf.1
title: Data for: Differential scanning calorimetry for quantification of polymer biodegradability in compost

description: roh data differential scanning calorimetry
DOI: 10.25550/1-awct
title: Thomas Diekwisch. Ameloblast Differentiation and Amelogenesis: Next-Generation Models to Define Key Mechanisms and Factors Involved in Biological Enamel Formation

description: Amelogenesis is a biological process by which highly specialized enamel organ epithelial cells called ameloblasts transport substantial amounts of calcium ions and enamel proteins into the secretory enamel matrix and manufacture a biomaterial of exceptional structural and mechanical properties: tooth enamel (Pandya and Diekwisch 2018). Recent studies have identified some of the calcium channels in dental enamel cells at the basal ameloblast aspect (Nurbaeva et al. 2015, Lacruz 2017). However, there is remarkably little agreement on the mechanisms of ion and protein trafficking at the secretory ameloblast pole and throughout the ameloblast cell body. In support of the present application we have developed three highly innovative models that will address knowledge gaps and advance our understanding of physiological and pathological amelogenesis, including (i) a conditional clathrin deletion mouse model, (ii) an ameloblast 3D bioreactor cell culture model, and (iii) a new liquid cell atomic resolution imaging technology for life in situ imaging of vesicular and extracellular enamel matrices. Establishment of a clathrin knockout model represents significant progress in the area of vesicular trafficking research and a powerful tool to study the function of coated vesicles during amelogenesis. Clathrin-coated vesicles are among the most abundant cellular vesicles, and loss of clathrin has been associated with severe and usually lethal phenotypes (Robinson 2015). Here we present exciting preliminary data demonstrating that clathrin depletion during amelogenesis resulted in altered enamel prism structure and crystal density. Our 3D bioreactor amelogenesis model marks another milestone in enamel research as it promoted the propagation of elongated amelogenin secreting cells, overcoming shortcomings of traditional 2D ameloblast cell culture technology. Third, our atomic resolution liquid chamber model facilitates unprecedented in situ imaging of vesicular contents and native enamel matrix, allowing for the identification of matrix/mineral clusters at the earliest stages of amelogenesis. In response to RFA-DE-19-004 we have now designed a research plan to develop and optimize these model systems (UG3 phase) and to validate their physiological relevance and usefulness for understanding mechanisms of enamel development and disease during the UH3 phase.
DOI: 10.5281/zenodo.3700197
title: IDTReeS 2020 Competition Data

description: Data provided by the Integrating Data science with Trees and Remote Sensing (IDTReeS) research group for use in the IDTReeS Competition. Geospatial and tabular data to be used in two data science tasks focused on using remote sensing data to quantify the locations, sizes and species identities of millions of trees and on determining how these methods generalize to other forests. Vector data are the geographic extents of Individual Tree Crown boundaries that have been identified by researchers in the IDTReeS group. The data were generated primarily by Sarah Graves, Sergio Marconi, and Benjamin Weinstein, with support from Stephanie Bohlman, Ethan White, and members of the IDTReeS group. Remote Sensing and Field data were generated by the National Ecological Observatory Network (NEON, Copyright © 2017 Battelle). Data were selected, downloaded, and packaged by Sergio Marconi. The most recent available data of the following products are provided: National Ecological Observatory Network. 2020. Data Product DP1.30010.001, High-resolution orthorectified camera imagery. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. National Ecological Observatory Network. 2020. Data Product DP1.30003.001, Discrete return LiDAR point cloud. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. National Ecological Observatory Network. 2020. Data Product DP1.10098.001, Woody plant vegetation structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. National Ecological Observatory Network. 2020. Data Product DP3.30015.001, Ecosystem structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. NEON has the following data policy: ‘The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program.’ THE NEON DATA PRODUCTS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE NEON DATA PRODUCTS BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE NEON DATA PRODUCTS.
description: This data is supported by the National Science Foundation through grant 1926542 and by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative through grant GBMF4563 to E.P. White, and the NSF Dimension of Biodiversity program grant (DEB-1442280) and USDA/NIFA McIntire-Stennis program (FLA-FOR-005470).
DOI: 10.5281/zenodo.3700277
title: Twitter Crawling for "Viral or Heboh" News

description: The data we take is about tweet form 10 official account of news portal on twitter that mentioned word "Viral" or "Heboh". The dataset contain 5 columns and more than 500 tweet sorted by time.
description: {"references": ["Nur Aini Rakhmawati. (2019). The Biodata of Legislative Candidates for Indonesian General Election 2019 (Version tanpapemenang) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3474543"]}
DOI: 10.5281/zenodo.3700278
title: Twitter Crawling for "Viral or Heboh" News

description: The data we take is about tweet form 10 official account of news portal on twitter that mentioned word "Viral" or "Heboh". The dataset contain 5 columns and 100 tweet sorted by time.
description: {"references": ["Nur Aini Rakhmawati. (2019). The Biodata of Legislative Candidates for Indonesian General Election 2019 (Version tanpapemenang) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3474543"]}
DOI: 10.13140/rg.2.2.23625.70245
title: Golden Droplets Episode 14
DOI: 10.5281/zenodo.3700233
title: DATA JDIH KABUPATEN/KOTA DI PROVINSI JAWA BARAT

description: Dataset JDIH kota / kabupaten di Provinsi Jawa Barat
DOI: 10.5281/zenodo.3700228
title: Cloud-free NDSI product across the Tibetan Plateau (2003-2018) with codes

description: Cloud-free NDSI product across the Tibetan Plateau.
DOI: 10.5281/zenodo.3700229
title: Cloud-free NDSI product across the Tibetan Plateau (2003-2018) with codes

description: Cloud-free NDSI product across the Tibetan Plateau.
DOI: 10.5281/zenodo.3696785
title: Wikidata Dump Machine Learning

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.3696786
title: Wikidata Dump Machine Learning

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.25919/5e6343c66b267
title: Parkes observations for project P1021 semester 2019OCTS_11

description: We propose to conduct observations of PSR J1653-45, a 951-ms pulsar in a long, 1.5-yr orbit. Binary pulsars are valuable objects of scientific study, allowing for a wide range of applications, including tests of gravity, probes of the neutron star equation of state, and fossil records of stellar evolution. Long spin-period pulsars in binary systems are generally much rarer than faster-spinning `recycled’ pulsars, and represent an under-explored region of pulsar binary evolution. In addition to its long orbital period, PSR J1653-45 also experiences extended eclipses, lasting up to half of the orbital period and whose nature is not well understood. These eclipses have also prevented the determination of an orbital of phase-connected timing solution, both vital steps in allowing for the continued study of this system. We propose a targeted, high-cadence observing campaign designed to monitor this pulsar as it transition into its eclipsing state. The campaign has the twin goals of both monitoring how the eclipse affects the pulsar’s emission properties, thereby revealed more about the nature of the eclipses themselves, and of extracting as much information regarding the pulsar’s orbit during its least-explored regions, thereby constrained and hopefully solving the pulsar’s orbit and ideally leading to the development of a timing solution. In both instances, we hope to gain a greater understanding of this pulsar’s properties and how it fits into this under-explored class of unrecycled pulsar binaries.
DOI: 10.25919/5e6343d91671a
title: Parkes observations for project P958 semester 2019OCTS_66

description: Fast radio bursts (FRBs) continue to raise more questions than they answer. Until last year, only one burst – the only one known to repeat - had been localized to a bright radio nebula (either a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. Over the last two year searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 31 bursts, five of which have been localised to host galaxies at redshifts ranging from 0.1 to 0.5. These bursts and their hosts are very unlike the repeater, suggesting there may be a dichotomy in the population. However one of the arcminute-localised ASKAP FRBs has recently been found to repeat. Here we propose to search and study repetitions in this FRB population. Our comprehensive sample and a dense monitoring campaign of well localised bursts, at a fluence limit more than 60 times lower than that of their detections to characterise the fraction of bursts that repeat. For bursts that repeat the unique wide bandwidth observations will be used to test leading models of burst emission.
DOI: 10.5281/zenodo.3700316
title: Supplementary Material for the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting

description: This is the dataset used in the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting. It is composed of: - Pre-trained models using active learning for document screening on HealthCLEF and Epistemonikos datasets. - Epistemonikos and HealthCLEF datasets containing medical questions and relevant/non relevant articles. - Embeddings and Document Representations used for experiments on both datasets. Scripts to run experiments can be found at: https://github.com/afcarvallo/active_learning_document_screening <strong>Paper abstract:</strong> Document screening is a fundamental task within Evidence-based Medicine (EBM), a practice that provides scientific evidence to support medical decisions. Several approaches have tried to reduce physicians' workload of screening and labeling vast amounts of documents to answer clinical questions. Previous works tried to semi-automate document screening, reporting promising results, but their evaluation was conducted on small datasets, which hinders generalization. Moreover, recent works in natural language processing have introduced neural language models, but none have compared their performance in EBM. In this paper, we evaluate the impact of several document representations such as TF-IDF along with neural language models (BioBERT, BERT, Word2vec, and GloVe) on an active learning-based setting for document screening in EBM. Our goal is to reduce the number of documents that physicians need to label to answer clinical questions. We evaluate these methods using both a small challenging dataset (HealthCLEF 2017) as well as a larger one but easier to rank (Epistemonikos). Our results indicate that word as well as textual neural embeddings always outperform the traditional TF-IDF representation. When comparing among neural and textual embeddings, in the HealthCLEF dataset the models BERT and BioBERT yielded the best results. On the larger dataset, Epistemonikos, Word2Vec and BERT were the most competitive, showing that BERT was the most consistent model across different corpuses. In term of active learning, an uncertainty sampling strategy combined with logistic regression achieved the best performance overall, above other methods under evaluation, and in fewer iterations.
DOI: 10.5281/zenodo.3700282
title: Annual 30-meter Dataset for Glacial Lakes in High Mountain Asia from 2008 to 2017

description: We developed a High Mountain Asia (HMA) Glacial Lake Inventory (Hi-MAG) database to characterize the annual coverage of glacial lakes from 2008 to 2017 at 30 m resolution. This is the first glacier lake inventory across the HMA providing details for size and type of glacial lakes at high spatial resolution, and the first with annual temporal resolution. Although the method of lake mapping was automatic, for quality control every lake polygon was inspected and was manually edited where needed. It can be used for studies on glacier-climate-lake interactions, glacio-hydrologic models, glacial lake outburst floods and potential downstream risks and water resources.
description: The creation of this data set was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19030101), the International Partnership Program of the Chinese Academy of Sciences (131C11KYSB20160061/131211KYSB20170046), and the National Key R&D Program of China (2017YFE0100800).
DOI: 10.17632/mwgmm2yhjn.1
title: Data for: Bioaccumulation mechanism of lead by microalgae in the presence of organic acids

description: Transportation of heavy metal in fresh water
DOI: 10.17632/w6h8fmm9g5.1
title: Population, density, GDP, and census travel to work attributes of Indian cities

description: The spreadsheet consists of multiple attributes of case study cities.
DOI: 10.17632/jwv5pvnbg2.1
title: Data for: The polyphenol Ellagic Acid exerts anti-inflammatory actions via disruption of store-operated calcium entry (SOCE) pathway activators and coupling mediators

description: Data from Western Blot. Protocols for immunoblotting.
DOI: 10.5281/zenodo.3700360
title: Wordpress blog export, posts from 2006--7 March, 2020..

description: An XML archive of the blogs posted at https://www.ch.ic.ac.uk/rzepa/blog during the period 2008-2020.
description: For loading into a Wordpress blog system
DOI: 10.5281/zenodo.3700361
title: Wordpress blog export, posts from 2006--7 March, 2020..

description: An XML archive of the blogs posted at https://www.ch.ic.ac.uk/rzepa/blog during the period 2008-2020.
description: For loading into a Wordpress blog system
DOI: 10.5517/ccdc.csd.cc1pxjrw
title: CCDC 1576559: Experimental Crystal Structure Determination

description: Related Article: K.L. Obydennov, T.A. Kalinina, O.A. Vysokova, P.A. Slepukhin, V.A. Pozdina, M.V.' Ulitko, T.V. Glukhareva|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|795|doi:10.1107/S2053229620009328
DOI: 10.5517/ccdc.csd.cc20z2nr
title: CCDC 1875954: Experimental Crystal Structure Determination

description: Related Article: K.L. Obydennov, T.A. Kalinina, O.A. Vysokova, P.A. Slepukhin, V.A. Pozdina, M.V.' Ulitko, T.V. Glukhareva|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|795|doi:10.1107/S2053229620009328
DOI: 10.5517/ccdc.csd.cc1lqd90
title: CCDC 1481282: Experimental Crystal Structure Determination

description: Related Article: K.L. Obydennov, T.A. Kalinina, O.A. Vysokova, P.A. Slepukhin, V.A. Pozdina, M.V.' Ulitko, T.V. Glukhareva|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|795|doi:10.1107/S2053229620009328
DOI: 10.5517/ccdc.csd.cc20z1wy
title: CCDC 1875930: Experimental Crystal Structure Determination

description: Related Article: K.L. Obydennov, T.A. Kalinina, O.A. Vysokova, P.A. Slepukhin, V.A. Pozdina, M.V.' Ulitko, T.V. Glukhareva|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|795|doi:10.1107/S2053229620009328
DOI: 10.5517/ccdc.csd.cc246tw2
title: CCDC 1972774: Experimental Crystal Structure Determination

description: Related Article: K.L. Obydennov, T.A. Kalinina, O.A. Vysokova, P.A. Slepukhin, V.A. Pozdina, M.V.' Ulitko, T.V. Glukhareva|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|795|doi:10.1107/S2053229620009328
DOI: 10.5517/ccdc.csd.cc1lqdc2
title: CCDC 1481284: Experimental Crystal Structure Determination

description: Related Article: K.L. Obydennov, T.A. Kalinina, O.A. Vysokova, P.A. Slepukhin, V.A. Pozdina, M.V.' Ulitko, T.V. Glukhareva|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|795|doi:10.1107/S2053229620009328
DOI: 10.5517/ccdc.csd.cc22f4g4
title: CCDC 1919255: Experimental Crystal Structure Determination

description: Related Article: A. Cleetus, G. Rani, G.B. Dharma Rao, D. Chopra|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|786|doi:10.1107/S2053229620009560
DOI: 10.5517/ccdc.csd.cc22f4l8
title: CCDC 1919259: Experimental Crystal Structure Determination

description: Related Article: A. Cleetus, G. Rani, G.B. Dharma Rao, D. Chopra|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|786|doi:10.1107/S2053229620009560
DOI: 10.5517/ccdc.csd.cc25pwmd
title: CCDC 2017034: Experimental Crystal Structure Determination

description: Related Article: P. Romo, J. Quiroga, J. Cobo, C. Glidewell|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|779|doi:10.1107/S2053229620009791
DOI: 10.5517/ccdc.csd.cc25pwnf
title: CCDC 2017035: Experimental Crystal Structure Determination

description: Related Article: P. Romo, J. Quiroga, J. Cobo, C. Glidewell|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|779|doi:10.1107/S2053229620009791
DOI: 10.5517/ccdc.csd.cc25q020
title: CCDC 2017141: Experimental Crystal Structure Determination

description: Related Article: A. Udvardy, S. De, T.G. Gál, G. Papp, C.E. Czégéni, F. Joó|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1353|doi:10.1107/S2056989020009779
DOI: 10.5517/ccdc.csd.cc25j1qh
title: CCDC 2011426: Experimental Crystal Structure Determination

description: Related Article: T.M. Seck, P.A. Gaye, C. Ndoye, I.E. Thiam, O. Diouf, P. Retailleau, M. Gaye|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1349|doi:10.1107/S2056989020009834
DOI: 10.5517/ccdc.csd.cc14ljlv
title: CCDC 1060497: Experimental Crystal Structure Determination

description: Related Article: H. Li, B. Yan, H. Ma, X. Ma, Z. Sun, Y. Ma|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|821|doi:10.1107/S2053229620009924
DOI: 10.5517/ccdc.csd.cc23081g
title: CCDC 1936664: Experimental Crystal Structure Determination

description: Related Article: E.B. Cinar, A. Zouitini, Y. Kandri Rodi, Y. Ouzidan, J. Marrot, D. Prim, N. Dege, E. Saif|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1361|doi:10.1107/S2056989020009895
DOI: 10.5517/ccdc.csd.cc25qp1n
title: CCDC 2017791: Experimental Crystal Structure Determination

description: Related Article: F.N. Naghiyev, G.Z. Mammadova, I.G. Mamedov, A.T. Huseynova, S.T. Çelikesir, M. Akkurt, A.A. Akobirshoeva|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1365|doi:10.1107/S2056989020009871
DOI: 10.5517/ccdc.csd.cc25qpwh
title: CCDC 2017817: Experimental Crystal Structure Determination

description: Related Article: A.R. Chadeayne, D.N.K. Pham, J.A. Golen, D.R. Manke|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1357|doi:10.1107/S2056989020010014
DOI: 10.5517/ccdc.csd.cc25qpxj
title: CCDC 2017818: Experimental Crystal Structure Determination

description: Related Article: A.R. Chadeayne, D.N.K. Pham, J.A. Golen, D.R. Manke|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1357|doi:10.1107/S2056989020010014
DOI: 10.5517/ccdc.csd.cc22f4j6
title: CCDC 1919257: Experimental Crystal Structure Determination

description: Related Article: A. Cleetus, G. Rani, G.B. Dharma Rao, D. Chopra|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|786|doi:10.1107/S2053229620009560
DOI: 10.5517/ccdc.csd.cc25rttl
title: CCDC 2018900: Experimental Crystal Structure Determination

description: Related Article: A. Gupta, R. Deka, R.J. Butcher, H.B. Singh|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|828|doi:10.1107/S2053229620010311
DOI: 10.5517/ccdc.csd.cc25rtvm
title: CCDC 2018901: Experimental Crystal Structure Determination

description: Related Article: A. Gupta, R. Deka, R.J. Butcher, H.B. Singh|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|828|doi:10.1107/S2053229620010311
DOI: 10.5517/ccdc.csd.cc25nh3g
title: CCDC 2015685: Experimental Crystal Structure Determination

description: Related Article: S.V. Shishkina, I.S. Konovalova, V.R. Karpina, S.S. Kovalenko, S.M. Kovalenko, N.D. Bunyatyan|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|836|doi:10.1107/S2053229620010414