cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5341804
title: Indoor Wireless Deterministic Anycast Transmissions Data from the FIT IoT-Lab testbed

description: This dataset contains the raw openwsn results generated by indoor experiments. The data was collected on the FIT IoT-Lab platform, using the m3 motes with a AT86RF231 radio chip, on the Grenoble's site. We rely on the following workflow: a modified version of openwsn that implements anycast transmissions at the link layer (CCA branch, https://github.com/ftheoleyre/openwsn-fw/releases/tag/duocast-mswim21). The firmware is implemented in C, and is executed by the m3 motes; a modified version of openvisualizer (https://github.com/ftheoleyre/openvisualizer/releases/tag/mswim21) a tool to process the dataset and compute the metrics: end-to-end reliability, number of transmissions, CCA events, etc. (https://github.com/ftheoleyre/openwsn-data/releases/tag/mswim21-duocast)
description: {"references": ["Fabrice Theoleyre, \"Duocast for Wireless Industrial Networks: an Experimental Study\", ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), Alicante (Spain), 2021"]}
DOI: 10.48493/6djk-mv61
title: Fourier Transform Infrared Spectroscopy data from archaeological and palaeontological tooth enamel
DOI: 10.6084/m9.figshare.16441584
title: Cav_agg_submission_data_zip.zip

description: Data set for the paper Metamaterial analogues of strongly coupled molecular ensemblesin ACS photonics
DOI: 10.6084/m9.figshare.16441584.v1
title: Cav_agg_submission_data_zip.zip

description: Data set for the paper Metamaterial analogues of strongly coupled molecular ensemblesin ACS photonics
DOI: 10.34740/kaggle/dsv/2575076
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.5345911
title: Oxypoda opaca SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5345910
title: Oxypoda opaca SPAdes preassembly

description: .
DOI: 10.15468/fz5qr3
title: Delicatophycus liuweii sp. nov., a new cymbelloid diatom (Bacillariophyceae) from an upper tributary of the Liujiang River, Guangxi, China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Liu, Wei, Li, Yan-Ling, Wu, Han, Kociolek, John Patrick (2021): Delicatophycus liuweii sp. nov., a new cymbelloid diatom (Bacillariophyceae) from an upper tributary of the Liujiang River, Guangxi, China. Phytotaxa 505 (1): 63-70, DOI: 10.11646/phytotaxa.505.1.4, URL: http://dx.doi.org/10.11646/phytotaxa.505.1.4
DOI: 10.18112/openneuro.ds003419.v1.0.2
title: MRI Lab Graz: A Two-Week Running Intervention Reduces Symptoms Related to Depression and Increases Hippocampal Volume in Young Adults
DOI: 10.5255/ukda-sn-855116
title: COVID-19: Burden and Impact in Care Homes: A Mixed Methods Study, 2020-2021

description: COVID-19 causes significant mortality in elderly and vulnerable people and spreads easily in care homes where one in seven individuals aged &gt; 85 years live. However, there is no surveillance for infection in care homes, nor are there systems (or research studies) monitoring the impact of the pandemic on individuals or systems. Usual practices are disrupted during the pandemic, and care home staff are taking on new and unfamiliar roles, such as advanced care planning. Understanding the nature of these changes is critical to mitigate the impact of COVID-19 on residents, relatives and staff. 20 care homes staff members were interviewed using semi-structured interviews.
DOI: 10.34740/kaggle/dsv/2575105
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.25345/c5583z
title: MassIVE MSV000088036 - Metabolomics analysis highlights Yashtimadhu (Glycyrrhiza glabra L.) mediated neuroprotection in a rotenone-induced cellular model of Parkinsons disease
title: In the present study we carried out global and targeted metabolomics of differentiated IMR32 cells to study the neuroprotective mechanism of Yashtimadhu (Glycyrrhiza glabra L.) in rotenone induced cellular model of parkinsons disease. Our mass spectrometry data highlighted 2403 and 2934 aligned metabolites from the positive and negative modes respectively. Among the aligned metabolites, 756 metabolites from positive and 731 metabolites from negative polarity, were mapped to a known metabolite, and the others remained unassigned. A total of 1,102 non-redundant metabolites from the positive and negative modes were assigned.
DOI: 10.5281/zenodo.5345641
title: DRIP: Segmenting Individual Requirements from the Paragraphs in Software Requirement Documents

description: Software Requirement Document datasets in past 5 years of RE, ICSE, REFSQ, REJ
DOI: 10.5281/zenodo.5346337
title: Stevia rebaudiana SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5346336
title: Stevia rebaudiana SPAdes preassembly

description: .
DOI: 10.5281/zenodo.4715924
title: Increased signal to noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components.

description: Data and scripts used to perform all analyses and produce all figures for publication.
DOI: 10.5281/zenodo.4715915
title: Increased signal to noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components.

description: Data and scripts used to perform all analyses and produce all figures for publication.
DOI: 10.5281/zenodo.5345808
title: Indicators of water quality in the Puruvesi Lake from 2008 to 2013 (OAL-FI)

description: This dataset contains monthly water quality indicators ( <strong>Chlorophyll-a, Particulate inorganic carbon (PIC) and Particulate organic carbon (P)C)) </strong>derived from Remote Sensing MODIS observations from 2008 to 2013. <strong>Chlorophyll-a</strong> Chlorophyll-a is a measure of the amount of algae growing in a waterbody. It can be used to classify the trophic condition of a waterbody. Although algae are a natural part of freshwater ecosystems, too much algae can cause aesthetic problems such as green scums and bad odors, and can result in decreased levels of dissolved oxygen. Some algae also produce toxins that can be of public health concern when they are found in high concentrations. <strong>Particulate inorganic carbon (PIC)</strong> Particulate inorganic carbon or calcium carbonate, is a major component of the global ocean carbon cycle. Through the process of calcification, marine organisms produce PIC shells and carbon dioxide from calcium ions and bicarbonate in seawater. These organisms, upon death, eventually sink to the ocean floor. During this process, the calcium carbonate shells may partially dissolve and what remains accounts for about 75% of carbon deposition on the seafloor (Groom & Holligan, <strong>1987</strong>). Moreover, calcium carbonate production leads to an increase in partial pressure of dissolved carbon dioxide in the surface layer of the ocean, weakening the effectiveness of the carbon dioxide sink produced by photosynthesis (Shutler et al., <strong>2013</strong>). <strong>Particulate onorganic carbon (POC)</strong> is one of the main pools of organic carbon found in the ocean. It is composed of living material (Phytoplankton, zooplanton, bacteria etc) and detritus. POC is important in terms of the global carbon cycle and it is the main pathway by which organic carbon formed via photosynthesis in the oceans surface layers is transferred to deeper ocean layers where it may be sequestered. In order to maintain the results in a easy to use platform, a GIS project is created using QGIS software (open source) to easily manage, edit, export and create layouts from the results. The layers are organized as follows: <strong>OPERANDUM_QGIS_prj.qgz</strong><strong>:</strong> main QGIS project file that includes all layers <strong>PuruvesiLake</strong><strong> (shapefile):</strong> Puruvesi lake exact boundary <strong>PuruvesiLake_with_buffer (shapefile): </strong>Puruvesi lake boundary with a buffer of 1 km to make sure that marginal pixels are within the boundary for the analysis <strong>P</strong><strong>I</strong><strong>CMa</strong><strong>p</strong><strong>_MODIS: </strong>60 monthly layers of estimated Particulate inorganic carbon from January 2008 until January 2013 <strong>Chlorophyll-a</strong><strong>:</strong> 60 monthly layers of estimated Chlorophyll-a content from January 2008 until January 2013 <strong>POCMap_MODIS</strong><strong>:</strong> 60 monthly layers of estimated Particulate organic carbon from January 2008 until January 2013
DOI: 10.5281/zenodo.5345807
title: Indicators of water quality in the Puruvesi Lake from 2008 to 2013 (OAL-FI)

description: This dataset contains monthly water quality indicators ( <strong>Chlorophyll-a, Particulate inorganic carbon (PIC) and Particulate organic carbon (P)C)) </strong>derived from Remote Sensing MODIS observations from 2008 to 2013. <strong>Chlorophyll-a</strong> Chlorophyll-a is a measure of the amount of algae growing in a waterbody. It can be used to classify the trophic condition of a waterbody. Although algae are a natural part of freshwater ecosystems, too much algae can cause aesthetic problems such as green scums and bad odors, and can result in decreased levels of dissolved oxygen. Some algae also produce toxins that can be of public health concern when they are found in high concentrations. <strong>Particulate inorganic carbon (PIC)</strong> Particulate inorganic carbon or calcium carbonate, is a major component of the global ocean carbon cycle. Through the process of calcification, marine organisms produce PIC shells and carbon dioxide from calcium ions and bicarbonate in seawater. These organisms, upon death, eventually sink to the ocean floor. During this process, the calcium carbonate shells may partially dissolve and what remains accounts for about 75% of carbon deposition on the seafloor (Groom & Holligan, <strong>1987</strong>). Moreover, calcium carbonate production leads to an increase in partial pressure of dissolved carbon dioxide in the surface layer of the ocean, weakening the effectiveness of the carbon dioxide sink produced by photosynthesis (Shutler et al., <strong>2013</strong>). <strong>Particulate onorganic carbon (POC)</strong> is one of the main pools of organic carbon found in the ocean. It is composed of living material (Phytoplankton, zooplanton, bacteria etc) and detritus. POC is important in terms of the global carbon cycle and it is the main pathway by which organic carbon formed via photosynthesis in the oceans surface layers is transferred to deeper ocean layers where it may be sequestered. In order to maintain the results in a easy to use platform, a GIS project is created using QGIS software (open source) to easily manage, edit, export and create layouts from the results. The layers are organized as follows: <strong>OPERANDUM_QGIS_prj.qgz</strong><strong>:</strong> main QGIS project file that includes all layers <strong>PuruvesiLake</strong><strong> (shapefile):</strong> Puruvesi lake exact boundary <strong>PuruvesiLake_with_buffer (shapefile): </strong>Puruvesi lake boundary with a buffer of 1 km to make sure that marginal pixels are within the boundary for the analysis <strong>P</strong><strong>I</strong><strong>CMa</strong><strong>p</strong><strong>_MODIS: </strong>60 monthly layers of estimated Particulate inorganic carbon from January 2008 until January 2013 <strong>Chlorophyll-a</strong><strong>:</strong> 60 monthly layers of estimated Chlorophyll-a content from January 2008 until January 2013 <strong>POCMap_MODIS</strong><strong>:</strong> 60 monthly layers of estimated Particulate organic carbon from January 2008 until January 2013
DOI: 10.5281/zenodo.5336400
title: Extent of severe flooding events in the Panaro river, Italy (OAL-IT)

description: The ambition of H2020 OPERANDUM project is to develop and document Nature Based Solutions (NBS) to mitigate risks associated with hydro-meteorological (HM) hazards. NBS mitigate risks by reducing the vulnerability of a particular system. The aim of this work is to demonstrate the use of multisource remote sensing data in documenting the impact of extreme HM events to advance knowledge on vulnerability and exposure. In particular the focus is to document past impacts due to extreme events selected from a characterization of recent (30 years) HM events in 11 Open Air Laboratories (OALs) where co-design, co-development and deployment of NBS are taking place. The impacts were documented by applying a wide spectrum of satellite image data and other, close – range, remote sensing techniques. A better understanding of the consequences due to extreme HM events in a particular area (OALs) is essential to identify elements at risk and expected to provide a reference to evaluate the reduction of vulnerability and mitigation of risks past the completion of NBS. This dataset contains Remote Sensing observations for the OAL-Italy , notably: - Flood maps of the identified extreme flood events occurred in the Panaro river (Italy) in the last 30 years derived by Space-borne Remote Sensing observations - Top and Bottom of Atmosphere reflectance, RS indicators and SAR backscatter used to derive the flood maps.
DOI: 10.5281/zenodo.5336399
title: Extent of severe flooding events in the Panaro river, Italy (OAL-IT)

description: The ambition of H2020 OPERANDUM project is to develop and document Nature Based Solutions (NBS) to mitigate risks associated with hydro-meteorological (HM) hazards. NBS mitigate risks by reducing the vulnerability of a particular system. The aim of this work is to demonstrate the use of multisource remote sensing data in documenting the impact of extreme HM events to advance knowledge on vulnerability and exposure. In particular the focus is to document past impacts due to extreme events selected from a characterization of recent (30 years) HM events in 11 Open Air Laboratories (OALs) where co-design, co-development and deployment of NBS are taking place. The impacts were documented by applying a wide spectrum of satellite image data and other, close – range, remote sensing techniques. A better understanding of the consequences due to extreme HM events in a particular area (OALs) is essential to identify elements at risk and expected to provide a reference to evaluate the reduction of vulnerability and mitigation of risks past the completion of NBS. This dataset contains Remote Sensing observations for the OAL-Italy , notably: - Flood maps of the identified extreme flood events occurred in the Panaro river (Italy) in the last 30 years derived by Space-borne Remote Sensing observations - Top and Bottom of Atmosphere reflectance, RS indicators and SAR backscatter used to derive the flood maps.
DOI: 10.15468/krjes5
title: The chimeric Onobrychis tenoreana (Fabaceae) and lectotypification of the names O. laconica and O. pentelica

description: This dataset contains the digitized treatments in Plazi based on the original journal article Guacchio, Emanuele Del, Bernardo, Liliana, Iamonico, Duilio (2021): The chimeric Onobrychis tenoreana (Fabaceae) and lectotypification of the names O. laconica and O. pentelica. Phytotaxa 505 (2): 240-244, DOI: 10.11646/phytotaxa.505.2.11, URL: http://dx.doi.org/10.11646/phytotaxa.505.2.11
DOI: 10.5281/zenodo.4001564
title: Antarctic time series of temperature, precipitation, and stable isotopes in precipitation from the ECHAM5/MPI-OM-wiso past1000 climate model simulation

description: This data set contains time series of two-metre air temperature (tas), surface temperature (ts), total precipitation (pr), oxygen-18 isotopic composition in precipitation (oxy), and deuterium isotopic composition in precipitation (dtr) from the past-millennium (800-1999 CE) simulation of the fully coupled ECHAM5/MPI-OM-wiso atmosphere-ocean general circulation model equipped with stable isotope diagnostics (Sjolte et al., 2018, Werner et al., 2016) used in the publication of Münch et al. (2021). The data here are provided for the Antarctic region, i.e., all model grid cells south of 60° S. The model's atmospheric component was run with a T31 spectral resolution (3.75° x 3.75°) and with 19 vertical levels, resulting in a total of N = 768 model grid cells covered by this data set. Note, however, that all time series off the continent of Antarctica have been set to NA values, so that the effectively available number of model grid cells is N<sub>eff</sub> = 442. Time series are provided at the original monthly resolution of the model output and on annual resolution obtained from the monthly resolution data. At annual resolution, the temperature and isotopic composition data are available as normal time averages and as precipitation-weighted time averages. In addition to the time series, the spatial field of time-invariant means is supplied, also as normal and precipitation-weighted time averages. Data are available as netcdf files and as R data files. In addition, processing code (bash and R scripts) are provided to reproduce the processing from monthly to annnual and time-invariant resolution and to read the data into the R data format. To process the R data, you will need the CRAN packages "ncdf4" and "lubridate", and the package "pfields" available on GitHub (see References).
description: {"references": ["pfields: An R package to analyse gridded field data. https://github.com/EarthSystemDiagnostics/pfields"]}
DOI: 10.5517/ccdc.csd.cc25ydr9
title: CCDC 2024261: Experimental Crystal Structure Determination

description: Related Article: Zhen Xu, Duane Hean, Clàudia Climent, David Casanova, Michael O. Wolf|2021|Mat.Advs.|2|5777|doi:10.1039/D1MA00314C
DOI: 10.5517/ccdc.csd.cc25ydsb
title: CCDC 2024262: Experimental Crystal Structure Determination

description: Related Article: Zhen Xu, Duane Hean, Clàudia Climent, David Casanova, Michael O. Wolf|2021|Mat.Advs.|2|5777|doi:10.1039/D1MA00314C
DOI: 10.5517/ccdc.csd.cc25ydtc
title: CCDC 2024263: Experimental Crystal Structure Determination

description: Related Article: Zhen Xu, Duane Hean, Clàudia Climent, David Casanova, Michael O. Wolf|2021|Mat.Advs.|2|5777|doi:10.1039/D1MA00314C
DOI: 10.5517/ccdc.csd.cc25ydvd
title: CCDC 2024264: Experimental Crystal Structure Determination

description: Related Article: Zhen Xu, Duane Hean, Clàudia Climent, David Casanova, Michael O. Wolf|2021|Mat.Advs.|2|5777|doi:10.1039/D1MA00314C
DOI: 10.5517/ccdc.csd.cc25ydwf
title: CCDC 2024265: Experimental Crystal Structure Determination

description: Related Article: Zhen Xu, Duane Hean, Clàudia Climent, David Casanova, Michael O. Wolf|2021|Mat.Advs.|2|5777|doi:10.1039/D1MA00314C
DOI: 10.5517/ccdc.csd.cc25ydxg
title: CCDC 2024266: Experimental Crystal Structure Determination

description: Related Article: Zhen Xu, Duane Hean, Clàudia Climent, David Casanova, Michael O. Wolf|2021|Mat.Advs.|2|5777|doi:10.1039/D1MA00314C
DOI: 10.5517/ccdc.csd.cc25ydyh
title: CCDC 2024267: Experimental Crystal Structure Determination

description: Related Article: Zhen Xu, Duane Hean, Clàudia Climent, David Casanova, Michael O. Wolf|2021|Mat.Advs.|2|5777|doi:10.1039/D1MA00314C
DOI: 10.5517/ccdc.csd.cc25yf1m
title: CCDC 2024270: Experimental Crystal Structure Determination

description: Related Article: Zhen Xu, Duane Hean, Clàudia Climent, David Casanova, Michael O. Wolf|2021|Mat.Advs.|2|5777|doi:10.1039/D1MA00314C
DOI: 10.5517/ccdc.csd.cc25yzz3
title: CCDC 2024826: Experimental Crystal Structure Determination

description: Related Article: Pavel A. Demakov, Alexey A. Ryadun, Pavel V. Dorovatovskii, Vladimir A. Lazarenko, Denis G. Samsonenko, Konstantin A. Brylev, Vladimir P. Fedin, Danil N. Dybtsev|2021|Dalton Trans.|50|11899|doi:10.1039/D1DT00872B
DOI: 10.5517/ccdc.csd.cc25z006
title: CCDC 2024827: Experimental Crystal Structure Determination

description: Related Article: Pavel A. Demakov, Alexey A. Ryadun, Pavel V. Dorovatovskii, Vladimir A. Lazarenko, Denis G. Samsonenko, Konstantin A. Brylev, Vladimir P. Fedin, Danil N. Dybtsev|2021|Dalton Trans.|50|11899|doi:10.1039/D1DT00872B
DOI: 10.5517/ccdc.csd.cc25z017
title: CCDC 2024828: Experimental Crystal Structure Determination

description: Related Article: Pavel A. Demakov, Alexey A. Ryadun, Pavel V. Dorovatovskii, Vladimir A. Lazarenko, Denis G. Samsonenko, Konstantin A. Brylev, Vladimir P. Fedin, Danil N. Dybtsev|2021|Dalton Trans.|50|11899|doi:10.1039/D1DT00872B
DOI: 10.5517/ccdc.csd.cc25z028
title: CCDC 2024829: Experimental Crystal Structure Determination

description: Related Article: Pavel A. Demakov, Alexey A. Ryadun, Pavel V. Dorovatovskii, Vladimir A. Lazarenko, Denis G. Samsonenko, Konstantin A. Brylev, Vladimir P. Fedin, Danil N. Dybtsev|2021|Dalton Trans.|50|11899|doi:10.1039/D1DT00872B
DOI: 10.5517/ccdc.csd.cc25z039
title: CCDC 2024830: Experimental Crystal Structure Determination

description: Related Article: Pavel A. Demakov, Alexey A. Ryadun, Pavel V. Dorovatovskii, Vladimir A. Lazarenko, Denis G. Samsonenko, Konstantin A. Brylev, Vladimir P. Fedin, Danil N. Dybtsev|2021|Dalton Trans.|50|11899|doi:10.1039/D1DT00872B
DOI: 10.5517/ccdc.csd.cc25z04b
title: CCDC 2024831: Experimental Crystal Structure Determination

description: Related Article: Pavel A. Demakov, Alexey A. Ryadun, Pavel V. Dorovatovskii, Vladimir A. Lazarenko, Denis G. Samsonenko, Konstantin A. Brylev, Vladimir P. Fedin, Danil N. Dybtsev|2021|Dalton Trans.|50|11899|doi:10.1039/D1DT00872B
DOI: 10.5517/ccdc.csd.cc265hfb
title: CCDC 2031071: Experimental Crystal Structure Determination

description: Related Article: Niroshani S. Abeynayake, Julio Zamora-Moreno, Saidulu Gorla, Bruno Donnadieu, Miguel A. Muñoz-Hernández, Virginia Montiel-Palma|2021|Dalton Trans.|50|11783|doi:10.1039/D1DT00677K
DOI: 10.5517/ccdc.csd.cc265hhd
title: CCDC 2031073: Experimental Crystal Structure Determination

description: Related Article: Niroshani S. Abeynayake, Julio Zamora-Moreno, Saidulu Gorla, Bruno Donnadieu, Miguel A. Muñoz-Hernández, Virginia Montiel-Palma|2021|Dalton Trans.|50|11783|doi:10.1039/D1DT00677K
DOI: 10.5517/ccdc.csd.cc135rhj
title: CCDC 1018427: Experimental Crystal Structure Determination

description: Related Article: Jie Yang, Shunlin Zhang, Zhe Feng, Ying Cao, Dun-Ru Zhu|2021|Dalton Trans.|50|11975|doi:10.1039/D1DT02116H
DOI: 10.5517/ccdc.csd.cc265hjf
title: CCDC 2031074: Experimental Crystal Structure Determination

description: Related Article: Niroshani S. Abeynayake, Julio Zamora-Moreno, Saidulu Gorla, Bruno Donnadieu, Miguel A. Muñoz-Hernández, Virginia Montiel-Palma|2021|Dalton Trans.|50|11783|doi:10.1039/D1DT00677K
DOI: 10.5517/ccdc.csd.cc1p987h
title: CCDC 1558036: Experimental Crystal Structure Determination

description: Related Article: Jie Yang, Shunlin Zhang, Zhe Feng, Ying Cao, Dun-Ru Zhu|2021|Dalton Trans.|50|11975|doi:10.1039/D1DT02116H
DOI: 10.5517/ccdc.csd.cc26cmy5
title: CCDC 2036977: Experimental Crystal Structure Determination

description: Related Article: Nicholas R. Wiedmaier, Hartmut Schubert, Hermann A. Mayer, Lars Wesemann|2021|Dalton Trans.|50|11814|doi:10.1039/D1DT02266K
DOI: 10.5517/ccdc.csd.cc26cmz6
title: CCDC 2036978: Experimental Crystal Structure Determination

description: Related Article: Nicholas R. Wiedmaier, Hartmut Schubert, Hermann A. Mayer, Lars Wesemann|2021|Dalton Trans.|50|11814|doi:10.1039/D1DT02266K
DOI: 10.5517/ccdc.csd.cc26cn08
title: CCDC 2036979: Experimental Crystal Structure Determination

description: Related Article: Nicholas R. Wiedmaier, Hartmut Schubert, Hermann A. Mayer, Lars Wesemann|2021|Dalton Trans.|50|11814|doi:10.1039/D1DT02266K
DOI: 10.5517/ccdc.csd.cc106mvp
title: CCDC 929902: Experimental Crystal Structure Determination

description: Related Article: Jie Yang, Shunlin Zhang, Zhe Feng, Ying Cao, Dun-Ru Zhu|2021|Dalton Trans.|50|11975|doi:10.1039/D1DT02116H
DOI: 10.5517/ccdc.csd.cc26wnj9
title: CCDC 2052371: Experimental Crystal Structure Determination

description: Related Article: Zhen Xu, Duane Hean, Clàudia Climent, David Casanova, Michael O. Wolf|2021|Mat.Advs.|2|5777|doi:10.1039/D1MA00314C
DOI: 10.5517/ccdc.csd.cc27d62y
title: CCDC 2067299: Experimental Crystal Structure Determination

description: Related Article: Atsuko Tabuchi, Teruaki Hayakawa, Shigeki Kuwata, Ryohei Ishige, Shinji Ando|2021|Mat.Advs.|2|5629|doi:10.1039/D1MA00308A