cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/ff1a230c98018e1363ffafacaf1d955b
title: Jornada Basin LTER: Wireless meteorological station at NPP G-IBPE site: 5-minute summary wind and air temperature data: 2013 - ongoing

description: 5-minute summary data at NPP G-IBPE met station. Average air temperature, wind speed and wind direction are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP G-IBPE site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.5281/zenodo.4265067
title: Atom probe characterisation of segregation driven Cu and Mn–Ni–Si co-precipitation in neutron irradiated T91 tempered-martensitic steel - data

description: Data for 'Atom probe characterisation of segregation driven Cu and Mn–Ni–Si co-precipitation in neutron irradiated T91 tempered-martensitic steel' paper (https://doi.org/10.1016/j.mtla.2020.100946)
DOI: 10.5281/zenodo.4265066
title: Atom probe characterisation of segregation driven Cu and Mn–Ni–Si co-precipitation in neutron irradiated T91 tempered-martensitic steel - data

description: Data for 'Atom probe characterisation of segregation driven Cu and Mn–Ni–Si co-precipitation in neutron irradiated T91 tempered-martensitic steel' paper (https://doi.org/10.1016/j.mtla.2020.100946)
DOI: 10.6073/pasta/9f4810c6cb63ab468a8037dcd45f2ad5
title: Jornada Basin LTER: Wireless meteorological station at NPP G-IBPE site: 30-minute summary data: 2013 - ongoing

description: 30-minute summary data at NPP G-IBPE met station. Average air temperature, relative humidity, wind speed and wind direction are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP G-IBPE site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.6073/pasta/0c7481ad745c05bf98894a311040f4a4
title: Jornada Basin LTER: Wireless meteorological station at NPP G-IBPE site: Daily summary data: 2013 - ongoing

description: Daily summary data at NPP G-IBPE met station. Average/maximum/minimum air temperature; average/maximum relative humidity and wind speed and average wind direction. These are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP G-IBPE site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.6073/pasta/c43455a1c364d7606986684f0dc116b9
title: Jornada Basin LTER: Wireless meteorological station at NPP G-IBPE site: 1-second summary precipitation data: 2013 - ongoing

description: 1-second summary precipitation data at NPP G-IBPE met station. Total precipitation per second is recorded only if there is measurable precipitation. This is recorded at an automated meteorological station installed at Jornada LTER NPP G-IBPE site.. This climate station is operated by the Jornada LTER Program. IMPORTANT: Rainfall is only recorded when a bucket tip occurs on the tipping bucket rain gauge. If there is missing data due to equipment or other problems, there will be no indication of this within this dataset. The user needs to review precipitation values in the 30 minute or hourly summary datasets to determine if there are missing data in this 1 second detailed precipitation dataset. Reference the package i.d.'s knb-lter-jrn.210437020, knb-lter-jrn.210437035, and knb-lter-jrn.210437050 for the NPP G-IBPE met station 30 minute, hourly, and daily summary datasets, respectively. This is an ONGOING dataset.
DOI: 10.5067/qfj13gegdi99
title: MERRA-2 statM_2d_edi_Nx: 2d, Single-Level, Monthly Extremes Detection Indices V1
DOI: 10.5067/hwsze7yk7l81
title: MERRA-2 tavgC_2d_ltm_Nx: 2d, Single-Level, Long Term Mean Diagnostics V1
DOI: 10.5067/jgav2vdlry9g
title: MERRA-2 statM_2d_pct_Nx: 2d, Single-Level, Monthly Percentiles V1
DOI: 10.5067/hqr1d9mpsejn
title: MERRA-2 tavgC_3d_ltm_Np: 3d, Long Term Mean 3-Dimensional Meteorological Fields V1
DOI: 10.5281/zenodo.4252783
title: Inferring quantitative species interactions of seeds and seed-feeding carabid beetles from ecological survey data

description: Here, we develop a trait-based approach suitable for creating quantitative networks, i.e. with varying interaction strengths. We applied this method to existing ecological survey data from an arable field of carabid ground beetles (Coleoptera: Carabidae) from pitfall traps and plant seeds from seed rain traps. We used existing data in the literature to predict a per-individual interaction cost index from carabid and seed size, based on frequency-dependent prey selection and the energetic intake of seeds by carabids. This was scaled up to the population level to create predicted inferred weighted networks using the sampled abundance of carabids and seeds, energetic intake rates in the literature, and assuming bottom up control. From this we calculated a novel predation pressure ratio which was the predicted seed predation (the sum of interaction strengths) relative to seed abundance. This is made available as an R markdown file with associated data files.
description: The raw FSE data can be requested from the Environmental Information Data Centre (https://catalogue.ceh.ac.uk/documents/876358e4-62f7-4386-99e1-7d3eac223e03). The seed trait data is shared with permission from Jeremy Wilson and David Gibbons. Data from seed feeding experiments were extracted from Honek et al. (2003; https://doi.org/10.14411/eje.2003.081) and Petit et al. (2014; https://doi.org/10.14411/eje.2014.086). Carabid trait data were extracted from Luff (2007; The Carabidae (ground beetles) of Britain and Ireland. RES Handbooks for the identification of British Insects).
DOI: 10.5281/zenodo.4252782
title: Inferring quantitative species interactions of seeds and seed-feeding carabid beetles from ecological survey data

description: Here, we develop a trait-based approach suitable for creating quantitative networks, i.e. with varying interaction strengths. We applied this method to existing ecological survey data from an arable field of carabid ground beetles (Coleoptera: Carabidae) from pitfall traps and plant seeds from seed rain traps. We used existing data in the literature to predict a per-individual interaction cost index from carabid and seed size, based on frequency-dependent prey selection and the energetic intake of seeds by carabids. This was scaled up to the population level to create predicted inferred weighted networks using the sampled abundance of carabids and seeds, energetic intake rates in the literature, and assuming bottom up control. From this we calculated a novel predation pressure ratio which was the predicted seed predation (the sum of interaction strengths) relative to seed abundance. This is made available as an R markdown file with associated data files.
description: The raw FSE data can be requested from the Environmental Information Data Centre (https://catalogue.ceh.ac.uk/documents/876358e4-62f7-4386-99e1-7d3eac223e03). The seed trait data is shared with permission from Jeremy Wilson and David Gibbons. Data from seed feeding experiments were extracted from Honek et al. (2003; https://doi.org/10.14411/eje.2003.081) and Petit et al. (2014; https://doi.org/10.14411/eje.2014.086). Carabid trait data were extracted from Luff (2007; The Carabidae (ground beetles) of Britain and Ireland. RES Handbooks for the identification of British Insects).
DOI: 10.6084/m9.figshare.7731164.v1
title: Stabilizing role of structural elements within the 5´ Untranslated Region (UTR) and gag sequences in Mason-Pfizer monkey virus (MPMV) genomic RNA packaging

description: The Mason-Pfizer monkey virus (MPMV) genomic RNA (gRNA) packaging signal is a highly-structured element with several stem-loops held together by two phylogenetically conserved long-range interactions (LRIs) between U5 and <i>gag</i> complementary sequences. These LRIs play a critical role in maintaining the structure of the 5´ end of the MPMV gRNA. Thus, one could hypothesize that the overall RNA secondary structure of this region is further architecturally held together by three other stem loops (SL3, Gag SL1, and Gag SL2) comprising of sequences from the distal parts of the 5´untranslated region (5ʹ UTR) to ~ 120 nucleotides into <i>gag</i>, excluding <i>gag</i> sequences involved in forming the U5-Gag LRIs. To provide functional evidence for the biological significance of these stem loops during gRNA encapsidation, these structural motifs were mutated and their effects on MPMV RNA packaging and propagation were tested in a single round <i>trans</i>-complementation assay. The mutant RNA structures were further studied by high throughput SHAPE (hSHAPE) assay. Our results reveal that sequences involved in forming these three stem loops do not play crucial roles at an individual level during MPMV gRNA packaging or propagation. Further structure–function analysis indicates that the U5-Gag LRIs have a more important architectural role in stabilizing the higher order structure of the 5´ UTR than the three stem loops which have a more secondary and perhaps indirect role in stabilizing the overall RNA secondary structure of the region. Our work provides a better understanding of the molecular interactions that take place during MPMV gRNA packaging.
DOI: 10.5281/zenodo.4265284
title: CarrefourDailyTracker

description: En el presente proyecto, hemos creado un web crawler capaz de navegar por la web del supermercado <em>Carrefour</em>, extrayendo los datos de los diferentes productos que allí se exponen y sus atributos principales (precios, ofertas y promociones). Este ejercicio tiene como finalidad entender y poner en práctica los conceptos aprendidos en la asignatura de Tipología y Ciclo de Vida de los Datos, asignatura perteneciente al Máster en Ciencia de los Datos de la Universitat Oberta de Catalunya, para realizar un Web Scraping. El dataset <em>CarrefourDailyPricing</em> está formado por una lista con los productos del supermercado <em>Carrefour</em>, guardando los datos más importantes como: la categoría del producto, su precio actual, precio por kilogramo, ofertas y promociones. El dataset <em>CarrefourDailyPricing</em> contiene los siguientes atributos: •<strong>Categoría</strong>: Sección del supermercado a la que pertenece un producto. •<strong>Descripción</strong>: Nombre que tiene asociado elproducto en el supermercado. •<strong>Precio</strong>: Precio de venta actual. •<strong>Precio Medida</strong>: Unidad de medida (Kg/L/ud/..) •<strong>Precio Previo:</strong> anterior a la oferta. •<strong>Precio Oferta:</strong> Precio de venta durante la oferta. •<strong>Promociones:</strong> Promociones activas para cada producto. •<strong>Enlace:</strong> Enlace donde se puede encontrar el producto en cuestión. La araña está programada para actualizar el dataset una vez al día, a las 8:00hrs, generando un csv nuevocogiendo los atributos ya mencionados. Este conjunto de datos busca recoger diariamente información de interés sobre cada producto en el supermercado <em>Carrefour</em>. Durante la elaboración de este proyecto, no solo buscamos la extracción de datos, sino también poder llevar un registro donde comparar las subidas y bajadas de precios en un período de tiempo determinado. Por esa razón, hemos programado la araña para que recorra la web diariamente, y así poder remarcar los productos en oferta, cada cuánto tiempo ocurren y su evolución en el tiempo. También, serviría para estudiar el impacto que tienen sobre la economía las crisis como la que vivimos actualmente del covid.
DOI: 10.5281/zenodo.4265283
title: CarrefourDailyTracker

description: En el presente proyecto, hemos creado un web crawler capaz de navegar por la web del supermercado <em>Carrefour</em>, extrayendo los datos de los diferentes productos que allí se exponen y sus atributos principales (precios, ofertas y promociones). Este ejercicio tiene como finalidad entender y poner en práctica los conceptos aprendidos en la asignatura de Tipología y Ciclo de Vida de los Datos, asignatura perteneciente al Máster en Ciencia de los Datos de la Universitat Oberta de Catalunya, para realizar un Web Scraping. El dataset <em>CarrefourDailyPricing</em> está formado por una lista con los productos del supermercado <em>Carrefour</em>, guardando los datos más importantes como: la categoría del producto, su precio actual, precio por kilogramo, ofertas y promociones. El dataset <em>CarrefourDailyPricing</em> contiene los siguientes atributos: •<strong>Categoría</strong>: Sección del supermercado a la que pertenece un producto. •<strong>Descripción</strong>: Nombre que tiene asociado elproducto en el supermercado. •<strong>Precio</strong>: Precio de venta actual. •<strong>Precio Medida</strong>: Unidad de medida (Kg/L/ud/..) •<strong>Precio Previo:</strong> anterior a la oferta. •<strong>Precio Oferta:</strong> Precio de venta durante la oferta. •<strong>Promociones:</strong> Promociones activas para cada producto. •<strong>Enlace:</strong> Enlace donde se puede encontrar el producto en cuestión. La araña está programada para actualizar el dataset una vez al día, a las 8:00hrs, generando un csv nuevocogiendo los atributos ya mencionados. Este conjunto de datos busca recoger diariamente información de interés sobre cada producto en el supermercado <em>Carrefour</em>. Durante la elaboración de este proyecto, no solo buscamos la extracción de datos, sino también poder llevar un registro donde comparar las subidas y bajadas de precios en un período de tiempo determinado. Por esa razón, hemos programado la araña para que recorra la web diariamente, y así poder remarcar los productos en oferta, cada cuánto tiempo ocurren y su evolución en el tiempo. También, serviría para estudiar el impacto que tienen sobre la economía las crisis como la que vivimos actualmente del covid.
DOI: 10.6073/pasta/c88bafe9a7d2a1c656b1e22b992f91ee
title: Jornada Basin LTER: Wireless meteorological station at NPP G-SUMM site: 5-minute summary wind and air temperature data: 2013 - ongoing

description: 5-minute summary data at NPP G-SUMM met station. Average air temperature, wind speed and wind direction are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP G-SUMM site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.6073/pasta/97acbf2c51f3b6429d18f848841d6ed9
title: Jornada Basin LTER: Wireless meteorological station at NPP G-SUMM site: 1-hour summary data: 2013 - ongoing

description: Hourly summary data at NPP G-SUMM met station. Average/maximum/minimum air temperature; average relative humidity and wind direction; average/maximum wind speed; solar radiation; albedo. These are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP G-SUMM site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. Solar radiation is measured at 3m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.5517/ccdc.csd.cc25mjn0
title: CCDC 2014772: Experimental Crystal Structure Determination

description: Related Article: Antonino Arenaza-Corona, M. Delfina Couce-Fortúnez, Andrés de Blas, David Morales-Morales, Rosa Santillan, Herbert Höpfl, Teresa Rodríguez-Blas, Victor Barba|2020|Inorg.Chem.|59|15120|doi:10.1021/acs.inorgchem.0c02068
DOI: 10.5517/ccdc.csd.cc25n6bd
title: CCDC 2015413: Experimental Crystal Structure Determination

description: Related Article: Moumita Bhattacharya, Sepehr Sebghati, Ryan T. VanderLinden, Caroline T. Saouma|2020|J.Am.Chem.Soc.|142|17589|doi:10.1021/jacs.0c07763
DOI: 10.5517/ccdc.csd.cc25pdz8
title: CCDC 2016580: Experimental Crystal Structure Determination

description: Related Article: Airi Kawamura, Jiaze Xie, Jan-Niklas Boyn, Kate A. Jesse, Andrew J. McNeece, Ethan A. Hill, Kelsey A. Collins, Juan A. Valdez-Moreira, Alexander S. Filatov, Josh W. Kurutz, David A. Mazziotti, John S. Anderson|2020|J.Am.Chem.Soc.|142|17670|doi:10.1021/jacs.0c08307
DOI: 10.5517/ccdc.csd.cc25pf0b
title: CCDC 2016581: Experimental Crystal Structure Determination

description: Related Article: Airi Kawamura, Jiaze Xie, Jan-Niklas Boyn, Kate A. Jesse, Andrew J. McNeece, Ethan A. Hill, Kelsey A. Collins, Juan A. Valdez-Moreira, Alexander S. Filatov, Josh W. Kurutz, David A. Mazziotti, John S. Anderson|2020|J.Am.Chem.Soc.|142|17670|doi:10.1021/jacs.0c08307
DOI: 10.5517/ccdc.csd.cc25pf1c
title: CCDC 2016582: Experimental Crystal Structure Determination

description: Related Article: Airi Kawamura, Jiaze Xie, Jan-Niklas Boyn, Kate A. Jesse, Andrew J. McNeece, Ethan A. Hill, Kelsey A. Collins, Juan A. Valdez-Moreira, Alexander S. Filatov, Josh W. Kurutz, David A. Mazziotti, John S. Anderson|2020|J.Am.Chem.Soc.|142|17670|doi:10.1021/jacs.0c08307
DOI: 10.5517/ccdc.csd.cc25qqrd
title: CCDC 2017844: Experimental Crystal Structure Determination

description: Related Article: Jaylan R. Billups, Zoe N. Fokakis, Sidney E. Creutz|2020|Inorg.Chem.|59|15228|doi:10.1021/acs.inorgchem.0c02211
DOI: 10.5517/ccdc.csd.cc25qqsf
title: CCDC 2017845: Experimental Crystal Structure Determination

description: Related Article: Jaylan R. Billups, Zoe N. Fokakis, Sidney E. Creutz|2020|Inorg.Chem.|59|15228|doi:10.1021/acs.inorgchem.0c02211
DOI: 10.5517/ccdc.csd.cc25qqtg
title: CCDC 2017846: Experimental Crystal Structure Determination

description: Related Article: Jaylan R. Billups, Zoe N. Fokakis, Sidney E. Creutz|2020|Inorg.Chem.|59|15228|doi:10.1021/acs.inorgchem.0c02211
DOI: 10.5517/ccdc.csd.cc25r8z6
title: CCDC 2018378: Experimental Crystal Structure Determination

description: Related Article: Rubén Turo-Cortés, Carlos Bartual-Murgui, Javier Castells-Gil, M. Carmen Muñoz, Carlos Martí-Gastaldo, José Antonio Real|2020|Chemical Science|11|11224|doi:10.1039/D0SC04246C
DOI: 10.5517/ccdc.csd.cc25r908
title: CCDC 2018379: Experimental Crystal Structure Determination

description: Related Article: Rubén Turo-Cortés, Carlos Bartual-Murgui, Javier Castells-Gil, M. Carmen Muñoz, Carlos Martí-Gastaldo, José Antonio Real|2020|Chemical Science|11|11224|doi:10.1039/D0SC04246C
DOI: 10.5517/ccdc.csd.cc25r92b
title: CCDC 2018381: Experimental Crystal Structure Determination

description: Related Article: Rubén Turo-Cortés, Carlos Bartual-Murgui, Javier Castells-Gil, M. Carmen Muñoz, Carlos Martí-Gastaldo, José Antonio Real|2020|Chemical Science|11|11224|doi:10.1039/D0SC04246C
DOI: 10.5517/ccdc.csd.cc25r93c
title: CCDC 2018382: Experimental Crystal Structure Determination

description: Related Article: Rubén Turo-Cortés, Carlos Bartual-Murgui, Javier Castells-Gil, M. Carmen Muñoz, Carlos Martí-Gastaldo, José Antonio Real|2020|Chemical Science|11|11224|doi:10.1039/D0SC04246C
DOI: 10.5517/ccdc.csd.cc25rpc0
title: CCDC 2018762: Experimental Crystal Structure Determination

description: Related Article: Haibin Qu, Shasha Jin, Junbo Gong, Shichao Du, Lina Jia, Songgu Wu|2020|Cryst.Growth Des.|20|7356|doi:10.1021/acs.cgd.0c01038
DOI: 10.5517/ccdc.csd.cc25rpd1
title: CCDC 2018763: Experimental Crystal Structure Determination

description: Related Article: Haibin Qu, Shasha Jin, Junbo Gong, Shichao Du, Lina Jia, Songgu Wu|2020|Cryst.Growth Des.|20|7356|doi:10.1021/acs.cgd.0c01038
DOI: 10.5517/ccdc.csd.cc25ryb7
title: CCDC 2019009: Experimental Crystal Structure Determination

description: Related Article: Siwattra Choodej, Sujitra Hanthanong, Thammarat Aree, Khanitha Pudhom|2020|Phytochemistry|180|112523|doi:10.1016/j.phytochem.2020.112523
DOI: 10.5517/ccdc.csd.cc25sd3h
title: CCDC 2019436: Experimental Crystal Structure Determination

description: Related Article: Harsh S. Shah, Kaushalendra Chaturvedi, Rutesh H. Dave, Simon Bates, Rahul V. Haware, Kenneth R. Morris|2020|Cryst.Growth Des.|20|7328|doi:10.1021/acs.cgd.0c01003
DOI: 10.5517/ccdc.csd.cc25svpj
title: CCDC 2019888: Experimental Crystal Structure Determination

description: Related Article: Shen Zhang, Wen-Min Wang, Yan Guo, Chao-Ying Li, Yu-Yu Guo, Yin-Ling Hou|2020|Polyhedron|191|114808|doi:10.1016/j.poly.2020.114808
DOI: 10.5517/ccdc.csd.cc25tgq6
title: CCDC 2020478: Experimental Crystal Structure Determination

description: Related Article: Luca Buzzetti, Mikus Puriņš, Phillip D. G. Greenwood, Jerome Waser|2020|J.Am.Chem.Soc.|142|17334|doi:10.1021/jacs.0c09177
DOI: 10.5517/ccdc.csd.cc25mb9g
title: CCDC 2014575: Experimental Crystal Structure Determination

description: Related Article: Jean-François Longevial, Kazuya Miyagawa, Hiroshi Shinokubo|2020|Dalton Trans.|49|14786|doi:10.1039/D0DT02727H
DOI: 10.5517/ccdc.csd.cc25v103
title: CCDC 2021014: Experimental Crystal Structure Determination

description: Related Article: Hui-Min Wen, Kai Shao, Wei Zhou, Bin Li, Banglin Chen|2020|Chem.Commun.|56|13117|doi:10.1039/D0CC05504B
DOI: 10.5517/ccdc.csd.cc25vyvv
title: CCDC 2021908: Experimental Crystal Structure Determination

description: Related Article: Yuzhong Wang, Yaoming Xie, Pingrong Wei, Soshawn A. Blair, Dongtao Cui, Michael K. Johnson, Henry F. Schaefer, Gregory H. Robinson|2020|J.Am.Chem.Soc.|142|17301|doi:10.1021/jacs.0c08495
DOI: 10.5517/ccdc.csd.cc25vyww
title: CCDC 2021909: Experimental Crystal Structure Determination

description: Related Article: Yuzhong Wang, Yaoming Xie, Pingrong Wei, Soshawn A. Blair, Dongtao Cui, Michael K. Johnson, Henry F. Schaefer, Gregory H. Robinson|2020|J.Am.Chem.Soc.|142|17301|doi:10.1021/jacs.0c08495
DOI: 10.5517/ccdc.csd.cc25vyxx
title: CCDC 2021910: Experimental Crystal Structure Determination

description: Related Article: Yuzhong Wang, Yaoming Xie, Pingrong Wei, Soshawn A. Blair, Dongtao Cui, Michael K. Johnson, Henry F. Schaefer, Gregory H. Robinson|2020|J.Am.Chem.Soc.|142|17301|doi:10.1021/jacs.0c08495
DOI: 10.5517/ccdc.csd.cc25vyyy
title: CCDC 2021911: Experimental Crystal Structure Determination

description: Related Article: Yuzhong Wang, Yaoming Xie, Pingrong Wei, Soshawn A. Blair, Dongtao Cui, Michael K. Johnson, Henry F. Schaefer, Gregory H. Robinson|2020|J.Am.Chem.Soc.|142|17301|doi:10.1021/jacs.0c08495
DOI: 10.5517/ccdc.csd.cc25b14q
title: CCDC 2005642: Experimental Crystal Structure Determination

description: Related Article: Anna Carissa M. San Esteban, Naoto Kuwamura, Tatsuhiro Kojima, Takumi Konno|2020|Inorg.Chem.|59|14847|doi:10.1021/acs.inorgchem.0c01775
DOI: 10.5517/ccdc.csd.cc25b15r
title: CCDC 2005643: Experimental Crystal Structure Determination

description: Related Article: Anna Carissa M. San Esteban, Naoto Kuwamura, Tatsuhiro Kojima, Takumi Konno|2020|Inorg.Chem.|59|14847|doi:10.1021/acs.inorgchem.0c01775
DOI: 10.5517/ccdc.csd.cc25w9gv
title: CCDC 2022237: Experimental Crystal Structure Determination

description: Related Article: Jitti Suebphanpho, Suttipong Wannapaiboon, Sujittra Youngme, Jaursup Boonmak|2020|Cryst.Growth Des.|20|7439|doi:10.1021/acs.cgd.0c01113
DOI: 10.5517/ccdc.csd.cc25w9hw
title: CCDC 2022238: Experimental Crystal Structure Determination

description: Related Article: Jitti Suebphanpho, Suttipong Wannapaiboon, Sujittra Youngme, Jaursup Boonmak|2020|Cryst.Growth Des.|20|7439|doi:10.1021/acs.cgd.0c01113
DOI: 10.5517/ccdc.csd.cc25hp9p
title: CCDC 2011072: Experimental Crystal Structure Determination

description: Related Article: Guo-Xia Jin, Teng Wang, Yanyan Sun, Yu-Long Li, Jian-Ping Ma|2020|Inorg.Chem.|59|15019|doi:10.1021/acs.inorgchem.0c01845