cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13134860.v1
title: Positive cascading effects of epiphytes enhance the persistence of a habitat-forming macroalga and the biodiversity of the associated invertebrate community under increasing stress

description: CorrelativeStudy_Dataset_Data in this dataset are used to analyze the biomass of the macroalga, <i>H. incurva,</i> and that of its epiphyte, <i>J. rubens</i> along a depth gradient in December 2016 and April 2017. In addition, data in this dataset are used to analyze the photosynthetic efficiency of <i>H. incurva</i> along the depth gradient at both sampling dates.<br>Dataset_Experiments_Data in this dataset are used to analyze the physiology of <i>H. incurva</i> (photosynthetic efficiency, photosynthetic pigments and secondary metabolites, reproductive output) and the structure of the associated invertebrate assemblage under different experimental conditions, in both shallow and deep habitats.
DOI: 10.5517/ccdc.csd.cc24yvpn
title: CCDC 1994902: Experimental Crystal Structure Determination

description: Related Article: Subhasis Paul, Joyram Guin|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc24yxz0
title: CCDC 1994973: Experimental Crystal Structure Determination

description: Related Article: Subhasis Paul, Joyram Guin|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc24z99r
title: CCDC 1995324: Experimental Crystal Structure Determination

description: Related Article: Subhasis Paul, Joyram Guin|2020|CSD Communication|||
DOI: 10.6084/m9.figshare.13134953
title: Effects of different plants extract by methanol on inhibition percentages of mycelial growth of the Fusarium species

description: <b>Effects of different plants extract by methanol on inhibition percentages of mycelial growth of the <i>Fusarium</i> species. </b>Mycelia were collected after seven days of incubation on PDA at 35°C in dark, 90 mm Petri plates were used to culture the tested fungus.
DOI: 10.6084/m9.figshare.13134959
title: Transplant Observatory data

description: Obtained from the WHO transplant observatory website.
DOI: 10.6084/m9.figshare.13134962
title: Effect of antagonistic agents on inhibition of mycelial growth of the Fusarium species.

description: Effect of <i>Escherichia coli</i>, <i>Rhizobium phaseoli,</i> <i>Rhizobium leguminosarum</i>, <i>Neofusicoccum mangifera,</i> <i>Trichoderma viride</i> and <i>Pestalotiopsis sp.</i>, on inhibition of mycelial growth of the <i>Fusarium</i> species. Mycelia were collected after seven days of incubation on PDA at 35°C in dark.
DOI: 10.6084/m9.figshare.13134962.v1
title: Effect of antagonistic agents on inhibition of mycelial growth of the Fusarium species.

description: Effect of <i>Escherichia coli</i>, <i>Rhizobium phaseoli,</i> <i>Rhizobium leguminosarum</i>, <i>Neofusicoccum mangifera,</i> <i>Trichoderma viride</i> and <i>Pestalotiopsis sp.</i>, on inhibition of mycelial growth of the <i>Fusarium</i> species. Mycelia were collected after seven days of incubation on PDA at 35°C in dark.
DOI: 10.5281/zenodo.4118228
title: Maximum moisture content of contemporary birch bark.

description: Data accompanying the article published on the Icom-CC proceedings 2017 Copenhagen: https://www.icom-cc-publications-online.org/PublicationDetail.aspx?cid=92b3e81e-ba73-46d2-875a-0fe7b3b4ffb6
DOI: 10.5281/zenodo.4118227
title: Maximum moisture content of contemporary birch bark.

description: Data accompanying the article published on the Icom-CC proceedings 2017 Copenhagen: https://www.icom-cc-publications-online.org/PublicationDetail.aspx?cid=92b3e81e-ba73-46d2-875a-0fe7b3b4ffb6
DOI: 10.25345/c5db7b
title: MassIVE MSV000086360 - Bacterized plants 2 - control and 3610
title: Comparison of samples from the whole plant after seed bacterization with 3610 compared to the non-bacterized plant
DOI: 10.17028/rd.lboro.13084316
title: Dataset 1 - Ten Hashtags on Twitter

description: The here available Twitter data (tweet IDs) was originally collected live, using the official Twitter REST API search endpoint, following their terms of service and developer guidelines. <br> Tweet IDs are usually 18-19 long numeric IDs, and these tweet IDs can be rehydrated, which is a simple process involving sending requests to the official Twitter API /statuses/lookup endpoint with tweet IDs uniquely identifying a tweet, and Twitter responding appropriately with the tweet and metadata. This helps ensure that if a user has set their account to private or deleted their tweet, effectively withdrawing consent, their tweet would not be available for further analysis. <br>There are several convenient utilities that researchers can use to rehydrate Twitter data from tweet IDs. One such tool is the DocNow Hydrator, available at https://github.com/DocNow/hydrator, as well as a useful detailed guide on how to go about it, available at https://programminghistorian.org/en/lessons/beginners-guide-to-twitter-data#hydrating.
DOI: 10.17028/rd.lboro.13084316.v1
title: Dataset 1 - Ten Hashtags on Twitter

description: The here available Twitter data (tweet IDs) was originally collected live, using the official Twitter REST API search endpoint, following their terms of service and developer guidelines. <br> Tweet IDs are usually 18-19 long numeric IDs, and these tweet IDs can be rehydrated, which is a simple process involving sending requests to the official Twitter API /statuses/lookup endpoint with tweet IDs uniquely identifying a tweet, and Twitter responding appropriately with the tweet and metadata. This helps ensure that if a user has set their account to private or deleted their tweet, effectively withdrawing consent, their tweet would not be available for further analysis. <br>There are several convenient utilities that researchers can use to rehydrate Twitter data from tweet IDs. One such tool is the DocNow Hydrator, available at https://github.com/DocNow/hydrator, as well as a useful detailed guide on how to go about it, available at https://programminghistorian.org/en/lessons/beginners-guide-to-twitter-data#hydrating.
DOI: 10.17028/rd.lboro.13084514
title: Dataset 2 - 25 Unique Topics/Events on Twitter

description: The here available Twitter data (tweet IDs) was originally collected live, using the official Twitter REST API search endpoint, following their terms of service and developer guidelines.<br>Tweet IDs are usually 18-19 long numeric IDs, and these tweet IDs can be rehydrated, which is a simple process involving sending requests to the official Twitter API /statuses/lookup endpoint with tweet IDs uniquely identifying a tweet, and Twitter responding appropriately with the tweet and metadata. This helps ensure that if a user has set their account to private or deleted their tweet, effectively withdrawing consent, their tweet would not be available for further analysis.<br>There are several convenient utilities that researchers can use to rehydrate Twitter data from tweet IDs. One such tool is the DocNow Hydrator, available at https://github.com/DocNow/hydrator, as well as a useful detailed guide on how to go about it, available at https://programminghistorian.org/en/lessons/beginners-guide-to-twitter-data#hydrating.
DOI: 10.17028/rd.lboro.13084514.v1
title: Dataset 2 - 25 Unique Topics/Events on Twitter

description: The here available Twitter data (tweet IDs) was originally collected live, using the official Twitter REST API search endpoint, following their terms of service and developer guidelines.<br>Tweet IDs are usually 18-19 long numeric IDs, and these tweet IDs can be rehydrated, which is a simple process involving sending requests to the official Twitter API /statuses/lookup endpoint with tweet IDs uniquely identifying a tweet, and Twitter responding appropriately with the tweet and metadata. This helps ensure that if a user has set their account to private or deleted their tweet, effectively withdrawing consent, their tweet would not be available for further analysis.<br>There are several convenient utilities that researchers can use to rehydrate Twitter data from tweet IDs. One such tool is the DocNow Hydrator, available at https://github.com/DocNow/hydrator, as well as a useful detailed guide on how to go about it, available at https://programminghistorian.org/en/lessons/beginners-guide-to-twitter-data#hydrating.
DOI: 10.17028/rd.lboro.13084817
title: Overview of the 25 Unique Events/Topics

description: The here available Twitter data (tweet IDs) was originally collected live, using the official Twitter REST API search endpoint, following their terms of service and developer guidelines. <br> Tweet IDs are usually 18-19 long numeric IDs, and these tweet IDs can be rehydrated, which is a simple process involving sending requests to the official Twitter API /statuses/lookup endpoint with tweet IDs uniquely identifying a tweet, and Twitter responding appropriately with the tweet and metadata. This helps ensure that if a user has set their account to private or deleted their tweet, effectively withdrawing consent, their tweet would not be available for further analysis. <br>There are several convenient utilities that researchers can use to rehydrate Twitter data from tweet IDs. One such tool is the DocNow Hydrator, available at https://github.com/DocNow/hydrator, as well as a useful detailed guide on how to go about it, available at https://programminghistorian.org/en/lessons/beginners-guide-to-twitter-data#hydrating.
DOI: 10.17028/rd.lboro.13084817.v1
title: Overview of the 25 Unique Events/Topics

description: The here available Twitter data (tweet IDs) was originally collected live, using the official Twitter REST API search endpoint, following their terms of service and developer guidelines. <br> Tweet IDs are usually 18-19 long numeric IDs, and these tweet IDs can be rehydrated, which is a simple process involving sending requests to the official Twitter API /statuses/lookup endpoint with tweet IDs uniquely identifying a tweet, and Twitter responding appropriately with the tweet and metadata. This helps ensure that if a user has set their account to private or deleted their tweet, effectively withdrawing consent, their tweet would not be available for further analysis. <br>There are several convenient utilities that researchers can use to rehydrate Twitter data from tweet IDs. One such tool is the DocNow Hydrator, available at https://github.com/DocNow/hydrator, as well as a useful detailed guide on how to go about it, available at https://programminghistorian.org/en/lessons/beginners-guide-to-twitter-data#hydrating.
DOI: 10.5281/zenodo.4120887
title: Zipf's laws of meaning in Catalan Datasets

description: This are the datasets referenced in the paper "Zipf’s laws of meaning in Catalan". The datasets are: <strong>DIEC2_CTILC_senseCG</strong>. It contains the following information: lema: lemma in Catalan n_sentits: number of meanings for that lemma, freq: frequency for that lemma. rank of that lemma. Those lemmas are contained in both of the following corpuses: CTILC: https://ctilc.iec.cat. DIEC: Institut d’Estudis Catalans. <em>Diccionari de la llengua catalana</em> [en línia]. 2a ed. Barcelona: Edicions 62: Enciclopèdia Catalana, 2007. [1a ed., 1995] &lt;https://dlc.iec.cat/&gt; [Consulta: October 2020] <strong>DIEC2_GLISSANDO_senseCG. </strong> It contains the following information: lema: lemma in Catalan n_sentits: number of meanings for that lemma, freq: frequency for that lemma. rank of that lemma. Those lemmas are contained in both of the following corpuses: Glissando: http://catalog.elra.info/en-us/repository/browse/ELRA-S0407/ DIEC: Institut d’Estudis Catalans. <em>Diccionari de la llengua catalana</em> [en línia]. 2a ed. Barcelona: Edicions 62: Enciclopèdia Catalana, 2007. [1a ed., 1995] &lt;https://dlc.iec.cat/&gt; [Consulta: October 2020]
description: This work has been funded by the project PRO2020-S03 (RCO03080449Ling ̈u ́ıstica Quantitativa, Institut d'Estudis Catalans). JB, RFC and AHF450are also funded by the grant TIN2017-89244-R from Ministerio de Econo-451mia, Industria y Competitividad (Gobierno de Espa ̃na) and the recognition4522017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya).
description: {"references": ["Institut d'Estudis Catalans. Diccionari de la llengua catalana [en l\u00ednia]. 2a ed. Barcelona: Edicions 62: Enciclop\u00e8dia Catalana, 2007. [1a ed., 1995] [Consulta: October 2020]"]}
DOI: 10.5281/zenodo.4120886
title: Zipf's laws of meaning in Catalan Datasets

description: This are the datasets referenced in the paper "Zipf’s laws of meaning in Catalan". The datasets are: <strong>DIEC2_CTILC_senseCG</strong>. It contains the following information: lema: lemma in Catalan n_sentits: number of meanings for that lemma, freq: frequency for that lemma. rank of that lemma. Those lemmas are contained in both of the following corpuses: CTILC: https://ctilc.iec.cat. DIEC: Institut d’Estudis Catalans. <em>Diccionari de la llengua catalana</em> [en línia]. 2a ed. Barcelona: Edicions 62: Enciclopèdia Catalana, 2007. [1a ed., 1995] &lt;https://dlc.iec.cat/&gt; [Consulta: October 2020] <strong>DIEC2_GLISSANDO_senseCG. </strong> It contains the following information: lema: lemma in Catalan n_sentits: number of meanings for that lemma, freq: frequency for that lemma. rank of that lemma. Those lemmas are contained in both of the following corpuses: Glissando: http://catalog.elra.info/en-us/repository/browse/ELRA-S0407/ DIEC: Institut d’Estudis Catalans. <em>Diccionari de la llengua catalana</em> [en línia]. 2a ed. Barcelona: Edicions 62: Enciclopèdia Catalana, 2007. [1a ed., 1995] &lt;https://dlc.iec.cat/&gt; [Consulta: October 2020]
description: This work has been funded by the project PRO2020-S03 (RCO03080449Ling ̈u ́ıstica Quantitativa, Institut d'Estudis Catalans). JB, RFC and AHF450are also funded by the grant TIN2017-89244-R from Ministerio de Econo-451mia, Industria y Competitividad (Gobierno de Espa ̃na) and the recognition4522017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya).
description: {"references": ["Institut d'Estudis Catalans. Diccionari de la llengua catalana [en l\u00ednia]. 2a ed. Barcelona: Edicions 62: Enciclop\u00e8dia Catalana, 2007. [1a ed., 1995] [Consulta: October 2020]"]}
DOI: 10.25625/twsfru/gm9kdy
title: Tchiofo-Lontsi et al_GHG-fluxes_data.tab

description: :unav
DOI: 10.25625/twsfru
title: Dataset on the impact of conventional selective and reduced-impact logging on soil greenhouse gas fluxes in a Congo Basin Rainforest

description: The dataset includes monthly and annual soil CO2, N2O and CH4 fluxes, and their soil controlling factors measured at two forests that had undergone conventional selective logging and reduced-impact logging in Cameroon. Each logging system had four replicate plots and each plot included the disturbed strata (road, logging deck, skidding trail, and felling gap) and an undisturbed reference area. Measurements were conducted monthly over one year. Annual fluxes are estimated based on trapezoidal rule between measured fluxes and sampling day intervals, assuming constant flux rates per day.
DOI: 10.17028/rd.lboro.12117993
title: List of references of Social Value definitions in the Digitalised Construction Environment

description: This is a list of reference product of a literature review. The reference show the definition of Social Value in construction.
DOI: 10.17028/rd.lboro.12117993.v1
title: List of references of Social Value definitions in the Digitalised Construction Environment

description: This is a list of reference product of a literature review. The reference show the definition of Social Value in construction.
DOI: 10.6084/m9.figshare.13134782
title: Gaps in secondary prevention among stroke survivors in rural Gadchiroli, India: a community-based cross-sectional study

description: <b>Background</b>: With epidemiological transition, stroke has emerged as a public health priority in rural India. However, population-level information on secondary prevention of stroke from rural areas of India and other low- and middle-income countries remains exceedingly rare.<br> <b>Methods</b>: In a cross-sectional community-based survey, trained surveyors screened a well-defined population of 74,095 individuals living in 64 villages in Gadchiroli district of India for symptoms of stroke. A trained physician evaluated screen positive patients, diagnosed stroke, measured blood pressure and collected information on prior diagnosis of risk factors and current use of medications using a structured questionnaire. <b>Results</b>: A total of 265 stroke survivors were identified. Prior diagnosis of hypertension was made in 57.4%, diabetes in 9.8%, hyperlipidaemia in 0.4%, ischaemic heart disease in 1.5%. and atrial fibrillation in 1.1%. Blood pressure was uncontrolled (&gt;140/90) in 46% of stroke survivors. Among men 71.2% used tobacco and 30% used alcohol, while among women 38.2% used tobacco and none used alcohol. Only 40.8% of stroke survivors were receiving antihypertensive medications, while 10.6% were on antiplatelet agents and 4.9% were on statins. In a multivariate analysis, age &lt;50 years (OR 0.2, 95% CI 0.1-0.5), male sex (OR 0.2, 95% CI 0.2-0.8) and lower economic status (no assets vs four assets; OR 0.3, 95% CI 0.1-0.9) were associated with lower odds of receiving medications for secondary prevention of stroke. <b>Conclusions</b>: There were significant gaps in secondary prevention of stroke in rural Gadchiroli. Healthcare programmes for secondary prevention of stroke in rural areas will have to ensure that blood pressure is adequately controlled, alcohol and tobacco cessation is promoted and special attention is paid to those who are younger, men and economically weaker.<b> </b>
DOI: 10.6084/m9.figshare.13133630
title: R0311_20191113a_videos

description: Rat 0311, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13133630.v1
title: R0311_20191113a_videos

description: Rat 0311, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13133615.v1
title: R0310_20191124a_videos

description: Rat 0310, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13133615
title: R0310_20191124a_videos

description: Rat 0310, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.21227/93kw-dt15
title: Model 4

description: Similar to Model 3, but filtering profiles with large standard deviations
DOI: 10.6084/m9.figshare.13135124.v1
title: random_number.txt

description: 1000 random numbers over the range 0-100
DOI: 10.6084/m9.figshare.13135124
title: random_number.txt

description: 1000 random numbers over the range 0-100
DOI: 10.5281/zenodo.4120346
title: Dati a supporto della valutazione e della gestione della qualità dell'aria in Friuli Venezia Giulia

description: <strong>Valutazioni annuali</strong> Sono resi disponibili gli indicatori annuali di qualità dell'aria calcolati sui dati misurati dalle stazioni di monitoraggio del Friuli Venezia Giulia e interpolati con tecnica di <em>universal kriging </em>su un grigliato regolare a 2km di risoluzione, usando come "campo guida" il medesimo indicatore calcolato dalla catena modellistica FARM. I file dei dati hanno nome &lt;POLL&gt;_&lt;YEAR&gt;_CivilYear_&lt;IND&gt;_*.&lt;EXT&gt;, dove &lt;POLL&gt; indica l'inquinante (PM25 indica il PM2,5) &lt;YEAR&gt; l'anno di riferimento &lt;IND&gt; l'indicatore di qualità dell'aria come definito dalla normativa <em>Avg </em>media annua <em>8hRunningAvgs-DailyMaxes-Nexc120 </em>superamenti della soglia 120 per il massimo giornaliero della media mobile su 8 ore <em>DailyAvgs-Nexc50</em> superamenti giornalieri della soglia 50 &lt;EXT&gt; indica il formato del file <em>asc </em>ESRI ASCII raster <em>xyz </em>file di testo con intestazione descrittiva <em>png </em>mappa
DOI: 10.6084/m9.figshare.13135100
title: using_equation.txt

description: Generate new numbers from the original 1000 using the equation y=3x+6
DOI: 10.6084/m9.figshare.13135100.v1
title: using_equation.txt

description: Generate new numbers from the original 1000 using the equation y=3x+6
DOI: 10.22008/hokkaido/data/meltwater_discharge/qaanaaq/1lhv6y
title: discharge2018.txt

description: 2018 discharge
DOI: 10.22008/hokkaido/data/meltwater_discharge/qaanaaq/7euqol
title: DischargeMeasurement200214.pdf

description: :unav
DOI: 10.22008/hokkaido/data/meltwater_discharge/qaanaaq/3qedum
title: discharge2017.txt

description: 2017 discharge
DOI: 10.22008/hokkaido/data/meltwater_discharge/qaanaaq/sasejo
title: discharge2019.txt

description: 2019 discharge
DOI: 10.22008/hokkaido/data/meltwater_discharge/qaanaaq/ujkror
title: README.org

description: README
DOI: 10.22008/hokkaido/data/meltwater_discharge/qaanaaq/eoujqb
title: README.txt

description: README
DOI: 10.22008/hokkaido/data/meltwater_discharge/qaanaaq/xzwtpa
title: discharge2017.txt

description: 2017 discharge
DOI: 10.22008/hokkaido/data/meltwater_discharge/qaanaaq/gjn76e
title: discharge2018.txt

description: 2018 discharge
DOI: 10.22008/hokkaido/data/meltwater_discharge/qaanaaq/4z2gaw
title: discharge2019.txt

description: 2019 discharge
DOI: 10.22008/hokkaido/data/meltwater_discharge/qaanaaq/cozezq
title: README.txt

description: README
DOI: 10.5526/erdr-00000128
title: Contribution of Differing Visual Cues on Distance Perception in Virtual Reality
DOI: 10.5526/erdr-00000126
title: Modification of The Rosenberg Scale to Assess Self-Esteem in Children
DOI: 10.5517/ccdc.csd.cc20l2f4
title: CCDC 1864415: Experimental Crystal Structure Determination

description: Related Article: Q.-J. Deng, M. Chen, D.-C. Chen, H.-Y. Long, C.-A. Chen|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|655|doi:10.1107/S2053229620006701
DOI: 10.5517/ccdc.csd.cc20l2d3
title: CCDC 1864414: Experimental Crystal Structure Determination

description: Related Article: Q.-J. Deng, M. Chen, D.-C. Chen, H.-Y. Long, C.-A. Chen|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|655|doi:10.1107/S2053229620006701
DOI: 10.5517/ccdc.csd.cc25bd64
title: CCDC 2005985: Experimental Crystal Structure Determination

description: Related Article: C. Arderne, K.F. Batchelor, B. Uprety, R. Chandran, H. Abrahamse|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|663|doi:10.1107/S2053229620007123
DOI: 10.5517/ccdc.csd.cc25bd75
title: CCDC 2005986: Experimental Crystal Structure Determination

description: Related Article: C. Arderne, K.F. Batchelor, B. Uprety, R. Chandran, H. Abrahamse|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|663|doi:10.1107/S2053229620007123
DOI: 10.5517/ccdc.csd.cc25bd86
title: CCDC 2005987: Experimental Crystal Structure Determination

description: Related Article: C. Arderne, K.F. Batchelor, B. Uprety, R. Chandran, H. Abrahamse|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|663|doi:10.1107/S2053229620007123
DOI: 10.5517/ccdc.csd.cc25fd78
title: CCDC 2008869: Experimental Crystal Structure Determination

description: Related Article: M. Szczesio, K. Gobis, I. Korona-Głowniak, I. Mazerant-Politowicz, D. Ziembicka, H. Foks, M. Główka, A. Olczak|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|673|doi:10.1107/S2053229620007822
DOI: 10.5517/ccdc.csd.cc25fd89
title: CCDC 2008870: Experimental Crystal Structure Determination

description: Related Article: M. Szczesio, K. Gobis, I. Korona-Głowniak, I. Mazerant-Politowicz, D. Ziembicka, H. Foks, M. Główka, A. Olczak|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|673|doi:10.1107/S2053229620007822
DOI: 10.5517/ccdc.csd.cc25fd9b
title: CCDC 2008871: Experimental Crystal Structure Determination

description: Related Article: M. Szczesio, K. Gobis, I. Korona-Głowniak, I. Mazerant-Politowicz, D. Ziembicka, H. Foks, M. Główka, A. Olczak|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|673|doi:10.1107/S2053229620007822
DOI: 10.5517/ccdc.csd.cc25fdbc
title: CCDC 2008872: Experimental Crystal Structure Determination

description: Related Article: M. Szczesio, K. Gobis, I. Korona-Głowniak, I. Mazerant-Politowicz, D. Ziembicka, H. Foks, M. Główka, A. Olczak|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|673|doi:10.1107/S2053229620007822