cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34968/nims.1319
title: Longed-for functions for digital repository learned from my self-archiving activity
DOI: 10.34968/nims.1321
title: eScienceとしての材料研究リポジトリ“NIMS eSciDoc”の可能性
DOI: 10.34968/nims.1322
title: NIMS eSciDoc: eScience向け基盤ソフトウェア開発と実践の現場から
DOI: 10.34968/nims.1324
title: NIMS eSciDoc Update -研究者IDマッピングと応用
DOI: 10.34968/nims.1308
title: Ultrahigh-speed videography of fiber fuse propagation: a tool for studying void formation
DOI: 10.34968/nims.1305
title: In-situ Observation of Fiber-fuse Ignition
DOI: 10.34968/nims.1303
title: In-Situ Observation of Fiber-Fuse Propagation
DOI: 10.34968/nims.1362
title: Low loss optical coupling structure between two ends of silica glass optical fibers by inserting TeO2 melt
DOI: 10.34968/nims.1302
title: Formation of optical coupling structure between silica glass waveguides and molten tellurite glass droplet
DOI: 10.34968/nims.1433
title: NITE Polymer Fracture Database
title: NITE 高分子破壊データベース
DOI: 10.6084/m9.figshare.21107032
title: Water use habits of residential users in Indian Urban Agglomeration.xlsx

description: The data set was created by collecting the information from residential users of Indian Urban agglomerations. The survey was conduted using the questionnaire supplied to users through a google form. Users were asked to provide the approximate answers to the questions in the survey.
DOI: 10.6084/m9.figshare.21107032.v1
title: Data set_Survey Responses_Indian Urban Agglomeration.xlsx

description: The data set was created by collecting the information from residential users of Indian Urban agglomerations. The survey was conduted using the questionnaire supplied to users through a google form. Users were asked to provide the approximate answers to the questions in the survey.
DOI: 10.5281/zenodo.7074776
title: Supplementary Data for DISCAN

description: Supplementary data for "The frequency of somatic mutations in cancer predicts the phenotypic relevance of germline mutations". This entry includes two data tables, uploaded as compressed text files: <strong>features.csv.gz</strong> contains all the covariates used to train the models. <strong>phenotype_probabilities.csv.gz</strong> contains the Random Forest probabilities for 1,361 phenotypes Gene identifiers are reported as Entrez ID.
DOI: 10.5281/zenodo.7074775
title: Supplementary Data for DISCAN

description: Supplementary data for "The frequency of somatic mutations in cancer predicts the phenotypic relevance of germline mutations". This entry includes two data tables, uploaded as compressed text files: <strong>features.csv.gz</strong> contains all the covariates used to train the models. <strong>phenotype_probabilities.csv.gz</strong> contains the Random Forest probabilities for 1,361 phenotypes Gene identifiers are reported as Entrez ID.
DOI: 10.5281/zenodo.7056615
title: Respiration regimes in rivers: Partitioning source‐specific respiration from metabolism time series

description: This repository hosts preprocessed input data and model codes of the paper: "Respiration regimes in rivers: Partitioning source‐specific respiration from metabolism time series" <br> Limonology and Oceanography, 2022.<br> DOI: 10.1002/lno.12207
DOI: 10.5281/zenodo.7056616
title: Respiration regimes in rivers: Partitioning source‐specific respiration from metabolism time series

description: This repository hosts preprocessed input data and model codes of the paper: "Respiration regimes in rivers: Partitioning source‐specific respiration from metabolism time series" <br> Limonology and Oceanography, 2022.<br> DOI: 10.1002/lno.12207
DOI: 10.5281/zenodo.7079451
title: Respiration regimes in rivers: Partitioning source‐specific respiration from metabolism time series

description: This repository hosts preprocessed input data and model codes of the paper: "Respiration regimes in rivers: Partitioning source‐specific respiration from metabolism time series" <br> Limonology and Oceanography, 2022.<br> DOI: 10.1002/lno.12207
DOI: 10.34820/fk2/ruyzjt/j26xzo
title: Metadata-3.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/rpveqv
title: Accelerometer-3.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/svvyp3
title: Magnetometer.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/efxljy
title: Gyroscope-4.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/jq0bd8
title: Magnetometer-7.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/kyxlke
title: Gyroscope-7.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/r00rgw
title: Magnetometer-6.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/tiknbo
title: Location-2.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/e18mbw
title: Location-5.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/gdt8oz
title: Magnetometer-2.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/h9rwne
title: Location-4.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/maphbb
title: Location-3.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/pb9fgw
title: Gyroscope.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/wxmw5x
title: Magnetometer-5.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/1la1lz
title: Magnetometer-4.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/da6uxp
title: Accelerometer-7.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/nxzdtt
title: Metadata-5.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/omd9il
title: Metadata-1.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/4ubwev
title: Gyroscope-3.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/f29jeu
title: Metadata-2.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/o47f3a
title: Gyroscope-2.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/tcwcjo
title: Magnetometer-1.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/tptzgd
title: Location-6.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/5zjlrb
title: Metadata-7.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/cldsbz
title: Metadata-6.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/mwqoz8
title: Accelerometer.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/otqje9
title: Magnetometer-3.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/tybavk
title: Accelerometer-6.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/h2lwig
title: Gyroscope-1.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/nqbsof
title: Metadata-4.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/pvxqzt
title: Location.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/stxfpw
title: Gyroscope-5.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/tydgdx
title: Accelerometer-2.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/3rotws
title: Location-1.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/6lvni6
title: Metadata.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/dmp2nv
title: Accelerometer-1.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/irnlas
title: Location-7.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/reheub
title: Accelerometer-5.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt
title: Data Akselero, Gyro, Magneto, dan Lokasi (GPS)

description: Data Akselero, Gyro, Magneto, dan Lokasi (GPS)
DOI: 10.34820/fk2/ruyzjt/5lyqjf
title: Gyroscope-6.tab

description: :unav
DOI: 10.34820/fk2/ruyzjt/hhohvc
title: Accelerometer-4.tab

description: :unav
DOI: 10.34740/kaggle/dsv/4203945
title: Elon Musk Tweets
DOI: 10.25921/w4sr-8089
title: ES60 Water Column Sonar Data Collected During CHS12AK
DOI: 10.6084/m9.figshare.14981181
title: Scallop

description: Datasets, metadata and jupyter notebooks for the analysis of cell-to-cell variability and cell type enrichment of the murine aging lung.<br>
DOI: 10.6084/m9.figshare.14981181.v1
title: Scallop

description: Datasets, metadata and jupyter notebooks for the analysis of cell-to-cell variability and cell type enrichment of the murine aging lung.<br>
DOI: 10.6084/m9.figshare.21107626
title: Haemoglobin, red blood cell and iron status parameters from healthy adults in Kenya

description: This dataset contains data for haemoglobin, red blood cell parameters and biomarkers of iron status derived from healthy Kenyan adults aged 18-65 years who were recruited as part of a global study conducted by the International Federation of Clinical Chemistry to determine reference intervals for selected chemistry and immunoassay tests. It was used to estimate the burden of iron deficiency in a healthy adult population residing in Kenya and to determine the association between ferritin cut-offs used to define iron deficiency and anaemia in a population known to have chronic low-grade inflammation.
DOI: 10.6084/m9.figshare.21107626.v1
title: Haemoglobin, red blood cell and iron status parameters from healthy adults in Kenya

description: This dataset contains data for haemoglobin, red blood cell parameters and biomarkers of iron status derived from healthy Kenyan adults aged 18-65 years who were recruited as part of a global study conducted by the International Federation of Clinical Chemistry to determine reference intervals for selected chemistry and immunoassay tests. It was used to estimate the burden of iron deficiency in a healthy adult population residing in Kenya and to determine the association between ferritin cut-offs used to define iron deficiency and anaemia in a population known to have chronic low-grade inflammation.
DOI: 10.15472/xhqqvt
title: Lista de especies de peces de los órdenes Characiformes y Gymnotiformes para el Putumayo (Orito)

description: Bajo el Convenio No. 22-075 suscrito con la Agencia Nacional de Hidrocarburos -ANH, se llevó a cabo el levantamiento de la segunda fase de la Línea Base Regional de biodiversidad para el Putumayo. En jurisdicción del municipio de Orito, se realizaron muestreos de biodiversidad de diferentes grupos biológicos, en este recurso se presentan los resultados de los ordenes Characiformes y Gymnotiformes obtenidos a partir de la revisión de fuentes secundarias. Para el levantamiento de la información secundaria, se utilizó la ecuación de búsqueda “Fish AND Putumayo” para buscar en las bases de datos la información orientada en la composición, riqueza y distribución de la fauna íctica de la cuenca alta del Río Putumayo. Se revisaron un total de 3530 resultados, de los cuales se extrajeron 20 documentos de interés para el proyecto. Se obtuvo un total de 171 especies.
DOI: 10.25403/upresearchdata.20319696
title: Semiotic figures and instagram poems = semiotieke figuur en instagramgedigte

description: These figures are related to Jakobson's communication theory and de Saussure's semiotic theory. The majority of these figures are Instagram poems that were used as examples of contemporary Afrikaans e-poetry and poems that were used for analyses from a multimodal perspective via close-reading for my study Hierdie figure het te doen met Jakobson se kommunikasieteorie en de Saussure se semiotieke teorie. Meeste van die figure is Instagramgedigte wat as voorbeelde dien vir eietydse Afrikaanse e-poësie en gedigte wat ek gestiplees en analiseer het vanuit 'n multimodale perspektief.
DOI: 10.25403/upresearchdata.20319696.v1
title: Semiotic figures and instagram poems = semiotieke figuur en instagramgedigte

description: These figures are related to Jakobson's communication theory and de Saussure's semiotic theory. The majority of these figures are Instagram poems that were used as examples of contemporary Afrikaans e-poetry and poems that were used for analyses from a multimodal perspective via close-reading for my study Hierdie figure het te doen met Jakobson se kommunikasieteorie en de Saussure se semiotieke teorie. Meeste van die figure is Instagramgedigte wat as voorbeelde dien vir eietydse Afrikaanse e-poësie en gedigte wat ek gestiplees en analiseer het vanuit 'n multimodale perspektief.
DOI: 10.5066/p9j53qqc
title: Bias-corrected daily precipitation at 1-kilometer resolution for Puerto Rico from Weather Research and Forecasting (WRF) dynamical downscaled historical (1985-2005) and projected (2040-60) climate

description: This data release consists of four network Common Data Form (netCDF) files of bias-corrected daily precipitation at 1-kilometer (km) scale for historical and projected climate of Puerto Rico. The Weather Research and Forecasting (WRF) model was used by Bowden and others (2018) to downscale two general circulation models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5): (1) the Community Climate System Model (CCSM4 or CESM), and (2) the Centre National de Recherches Meteorologiques-CERFACS (CNRM). The two models were dynamically downscaled using WRF for a historical (1985-2005) time slice and a future time slice (2040&ndash;60) under the high greenhouse gas emission scenario known as Representative Concentration Pathway [...]
DOI: 10.6084/m9.figshare.20043665.v7
title: AQME paper examples

description: This dataset contains example for our newly developed program AQME : Automated Quantum Mechnical Envirnments for chemical analysis. We show the utility of independent modules in multiple end-to-end examples covering both jupyter notebook and HPC computing.
DOI: 10.25403/upresearchdata.20439294
title: Enhancing the photo-current and photo-stability of dip-coated copper (II) oxide thin films for hydrogen production

description: The data emphasize the importance of optimizing the processing parameters of preparing dip-coated CuO films for photoelectrochemical (PEC) applications, before improving their photo-stability in electrolytes. Furthermore, enhancing the photo-stability of CuO films in electrolyte is paramount to their practical application in PEC water-splitting for hydrogen production.
DOI: 10.25403/upresearchdata.20439294.v1
title: Enhancing the photo-current and photo-stability of dip-coated copper (II) oxide thin films for hydrogen production

description: The data emphasize the importance of optimizing the processing parameters of preparing dip-coated CuO films for photoelectrochemical (PEC) applications, before improving their photo-stability in electrolytes. Furthermore, enhancing the photo-stability of CuO films in electrolyte is paramount to their practical application in PEC water-splitting for hydrogen production.
DOI: 10.6084/m9.figshare.21108010
title: SupData.pdf

description: Supplemental materiel for submitted article
DOI: 10.6084/m9.figshare.20043665.v8
title: AQME paper examples

description: This dataset contains example for our newly developed program AQME : Automated Quantum Mechnical Envirnments for chemical analysis. We show the utility of independent modules in multiple end-to-end examples covering both jupyter notebook and HPC computing.
DOI: 10.5281/zenodo.3835823
title: CLDF dataset derived from Wichmann et al.'s "ASJP Database" from 2010

description: Cite the source of the dataset as: Wichmann, Søren, André Müller, Viveka Velupillai, Cecil H. Brown, Eric W. Holman, Pamela Brown, Matthias Urban, Sebastian Sauppe, Oleg Belyaev, Zarina Molochieva, Annkathrin Wett, Dik Bakker, Johann-Mattis List, Dmitry Egorov, Robert Mailhammer, and Helen Geyer. 2010. The ASJP Database (Version 12).
DOI: 10.5281/zenodo.3835872
title: CLDF dataset derived from Wichmann et al.'s "ASJP Database" v13 from 2010

description: Cite the source of the dataset as: Wichmann, Søren, André Müller, Viveka Velupillai, Cecil H. Brown, Eric W. Holman, Pamela Brown, Sebastian Sauppe, Oleg Belyaev, Matthias Urban, Zarina Molochieva, Annkathrin Wett, Dik Bakker, Johann-Mattis List, Dmitry Egorov, Robert Mailhammer, David Beck, and Helen Geyer. 2010. The ASJP Database (version 13).
DOI: 10.5281/zenodo.3835887
title: CLDF dataset derived from Wichmann et al.'s "ASJP Database" v14 from 2011

description: Cite the source of the dataset as: Wichmann, Søren, André Müller, Viveka Velupillai, Annkathrin Wett, Cecil H. Brown, Zarina Molochieva, Sebastian Sauppe, Eric W. Holman, Pamela Brown, Julia Bishoffberger, Dik Bakker, Johann-Mattis List, Dmitry Egorov, Oleg Belyaev, Matthias Urban, Robert Mailhammer, Helen Geyer, David Beck, Evgenia Korovina, Pattie Epps, Pilar Valenzuela, Anthony Grant, and Harald Hammarström. 2011. The ASJP Database (version 14).
DOI: 10.5281/zenodo.3835901
title: CLDF dataset derived from Wichmann et al.'s "ASJP Database" v15 from 2012

description: Cite the source of the dataset as: Wichmann, Søren, André Müller, Viveka Velupillai, Annkathrin Wett, Cecil H. Brown, Zarina Molochieva, Julia Bishoffberger, Eric W. Holman, Sebastian Sauppe, Pamela Brown, Dik Bakker, Johann-Mattis List, Dmitry Egorov, Oleg Belyaev, Matthias Urban, Harald Hammarström, Agustina Carrizo, Robert Mailhammer, Helen Geyer, David Beck, Evgenia Korovina, Pattie Epps, Pilar Valenzuela, and Anthony Grant. 2012. The ASJP Database (version 15).
DOI: 10.5281/zenodo.3835925
title: CLDF dataset derived from Wichmann et al.'s "ASJP Database" v16 from 2013

description: Cite the source of the dataset as: Wichmann, Søren, André Müller, Annkathrin Wett, Viveka Velupillai, Julia Bischoffberger, Cecil H. Brown, Eric W. Holman, Sebastian Sauppe, Zarina Molochieva, Pamela Brown, Harald Hammarström, Oleg Belyaev, Johann-Mattis List, Dik Bakker, Dmitry Egorov, Matthias Urban, Robert Mailhammer, Agustina Carrizo, Matthew S. Dryer, Evgenia Korovina, David Beck, Helen Geyer, Pattie Epps, Anthony Grant, and Pilar Valenzuela. 2013. The ASJP Database (version 16).
DOI: 10.5281/zenodo.3835952
title: CLDF dataset derived from Wichmann et al.'s "ASJP Database" v18 from 2018

description: Cite the source of the dataset as: Wichmann, Søren, Eric W. Holman, and Cecil H. Brown (eds.). 2018. The ASJP Database (version 18).
DOI: 10.5281/zenodo.3840975
title: CLDF dataset derived from Wichmann et al.'s "ASJP Database" v19 from 2020

description: Cite the source of the dataset as: Wichmann, Søren, Eric W. Holman, and Cecil H. Brown (eds.). 2020. The ASJP Database (version 19).
DOI: 10.5281/zenodo.7079637
title: CLDF dataset derived from Wichmann et al.'s "ASJP Database" v20 from 2022

description: Cite the source of the dataset as: Wichmann, Søren, Eric W. Holman, and Cecil H. Brown (eds.). 2022. The ASJP Database (version 20).
DOI: 10.34740/kaggle/dsv/4204158
title: Mahanagar Yatayat Dataset
DOI: 10.5281/zenodo.7064211
title: Machine Learning the Hohenberg-Kohn Map to Molecular Excited States

description: The dataset and code used in paper”Machine Learning the Hohenberg-Kohn Map to Molecular Excited States” For detailed information of each file, see Readme.txt
description: WJG acknowledges financial support from the National Natural Science Foundation of China (NSFC), grant no.~22173060), the NSFC Fund for International Excellent Young Scientists (grant no.~22150610466), the Ministry of Science and Technology of the People's Republic of China (MOST) National Foreign Experts Program Fund (grant No.~ QN2021013001L), and the MOST Foreign Young Talents Program (grant no.~WGXZ2022006L). MET acknowledges support from the National Science Foundation (grant no. CHE-1955381). LV-M acknowledges support from the NYU University Research Challenge Fund. This project was supported in whole (or in part) by the NYU Shanghai Boost Fund.
DOI: 10.5281/zenodo.7064212
title: Machine Learning the Hohenberg-Kohn Map to Molecular Excited States

description: The dataset and code used in paper”Machine Learning the Hohenberg-Kohn Map to Molecular Excited States” For detailed information of each file, see Readme.txt
description: WJG acknowledges financial support from the National Natural Science Foundation of China (NSFC), grant no.~22173060), the NSFC Fund for International Excellent Young Scientists (grant no.~22150610466), the Ministry of Science and Technology of the People's Republic of China (MOST) National Foreign Experts Program Fund (grant No.~ QN2021013001L), and the MOST Foreign Young Talents Program (grant no.~WGXZ2022006L). MET acknowledges support from the National Science Foundation (grant no. CHE-1955381). LV-M acknowledges support from the NYU University Research Challenge Fund. This project was supported in whole (or in part) by the NYU Shanghai Boost Fund.
DOI: 10.5281/zenodo.7079523
title: Machine Learning the Hohenberg-Kohn Map to Molecular Excited States

description: The dataset and code used in paper”Machine Learning the Hohenberg-Kohn Map to Molecular Excited States” For detailed information of each file, see Readme.txt
description: WJG acknowledges financial support from the National Natural Science Foundation of China (NSFC), grant no.~22173060), the NSFC Fund for International Excellent Young Scientists (grant no.~22150610466), the Ministry of Science and Technology of the People's Republic of China (MOST) National Foreign Experts Program Fund (grant No.~ QN2021013001L), and the MOST Foreign Young Talents Program (grant no.~WGXZ2022006L). MET acknowledges support from the National Science Foundation (grant no. CHE-1955381). LV-M acknowledges support from the NYU University Research Challenge Fund. This project was supported in whole (or in part) by the NYU Shanghai Boost Fund.
DOI: 10.5517/ccdc.csd.cc2bwj2v
title: CCDC 2171397: Experimental Crystal Structure Determination

description: Related Article: Deysy Andrea Molina Giraldo, Luz Amalia Ríos Vásquez, Rubén A. Toscano, Rogelio Ocampo-Cardona, Jessica Johanna Gomez-Peña, Mario A. Macías|2022|J.Mol.Struct.|1271|133962|doi:10.1016/j.molstruc.2022.133962
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bwj3w
title: CCDC 2171398: Experimental Crystal Structure Determination

description: Related Article: Deysy Andrea Molina Giraldo, Luz Amalia Ríos Vásquez, Rubén A. Toscano, Rogelio Ocampo-Cardona, Jessica Johanna Gomez-Peña, Mario A. Macías|2022|J.Mol.Struct.|1271|133962|doi:10.1016/j.molstruc.2022.133962
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bwj4x
title: CCDC 2171399: Experimental Crystal Structure Determination

description: Related Article: Deysy Andrea Molina Giraldo, Luz Amalia Ríos Vásquez, Rubén A. Toscano, Rogelio Ocampo-Cardona, Jessica Johanna Gomez-Peña, Mario A. Macías|2022|J.Mol.Struct.|1271|133962|doi:10.1016/j.molstruc.2022.133962
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bx3cr
title: CCDC 2171964: Experimental Crystal Structure Determination

description: Related Article: Cian-Wei Yang, Suman Mallick, Yi-Chou Tsai|2022|J.Chin.Chem.Soc.(Taipei)|69|1431|doi:10.1002/jccs.202200224
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c2jff
title: CCDC 2177174: Experimental Crystal Structure Determination

description: Related Article: Uday Kumar Neelam, Bhatraju Daveedu, Vekariya Nagaji Ambabhai, Mahender Rao Siripragada, Satyasree Rajendra kumar, Sridhar Balasubramanian|2023|J.Mol.Struct.|1271|134024|doi:10.1016/j.molstruc.2022.134024
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.