cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3701440
title: HPC-ODA Dataset Collection

description: HPC-ODA is a collection of datasets acquired on production HPC systems, which are representative of several real-world use cases in the field of Operational Data Analytics (ODA) for the improvement of reliability and energy efficiency. The datasets are composed of monitoring sensor data, acquired from the components of different HPC systems depending on the specific use case. Two tools, whose overhead is proven to be very light, were used to acquire data in HPC-ODA: these are the DCDB and LDMS monitoring frameworks. The aim of HPC-ODA is to provide several vertical slices (here named segments) of the monitoring data available in a large-scale HPC installation. The segments all have different granularities, in terms of data sources and time scale, and provide several use cases on which models and approaches to data processing can be evaluated. While having a production dataset from a whole HPC system - from the infrastructure down to the CPU core level - at a fine time granularity would be ideal, this is often not feasible due to the confidentiality of the data, as well as the sheer amount of storage space required. HPC-ODA includes 5 different segments: Power Consumption Prediction: a fine-granularity dataset that was collected from a single compute node in a HPC system. It contains both node-level data as well as per-CPU core metrics, and can be used to perform regression tasks such as power consumption prediction. Fault Detection: a medium-granularity dataset that was collected from a single compute node while it was subjected to fault injection. It contains only node-level data, as well as the labels for both the applications and faults being executed on the HPC node in time. This dataset can be used to perform fault classification. Application Classification: a medium-granularity dataset that was collected from 16 compute nodes in a HPC system while running different parallel MPI applications. Data is at the compute node level, separated for each of them, and is paired with the labels of the applications being executed. This dataset can be used for tasks such as application classification. Infrastructure Management: a coarse-granularity dataset containing cluster-wide data from a HPC system, about its warm water cooling system as well as power consumption. The data is at the rack level, and can be used for regression tasks such as outlet water temperature or removed heat prediction. Cross-architecture: a medium-granularity dataset that is a variant of the Application Classification one, and shares the same ODA use case. Here, however, single-node configurations of the applications were executed on three different compute node types with different CPU architectures. This dataset can be used to perform cross-architecture application classification, or performance comparison studies. The HPC-ODA dataset collection includes a readme document containing all necessary usage information, as well as a lightweight Python framework to carry out the ODA tasks described for each dataset.
DOI: 10.14457/mju.res.2010.71
title: การสำรวจผลกระทบของการดำเนินงานของเกสต์เฮาส์ต่ออุตสาหกรรมท่องเที่ยวในเขตอำเภอเมือง จังหวัดเชียงใหม่
DOI: 10.14457/mju.res.2015.24
title: การจัดการเชิงกลยุทธ์การท่องเที่ยวชุมชนอย่างยั่งยืนในแขวงอุดมไซ และแขวงหลวงน้ำทา สาธารณรัฐประชาธิปไตยประชาชนลาว เพื่อรองรับการเข้าสู่ประชาคมเศรษฐกิจอาเซียน
DOI: 10.14457/mju.res.2015.25
title: กลยุทธ์การจัดการพฤติกรรมการบริโภคผลิตภัณฑ์การท๋องเที่ยวชุมชนของนักท๋องเที่ยวในประเทศไทยและประเทศสาธารณรัฐประชาธิปไตยประชาชนลาวเพื่อรองรับการเข้าสู่ ประชาคมเศรษฐกิจอาเซียน
DOI: 10.14457/mju.res.2015.26
title: ระบบการจัดการสารสนเทศเพื่อสนับสนุนการตัดสินใจเรื่องผลกระทบดัานการท่องเที่ยวชุมชนใน สาธารณรัฐประชาธิปไตยประชาชนลาว และ ประเทศไทย เพื่อรองรับการเข้าสู่ ประชาคมเศรษฐกิจอาเซียน
DOI: 10.14457/mju.res.2017.9
title: ประสิทธิภาพการผลิตลำไยของเกษตรกรในเขตภาคเหนือตอนบน
DOI: 10.14457/mju.res.2016.17
title: การประดิษฐ์อุปกรณ์มีโซฟลูอิดิกส์แบบใช้แล้วทิ้ง สำหรับการวิเคราะห์สารต้านจุลชีพบางชนิด โดยการตรวจวัดการเปล่งแสงเคมิลูมิเนสเซนต์ชนิดเหนี่ยวนำด้วยไฟฟ้า
DOI: 10.14457/mju.res.2015.27
title: การผลิตและทดสอบสมบัติของกระดาษจากสาหร่ายน้ำจืดสีเขียว
DOI: 10.14457/mju.res.2015.28
title: การศึกษาคุณสมบัติในการต้านเชื้อแบคทีเรียก่อโรคที่มาจากอาหารและแอนติออกซิแดน ของสมุนไพรและเครื่องเทศของไทย
DOI: 10.14457/mju.res.2014.41
title: อิเลคโทรเคมิลูมิเนสเซนต์เซนเซอร์ออนอะชิปสำหรับการวิเคราะห์สารต้านจุลชีพบางชนิดที่ปนเปื้อนในอาหาร
DOI: 10.14457/mju.res.2016.18
title: การวางแผนพัฒนาชุมชนเกษตรอย่างยั่งยืนโดยการมีส่วนร่วม กรณีศึกษา เทศบาลตำบลป่าไผ่ อำเภอสันทราย จังหวัดเชียงใหม่
DOI: 10.14457/mju.res.2014.42
title: การพัฒนาระบบการจัดทำบัญชีครัวเรือนแบบมีส่วนร่วมเพื่อการพัฒนาอย่างยั่งยืน ในเขตอำเภอสันทราย จังหวัดเชียงใหม่
DOI: 10.14457/mju.res.2014.43
title: การศึกษาความเป็นไปได้ของโครงการทางด้านการเงินในการออกแบบบรรจุภัณฑ์ อาหารว่างสำหรับกลุ่มแม่บ้านเทศบาลแม่โจ้ อำเภอสันทราย จังหวัดเชียงใหม่
DOI: 10.14457/mju.res.2014.44
title: เครือข่ายความร่วมมือการผลิตสินค้าหนึ่งตำบลหนึ่งผลิตภัณฑ์ (OTOP) เพื่อการจัดการท่องเที่ยวตามแนวปรัชญาเศรษฐกิจพอเพียง
DOI: 10.14457/mju.res.2016.19
title: การพัฒนาเส้นทางการท่องเที่ยวทางวัฒนธรรมอําเภอหลังสวนจังหวัดชุมพร
DOI: 10.14457/mju.res.2020.1
title: โครงการ “ระบบการสร้างอาชีพการเลี้ยงไก่ประดู่หางดำแก่เกษตรกรรายย่อย เส้นทางผลลัพธ์ต้นแบบธุรกิจชุมชน”
DOI: 10.15129/f4eb231f-765f-4c18-a4fa-be5bac4566c2
title: Data for: "Anomalous Ising freezing times"

description: Dataset associated with the Journal of Statistical Mechanics paper. In our paper, we simulate zero-temperature quenches of the Ising model using a basic continuous-time kinetic Monte Carlo method—which is detailed in the manuscript. The data underpinning this work are stored in csv files, and can be opened with virtually any computer software used to analyse data—the authors recommend the freely available Python library, ``Pandas''. See the included README file for further details on the data
DOI: 10.5281/zenodo.3997728
title: Fluorescently-labelled zebrafish pronephroi + ground truth classes (normal/cystic) + trained CNN model

description: This upload contains : - <strong>images.zip: </strong> microscope images of fluorescently-labelled pronephroi in larvae of the <em>Tg(wt1b:EGFP)</em> transgenic zebrafish line showing 2 morphologies (normal vs cystic) upon injection with Co-Mo or ift172-MO, respectively. Images were obtained using an ACQUIFER Imaging Machine widefield high content screening microscope. Reference: Pandey, G., Westhoff, J., Schaefer, F. and Gehrig, J. (2019). <strong>A Smart Imaging Workflow for Organ-Specific Screening in a Cystic Kidney Zebrafish Disease Model</strong>. International Journal of Molecular Sciences <em>20</em>, 1290, doi:10.3390/ijms20061290. - <strong>Annotations-***.csv : </strong>Tables containing ground-truth category classes (normal vs cystic) for the images in the zip file. The tables contain columns with the image filename, folder and category. Note : <strong>the Folder column should be updated with the root folder directory once downloaded on your machine.</strong> These files were generated with the Fiji plugin <em>single-class (button)</em> from the <em>Qualitative-Annotations</em> update site. The 2 files contain the same information, they only differ in the formatting of the category, the <em>singleColumn </em>file has a single category column while the <em>multiColumn</em> has 2 columns (normal/cystic) with 0/1 encoding. The choice of category encoding solely depends on how the table is used, i.e. in which training workflow, home-made script or software. - <strong>trainedModel.zip : </strong>This archive contains 2 files: <strong>(1) </strong>a h5 file corresponding to a trained deep-learning model to classify the images of the dataset in the 2 categories (normal vs cystic), and <strong>(2)</strong> a text file containing the class names. Both files are necessary to predict the category of new images similar to the one in the dataset, for instance using the published KNIME workflows.
description: {"references": ["Pandey, G., Westhoff, J., Schaefer, F. and Gehrig, J. (2019). A Smart Imaging Workflow for Organ-Specific Screening in a Cystic Kidney Zebrafish Disease Model. International Journal of Molecular Sciences 20, 1290, doi:10.3390/ijms20061290."]}
DOI: 10.5281/zenodo.3997727
title: Fluorescently-labelled zebrafish pronephroi + ground truth classes (normal/cystic) + trained CNN model

description: This upload contains : - <strong>images.zip: </strong> microscope images of fluorescently-labelled pronephroi in larvae of the <em>Tg(wt1b:EGFP)</em> transgenic zebrafish line showing 2 morphologies (normal vs cystic) upon injection with Co-Mo or ift172-MO, respectively. Images were obtained using an ACQUIFER Imaging Machine widefield high content screening microscope. Reference: Pandey, G., Westhoff, J., Schaefer, F. and Gehrig, J. (2019). <strong>A Smart Imaging Workflow for Organ-Specific Screening in a Cystic Kidney Zebrafish Disease Model</strong>. International Journal of Molecular Sciences <em>20</em>, 1290, doi:10.3390/ijms20061290. - <strong>Annotations-***.csv : </strong>Tables containing ground-truth category classes (normal vs cystic) for the images in the zip file. The tables contain columns with the image filename, folder and category. Note : <strong>the Folder column should be updated with the root folder directory once downloaded on your machine.</strong> These files were generated with the Fiji plugin <em>single-class (button)</em> from the <em>Qualitative-Annotations</em> update site. The 2 files contain the same information, they only differ in the formatting of the category, the <em>singleColumn </em>file has a single category column while the <em>multiColumn</em> has 2 columns (normal/cystic) with 0/1 encoding. The choice of category encoding solely depends on how the table is used, i.e. in which training workflow, home-made script or software. - <strong>trainedModel.zip : </strong>This archive contains 2 files: <strong>(1) </strong>a h5 file corresponding to a trained deep-learning model to classify the images of the dataset in the 2 categories (normal vs cystic), and <strong>(2)</strong> a text file containing the class names. Both files are necessary to predict the category of new images similar to the one in the dataset, for instance using the published KNIME workflows.
description: {"references": ["Pandey, G., Westhoff, J., Schaefer, F. and Gehrig, J. (2019). A Smart Imaging Workflow for Organ-Specific Screening in a Cystic Kidney Zebrafish Disease Model. International Journal of Molecular Sciences 20, 1290, doi:10.3390/ijms20061290."]}
DOI: 10.23725/6272-xv03
title: NWD170519.freeze5.v1.vcf.gz.csi

description: TOPMed: NWD170519.freeze5.v1.vcf.gz.csi <br>File: VCF CSI file
DOI: 10.6084/m9.figshare.12423623
title: Contrasting stripes are a widespread feature of group living in birds, mammals and fishes

description: Datasets used for phyloglm analyses. See Negro et al. 2020 for more details.
DOI: 10.6084/m9.figshare.12423623.v1
title: Contrasting stripes are a widespread feature of group living in birds, mammals and fishes

description: Datasets used for phyloglm analyses. See Negro et al. 2020 for more details.
DOI: 10.21227/61km-cg97
title: Datasets and codes for "Deep Probabilistic Learning for Process Quality Evaluation with a Case Study of Gear Hobbing Process"

description: Datasets and codes for "Deep Probabilistic Learning for Process Quality Evaluation with a Case Study of Gear Hobbing Process"
DOI: 10.5281/zenodo.4031715
title: CLEIT Dataset

description: This file includes the dataset used for "A Cross-Level Information Transmission Network for Predicting Phenotype from New Genotype: Application to Cancer Precision Medicine"
DOI: 10.5281/zenodo.4032373
title: DFT Calculated xyz Files in Support of "Reaction Mechanism of Pd-catalyzed "CO-free" Carbonylation Reaction Uncovered by In situ Spectroscopy: The Formyl Mechanism"

description: Theoretically calculated xyz files for hydrogen, carbon monoxide, carbon dioxide, methane, methanol, formaldehyde, methyl formate, butene, methyl pentanoate and multiple Pd-dtbpx complexes (dtbpx = 1,2-Bis(di-<em>tert</em>-butylphosphino)xylene) which catalyze the addition of HCHO/CO onto butene to form methyl pentanoate. All quantum chemical simulations were performed using DFT with the global hybrid functional B3LYP and a non-augmented double-ζ-basis set of slater-type orbitals with one polarization function (DZP) implemented in the Amsterdam Density Functional (ADF) Modeling Suite 2016. For palladium a non-augmented triple-ζ-basis set of slater-type orbitals with one polarization function (TZP) was used. No electron approximations were made (no frozen core), relativistic effects were included via the scalar zeroth-order regular approximated (ZORA) relativistic equation and the numerical quality was ’Good’.
DOI: 10.25345/c56j2n
title: MassIVE MSV000086150 - GNPS_GC_MS_BA_4898452121130879878trhbcxfvf
title: Congenital biliary atresia, biliary atresia accounts for half of the cases of neonatal long-term obstructive jaundice, and its incidence is 1:8000-1:14000 live births, but there are big differences in region and ethnicity. Most cases have been reported in Asia. The incidence of Eastern peoples is 4-5 times higher, and the ratio of men to women is 1:2. Congenital biliary atresia is a kind of obstruction of the bile ducts inside and outside the liver, which can lead to cholestatic liver cirrhosis. Congenital biliary atresia and eventually liver failure. It is one of the most important digestive surgery diseases in the field of pediatric surgery, and it is also a pediatric liver transplant. The most common indication.
DOI: 10.6084/m9.figshare.12961664
title: Cost-benefit analysis for Operational Earthquake Forecasting using probabilistic seismic hazard assessment for circular area using Excel

description: This is a Microsoft Excel spreadsheet to under cost-benefit analyses for operational earthquake forecasting actions based on the probabilistic seismic hazard for a circular area of uniform seismicity characterised by a Gutenberg-Richter relation and using a simple ground motion prediction equation (Cauzzi and Faccoli, 2008). The circular area is split into 10 annuli to integrate over the area. The hazard curve is computed as well as various associated parameters (e.g. mean disaggregated scenario).<br>The numbers in yellow can be changed to assess the benefit-to-cost ratio for different actions. It is an extension of my spreadsheet for PSHA for a circular source, which is also available here.<br>For more details please see the article by Douglas and Azarbakht (2020).
DOI: 10.6084/m9.figshare.12961664.v1
title: Cost-benefit analysis for Operational Earthquake Forecasting using probabilistic seismic hazard assessment for circular area using Excel

description: This is a Microsoft Excel spreadsheet to under cost-benefit analyses for operational earthquake forecasting actions based on the probabilistic seismic hazard for a circular area of uniform seismicity characterised by a Gutenberg-Richter relation and using a simple ground motion prediction equation (Cauzzi and Faccoli, 2008). The circular area is split into 10 annuli to integrate over the area. The hazard curve is computed as well as various associated parameters (e.g. mean disaggregated scenario).<br>The numbers in yellow can be changed to assess the benefit-to-cost ratio for different actions. It is an extension of my spreadsheet for PSHA for a circular source, which is also available here.<br>For more details please see the article by Douglas and Azarbakht (2020).
DOI: 10.21227/7907-sn67
title: Facial Masks and Respirators Database (FMR-DB)

description: Recently, the coronavirus pandemic has made the use of facial masks and respirators common, the former to reduce the likelihood of spreading saliva droplets and the latter as Personal Protective Equipment (PPE). As a result, this caused problems for the existing face detection algorithms. For this reason, and for the implementation of other more sophisticated systems, able to recognize the type of facial mask or respirator and to react given this information, we created the Facial Masks and Respirators Database (FMR-DB).The database consists of 2565 images taken from the Internet, divided into the following main classes:With Facial Mask or RespiratorDisposable Respirators With Valve Disposable Respirators Without Valve Full-Face RespiratorsHalf-Face Respirators Non-Medical MasksSurgical MasksWithout Facial Mask or RespiratorWith OcclusionsHands On MouthHatsNeck Warmers And BandanasSunglassesWithout OcclusionsImages depicting people wearing a mask or a respirator also have a classification on the additional protective clothing worn: eye protection, head protection or both. The images depicting people wearing disposable respirators are also classified according to the respirator protection class.
DOI: 10.5281/zenodo.4032424
title: Reproduction package for "ClG 0217+70: A massive merging galaxy cluster with a large radio halo and relics"

description: This is the reproduction package for "ClG 0217+70: A massive merging galaxy cluster with a large radio halo and relics", which has been accepted for publication in A&A. To use this package, please read the README. This package is tested in an environment that contains: SPEX v3.06 CIAO v4.12 python 3.6.5 numpy 1.14.3 astropy 3.0.2 astroquery 0.4 scipy 1.1.0 matplotlib 2.2.2
DOI: 10.5281/zenodo.4032423
title: Reproduction package for "ClG 0217+70: A massive merging galaxy cluster with a large radio halo and relics"

description: This is the reproduction package for "ClG 0217+70: A massive merging galaxy cluster with a large radio halo and relics", which has been accepted for publication in A&A. To use this package, please read the README. This package is tested in an environment that contains: SPEX v3.06 CIAO v4.12 python 3.6.5 numpy 1.14.3 astropy 3.0.2 astroquery 0.4 scipy 1.1.0 matplotlib 2.2.2
DOI: 10.5281/zenodo.4032418
title: Data for 'Slip-hydride interactions in Zircaloy-4: Multiscale mechanical testing and characterisation'

description: This is the data bundle for 'Slip-hydride interactions in Zircaloy-4: Multiscale mechanical testing and characterisation' Arxiv: https://arxiv.org/abs/2003.13610 Acta Materialia: https://doi.org/10.1016/j.actamat.2020.09.038 Authors: <br> Siyang Wang, Finn Giuliani, T. Ben Britton<br> Department of Materials, Royal School of Mines, Imperial College London, London, SW7 2AZ, UK This data bundle consists of three folders:<br> 'DIC' contains the raw SEM images and the data from DIC analysis;<br> 'EBSD' contains the .ctf files from EBSD experiments;<br> 'Mech_curves' contains the data from mechanical tests. Any queries please contact Dr. Siyang Wang (siyang.wang15@imperial.ac.uk).
DOI: 10.5281/zenodo.4032417
title: Data for 'Slip-hydride interactions in Zircaloy-4: Multiscale mechanical testing and characterisation'

description: This is the data bundle for 'Slip-hydride interactions in Zircaloy-4: Multiscale mechanical testing and characterisation' Arxiv: https://arxiv.org/abs/2003.13610 Acta Materialia: https://doi.org/10.1016/j.actamat.2020.09.038 Authors: <br> Siyang Wang, Finn Giuliani, T. Ben Britton<br> Department of Materials, Royal School of Mines, Imperial College London, London, SW7 2AZ, UK This data bundle consists of three folders:<br> 'DIC' contains the raw SEM images and the data from DIC analysis;<br> 'EBSD' contains the .ctf files from EBSD experiments;<br> 'Mech_curves' contains the data from mechanical tests. Any queries please contact Dr. Siyang Wang (siyang.wang15@imperial.ac.uk).
DOI: 10.5517/ccdc.csd.cc1zqrxc
title: CCDC 1839134: Experimental Crystal Structure Determination

description: Related Article: Min-Yu Li, Fei Wang, Jian Zhang|2020|Cryst.Growth Des.|20|5644|doi:10.1021/acs.cgd.0c00817
DOI: 10.5517/ccdc.csd.cc2265xd
title: CCDC 1912573: Experimental Crystal Structure Determination

description: Related Article: Xia Wang, Xiaokang Wang, Xiurong Zhang, Weidong Fan, Qing Li, Weifeng Jiang, Fangna Dai, Daofeng Sun|2020|Cryst.Growth Des.|20|5670|doi:10.1021/acs.cgd.0c00925
DOI: 10.5517/ccdc.csd.cc250ktl
title: CCDC 1996549: Experimental Crystal Structure Determination

description: Related Article: Surendran Sujith, Eun Bi Nam, Junseong Lee, Sang Uck Lee, Min Hyung Lee|2020|Inorg.Chem.Front.|7|3456|doi:10.1039/D0QI00535E
DOI: 10.5517/ccdc.csd.cc2555n6
title: CCDC 2000977: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc2555p7
title: CCDC 2000978: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc2555q8
title: CCDC 2000979: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc2555r9
title: CCDC 2000980: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc2555sb
title: CCDC 2000981: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc2555tc
title: CCDC 2000982: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc2555vd
title: CCDC 2000983: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc2555wf
title: CCDC 2000984: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc2555xg
title: CCDC 2000985: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc2555yh
title: CCDC 2000986: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc2555zj
title: CCDC 2000987: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc25560l
title: CCDC 2000988: Experimental Crystal Structure Determination

description: Related Article: Essi Tervola, Khai-Nghi Truong, Jas S. Ward, Arri Priimagi, Kari Rissanen|2020|RSC Advances|10|29385|doi:10.1039/D0RA04691D
DOI: 10.5517/ccdc.csd.cc25561m
title: CCDC 2000989: Experimental Crystal Structure Determination

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