cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc200ybb
title: CCDC 1847951: Experimental Crystal Structure Determination

description: Related Article: Norbert Szimhardt,  Maximilian H. H. Wurzenberger,  Lukas Zeisel,  Michael S. Gruhne,  Marcus Lommel,  Thomas M. Klapötke,  Jörg Stierstorfer|2019|Chem.-Eur.J.|25|1963|doi:10.1002/chem.201803372
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.cc200ycc
title: CCDC 1847952: Experimental Crystal Structure Determination

description: Related Article: Norbert Szimhardt,  Maximilian H. H. Wurzenberger,  Lukas Zeisel,  Michael S. Gruhne,  Marcus Lommel,  Thomas M. Klapötke,  Jörg Stierstorfer|2019|Chem.-Eur.J.|25|1963|doi:10.1002/chem.201803372
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.cc2010jm
title: CCDC 1848019: Experimental Crystal Structure Determination

description: Related Article: Norbert Szimhardt,  Maximilian H. H. Wurzenberger,  Lukas Zeisel,  Michael S. Gruhne,  Marcus Lommel,  Thomas M. Klapötke,  Jörg Stierstorfer|2019|Chem.-Eur.J.|25|1963|doi:10.1002/chem.201803372
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.cc213vbc
title: CCDC 1880532: Experimental Crystal Structure Determination

description: Related Article: Fuchao Yu|2018|CSD Communication|||
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.cc213vkl
title: CCDC 1880539: Experimental Crystal Structure Determination

description: Related Article: Li-Qian Ji, Jing Yang, Zi-You Zhang, Yong Qian, Zhi Su, Min Han, Hong-Ke Liu|2018|CSD Communication|||
description: Related Article: Li-Qian Ji, Jing Yang, Zi-You Zhang, Yong Qian, Zhi Su, Min Han, Hong-Ke Liu|2019|Cryst.Growth Des.|19|2991|doi:10.1021/acs.cgd.9b00190
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: 10.7910/dvn/s9kuhq/wd8ano
title: dp 2016-3.pdf
DOI: 10.5061/dryad.n5b54j0/1
title: JAE-2018-00480.R2 - raw data & SAS code

description: Raw data collected during experiments undertaken at the Virginia Institute of Marine Science, Eastern Shore Laboratory, in 2016.
DOI: 10.5281/zenodo.1484260
title: Non-coding regions are the main source of targetable tumor-specific antigens - DATASETS (k=24)

description: Tumor-specific antigens (TSAs) represent ideal targets for cancer immunotherapy, but few have been identified thus far. We therefore developed a proteogenomic approach to enable the high-throughput discovery of TSAs coded by potentially all genomic regions. In two murine cancer cell lines and seven human primary tumors, we identified a total of 40 TSAs, about 90% of which derived from allegedly non-coding regions and would have been missed by standard exome-based approaches. Moreover, the majority of these TSAs derived from non-mutated yet aberrantly expressed transcripts (such as endogenous retroelements) that could be shared by multiple tumor types. In mice, the efficacy of TSA vaccination was influenced by two parameters that can be estimated in humans and could serve for TSA prioritization in clinical studies: TSA expression and the frequency of TSA-responsive T cells in the pre-immune repertoire. In conclusion, the strategy reported herein could considerably facilitate the identification and prioritization of actionable human TSAs.
description: k-mer database (k=24 nucleotides) generated from concatenated RNA-sequencing reads from human TEC (n=2) and mTEC (n=4), which was used in the following publication Laumont C.M., Vincent K. et al. Sci Trans Med (2018). If you use the data, please cite this paper.

For methodological details, see section Study design of the Materials and Methods and sections Human TEC and mTEC extraction, RNA extraction, library preparation and sequencing and Generation of cancer and normal k-mer databases of the Supplementary Materials and Methods of the article cited above.

For any questions or for assistance, please contact us using the following email address: perreault.lab@iric.ca
DOI: 10.5281/zenodo.1484261
title: Non-coding regions are the main source of targetable tumor-specific antigens - DATASETS (k=24)

description: Tumor-specific antigens (TSAs) represent ideal targets for cancer immunotherapy, but few have been identified thus far. We therefore developed a proteogenomic approach to enable the high-throughput discovery of TSAs coded by potentially all genomic regions. In two murine cancer cell lines and seven human primary tumors, we identified a total of 40 TSAs, about 90% of which derived from allegedly non-coding regions and would have been missed by standard exome-based approaches. Moreover, the majority of these TSAs derived from non-mutated yet aberrantly expressed transcripts (such as endogenous retroelements) that could be shared by multiple tumor types. In mice, the efficacy of TSA vaccination was influenced by two parameters that can be estimated in humans and could serve for TSA prioritization in clinical studies: TSA expression and the frequency of TSA-responsive T cells in the pre-immune repertoire. In conclusion, the strategy reported herein could considerably facilitate the identification and prioritization of actionable human TSAs.
description: k-mer database (k=24 nucleotides) generated from concatenated RNA-sequencing reads from human TEC (n=2) and mTEC (n=4), which was used in the following publication Laumont C.M., Vincent K. et al. Sci Trans Med (2018). If you use the data, please cite this paper.

For methodological details, see section Study design of the Materials and Methods and sections Human TEC and mTEC extraction, RNA extraction, library preparation and sequencing and Generation of cancer and normal k-mer databases of the Supplementary Materials and Methods of the article cited above.

For any questions or for assistance, please contact us using the following email address: perreault.lab@iric.ca
DOI: 10.5517/ccdc.csd.cc1zgcb4
title: CCDC 1831056: Experimental Crystal Structure Determination

description: Related Article: Amal Al-Azmi|2019|J.Mol.Struct.|1180|179|doi:10.1016/j.molstruc.2018.11.043
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.cc1zjgcb
title: CCDC 1833072: Experimental Crystal Structure Determination

description: Related Article: Guojun Hu, Ning Xiao, Lejia Wang, Lianjun Shen, Xiaofang Li, Hai Xu, Lei Han, Xunwen Xiao|2019|J.Solid State Chem.|270|247|doi:10.1016/j.jssc.2018.11.031
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.cc213vlm
title: CCDC 1880540: Experimental Crystal Structure Determination

description: Related Article: Li-Qian Ji, Jing Yang, Zi-You Zhang, Yong Qian, Zhi Su, Min Han, Hong-Ke Liu|2018|CSD Communication|||
description: Related Article: Li-Qian Ji, Jing Yang, Zi-You Zhang, Yong Qian, Zhi Su, Min Han, Hong-Ke Liu|2019|Cryst.Growth Des.|19|2991|doi:10.1021/acs.cgd.9b00190
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.cc1zgcd6
title: CCDC 1831058: Experimental Crystal Structure Determination

description: Related Article: Amal Al-Azmi|2019|J.Mol.Struct.|1180|179|doi:10.1016/j.molstruc.2018.11.043
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.cc204bgz
title: CCDC 1851210: Experimental Crystal Structure Determination

description: Related Article: Xiao-Jie Wei, Dong Liu, Yue-Hua Li, Guang-Hua Cui|2019|Polyhedron|158|357|doi:10.1016/j.poly.2018.11.014
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.cc204bj1
title: CCDC 1851212: Experimental Crystal Structure Determination

description: Related Article: Xiao-Jie Wei, Dong Liu, Yue-Hua Li, Guang-Hua Cui|2019|Polyhedron|158|357|doi:10.1016/j.poly.2018.11.014
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.cc20cdtm
title: CCDC 1858010: Experimental Crystal Structure Determination

description: Related Article: Kiyoshi Fujisawa, Kengo Tobita, Syuhei Sakuma, Didier Savard, Daniel B. Leznoff|2019|Inorg.Chim.Acta|486|582|doi:10.1016/j.ica.2018.10.036
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.cc20cdvn
title: CCDC 1858011: Experimental Crystal Structure Determination

description: Related Article: Kiyoshi Fujisawa, Kengo Tobita, Syuhei Sakuma, Didier Savard, Daniel B. Leznoff|2019|Inorg.Chim.Acta|486|582|doi:10.1016/j.ica.2018.10.036
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: 10.7910/dvn/q0wigs/wigleb
title: google-politics.tgz
DOI: 10.15129/8763783e-813f-4753-a178-e81b4fd3011f
title: Data for:"Microfluidic-assisted silk nanoparticle tuning"

description: The current manufacture of silk nanoparticles using desolvation method is time-consuming and lack ability to tune nanoparticle characteristics. Over the last decade, microfluidics has been developed which enables to manipulate particle properties by fine-tuning process parameters such as the total flow rate and flow rate ratios. Our aim was therefore to manufacture silk nanoparticles by desolvation using the NanoAssemblrTM  microfluidic setup.

All datasets for each figure provided in separate zipped folders. All measurements, equipment and software used in this study are detailed in the Readme file provided
DOI: 10.5517/ccdc.csd.cc20cdwp
title: CCDC 1858012: Experimental Crystal Structure Determination

description: Related Article: Kiyoshi Fujisawa, Kengo Tobita, Syuhei Sakuma, Didier Savard, Daniel B. Leznoff|2019|Inorg.Chim.Acta|486|582|doi:10.1016/j.ica.2018.10.036
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.cc20hv5k
title: CCDC 1862268: Experimental Crystal Structure Determination

description: Related Article: Marwa Yahyaoui, Med Abderrahmane Sanhoury, Cameron L. Carpenter-Warren, Alexandra M.Z. Slawin, J. Derek Woollins, Soufiane Touil, Ali Samarat|2018|Chem. Data Collect.|17-18|483|doi:10.1016/j.cdc.2018.11.009
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: 10.7910/dvn/27846/h8flmt
title: Permission_HU Multinational Enterprise Data Banks_v2015.pdf
DOI: 10.7910/dvn/pe8twp/0xgiww
title: data_ipod.dta
DOI: 10.7910/dvn/r58enb/hlpimk
title: CONTROL
DOI: 10.7910/dvn/r58enb/jdjgsz
title: IND
DOI: 10.7910/dvn/r58enb/x2u8ww
title: Global.txt
DOI: 10.7910/dvn/pe8twp/2nhdav
title: Codebook.xlsx
DOI: 10.7910/dvn/pe8twp/mssbz9
title: data_ipod.tab
DOI: 10.7910/dvn/skspsy/jfk2rm
title: efrinuline.csv
DOI: 10.25582/data.2021-07.2267637/1954225
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273303/1954226
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269087/1954227
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274845/1954228
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270716/1954229
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273896/1954230
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270199/1954231
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276000/1954232
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268525/1954233
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276086/1954234
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273135/1954235
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2266992/1954236
title: Data for EMSL Project 50686 from July 2021
DOI: 10.6084/m9.figshare.22126796
title: Supplementary Material for: Brain Biometry Reveals Impaired Brain Growth in Preterm Neonates with Intraventricular Hemorrhage

description: <b><i>Introduction:</i></b> Preterm birth and cerebral hemorrhage have adverse effects on brain development. Alterations in regional brain size on magnetic resonance imaging (MRI) can be assessed using 2D biometrical analysis, an easily applicable technique showing good correlation with 3D brain volumes. <b><i>Methods:</i></b> This retrospective study included 74 preterm neonates with intraventricular hemorrhage (IVH) born &lt;32<sup>+0</sup> weeks of gestation between 2011 and 2019. Cerebral MRI was performed at term-equivalent age, and 2D measurement techniques were used for biometrical analysis and compared to normative data of two control groups. Finally, the correlation and association of brain parameters and patterns of impaired brain growth and outcome at 2 and 3 years of age were evaluated. <b><i>Results:</i></b> Interhemispheric distance (IHD), the 3rd ventricle, and lateral ventricles presented larger, in contrast, cerebral biparietal width (cBPW), fronto-occipital diameter (FOD), and the length of the corpus callosum were smaller in IVH patients compared to respective controls. The strongest correlations with outcome were observed for the parameters FOD, anteroposterior diameter of the vermis, transverse cerebellar diameter (tCD), corpus callosum, 3rd ventricle, and left ventricular index. Patients with the small FOD, small BPW, and increased IHD pattern reached overall lower outcome scores at follow-up. <b><i>Discussion:</i></b> Preterm neonates with IVH showed reduced total brain sizes and enlarged pericerebral spaces compared to neurologically healthy controls. Biometric analysis revealed that several 2D brain parameters as well as different patterns of impaired brain growth were associated with neurodevelopmental impairment in early childhood. These findings may support prediction of long-term outcome and parental counseling in patients with IVH.
DOI: 10.6084/m9.figshare.22126796.v1
title: Supplementary Material for: Brain Biometry Reveals Impaired Brain Growth in Preterm Neonates with Intraventricular Hemorrhage

description: <b><i>Introduction:</i></b> Preterm birth and cerebral hemorrhage have adverse effects on brain development. Alterations in regional brain size on magnetic resonance imaging (MRI) can be assessed using 2D biometrical analysis, an easily applicable technique showing good correlation with 3D brain volumes. <b><i>Methods:</i></b> This retrospective study included 74 preterm neonates with intraventricular hemorrhage (IVH) born &lt;32<sup>+0</sup> weeks of gestation between 2011 and 2019. Cerebral MRI was performed at term-equivalent age, and 2D measurement techniques were used for biometrical analysis and compared to normative data of two control groups. Finally, the correlation and association of brain parameters and patterns of impaired brain growth and outcome at 2 and 3 years of age were evaluated. <b><i>Results:</i></b> Interhemispheric distance (IHD), the 3rd ventricle, and lateral ventricles presented larger, in contrast, cerebral biparietal width (cBPW), fronto-occipital diameter (FOD), and the length of the corpus callosum were smaller in IVH patients compared to respective controls. The strongest correlations with outcome were observed for the parameters FOD, anteroposterior diameter of the vermis, transverse cerebellar diameter (tCD), corpus callosum, 3rd ventricle, and left ventricular index. Patients with the small FOD, small BPW, and increased IHD pattern reached overall lower outcome scores at follow-up. <b><i>Discussion:</i></b> Preterm neonates with IVH showed reduced total brain sizes and enlarged pericerebral spaces compared to neurologically healthy controls. Biometric analysis revealed that several 2D brain parameters as well as different patterns of impaired brain growth were associated with neurodevelopmental impairment in early childhood. These findings may support prediction of long-term outcome and parental counseling in patients with IVH.
DOI: 10.25582/data.2021-07.2275961/1954237
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275795/1954238
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273757/1954239
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274144/1954240
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270295/1954241
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267073/1954242
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267578/1954243
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270773/1954244
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275474/1954245
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269762/1954246
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268423/1954247
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273376/1954248
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276498/1954249
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2266951/1954250
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2266982/1954251
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275583/1954252
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273186/1954253
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2266857/1954254
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267575/1954255
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276436/1954256
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267030/1954257
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267609/1954258
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267127/1954259
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273947/1954260
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267392/1954262
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268429/1954263
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273813/1954264
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275572/1954265
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270640/1954266
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267990/1954267
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268384/1954268
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269743/1954269
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268046/1954270
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268637/1954271
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275273/1954272
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268960/1954273
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2266817/1954274
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276135/1954275
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268498/1954276
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273249/1954277
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275107/1954278
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275464/1954279
title: Data for EMSL Project 50686 from July 2021