cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc28rnzp
title: CCDC 2108123: Experimental Crystal Structure Determination

description: Related Article: Jianfei Yu, Fanping Huang, Wei Fang, Congcong Yin, Chuan Shi, Qiwei Lang, Gen-Qiang Chen, Xumu Zhang|2022|Green Synth. Cat.|3|175|doi:10.1016/j.gresc.2022.03.004
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.cc28wrtr
title: CCDC 2112055: Experimental Crystal Structure Determination

description: Related Article: Sanliang Li, Qiaoyu Chen, Zhan-Ming Zhang, Junliang Zhang|2021|Green Synth. Cat.|2|374|doi:10.1016/j.gresc.2021.10.006
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.cc29wttv
title: CCDC 2141908: Experimental Crystal Structure Determination

description: Related Article: Fen Tan, Luojia Chen, Yuncong Yuan, Xiao-Yu He, Yi-Wen Su, Shu-Qiang Cao, Conghua Xie, Meijia Gu, You-Quan Zou|2022|Green Synth. Cat.|3|357|doi:10.1016/j.gresc.2022.09.003
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.cc29z4n3
title: CCDC 2144135: Experimental Crystal Structure Determination

description: Related Article: Le Jia, Xue Wang, Xiaolong Gao, Mohamed Makha, Xi-Cun Wang, Yuehui Li|2022|Green Synth. Cat.|3|259|doi:10.1016/j.gresc.2022.03.008
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.cc2bv52g
title: CCDC 2170095: Experimental Crystal Structure Determination

description: Related Article: Fen Tan, Luojia Chen, Yuncong Yuan, Xiao-Yu He, Yi-Wen Su, Shu-Qiang Cao, Conghua Xie, Meijia Gu, You-Quan Zou|2022|Green Synth. Cat.|3|357|doi:10.1016/j.gresc.2022.09.003
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: 10.48331/scielo.rhldvj/afolkg
title: Karunika Kardak Presentation 18 May 2023.pdf

description: :unav
DOI: 10.48331/scielo.rhldvj
title: Succession Planning Recruiting a New Editor in Chief

description: Presentation by Dr Karunika Kardak at the ABEC Brazil - SciELO 25 Years Seminar: Editor succession models. Part 1 - Experience of journals from commercial publishers. Event page: https://25.scielo.org/en/seminars/abec-editor-pt1/
DOI: 10.5281/zenodo.7544968
title: Friction extrusion processing of aluminum powders: microstructure and mechanical properties assessment

description: This dataset contains the data from the publication <strong>"Friction extrusion processing of aluminum powders: microstructure and mechanical properties assessment"</strong>
DOI: 10.5281/zenodo.7946528
title: Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties

description: This dataset contains the data from the publication <strong>"Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties"</strong>
DOI: 10.5281/zenodo.7544967
title: Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties

description: This dataset contains the data from the publication <strong>"Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties"</strong>
DOI: 10.48331/scielo.mlbx8w/hwbr13
title: Talk Scielo-ABEC May 2023.ppt

description: :unav
DOI: 10.48331/scielo.mlbx8w
title: Modelos de sucessão de editores. Parte 1 - Experiência de periódicos de editoras comerciais: Annals of Applied Biology

description: Apresentação do Prof Ricardo A. Azevedo no Seminário ABEC Brasil – SciELO 25 Anos: Modelos de sucessão de editores. Parte 1 - Experiência de periódicos de editoras comerciais. Página do evento: https://25.scielo.org/seminarios/abec-editores-pt1/
DOI: 10.5517/ccdc.csd.cc10qkz8
title: CCDC 945220: Experimental Crystal Structure Determination

description: Related Article: I. Rama, A. Subashini, R. Selvameena|2020|Indian J.Chem.,Sect.A:Inorg.,Bio-inorg.,Phys.,Theor.Anal.Chem.|59A|1449|
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: 10.5281/zenodo.7661222
title: Supplementary materials for "Machine Learning Electronic Structure Methods Based On The One-Electron Reduced Density Matrix"

description: <strong>Supplementary materials for "Machine Learning Electronic Structure Methods Based On The One-Electron Reduced Density Matrix"</strong> by X. Shao, L. Paetow, M. E. Tuckerman and M. Pavanello <strong>The QMLearn software</strong> A current snapshot of the QMLearn software is available on GitLab at https://gitlab.com/pavanello-research-group/qmlearn. Video tutorials and other examples (including Jupyter Notebooks) are available at http://qmlearn.rutgers.edu <strong>Data availability</strong> We share all training/test sets and notebooks needed to reproduce Table II and Figures 1-7. For all figures, we provide Jupyter notebooks and the needed data to exactly reproduce the figures. Trajectory files for all IR spectra are shared. The collection of all materials is available in this dataset. The QMLearn version used for this work is 0.0.1. Specific QMLearn dependencies and their version used for all calculations are listed as follows:<br> - ase (3.22.1)<br> - h5py (3.7.0)<br> - numpy (1.23.1)<br> - pyscf (2.0.1)<br> - scikit-learn (1.1.1)<br> - scipy (1.8.1)
DOI: 10.5281/zenodo.7661223
title: Supplementary materials for "Machine Learning Electronic Structure Methods Based On The One-Electron Reduced Density Matrix"

description: <strong>Supplementary materials for "Machine Learning Electronic Structure Methods Based On The One-Electron Reduced Density Matrix"</strong> by X. Shao, L. Paetow, M. E. Tuckerman and M. Pavanello <strong>The QMLearn software</strong> A current snapshot of the QMLearn software is available on GitLab at https://gitlab.com/pavanello-research-group/qmlearn. Video tutorials and other examples (including Jupyter Notebooks) are available at http://qmlearn.rutgers.edu <strong>Data availability</strong> We share all training/test sets and notebooks needed to reproduce Table II and Figures 1-5. For all figures, we provide Jupyter notebooks and the needed data to exactly reproduce the figures. Trajectory files for all IR spectra are shared. The collection of all materials is available in this dataset. The QMLearn version used for this work is 0.0.1. Specific QMLearn dependencies and their version used for all calculations are listed as follows:<br> - ase (3.22.1)<br> - h5py (3.7.0)<br> - numpy (1.23.1)<br> - pyscf (2.0.1)<br> - scikit-learn (1.1.1)<br> - scipy (1.8.1)
DOI: 10.5281/zenodo.7946445
title: Bioverse: The Habitable Zone Inner Edge Discontinuity as an Imprint of Runaway Greenhouse Climates on Exoplanet Demographics

description: This repository contains data required to run the pipeline producing the results and figures in Schlecker+2023, in particular results objects created with expensive model grid runs of Bioverse.
DOI: 10.5281/zenodo.7941542
title: Directed Stepwise Tracing of Polysynaptic Neuronal Circuits With Replication-deficient Pseudorabies Virus

description: Data for paper: Directed Stepwise Tracing of Polysynaptic Neuronal Circuits With Replication-deficient Pseudorabies Virus. Brain functions are accomplished by polysynaptic circuits formed by neurons wired together through multiple orders of synaptic connections. Polysynaptic connectivity has been difficult to examine due to a lack of methods of continuously tracing the pathways in a controlled manner. Here we demonstrate directed, stepwise retrograde polysynaptic tracing by inducible reconstitution of replication-deficient transneuronal pseudorabies virus (PRV<sup>∆IE</sup>) in the brain. Furthermore, PRV<sup>∆IE</sup> replication can be temporally restricted to minimize its neurotoxicity. With this tool, we delineate a wiring diagram between the hippocampus and striatum-- two major brain systems for learning, memory and navigation--which consist of projections from specific hippocampal domains to specific striatal areas via distinct intermediate brain regions. Therefore, this inducible PRV<sup>∆IE </sup>system provides a tool for dissecting polysynaptic circuits underlying complex brain functions.
DOI: 10.5281/zenodo.7941541
title: Directed Stepwise Tracing of Polysynaptic Neuronal Circuits With Replication-deficient Pseudorabies Virus

description: Data for paper: Directed Stepwise Tracing of Polysynaptic Neuronal Circuits With Replication-deficient Pseudorabies Virus. Brain functions are accomplished by polysynaptic circuits formed by neurons wired together through multiple orders of synaptic connections. Polysynaptic connectivity has been difficult to examine due to a lack of methods of continuously tracing the pathways in a controlled manner. Here we demonstrate directed, stepwise retrograde polysynaptic tracing by inducible reconstitution of replication-deficient transneuronal pseudorabies virus (PRV<sup>∆IE</sup>) in the brain. Furthermore, PRV<sup>∆IE</sup> replication can be temporally restricted to minimize its neurotoxicity. With this tool, we delineate a wiring diagram between the hippocampus and striatum-- two major brain systems for learning, memory and navigation--which consist of projections from specific hippocampal domains to specific striatal areas via distinct intermediate brain regions. Therefore, this inducible PRV<sup>∆IE </sup>system provides a tool for dissecting polysynaptic circuits underlying complex brain functions.
DOI: 10.6084/m9.figshare.22904132
title: iss_training

description: iss training data
DOI: 10.34740/kaggle/dsv/5701771
title: "Dubai's Finest Eats: 13k+ Restaurant Dataset"
DOI: 10.6084/m9.figshare.22817318
title: genome_browser_5mC

description: 5mC data for genome browser test
DOI: 10.6084/m9.figshare.22817318.v1
title: genome_browser_5mC

description: 5mC data for genome browser test
DOI: 10.6084/m9.figshare.22817318.v2
title: genome_browser_5mC

description: 5mC data for genome browser test
DOI: 10.34740/kaggle/dsv/5710763
title: Balochi Language to English Translation Dataset
DOI: 10.5517/ccdc.csd.cc2fd8vz
title: CCDC 2246131: Experimental Crystal Structure Determination

description: Related Article: Seonghyeon Nam, Sunhee Lee, Woojin Kim, Ikyon Kim|2023|Org.Biomol.Chem.|21|3881|doi:10.1039/D3OB00471F
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.cc2fd8w0
title: CCDC 2246132: Experimental Crystal Structure Determination

description: Related Article: Seonghyeon Nam, Sunhee Lee, Woojin Kim, Ikyon Kim|2023|Org.Biomol.Chem.|21|3881|doi:10.1039/D3OB00471F
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: 10.5281/zenodo.7925768
title: SpliceAI_rocksdb_hg19_chr3

description: SpliceAI RocksDB for chromosome 3 of hg19 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.5281/zenodo.7925767
title: SpliceAI_rocksdb_hg19_chr3

description: SpliceAI RocksDB for chromosome 3 of hg19 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.5281/zenodo.7925735
title: SpliceAI_rocksdb_hg19_chr2

description: SpliceAI RocksDB for chromosome 2 of hg19 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.5281/zenodo.7925734
title: SpliceAI_rocksdb_hg19_chr2

description: SpliceAI RocksDB for chromosome 2 of hg19 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.5281/zenodo.7925611
title: SpliceAI_rocksdb_hg19_chr1

description: SpliceAI RocksDB for chromosome 1 of hg19 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.5281/zenodo.7925610
title: SpliceAI_rocksdb_hg19_chr1

description: SpliceAI RocksDB for chromosome 1 of hg19 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.5281/zenodo.7925908
title: SpliceAI_rocksdb_hg19_chr6

description: SpliceAI RocksDB for chromosome 6 of hg19 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.5281/zenodo.7925907
title: SpliceAI_rocksdb_hg19_chr6

description: SpliceAI RocksDB for chromosome 6 of hg19 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master
DOI: 10.6084/m9.figshare.22904479
title: Multiomics analysis of adaptation to repeated DNA damage in prostate cancer cells

description: DNA damage is frequently utilized as the basis for cancer therapies; however, resistance to DNA damage remains one of the biggest challenges for successful treatment outcomes. Critically, the molecular drivers behind resistance are poorly understood. To address this question, we created an isogenic model of prostate cancer exhibiting more aggressive characteristics to better understand the molecular signatures associated with resistance and metastasis. 22Rv1 cells were repeatedly exposed to DNA damage daily for 6 weeks, similar to patient treatment regimes. Using Illumina Methylation EPIC arrays and RNA-seq, we compared DNA methylation and transcriptional profiles between the parental 22Rv1 cell line and the lineage exposed to prolonged DNA damage. Here we show that repeated DNA damage drives the molecular evolution of cancer cells to a more aggressive phenotype and identify molecular candidates behind this process. Total DNA methylation was increased while RNA-seq demonstrated these cells had dysregulated expression of genes involved in metabolism and the unfolded protein response (UPR) with Asparagine synthetase (ASNS) identified as central to this process. Despite the limited overlap between RNA-seq and DNA methylation, oxoglutarate dehydrogenase-like (OGDHL) was identified as altered in both data sets. Utilising a second approach we profiled the proteome in 22Rv1 cells following a single dose of radiotherapy. This analysis also highlighted the UPR in response to DNA damage. Together, these analyses identified dysregulation of metabolism and the UPR and identified ASNS and OGDHL as candidates for resistance to DNA damage. This work provides critical insight into molecular changes which underpin treatment resistance and metastasis.
DOI: 10.6084/m9.figshare.22904479.v1
title: Multiomics analysis of adaptation to repeated DNA damage in prostate cancer cells

description: DNA damage is frequently utilized as the basis for cancer therapies; however, resistance to DNA damage remains one of the biggest challenges for successful treatment outcomes. Critically, the molecular drivers behind resistance are poorly understood. To address this question, we created an isogenic model of prostate cancer exhibiting more aggressive characteristics to better understand the molecular signatures associated with resistance and metastasis. 22Rv1 cells were repeatedly exposed to DNA damage daily for 6 weeks, similar to patient treatment regimes. Using Illumina Methylation EPIC arrays and RNA-seq, we compared DNA methylation and transcriptional profiles between the parental 22Rv1 cell line and the lineage exposed to prolonged DNA damage. Here we show that repeated DNA damage drives the molecular evolution of cancer cells to a more aggressive phenotype and identify molecular candidates behind this process. Total DNA methylation was increased while RNA-seq demonstrated these cells had dysregulated expression of genes involved in metabolism and the unfolded protein response (UPR) with Asparagine synthetase (ASNS) identified as central to this process. Despite the limited overlap between RNA-seq and DNA methylation, oxoglutarate dehydrogenase-like (OGDHL) was identified as altered in both data sets. Utilising a second approach we profiled the proteome in 22Rv1 cells following a single dose of radiotherapy. This analysis also highlighted the UPR in response to DNA damage. Together, these analyses identified dysregulation of metabolism and the UPR and identified ASNS and OGDHL as candidates for resistance to DNA damage. This work provides critical insight into molecular changes which underpin treatment resistance and metastasis.
DOI: 10.5281/zenodo.7925923
title: SpliceAI_rocksdb_hg19_chr7

description: SpliceAI RocksDB for chromosome 7 of hg19 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3 Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master