cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2ffsnb
title: CCDC 2247582: Experimental Crystal Structure Determination

description: Related Article: D. Havlíček, S. Tepavitcharova, M. Fridrichová, J. Plocek, R. Gergulova, D. Rabadjieva|2023|J.Mol.Struct.|1290|135972|doi:10.1016/j.molstruc.2023.135972
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.cc2fk76g
title: CCDC 2250885: Experimental Crystal Structure Determination

description: Related Article: Tucker J. Ball, Matthew J. Polinski|2023|Inorg.Chem.|62|9618|doi:10.1021/acs.inorgchem.3c01099
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.cc2fk77h
title: CCDC 2250886: Experimental Crystal Structure Determination

description: Related Article: Tucker J. Ball, Matthew J. Polinski|2023|Inorg.Chem.|62|9618|doi:10.1021/acs.inorgchem.3c01099
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.cc2fk78j
title: CCDC 2250887: Experimental Crystal Structure Determination

description: Related Article: Tucker J. Ball, Matthew J. Polinski|2023|Inorg.Chem.|62|9618|doi:10.1021/acs.inorgchem.3c01099
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.8087816
title: Phenological data of Pentaclethra macroloba in high and low várzea forest in the Amazon estuary

description: Phenological data of Pentaclethra macroloba in high and low várzea forest in the Amazon estuary during the period from February 2018 to December 2019 using the Founier methodology.
DOI: 10.5281/zenodo.8087815
title: Phenological data of Pentaclethra macroloba in high and low várzea forest in the Amazon estuary

description: Phenological data of Pentaclethra macroloba in high and low várzea forest in the Amazon estuary during the period from February 2018 to December 2019 using the Founier methodology.
DOI: 10.5281/zenodo.8087804
title: DEMIX Difference Distribution Histograms

description: DEMIX seeks to rank 6 global 1 arc sec DEMs (COPDEM, FABDEM, ALOS, NASADEM, SRTM, and ASTER) [1]. It uses the difference between the global DEM and a comparable reference DEM aggregated from 1-5 m lidar-derived UTM DEMs. For each DEM it calculates 5 criteria for describing the elevation, slope, and roughness distributions. Evaluations for each criterion are included in the DEMIX GIS database [2]. These distributions have been used in several presentations [1,5,6]. This data set includes: A CSV format database table listing the 236 tiles used by DEMIX {1], summary statistics for the reference DTM version of the tile, and a link to the PNG graphic with the 3 histograms (if the tile does not have a DSM), and 6 histograms (if the tile has both DSM and DTM). The MICRODEM GIS program [3,4] can display this database, and display the histogram for selected tiles. Filtering of the GIS database allows display of just the histograms for tiles with selected characteristics. DBF version of the same database 236 PNG files with the difference distributions. The histograms cover only the central part of the distributions, which should show a mode close to 0 if the DEM is unbiased. If the histogram does not show a mode for a DEM, it's a clear indication that DEM is not very good in that tile. References: Bielski, C.; López-Vázquez, C.; Guth. P.L.; Grohmann, C.H. and the TMSG DEMIX Working Group, 2023. DEMIX Wine Contest Method Ranks ALOS AW3D30, COPDEM, and FABDEM as Top 1” Global DEMs: https://arxiv.org/pdf/2302.08425.pdf. A revised version of this preprint will be posted soon. Guth, P. L., 2023. DEMIX GIS Database Version 2 (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8062008 . Guth, P.L., 2023, git_microdem: https://github.com/prof-pguth/git_microdem , open source software. Guth, P.L., 2023, MICRODEM: Open-source GIS with a focus on Geomorphometry: https://microdem.org/ , download for Windows-64 installation program and CHM help file. Guth, P.L., Grohmann, C.H., & Trevisani, S., 2023, Geomorphometric and Geospatial Patterns in Differences Between ALOS and COPDEM: Geomorphometry2023. https://doi.org/10.5281/zenodo.8057703 Coming soon
DOI: 10.5281/zenodo.8087803
title: DEMIX Difference Distribution Histograms

description: DEMIX seeks to rank 6 global 1 arc sec DEMs (COPDEM, FABDEM, ALOS, NASADEM, SRTM, and ASTER) [1]. It uses the difference between the global DEM and a comparable reference DEM aggregated from 1-5 m lidar-derived UTM DEMs. For each DEM it calculates 5 criteria for describing the elevation, slope, and roughness distributions. Evaluations for each criterion are included in the DEMIX GIS database [2]. These distributions have been used in several presentations [1,5,6]. This data set includes: A CSV format database table listing the 236 tiles used by DEMIX {1], summary statistics for the reference DTM version of the tile, and a link to the PNG graphic with the 3 histograms (if the tile does not have a DSM), and 6 histograms (if the tile has both DSM and DTM). The MICRODEM GIS program [3,4] can display this database, and display the histogram for selected tiles. Filtering of the GIS database allows display of just the histograms for tiles with selected characteristics. DBF version of the same database 236 PNG files with the difference distributions. The histograms cover only the central part of the distributions, which should show a mode close to 0 if the DEM is unbiased. If the histogram does not show a mode for a DEM, it's a clear indication that DEM is not very good in that tile. References: Bielski, C.; López-Vázquez, C.; Guth. P.L.; Grohmann, C.H. and the TMSG DEMIX Working Group, 2023. DEMIX Wine Contest Method Ranks ALOS AW3D30, COPDEM, and FABDEM as Top 1” Global DEMs: https://arxiv.org/pdf/2302.08425.pdf. A revised version of this preprint will be posted soon. Guth, P. L., 2023. DEMIX GIS Database Version 2 (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8062008 . Guth, P.L., 2023, git_microdem: https://github.com/prof-pguth/git_microdem , open source software. Guth, P.L., 2023, MICRODEM: Open-source GIS with a focus on Geomorphometry: https://microdem.org/ , download for Windows-64 installation program and CHM help file. Guth, P.L., Grohmann, C.H., & Trevisani, S., 2023, Geomorphometric and Geospatial Patterns in Differences Between ALOS and COPDEM: Geomorphometry2023. https://doi.org/10.5281/zenodo.8057703 Coming soon
DOI: 10.6084/m9.figshare.23589243
title: Ribosomal DNA Copy Number Variation is Coupled with DNA Methylation Changes at the 45S rDNA Locus

description: The human ribosomal DNA (rDNA) copy number (CN) has been challenging to analyse, and its sequence has been excluded from reference genomes due to its highly repetitive nature. The 45S rDNA locus encodes essential components of the cell, nevertheless rDNA displays high inter-individual CN variation that could influence human health and disease. CN alterations in rDNA have been hypothesized as a possible factor in autism spectrum disorders (ASD) and were shown to be altered in Schizophrenia patients. We tested whether whole-genome bisulphite sequencing can be used to simultaneously quantify rDNA CN and measure DNA methylation at the 45S rDNA locus. Using this approach, we observed high inter-individual variation in rDNA CN, and limited intra-individual copy differences in several post-mortem tissues. Furthermore, we did not observe any significant alterations in rDNA CN or DNA methylation in Autism Spectrum Disorder (ASD) brains in 16 ASD vs 11 control samples. Similarly, no difference was detected when comparing neurons form 28 Schizophrenia (Scz) patients vs 25 controls or oligodendrocytes from 22 Scz samples vs 20 controls. However, our analysis revealed a strong positive correlation between CN and DNA methylation at the 45S rDNA locus in multiple tissues. This was observed in brain and confirmed in small intestine, adipose tissue, and gastric tissue. This should shed light on a possible dosage compensation mechanism that silences additional rDNA copies to ensure homoeostatic regulation of ribosome biogenesis.
DOI: 10.6084/m9.figshare.23589243.v1
title: Ribosomal DNA Copy Number Variation is Coupled with DNA Methylation Changes at the 45S rDNA Locus

description: The human ribosomal DNA (rDNA) copy number (CN) has been challenging to analyse, and its sequence has been excluded from reference genomes due to its highly repetitive nature. The 45S rDNA locus encodes essential components of the cell, nevertheless rDNA displays high inter-individual CN variation that could influence human health and disease. CN alterations in rDNA have been hypothesized as a possible factor in autism spectrum disorders (ASD) and were shown to be altered in Schizophrenia patients. We tested whether whole-genome bisulphite sequencing can be used to simultaneously quantify rDNA CN and measure DNA methylation at the 45S rDNA locus. Using this approach, we observed high inter-individual variation in rDNA CN, and limited intra-individual copy differences in several post-mortem tissues. Furthermore, we did not observe any significant alterations in rDNA CN or DNA methylation in Autism Spectrum Disorder (ASD) brains in 16 ASD vs 11 control samples. Similarly, no difference was detected when comparing neurons form 28 Schizophrenia (Scz) patients vs 25 controls or oligodendrocytes from 22 Scz samples vs 20 controls. However, our analysis revealed a strong positive correlation between CN and DNA methylation at the 45S rDNA locus in multiple tissues. This was observed in brain and confirmed in small intestine, adipose tissue, and gastric tissue. This should shed light on a possible dosage compensation mechanism that silences additional rDNA copies to ensure homoeostatic regulation of ribosome biogenesis.
DOI: 10.25345/c5hx1618n
title: MassIVE MSV000092282 - 060272023-Mobley-collaborative-project_Zn
title: Proteus mirabilis WGLW4 were cultured, supernatant was extracted using HLB-SPE and 80% MeOH/water and metabolome was analyzed with QExactive HF. Zinc infusion
DOI: 10.5519/qd.v1evg0oc
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (730 records)
DOI: 10.25345/c5d50g74t
title: MassIVE MSV000092283 - 22M038 Extractos de cafe por cromatografia liquida
title: caracterizacion de extractos de cafe mediante cromatografia liquida acoplada a espectrometria de masas de tiempo de vuelo
DOI: 10.6084/m9.figshare.23589270
title: RNA-seq data from a comparison of RMV7 epigenetic phase variants

description: Files containing the output of the analysis of an RNA-seq study of <em>Streptococcus pneumoniae</em> RMV7 using the Kallisto and Sleuth packages. Each dataset corresponds to one of two epigenetic phase variants of <em>S. pneumoniae</em> RMV7 (either the dominant or rare methylation pattern), sampled at one of three timepoints (either prior to competence stimulating peptide administration; 10 minutes post-CSP administration, or 20 minutes post-CSP administration). RMV7_RNA_seq_expression.csv contains the estimated level of transcription of each gene in <em>S. pneumoniae</em> RMV7 in each dataset. RMV7_RNA_seq_statistics.csv contains the output of statistical tests for differential expression; the two phase variants were compared to one another pre-CSP, and then each post-CSP sample was compared to the pre-CSP sample from the corresponding phase variant.
DOI: 10.6084/m9.figshare.23589270.v1
title: RNA-seq data from a comparison of RMV7 epigenetic phase variants

description: Files containing the output of the analysis of an RNA-seq study of <em>Streptococcus pneumoniae</em> RMV7 using the Kallisto and Sleuth packages. Each dataset corresponds to one of two epigenetic phase variants of <em>S. pneumoniae</em> RMV7 (either the dominant or rare methylation pattern), sampled at one of three timepoints (either prior to competence stimulating peptide administration; 10 minutes post-CSP administration, or 20 minutes post-CSP administration). RMV7_RNA_seq_expression.csv contains the estimated level of transcription of each gene in <em>S. pneumoniae</em> RMV7 in each dataset. RMV7_RNA_seq_statistics.csv contains the output of statistical tests for differential expression; the two phase variants were compared to one another pre-CSP, and then each post-CSP sample was compared to the pre-CSP sample from the corresponding phase variant.
DOI: 10.5281/zenodo.8087872
title: Compounding uncertainties in economic and population growth increase tail risks for relevant outcomes across sectors

description: Understanding the long-term effects of population and GDP changes requires a multisectoral and regional understanding of the coupled human-Earth system, as the long-term evolution of this coupled system is influenced by human decisions and the Earth system. This study investigates the impact of compounding economic and population growth uncertainties on long-term multisectoral outcomes. We use the Global Change Analysis Model (GCAM) to explore the influence of compounding and feedback between future GDP and population growth on four key sectors: final energy consumption, water withdrawal, staple food prices, and CO2 emissions. The results show that uncertainties in GDP and population compound, resulting in a magnification of tail risks for outcomes across sectors and regions. Compounding uncertainties significantly impact metrics such as CO2 emissions and final energy consumption, particularly at the upper tail at both global and regional levels. However, the impact of staple food prices and water withdrawal depends on regional factors. Additionally, an alternative low-carbon transition scenario could compound uncertainties and increase tail risk, particularly in staple food prices, highlighting the influence of emergent constraints on land availability and food-energy competition for land use. The findings underscore the importance of considering and adequately accounting for compounding uncertainties in key drivers of multisectoral systems to enhance our comprehensive understanding of the complex nature of multisectoral systems. The paper provides valuable insights into the potential implications of compounding uncertainties.
DOI: 10.18739/a29g5gf9n
title: Alaska Community Ice Observations - 2019-2022

description: This dataset contains community based observations of ice thickness throughout the winters of 2019/2020, 2020/2021, and 2021/2022. Observations are co-located within or near communities participating in the Fresh Eyes on Ice project, seeking to expand spatial coverage of ice observations and science literacy in classrooms around the state. This spatially distributed dataset provides valuable information about ice thickness through the winter for the period of 2019-2022 on lakes and rivers around Alaska, including Big Lake near Venetie, Alaska, Brown's Slough in Bethel, Alaska, Noyes Slough in Fairbanks, Alaska, the Tanana River near Fairbanks, Alaska, Shageluk Lake near Shageluk, Alaska, Third Lake near Noatak, Alaska, Alexander Lake in Galena, Alaska, Smith Lake near Fairbanks, Alaska, Big Trail Lake near Fairbanks, Alaska, Toolik Lake at Toolik Research Station, Alaska, Jan Lake near Tok, Alaska, Float Pond Lake near Nenana, Alaska, unnamed lakes in the communities of Kenny Lake, Alaska, Arctic Village, Alaska, Sleetmute, Alaska, and McGrath, Alaska, Pippen Lake near Tonsina, Alaska, Yukon River near Eagle, Alaska, Long Lake near Eagle Village, Alaska, Sculpin Lake near Chitina, Alaska, and Dog Pits Lakes near Fairbanks, Alaska . These observations were made by drilling of 3 holes in an undisturbed location at the site by students, teachers, and community members, as well as members of the Fresh Eyes on Ice project. Snow depth was recorded at each of the holes prior to drilling.
DOI: 10.5517/ccdc.csd.cc2fk7cm
title: CCDC 2250890: Experimental Crystal Structure Determination

description: Related Article: Tucker J. Ball, Matthew J. Polinski|2023|Inorg.Chem.|62|9618|doi:10.1021/acs.inorgchem.3c01099
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.cc2fk7js
title: CCDC 2250895: Experimental Crystal Structure Determination

description: Related Article: Tucker J. Ball, Matthew J. Polinski|2023|Inorg.Chem.|62|9618|doi:10.1021/acs.inorgchem.3c01099
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.cc2fk7mw
title: CCDC 2250898: Experimental Crystal Structure Determination

description: Related Article: Tucker J. Ball, Matthew J. Polinski|2023|Inorg.Chem.|62|9618|doi:10.1021/acs.inorgchem.3c01099
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.cc2fk7py
title: CCDC 2250900: Experimental Crystal Structure Determination

description: Related Article: Tucker J. Ball, Matthew J. Polinski|2023|Inorg.Chem.|62|9618|doi:10.1021/acs.inorgchem.3c01099
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.cc2fk7r0
title: CCDC 2250902: Experimental Crystal Structure Determination

description: Related Article: Tucker J. Ball, Matthew J. Polinski|2023|Inorg.Chem.|62|9618|doi:10.1021/acs.inorgchem.3c01099
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.cc2fk7s1
title: CCDC 2250903: Experimental Crystal Structure Determination

description: Related Article: Tucker J. Ball, Matthew J. Polinski|2023|Inorg.Chem.|62|9618|doi:10.1021/acs.inorgchem.3c01099
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.cc2fk7nx
title: CCDC 2250899: Experimental Crystal Structure Determination

description: Related Article: Tucker J. Ball, Matthew J. Polinski|2023|Inorg.Chem.|62|9618|doi:10.1021/acs.inorgchem.3c01099
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.cc2flgq7
title: CCDC 2252079: Experimental Crystal Structure Determination

description: Related Article: Masahito Murai, Mako Ito, Satoshi Takahashi, Shigehiro Yamaguchi|2023|Dalton Trans.|52|8631|doi:10.1039/D3DT01491F
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.cc2fr07g
title: CCDC 2256435: Experimental Crystal Structure Determination

description: Related Article: Liu Zhi, Yin Fan, Jian Yang, Xiao-Qing Guo, Lipeng Zhou, Chong-Bin Tian, Qing-Fu Sun|2023|Dalton Trans.|52|8670|doi:10.1039/D3DT01139A
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.cc2fr09j
title: CCDC 2256437: Experimental Crystal Structure Determination

description: Related Article: Liu Zhi, Yin Fan, Jian Yang, Xiao-Qing Guo, Lipeng Zhou, Chong-Bin Tian, Qing-Fu Sun|2023|Dalton Trans.|52|8670|doi:10.1039/D3DT01139A
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.cc2fr0bk
title: CCDC 2256438: Experimental Crystal Structure Determination

description: Related Article: Liu Zhi, Yin Fan, Jian Yang, Xiao-Qing Guo, Lipeng Zhou, Chong-Bin Tian, Qing-Fu Sun|2023|Dalton Trans.|52|8670|doi:10.1039/D3DT01139A
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.cc2bc8vv
title: CCDC 2155797: Experimental Crystal Structure Determination

description: Related Article: Siji Xu, Gui Xiong, Xiang-Yu Zhang, Kun Huang, Da-Bin Qin, Bin Zhao|2023|Cryst.Growth Des.|23|4214|doi:10.1021/acs.cgd.3c00028
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.