cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2cwp98
title: CCDC 2201350: Experimental Crystal Structure Determination

description: Related Article: Ferdinand Belaj, Lydia M. Peschel, Nadia C. Mösch-Zanetti|2022|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.cc2cwpb9
title: CCDC 2201351: Experimental Crystal Structure Determination

description: Related Article: Ferdinand Belaj, Lydia M. Peschel, Nadia C. Mösch-Zanetti|2022|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.cc2cwpcb
title: CCDC 2201352: Experimental Crystal Structure Determination

description: Related Article: Ferdinand Belaj, Lydia M. Peschel, Nadia C. Mösch-Zanetti|2022|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.cc2cwpdc
title: CCDC 2201353: Experimental Crystal Structure Determination

description: Related Article: Ferdinand Belaj, Lydia M. Peschel, Nadia C. Mösch-Zanetti|2022|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.cc2cwpfd
title: CCDC 2201354: Experimental Crystal Structure Determination

description: Related Article: Ferdinand Belaj, Lydia M. Peschel, Nadia C. Mösch-Zanetti|2022|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.cc2cwpgf
title: CCDC 2201355: Experimental Crystal Structure Determination

description: Related Article: Ferdinand Belaj, Lydia M. Peschel, Nadia C. Mösch-Zanetti|2022|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.cc2cwphg
title: CCDC 2201356: Experimental Crystal Structure Determination

description: Related Article: Ferdinand Belaj, Lydia M. Peschel, Nadia C. Mösch-Zanetti|2022|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.cc2cwpjh
title: CCDC 2201357: Experimental Crystal Structure Determination

description: Related Article: Ferdinand Belaj, Carina Vidovic, Lydia M. Peschel, Nadia C. Mösch-Zanetti|2022|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.cc2cwpkj
title: CCDC 2201358: Experimental Crystal Structure Determination

description: Related Article: Ferdinand Belaj, Carina Vidovic, Nadia C. Mösch-Zanetti|2022|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.cc24nn4m
title: CCDC 1986050: Experimental Crystal Structure Determination

description: Related Article: Pin-Pin Ma, Zhi-Min Hao, Pan Wang, Wen-Hua Zhang, David J. Young|2023|J.Solid State Chem.|317|123721|doi:10.1016/j.jssc.2022.123721
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.cc24nn5n
title: CCDC 1986051: Experimental Crystal Structure Determination

description: Related Article: Pin-Pin Ma, Zhi-Min Hao, Pan Wang, Wen-Hua Zhang, David J. Young|2023|J.Solid State Chem.|317|123721|doi:10.1016/j.jssc.2022.123721
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.7412139
title: ostorhinchus_cyanosoma_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7412138
title: ostorhinchus_cyanosoma_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.6084/m9.figshare.21441306
title: cases statistics_2022_11_01.xls

description: Autosomal short tandem repeats (STRs) are routinely used for forensic parentage testing and individual identification. Abnormal allelic genotypes of autosomal STRs, X-STRs, and Y-STRs may affect the interpretation of forensic results. However, the extent of the effect on forensic results is not known. Therefore, we assessed the influence of abnormal chromosomes on forensic analysis results. The two autosomal STR kits used were not sensitive enough to produce accurate results for the two chimeric Turner syndrome patients studied; for one of the patients, the amelogenin genotype could lead to the wrong interpretation of sex. The blood sample from an allogenic bone marrow transplantation patient was not suitable for forensic analysis. Finally, different azoospermia factor microdeletions of the Y chromosome resulted in different Y-STR dropout patterns. Therefore, regarding individuals with abnormal chromosome karyotypes, forensic conclusions were affected.
DOI: 10.6084/m9.figshare.21441306.v4
title: cases statistics_2022_11_01.xls

description: Autosomal short tandem repeats (STRs) are routinely used for forensic parentage testing and individual identification. Abnormal allelic genotypes of autosomal STRs, X-STRs, and Y-STRs may affect the interpretation of forensic results. However, the extent of the effect on forensic results is not known. Therefore, we assessed the influence of abnormal chromosomes on forensic analysis results. The two autosomal STR kits used were not sensitive enough to produce accurate results for the two chimeric Turner syndrome patients studied; for one of the patients, the amelogenin genotype could lead to the wrong interpretation of sex. The blood sample from an allogenic bone marrow transplantation patient was not suitable for forensic analysis. Finally, different azoospermia factor microdeletions of the Y chromosome resulted in different Y-STR dropout patterns. Therefore, regarding individuals with abnormal chromosome karyotypes, forensic conclusions were affected.
DOI: 10.6084/m9.figshare.21441306.v3
title: cases statistics_2022_11_01.xls

description: Autosomal short tandem repeats (STRs) are routinely used for forensic parentage testing and individual identification. Abnormal allelic genotypes of autosomal STRs, X-STRs, and Y-STRs may affect the interpretation of forensic results. However, the extent of the effect on forensic results is not known. Therefore, we assessed the influence of abnormal chromosomes on forensic analysis results. The two autosomal STR kits used were not sensitive enough to produce accurate results for the two chimeric Turner syndrome patients studied; for one of the patients, the amelogenin genotype could lead to the wrong interpretation of sex. The blood sample from an allogenic bone marrow transplantation patient was not suitable for forensic analysis. Finally, different azoospermia factor microdeletions of the Y chromosome resulted in different Y-STR dropout patterns. Therefore, regarding individuals with abnormal chromosome karyotypes, forensic conclusions were affected.
DOI: 10.6084/m9.figshare.21692411
title: Draft

description: Test
DOI: 10.6084/m9.figshare.21692411.v1
title: Jennifer Hudson PhD Thesis Appendices

description: Appendix II: Full list of publications reviewed in Chapter 2 Appendix III: Conserved C-terminal sorting domain containing proteins (TIGRfam: TIGR04183) present in <em>Aquimarina</em> sp. AD1, BL5 and AD10 Appendix IV: Annotation of <em>D. pulchra</em> transcripts Appendix V: Annotation of DEGs from the <em>D. pulchra</em> transcriptome
DOI: 10.6084/m9.figshare.21692411.v2
title: Draft

description: Test
DOI: 10.6084/m9.figshare.14463740
title: Impact of volumetric versus gravimetric assessment on Olsen P concentrations

description: Soil Olsen phosphorus is used for regional and national environmental reporting, and for farm soil fertility assessment. In New Zealand, the laboratory measurement using sieved and air-dried soil is undertaken gravimetrically (by weight), or volumetrically, so can be reported on either basis. Olsen P ‘stocks’ can also be calculated using field bulk density. These methods have led to inconsistencies in environmental reporting. This study compares and quantifies the relationships between the laboratory-determined gravimetric and volumetric Olsen P for a range of soil orders, by developing a statistical model. Our study showed there is a significant difference in Olsen P determined by volumetric and gravimetric methods, and the relationship varies with soil order. The gravimetric method gave elevated values for all soil orders compared with the volumetric method. Bulk density was generally significantly different from, and greater than, volume weight, for many soil orders. From the statistical modelling, look-up tables are provided for converting from volumetric-to-gravimetric, and gravimetric-to-volumetric, in the absence of volume weight, such as for legacy data. Several recommendations to improve national environmental reporting are provided, including that, if the volumetric method is used, volume weight should be requested from the laboratory to enable consistent conversion for national reporting.
DOI: 10.6084/m9.figshare.21692435
title: Survival.Raw Data.Zinc.xlsx

description: This dataset is the raw honey bee survivorshipship data resulting from varying levels of zinc exposure. <br> These data are associated with a manuscript titled: Zinc and laboratory emerge date impact honey bee gut microbiota and survival. <br> Abstract of manuscript: Honey bees may be exposed to a wide variety of chemicals in the environment, including pesticides, antibiotics, and metals. Zinc, for example, is commonly included in fertilizers, pesticides, and feed additives, and is found in agricultural runoff. Honey bees can be exposed to zinc directly or indirectly by consuming zinc-contaminated nectar and pollen. However, there is a paucity of studies addressing the putative effects of zinc on honeybee´s health. In this study, we tested the effects of zinc on honey bee survivorship and gut microbiota. To evaluate survivorship, we exposed bees to six concentrations of zinc (0, 50, 100, 250, 500, or 1000 mg/L) and assessed survival daily for 10 days. To evaluate effects of zinc on gut microbiota, we exposed bees to 5 or 100 mg/L zinc. Bees were sampled before (day 0) and after zinc exposure (days 3, 6, and 9). Abdominal contents underwent DNA extraction and 16S rRNA sequencing (V3-V4) on an Illumina MiSeq. Sequences were filtered and processed through QIIME2 and DADA2. Zinc treatment had minimal effects on bacterial DNA concentrations and absolute cell counts while emerge date (the date a bee emerged from the brood comb) had a significant effect with decreased bacterial concentrations and cell counts observed at later emerge dates. Survival was only minimally impacted (&gt;89% survival) at zinc concentrations up to 100 mg/L. Zinc had limited effects on overall gut microbial composition, diversity, and taxonomic abundances, with the greatest differences noted in the bee group exposed to the higher concentration of zinc (100 mg/L). In this group, several beneficial taxa (<em>Lactobacillus,</em> Rhizobiaceae<em>, Gilliamella</em>) were found at reduced abundances, while <em>Paenibacillus,</em> a potentially pathogenic taxa, was found at increased abundances. This suggests that zinc exposure, even at relatively low levels, may negatively impact honey bee health, even if survivorship is not dramatically impacted. Notably, emerge date effects were also observed in microbial composition. These results demonstrate the need to include assessments of honeybee gut microbiota in addition to other metrics of honey bee health and survivorship when evaluating the potential effects of agrochemicals on honey bees
DOI: 10.6084/m9.figshare.21692435.v1
title: Survival.Raw Data.Zinc.xlsx

description: This dataset is the raw honey bee survivorshipship data resulting from varying levels of zinc exposure. <br> These data are associated with a manuscript titled: Zinc and laboratory emerge date impact honey bee gut microbiota and survival. <br> Abstract of manuscript: Honey bees may be exposed to a wide variety of chemicals in the environment, including pesticides, antibiotics, and metals. Zinc, for example, is commonly included in fertilizers, pesticides, and feed additives, and is found in agricultural runoff. Honey bees can be exposed to zinc directly or indirectly by consuming zinc-contaminated nectar and pollen. However, there is a paucity of studies addressing the putative effects of zinc on honeybee´s health. In this study, we tested the effects of zinc on honey bee survivorship and gut microbiota. To evaluate survivorship, we exposed bees to six concentrations of zinc (0, 50, 100, 250, 500, or 1000 mg/L) and assessed survival daily for 10 days. To evaluate effects of zinc on gut microbiota, we exposed bees to 5 or 100 mg/L zinc. Bees were sampled before (day 0) and after zinc exposure (days 3, 6, and 9). Abdominal contents underwent DNA extraction and 16S rRNA sequencing (V3-V4) on an Illumina MiSeq. Sequences were filtered and processed through QIIME2 and DADA2. Zinc treatment had minimal effects on bacterial DNA concentrations and absolute cell counts while emerge date (the date a bee emerged from the brood comb) had a significant effect with decreased bacterial concentrations and cell counts observed at later emerge dates. Survival was only minimally impacted (&gt;89% survival) at zinc concentrations up to 100 mg/L. Zinc had limited effects on overall gut microbial composition, diversity, and taxonomic abundances, with the greatest differences noted in the bee group exposed to the higher concentration of zinc (100 mg/L). In this group, several beneficial taxa (<em>Lactobacillus,</em> Rhizobiaceae<em>, Gilliamella</em>) were found at reduced abundances, while <em>Paenibacillus,</em> a potentially pathogenic taxa, was found at increased abundances. This suggests that zinc exposure, even at relatively low levels, may negatively impact honey bee health, even if survivorship is not dramatically impacted. Notably, emerge date effects were also observed in microbial composition. These results demonstrate the need to include assessments of honeybee gut microbiota in addition to other metrics of honey bee health and survivorship when evaluating the potential effects of agrochemicals on honey bees
DOI: 10.6084/m9.figshare.21663401.v1
title: CIS Database - multiplex graph of Interdisciplinary PhD Program

description: This dataset is based on the model developed together with the PhD students of Communication and Information Sciences PhD program at University of Hawaii at Manoa, intended to help new students get relevant information. The model was presented at the iConference 2023, in a paper "Community Design of a Knowledge Graph to Support Interdisciplinary Ph.D. Students ". It contains publically available data collected from multiple web locations and indexes of the sample of publications from the people in this domain. Except for that, it contains my (first author's) personal graph that demonstrates progress through the program of a student in this degree. <br> It is intended to provide researchers with an opportunity to investigate multigraph visualization of the data modeled by the said model. <br> The database is created in Neo4J and here you can download the .dump file to import to this software. <br> <br> <br>
DOI: 10.5281/zenodo.7094901
title: Additional files of scTensor paper "scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data"

description: Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression. However, the analytical methods are still not mature; such methods cannot detect CCIs and the related L-R pairs simultaneously or also are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor could detect some hypergraphs, which cannot be detected by conventional methods, especially when those CCIs are many-to-many relationships.
DOI: 10.5281/zenodo.7098894
title: Additional files of scTensor paper "scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data"

description: Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression. However, the analytical methods are still not mature; such methods cannot detect CCIs and the related L-R pairs simultaneously or also are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor could detect some hypergraphs, which cannot be detected by conventional methods, especially when those CCIs are many-to-many relationships.
DOI: 10.5281/zenodo.7102640
title: Additional files of scTensor paper "scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data"

description: Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression. However, the analytical methods are still not mature; such methods cannot detect CCIs and the related L-R pairs simultaneously or also are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor could detect some hypergraphs, which cannot be detected by conventional methods, especially when those CCIs are many-to-many relationships.
DOI: 10.5281/zenodo.7402557
title: Additional files of scTensor paper "scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data"

description: Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression. However, the analytical methods are still not mature; such methods cannot detect CCIs and the related L-R pairs simultaneously or also are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor could detect some hypergraphs, which cannot be detected by conventional methods, especially when those CCIs are many-to-many relationships.
DOI: 10.5281/zenodo.7094900
title: Additional files of scTensor paper "scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data"

description: Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression. However, the analytical methods are still not mature; such methods cannot detect CCIs and the related L-R pairs simultaneously or also are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor could detect some hypergraphs, which cannot be detected by conventional methods, especially when those CCIs are many-to-many relationships.
DOI: 10.5281/zenodo.7407332
title: Additional files of scTensor paper "scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data"

description: Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression. However, the analytical methods are still not mature; such methods cannot detect CCIs and the related L-R pairs simultaneously or also are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor could detect some hypergraphs, which cannot be detected by conventional methods, especially when those CCIs are many-to-many relationships.