cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25375/uct.16616227
title: Effect of pulsatility on the transport of thrombin in an idealized cerebral aneurysm geometry

description: Datasets pertaining to the paper titled: "Effect of Pulsatility on the transport of thrombin in an idealized cerebral aneurysm geometry"<br>The first dataset, titled "PIV Validation Comparison", is derived from a comparison of CFD and PIV data for thrombin transport in an idealised cerebral aneurysm geometry. The second dataset, titled ''Function Comparison", examines the influence of pulsatility on transport of thrombin.<br>
DOI: 10.25375/uct.16616227.v1
title: Effect of pulsatility on the transport of thrombin in an idealized cerebral aneurysm geometry

description: Datasets pertaining to the paper titled: "Effect of Pulsatility on the transport of thrombin in an idealized cerebral aneurysm geometry"<br>The first dataset, titled "PIV Validation Comparison", is derived from a comparison of CFD and PIV data for thrombin transport in an idealised cerebral aneurysm geometry. The second dataset, titled ''Function Comparison", examines the influence of pulsatility on transport of thrombin.<br>
DOI: 10.34740/kaggle/dsv/2641256
title: UCI Heart-Disease/Cleveland Dataset | ML Dataset
DOI: 10.5281/zenodo.5524083
title: Supplementary Data for "Machine Learning Based Long-term Water Quality in the Turbid Pearl River Estuary, China"

description: The file "Ma_AGUSupplementary.xlsx" contains the in situ data used to train and validate the model of the original manuscript.
DOI: 10.6084/m9.figshare.16656982
title: endocrinogenesis_day15_sctransform.h5ad

description: Description adjusted from the sctransform documentation (https://satijalab.org/seurat/articles/sctransform_vignette.html):<br>"The results of sctransfrom are stored in layers with the “SCT” prefix. SCT_normalized contains the residuals (normalized values), and is used directly as input to PCA. To assist with visualization and interpretation. we also convert Pearson residuals back to ‘corrected’ UMI counts. You can interpret these as the UMI counts we would expect to observe if all cells were sequenced to the same depth. The ‘corrected’ UMI counts are stored in <code>SCT_corrected_UMI</code>. We store log-normalized versions of these corrected counts in <code>SCT_lognorm_corrected_UMI</code>, which are very helpful for visualization.You can use the corrected log-normalized counts for differential expression and integration. However, in principle, it would be most optimal to perform these calculations directly on the residuals (stored in the <code>SCT_normalized</code> slot) themselves."
DOI: 10.6084/m9.figshare.16656982.v3
title: sctransform_pancreas_hackathon.h5ad

description: The results of sctransfrom are stored in layers with the “SCT” prefix. SCT_normalized contains the residuals (normalized values), and is used directly as input to PCA. To assist with visualization and interpretation. we also convert Pearson residuals back to ‘corrected’ UMI counts. You can interpret these as the UMI counts we would expect to observe if all cells were sequenced to the same depth. The ‘corrected’ UMI counts are stored in <code>SCT_corrected_UMI</code>. We store log-normalized versions of these corrected counts in <code>SCT_lognorm_corrected_UMI</code>, which are very helpful for visualization.You can use the corrected log-normalized counts for differential expression and integration. However, in principle, it would be most optimal to perform these calculations directly on the residuals (stored in the <code>SCT_normalized</code> slot) themselves.
DOI: 10.5519/qd.3wgnsm5n
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (27 records)
DOI: 10.5519/qd.b42desfk
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (8 records)
DOI: 10.5519/qd.hvewbkde
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (84 records)
DOI: 10.6084/m9.figshare.10318034.v1
title: Diagnostic ability of hepcidin in predicting fetal outcome in preeclampsia

description: Low birth weight and prematurity are the major contributors to neonatal mortality and morbidity. Preeclampsia which is associated with both maternal and fetal mortality and morbidity is a major contributor to such poor fetal outcomes. Hepcidin an acute phase peptide hormone gets elevated in conditions of iron overload, inflammation, infections, and cytotoxicity. Hepcidin levels can get elevated in pregnancies with such pathologies which invariably will be having a poor fetal outcome. To study the role of hepcidin as a diagnostic marker in predicting a poor fetal outcome. A cross-sectional study with follow up was carried out in a South Indian Tamil population. Forty healthy pregnant women and forty preeclampsia patients were recruited between the gestational age of 34 ± 4 weeks and followed up till delivery. Serum levels of hepcidin were analyzed for all the participants and comparisons were done between preeclampsia and healthy pregnancies as well as between pregnancies with good and poor fetal outcomes. Fetal outcome variables such as birth weight, gestational age at the time of delivery and NICU admission status of the newborn were collected during the follow-up period. ROC curves were constructed to determine the ability of maternal serum hepcidin levels in predicting poor fetal outcomes with good sensitivity, specificity and likelihood ratios. Maternal hepcidin levels were found to be significantly elevated in preeclampsia patients (<i>p</i> p Above a cut off level of 615 pg/ml and at the gestational age of 34 ± 4 weeks, maternal hepcidin levels were able to predict poor fetal outcomes such as low birth weight, preterm delivery, and NICU admission.
DOI: 10.6084/m9.figshare.10318034
title: Diagnostic ability of hepcidin in predicting fetal outcome in preeclampsia

description: Low birth weight and prematurity are the major contributors to neonatal mortality and morbidity. Preeclampsia which is associated with both maternal and fetal mortality and morbidity is a major contributor to such poor fetal outcomes. Hepcidin an acute phase peptide hormone gets elevated in conditions of iron overload, inflammation, infections, and cytotoxicity. Hepcidin levels can get elevated in pregnancies with such pathologies which invariably will be having a poor fetal outcome. To study the role of hepcidin as a diagnostic marker in predicting a poor fetal outcome. A cross-sectional study with follow up was carried out in a South Indian Tamil population. Forty healthy pregnant women and forty preeclampsia patients were recruited between the gestational age of 34 ± 4 weeks and followed up till delivery. Serum levels of hepcidin were analyzed for all the participants and comparisons were done between preeclampsia and healthy pregnancies as well as between pregnancies with good and poor fetal outcomes. Fetal outcome variables such as birth weight, gestational age at the time of delivery and NICU admission status of the newborn were collected during the follow-up period. ROC curves were constructed to determine the ability of maternal serum hepcidin levels in predicting poor fetal outcomes with good sensitivity, specificity and likelihood ratios. Maternal hepcidin levels were found to be significantly elevated in preeclampsia patients (<i>p</i> p Above a cut off level of 615 pg/ml and at the gestational age of 34 ± 4 weeks, maternal hepcidin levels were able to predict poor fetal outcomes such as low birth weight, preterm delivery, and NICU admission.
DOI: 10.24381/cds.3a9c4f89
title: Extreme precipitation risk indicators for Europe and European cities from 1950 to 2019
DOI: 10.24381/cds.9d3db0eb
title: Flood risk indicators for European cities from 1989 to 2018
DOI: 10.5517/ccdc.csd.cc273rmr
title: CCDC 2059194: Experimental Crystal Structure Determination

description: Related Article: Fabio Mazzotta, Doris Kunz|2021|Organometallics|40|3003|doi:10.1021/acs.organomet.1c00351
DOI: 10.5517/ccdc.csd.cc27r4gm
title: CCDC 2077820: Experimental Crystal Structure Determination

description: Related Article: Filipp M. Kolomeychuk, Evgeniya A. Safonova, Marina A. Polovkova, Anna A. Sinelshchikova, Alexander G. Martynov, Alexander V. Shokurov, Gayane A. Kirakosyan, Nikolay N. Efimov, Aslan Yu. Tsivadze, Yulia G. Gorbunova|2021|J.Am.Chem.Soc.|143|14053|doi:10.1021/jacs.1c05831
DOI: 10.5517/ccdc.csd.cc27v2qx
title: CCDC 2080649: Experimental Crystal Structure Determination

description: Related Article: Mariia Beliaeva, Andrey Belyaev, Elena V. Grachova, Andreas Steffen, Igor O. Koshevoy|2021|J.Am.Chem.Soc.|143|15045|doi:10.1021/jacs.1c04413
DOI: 10.5517/ccdc.csd.cc27v2ry
title: CCDC 2080650: Experimental Crystal Structure Determination

description: Related Article: Mariia Beliaeva, Andrey Belyaev, Elena V. Grachova, Andreas Steffen, Igor O. Koshevoy|2021|J.Am.Chem.Soc.|143|15045|doi:10.1021/jacs.1c04413
DOI: 10.5517/ccdc.csd.cc27v2sz
title: CCDC 2080651: Experimental Crystal Structure Determination

description: Related Article: Mariia Beliaeva, Andrey Belyaev, Elena V. Grachova, Andreas Steffen, Igor O. Koshevoy|2021|J.Am.Chem.Soc.|143|15045|doi:10.1021/jacs.1c04413
DOI: 10.5517/ccdc.csd.cc27v2t0
title: CCDC 2080652: Experimental Crystal Structure Determination

description: Related Article: Mariia Beliaeva, Andrey Belyaev, Elena V. Grachova, Andreas Steffen, Igor O. Koshevoy|2021|J.Am.Chem.Soc.|143|15045|doi:10.1021/jacs.1c04413
DOI: 10.5517/ccdc.csd.cc27v2v1
title: CCDC 2080653: Experimental Crystal Structure Determination

description: Related Article: Mariia Beliaeva, Andrey Belyaev, Elena V. Grachova, Andreas Steffen, Igor O. Koshevoy|2021|J.Am.Chem.Soc.|143|15045|doi:10.1021/jacs.1c04413
DOI: 10.5517/ccdc.csd.cc279gst
title: CCDC 2064686: Experimental Crystal Structure Determination

description: Related Article: Jairus L. Lamola, Joy C. Shilubana, Lonwabo Ngodwana, Banele Vatsha, Adedapo S. Adeyinka, Munaka C. Maumela, Cedric W. Holzapfel, Edwin M. Mmutlane|2021|Eur.J.Inorg.Chem.|2021|2578|doi:10.1002/ejic.202100232
DOI: 10.5517/ccdc.csd.cc282dd5
title: CCDC 2087676: Experimental Crystal Structure Determination

description: Related Article: Qiqiang Xie, Guangbin Dong|2021|J.Am.Chem.Soc.|143|14422|doi:10.1021/jacs.1c06186
DOI: 10.5517/ccdc.csd.cc285zxc
title: CCDC 2091133: Experimental Crystal Structure Determination

description: Related Article: Yidong Wang, Sarah G. Scrivener, Xiao-Dong Zuo, Ruihan Wang, Philip N. Palermo, Ethan Murphy, Austin C. Durham, Yi-Ming Wang|2021|J.Am.Chem.Soc.|143|14998|doi:10.1021/jacs.1c07512
DOI: 10.6084/m9.figshare.16531027.v1
title: The genetic and cultural impact of the Steppe migration into Europe

description: During the early 3rd millennium BCE migration from Pontic Steppe, mainly related to Yamnaya culture, has affected European populations both culturally and genetically, however, it has long been debated to what extent this migration was male-driven, and how this replacement process took place which eliminated partially/largely Neolithic male lines over time. This paper aims to evaluate the influence of the Steppe migration on European Bronze Age populations by calculating both male and female genetic contributions of the Steppe-related ancestry to the European Bronze Age populations. With this approach, we will be able to clarify the hypotheses on whether it was male-biased migration or not. To evaluate the genetic impact and the proportion of the Steppe-related ancestry to the European Bronze Age populations, we performed PCA and <i>qpAdm</i> analyses by using published genome-wide data. In addition, we quantified male and female genetic contribution into Europe by using the analysis of uniparental markers and the X-chromosome. The Steppe migration had a considerable impact on the genetic makeup of the Bronze Age European populations. The data suggest that the Steppe-related ancestry arriving into Central Europe was male-driven, dominantly in the Corded Ware culture populations and lesser in the Bell Beaker populations. In fact, there is no evidence that this migration had a significant input on the mitochondrial genetic pool of all European Bronze Age populations. Our analyses suggest that the Steppe-related ancestry had genetic impact on mainly Central-Eastern Europe. Moreover, this migration was male-driven for most of the Central European populations belonging to the Corded Ware groups, and to a lesser extent for the Bell Beaker groups.
DOI: 10.6084/m9.figshare.16531030
title: The surname structure of Trentino (Italy) and its relationship with dialects and genes

description: Thanks to the availability of rich surname, linguistic and genetic information, together with its geographic and cultural complexity, Trentino (North-Eastern Italy) is an ideal place to test the relationships between genetic and cultural traits. We provide a comprehensive study of population structures based on surname and dialect variability and evaluate their relationships with genetic diversity in Trentino. Surname data were collected for 363 parishes, linguistic data for 57 dialects and genetic data for different sets of molecular markers (Y-chromosome, mtDNA, autosomal) in 10 populations. Analyses relied on different multivariate methods and correlation tests. Besides the expected isolation-by-distance-like patterns (with few local exceptions, likely related to sociocultural instances), we detected a significant and geography-independent association between dialects and surnames. As for molecular markers, only Y-chromosomal STRs seem to be associated with the dialects, although no significant result was obtained. No evidence for correlation between molecular markers and surnames was observed. Surnames act as cultural markers as do other words, although in this context they cannot be used as reliable proxies for genetic variability at a local scale.
DOI: 10.6084/m9.figshare.16531027
title: The genetic and cultural impact of the Steppe migration into Europe

description: During the early 3rd millennium BCE migration from Pontic Steppe, mainly related to Yamnaya culture, has affected European populations both culturally and genetically, however, it has long been debated to what extent this migration was male-driven, and how this replacement process took place which eliminated partially/largely Neolithic male lines over time. This paper aims to evaluate the influence of the Steppe migration on European Bronze Age populations by calculating both male and female genetic contributions of the Steppe-related ancestry to the European Bronze Age populations. With this approach, we will be able to clarify the hypotheses on whether it was male-biased migration or not. To evaluate the genetic impact and the proportion of the Steppe-related ancestry to the European Bronze Age populations, we performed PCA and <i>qpAdm</i> analyses by using published genome-wide data. In addition, we quantified male and female genetic contribution into Europe by using the analysis of uniparental markers and the X-chromosome. The Steppe migration had a considerable impact on the genetic makeup of the Bronze Age European populations. The data suggest that the Steppe-related ancestry arriving into Central Europe was male-driven, dominantly in the Corded Ware culture populations and lesser in the Bell Beaker populations. In fact, there is no evidence that this migration had a significant input on the mitochondrial genetic pool of all European Bronze Age populations. Our analyses suggest that the Steppe-related ancestry had genetic impact on mainly Central-Eastern Europe. Moreover, this migration was male-driven for most of the Central European populations belonging to the Corded Ware groups, and to a lesser extent for the Bell Beaker groups.
DOI: 10.6084/m9.figshare.16531030.v1
title: The surname structure of Trentino (Italy) and its relationship with dialects and genes

description: Thanks to the availability of rich surname, linguistic and genetic information, together with its geographic and cultural complexity, Trentino (North-Eastern Italy) is an ideal place to test the relationships between genetic and cultural traits. We provide a comprehensive study of population structures based on surname and dialect variability and evaluate their relationships with genetic diversity in Trentino. Surname data were collected for 363 parishes, linguistic data for 57 dialects and genetic data for different sets of molecular markers (Y-chromosome, mtDNA, autosomal) in 10 populations. Analyses relied on different multivariate methods and correlation tests. Besides the expected isolation-by-distance-like patterns (with few local exceptions, likely related to sociocultural instances), we detected a significant and geography-independent association between dialects and surnames. As for molecular markers, only Y-chromosomal STRs seem to be associated with the dialects, although no significant result was obtained. No evidence for correlation between molecular markers and surnames was observed. Surnames act as cultural markers as do other words, although in this context they cannot be used as reliable proxies for genetic variability at a local scale.
DOI: 10.5066/f74b2zd1
title: Great Lakes Commercial Fishing Catch 1929-2014

description: Since 1971 the Great Lakes Science Center (GLSC), formerly known as the National Fishery Center-Great Lakes (National Biological Service), the Great Lakes Fishery Laboratory (U.S. Fish and Wildlife Service)(FWS/GLFL), and the Ann Arbor Biological Laboratory (Bureau of Commercial Fisheries), have acted as repositories for the processing, reporting, and storage of U.S. Great Lakes commercial fishery statistics. The GLSC prepares annual summaries of these data for use by federal and state resource managers and other interested public and private organizations. The data collection system was organized in 1926 by Dr. John Van Oosten of the Bureau of Fisheries and was first put to use by the State of Michigan in 1927. Commercial fishermen licensed by that state were required to submit daily fishing reports indicating gear used and amount of catch. In succeeding years the system was improved and expanded until by 1950 all the Great Lakes states and the Province of Ontario, Canada, had similar systems.
DOI: 10.5066/f73t9fb3
title: Great Lakes Commercial Fishing Catches 1929-2014

description: Since 1971 the Great Lakes Science Center (GLSC), formerly known as the National Fishery Center-Great Lakes (National Biological Service), the Great Lakes Fishery Laboratory (U.S. Fish and Wildlife Service)(FWS/GLFL), and the Ann Arbor Biological Laboratory (Bureau of Commercial Fisheries), have acted as repositories for the processing, reporting, and storage of U.S. Great Lakes commercial fishery statistics. The GLSC prepares annual summaries of these data for use by federal and state resource managers and other interested public and private organizations. The data collection system was organized in 1926 by Dr. John Van Oosten of the Bureau of Fisheriesand was first put to use by the State of Michigan in 1927. Commercialfishermen licensed by that state were required to submit daily fishingreports indicating gear used and amount of catch. In succeeding yearsthe system was improved and expanded until by 1950 all the Great Lakes states and the Province of Ontario, Canada, had similar systems.
DOI: 10.6084/m9.figshare.24220390
title: Supplementary Data Ribes-Navarro et al from Examination of gammarid transcriptomes reveals a widespread occurrence of key metabolic genes from epibiont bdelloid rotifers in freshwater species

description: Supplementary Data Ribes-Navarro et al
DOI: 10.6084/m9.figshare.24220390.v1
title: Supplementary Data Ribes-Navarro et al from Examination of gammarid transcriptomes reveals a widespread occurrence of key metabolic genes from epibiont bdelloid rotifers in freshwater species

description: Supplementary Data Ribes-Navarro et al
DOI: 10.6084/m9.figshare.22811384.v1
title: Guppy and M. picta repeats_annotation

description: 1. The <em>Poecilia reticulata</em> files are in the folder “Guppy repeats_annotation”. The dataset was produced using RepeatModeler2 (Flynn<em> et al.</em> 2020) to generate an initial comprehensive set of candidate TE families in the male reference genome assembly (Fraser<em> et al.</em> 2020). RepeatMasker (Smit<em> et al.</em> http:// repeatmasker.org) was used to make a library of consensus sequences from the RepeatModeler2 analysis plus Teleostei repeats from Repbase (https://www.girinst.org/repbase/). A filtered list of candidates was created and manually annotated following the procedures and criteria described by Goubert<em> et al.</em> (2022). RepeatMasker was then run again to find and annotate repeat copies throughout the male reference genome. There are two files, as follows. consensus_library.fa (consensus TE library from RepeatModeler) guppy_ref.align_with_div.noCpG.size.out (RepeatMasker result) 2. <em>Micropoecilia picta</em><br> This folder contains files from the RepeatModeler and RepeatMasker analyses of the M. picta assembly. Files with .out and .gff suffixes are included for the whole genome and for just chromosome 12, the sex chromosome. The folder “RM_54167.FriNov121514092021” includes the consensus sequences produced by RepeatModeler. <strong>References</strong> Flynn, J. M., R. Hubley, C. Goubert, J. Rosen, A. G. Clark<em> et al.</em>, 2020 RepeatModeler2: automated genomic discovery of transposable element families. Proceedings of the National Academy of Sciences of the United States of America 117<strong>:</strong> 9451-9457. doi: 10.1073/pnas.1921046117 Fraser, B. A., J. R. Whiting, J. R. Paris, C. J. Weadick, P. J. Parsons<em> et al.</em>, 2020 Improved reference genome uncovers novel sex-linked regions in the guppy (<em>Poecilia reticulata)</em>. Genome Biology and Evolution 12<strong>:</strong> 1789–1805. doi: 10.1093/gbe/evaa187 Goubert, C., R. J. Craig, A. F. Bilat, V. Peona, A. A. Vogan<em> et al.</em>, 2022 A beginner's guide to manual curation of transposable elements. Mob DNA 13<strong>:</strong> 7. doi: 10.1186/s13100-021-00259-7 Smit, A., R. Hubley and P. Green, 2013-2015 RepeatMasker Open-4.0
DOI: 10.5281/zenodo.8390679
title: Supplementary file for "Primitive magnetotaxis in 1.88 Ga gigantic magnetofossils"

description: Gigantic magnetofossils' occurrence is very restricted along the geological records. Distinguishing them from ordinary nanoscopic magnetotactic organisms, the biosynthesized iron oxides these organisms produce are micrometric, and the biological function of synthesizing such large grains is still unknown. We approach the problem by micromagnetic modeling synthetic chains of gigantic magnetofossils built based on 3D-Ptychographic data of Precambrian (1.88 Ga) fossils. In this dataset, we include the meshes used in our simulations (.pat files), Python scripts, MERRILL scripts, and outputs achieved from the simulations (.out files).
DOI: 10.5281/zenodo.8390678
title: Supplementary file for "Primitive magnetotaxis in 1.88 Ga gigantic magnetofossils"

description: Gigantic magnetofossils' occurrence is very restricted along the geological records. Distinguishing them from ordinary nanoscopic magnetotactic organisms, the biosynthesized iron oxides these organisms produce are micrometric, and the biological function of synthesizing such large grains is still unknown. We approach the problem by micromagnetic modeling synthetic chains of gigantic magnetofossils built based on 3D-Ptychographic data of Precambrian (1.88 Ga) fossils. In this dataset, we include the meshes used in our simulations (.pat files), Python scripts, MERRILL scripts, and outputs achieved from the simulations (.out files).