cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.10047473.v1
title: MOESM2 of Ecological status of high altitude medicinal plants and their sustainability: Lingshi, Bhutan

description: Additional file 2. Priority species plant density at different survey points.
DOI: 10.6084/m9.figshare.10047416
title: The data of manuscripts number ECE-2019-10-01339 of the journal "ecology and evolution"

description: Data used by the manuscript "Sap flow characteristics of <i>Amorpha fruticosa</i>: Implications of water use strategy in a semiarid system with secondary salinization in northwestern China"<br>
DOI: 10.6084/m9.figshare.10047383
title: Supplementary Material for: Second Trimester Maternal Leptin Levels Are Associated with Body Mass Index and Gestational Weight Gain but not Birth Weight of the Infant

description: <b><i>Introduction:</i></b> Obesity is increasing among the pregnant population. Leptin has an important role in the regulation of energy balance and hunger. The aim of this study was to investigate the association between maternal leptin levels with maternal obesity, gestational weight gain (GWG), single nucleotide polymorphisms (SNPs) within the leptin gene, and the age-adjusted birth weight of the child. <b><i>Material and</i></b> <b><i>Methods:</i></b> Maternal leptin levels (<i>n</i> = 740) and SNPs (<i>n</i> = 504) were analyzed in blood samples taken within the Uppsala Biobank of Pregnant women at pregnancy weeks 16–19. <b><i>Results:</i></b> Maternal leptin levels differed significantly between body mass index (BMI) groups. Normal weight women had the lowest median leptin levels and levels increased with each BMI group. Leptin SNP genotype was not associated with leptin levels or BMI. There was also no association between maternal leptin levels and age-adjusted birth weight of the child except for a negative association between leptin levels and birth weight in the morbid obese group. <b><i>Discussion/Conclusion:</i></b> Maternal BMI was identified as the best positive explanatory factor for maternal leptin levels. Leptin was a strong positive explanatory factor for GWG. Birth weight of children of uncomplicated pregnancies was, however, dependent on maternal height, BMI, GWG, and parity but not leptin levels, except for in morbid obese women where a negative association between maternal leptin levels and birth weight was found. We speculate that this indicates altered placental function, not manifested in pregnancy complication. We conclude that maternal leptin levels do not affect the birth weight of the child more than BMI, GWG, and parity.
DOI: 10.6084/m9.figshare.10047410
title: Prediction of long-term quality of life after severe traumatic brain injury based on variables at hospital admission

description: <b>Objectives</b>: Variables collected early after severe traumatic brain injury (sTBI) could predict health-related quality of life (HRQoL). Our aim was to determine the prevalence of patients with a low HRQoL 4 years after sTBI and to develop a prediction model including early variables. <b>Methods</b>: Adult patients with both sTBI [abbreviated injury score of the head region (HAIS) &gt;3] and disease-specific HRQoL assessments using the ‘Quality of Life after Brain Injury’ (QOLIBRI) were included. The outcome was the total score (TS) of QOLIBRI; cutoff for low HRQoL: &lt;60 points. A multivariate logistic regression model and prediction model were performed. <b>Results</b>: One hundred-sixteen patients [median age 50.8 years (IQR 25.9–62.8; 21.6% &gt;65 years)] were included; 68 (58.6%) with HAIS = 4, 48 (41.4%) with HAIS = 5. Median Glasgow Coma Scale (GCS) was 13 (IQR 3–15). Median TS was 77 (IQR 60–88). Low HRQoL was observed in 28 patients (24.1%). Two variables were associated with low HRQoL: GCS &lt;13, working situation other than employed or retired. The prediction model had an AUROC of 0.765; calibration was moderate (Hosmer Lemeshow Chi2 6.82, <i>p</i> = .556). <b>Conclusion</b>: One in four patients had a low HRQoL after 4 years. A lower GCS and working situations were associated with low HRQoL.
DOI: 10.6084/m9.figshare.10047377
title: Seawater temperature and sea level data recorded between May 2007 and September 2008 in Nanwan Bay, southern Taiwan

description: Nanwan Bay (NW) is a small, semi-enclosed bay at the southern tip of Taiwan. NW features well-developed fringing reefs along the coast and to a depth of approximately 30 metres. <br>Data were captured at two locations in NW: the bottom of a seamount, S-rock (SR) and at the inlet of the Tai-power third nuclear power plant (IL). Seawater temperature alone was recorded at SR, while both temperature and sea level were recorded simultaneously at IL. Measurements were taken at a depth of 30 metres and 5 metres at SR and IL, respectively.<br>This dataset contains two files provided in the MATLAB .mat format: <b>nw_sroc_T_200705_200809.mat</b> and <b>nw_inlet_lvl_200705_200810.mat</b>.<br><b>nw_sroc_T_200705_200809.mat</b> contains the data recorded at SR. This file includes the following variables:<i>ti_roc</i> (day) - Day of temperature reading. Day 0 is January 1st 2007.<i>t_roc</i> (degrees Celsius) - Temperature readingThe subset of data used in the accompanying study is May 5th 2007 to September 9th 2009.<br><b>nw_inlet_lvl_200705_200810.mat</b> contains the data recorded at IL.<i>time </i>- Time and date of fate collection<i>time_j</i> - Julian day of date<i>el</i> (meters) - Elevation of sea level<i>t </i>(degrees Celsius) - Temperature reading<br>Analyses of these data are presented in the accompanying publication.
DOI: 10.6084/m9.figshare.10047383.v1
title: Supplementary Material for: Second Trimester Maternal Leptin Levels Are Associated with Body Mass Index and Gestational Weight Gain but not Birth Weight of the Infant

description: <b><i>Introduction:</i></b> Obesity is increasing among the pregnant population. Leptin has an important role in the regulation of energy balance and hunger. The aim of this study was to investigate the association between maternal leptin levels with maternal obesity, gestational weight gain (GWG), single nucleotide polymorphisms (SNPs) within the leptin gene, and the age-adjusted birth weight of the child. <b><i>Material and</i></b> <b><i>Methods:</i></b> Maternal leptin levels (<i>n</i> = 740) and SNPs (<i>n</i> = 504) were analyzed in blood samples taken within the Uppsala Biobank of Pregnant women at pregnancy weeks 16–19. <b><i>Results:</i></b> Maternal leptin levels differed significantly between body mass index (BMI) groups. Normal weight women had the lowest median leptin levels and levels increased with each BMI group. Leptin SNP genotype was not associated with leptin levels or BMI. There was also no association between maternal leptin levels and age-adjusted birth weight of the child except for a negative association between leptin levels and birth weight in the morbid obese group. <b><i>Discussion/Conclusion:</i></b> Maternal BMI was identified as the best positive explanatory factor for maternal leptin levels. Leptin was a strong positive explanatory factor for GWG. Birth weight of children of uncomplicated pregnancies was, however, dependent on maternal height, BMI, GWG, and parity but not leptin levels, except for in morbid obese women where a negative association between maternal leptin levels and birth weight was found. We speculate that this indicates altered placental function, not manifested in pregnancy complication. We conclude that maternal leptin levels do not affect the birth weight of the child more than BMI, GWG, and parity.
DOI: 10.6084/m9.figshare.10047368
title: For Dan WUSTL.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047371
title: For Dan Found all the data for the graph.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047410.v1
title: Prediction of long-term quality of life after severe traumatic brain injury based on variables at hospital admission

description: <b>Objectives</b>: Variables collected early after severe traumatic brain injury (sTBI) could predict health-related quality of life (HRQoL). Our aim was to determine the prevalence of patients with a low HRQoL 4 years after sTBI and to develop a prediction model including early variables. <b>Methods</b>: Adult patients with both sTBI [abbreviated injury score of the head region (HAIS) &gt;3] and disease-specific HRQoL assessments using the ‘Quality of Life after Brain Injury’ (QOLIBRI) were included. The outcome was the total score (TS) of QOLIBRI; cutoff for low HRQoL: &lt;60 points. A multivariate logistic regression model and prediction model were performed. <b>Results</b>: One hundred-sixteen patients [median age 50.8 years (IQR 25.9–62.8; 21.6% &gt;65 years)] were included; 68 (58.6%) with HAIS = 4, 48 (41.4%) with HAIS = 5. Median Glasgow Coma Scale (GCS) was 13 (IQR 3–15). Median TS was 77 (IQR 60–88). Low HRQoL was observed in 28 patients (24.1%). Two variables were associated with low HRQoL: GCS &lt;13, working situation other than employed or retired. The prediction model had an AUROC of 0.765; calibration was moderate (Hosmer Lemeshow Chi2 6.82, <i>p</i> = .556). <b>Conclusion</b>: One in four patients had a low HRQoL after 4 years. A lower GCS and working situations were associated with low HRQoL.
DOI: 10.6084/m9.figshare.10047365
title: Males For Dan.csv from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047377.v1
title: Seawater temperature and sea level data recorded between May 2007 and September 2008 in Nanwan Bay, southern Taiwan

description: Nanwan Bay (NW) is a small, semi-enclosed bay at the southern tip of Taiwan. NW features well-developed fringing reefs along the coast and to a depth of approximately 30 metres. <br>Data were captured at two locations in NW: the bottom of a seamount, S-rock (SR) and at the inlet of the Tai-power third nuclear power plant (IL). Seawater temperature alone was recorded at SR, while both temperature and sea level were recorded simultaneously at IL. Measurements were taken at a depth of 30 metres and 5 metres at SR and IL, respectively.<br>This dataset contains two files provided in the MATLAB .mat format: <b>nw_sroc_T_200705_200809.mat</b> and <b>nw_inlet_lvl_200705_200810.mat</b>.<br><b>nw_sroc_T_200705_200809.mat</b> contains the data recorded at SR. This file includes the following variables:<i>ti_roc</i> (day) - Day of temperature reading. Day 0 is January 1st 2007.<i>t_roc</i> (degrees Celsius) - Temperature readingThe subset of data used in the accompanying study is May 5th 2007 to September 9th 2009.<br><b>nw_inlet_lvl_200705_200810.mat</b> contains the data recorded at IL.<i>time </i>- Time and date of fate collection<i>time_j</i> - Julian day of date<i>el</i> (meters) - Elevation of sea level<i>t </i>(degrees Celsius) - Temperature reading<br>Analyses of these data are presented in the accompanying publication.
DOI: 10.6084/m9.figshare.10047362
title: WHO data.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047368.v1
title: For Dan WUSTL.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047356
title: For Dan U Penn.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047359
title: For Dan UConn.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047371.v1
title: For Dan Found all the data for the graph.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047353
title: Nadav R03.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047356.v1
title: For Dan U Penn.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047362.v1
title: WHO data.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047359.v1
title: For Dan UConn.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047365.v1
title: Males For Dan.csv from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047344
title: NYU data Demographics.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047347
title: For Dan Filtered Data SGA Final.csv from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047350
title: For Dan PIN Data.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047353.v1
title: Nadav R03.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047341
title: For Dan SGA Nadav R03_Data Removed for missing DOB.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.
DOI: 10.6084/m9.figshare.10047341.v1
title: For Dan SGA Nadav R03_Data Removed for missing DOB.xlsx from Gestational growth trajectories derived from a dynamic fetal–placental scaling law

description: Fetal trajectories characterizing growth rates <i>in utero</i> have relied primarily on goodness of fit rather than mechanistic properties exhibited <i>in utero</i>. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events <i>in utero</i>.