cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.10071140.v1
title: MOESM4 of RaGOO: fast and accurate reference-guided scaffolding of draft genomes

description: Additional file 4: Table S5. Maker gene annotation statistics.
DOI: 10.6084/m9.figshare.10071128
title: MOESM2 of RaGOO: fast and accurate reference-guided scaffolding of draft genomes

description: Additional file 2: Table S2. Chromosome construction accuracy and runtime statistics.
DOI: 10.6084/m9.figshare.10071128.v1
title: MOESM2 of RaGOO: fast and accurate reference-guided scaffolding of draft genomes

description: Additional file 2: Table S2. Chromosome construction accuracy and runtime statistics.
DOI: 10.6084/m9.figshare.10071134.v1
title: MOESM3 of RaGOO: fast and accurate reference-guided scaffolding of draft genomes

description: Additional file 3: Table S4. Accuracy of different polishing strategies of M82.
DOI: 10.6084/m9.figshare.10071122
title: MOESM2 of Aging progression of human gut microbiota

description: Additional file 2 Genera correlated with ages revealed by Spearman correlation. Validated correlations were recognized with P&lt;0.05.
DOI: 10.6084/m9.figshare.10071116
title: MOESM1 of Aging progression of human gut microbiota

description: Additional file 1 Genera significantly varied in different age groups.
DOI: 10.6084/m9.figshare.10071116.v1
title: MOESM1 of Aging progression of human gut microbiota

description: Additional file 1 Genera significantly varied in different age groups.
DOI: 10.6084/m9.figshare.10071131.v1
title: MOESM3 of Aging progression of human gut microbiota

description: Additional file 3 Critical genera identified by SPD underlying the progression of human gut microbial community along with aging.
DOI: 10.6084/m9.figshare.10071122.v1
title: MOESM2 of Aging progression of human gut microbiota

description: Additional file 2 Genera correlated with ages revealed by Spearman correlation. Validated correlations were recognized with P&lt;0.05.
DOI: 10.6084/m9.figshare.10071044
title: Additional file 6: of Tumors diagnosed as cerebellar glioblastoma comprise distinct molecular entities

description: Frequencies of PDGFRA amplification (amp), EGFR amp, CDKN2A/B loss and TERT promoter mutation in supratentorial GBMs IDH wt. (XLSX 36 kb)
DOI: 10.6084/m9.figshare.10071044.v1
title: Additional file 6: of Tumors diagnosed as cerebellar glioblastoma comprise distinct molecular entities

description: Frequencies of PDGFRA amplification (amp), EGFR amp, CDKN2A/B loss and TERT promoter mutation in supratentorial GBMs IDH wt. (XLSX 36 kb)
DOI: 10.6084/m9.figshare.10071032
title: MOESM2 of Role of chemical composition and redox modification of poorly soluble nanomaterials on their ability to enhance allergic airway sensitisation in mice

description: Additional file 2. Cytokines in bronchoalveolar lavage fluid.
DOI: 10.6084/m9.figshare.10071020
title: Additional file 3: of Tumors diagnosed as cerebellar glioblastoma comprise distinct molecular entities

description: a. Molecular and clinical data of all 86 cerebellar gliomas sorted by integrated diagnosis. b. mapping of cases 1–13 in the t-SNE (see Fig. 1). (ZIP 890 kb)
DOI: 10.6084/m9.figshare.10071023
title: MOESM1 of Role of chemical composition and redox modification of poorly soluble nanomaterials on their ability to enhance allergic airway sensitisation in mice

description: Additional file 1. Total and differential cell counts in bronchoalveolar lavage fluid.
DOI: 10.6084/m9.figshare.10071032.v1
title: MOESM2 of Role of chemical composition and redox modification of poorly soluble nanomaterials on their ability to enhance allergic airway sensitisation in mice

description: Additional file 2. Cytokines in bronchoalveolar lavage fluid.
DOI: 10.6084/m9.figshare.10071023.v1
title: MOESM1 of Role of chemical composition and redox modification of poorly soluble nanomaterials on their ability to enhance allergic airway sensitisation in mice

description: Additional file 1. Total and differential cell counts in bronchoalveolar lavage fluid.
DOI: 10.6084/m9.figshare.10071020.v1
title: Additional file 3: of Tumors diagnosed as cerebellar glioblastoma comprise distinct molecular entities

description: a. Molecular and clinical data of all 86 cerebellar gliomas sorted by integrated diagnosis. b. mapping of cases 1–13 in the t-SNE (see Fig. 1). (ZIP 890 kb)
DOI: 10.6084/m9.figshare.10070279
title: 2e Corn Intensification.csv

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and NOAA climate data. It has been processed to provide a basis for modelling intensification in Corn production. It factors in a county's suitability for growing corn, GM uptake, irrigation and other intensive inputs over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070285
title: 2f Soybean Intensification.csv

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and NOAA climate data. It has been processed to provide a basis for modelling intensification in Corn production. It factors in a county's suitability for growing soybean, GM uptake, irrigation and other intensive inputs over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070276
title: Palomino, Lopez Frutos y Sotillo.xlsx

description: Control inhibition over memory
DOI: 10.6084/m9.figshare.10070264
title: 2d Soy Extensification.csv

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and Conservation Reserve Programme. It has been processed to provide a basis for modelling change in acreage of soybean planted at the county level. It factors in a county's suitability for growing soybean, competition from other commodities, GM uptake and commodity price over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070276.v1
title: Palomino, Lopez Frutos y Sotillo.xlsx

description: Control inhibition over memory
DOI: 10.6084/m9.figshare.10070285.v1
title: 3b Soybean Intensification.csv

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and NOAA climate data. It has been processed to provide a basis for modelling intensification in Corn production. It factors in a county's suitability for growing soybean, GM uptake, irrigation and other intensive inputs over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070255
title: 2c Corn Extensification

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and Conservation Reserve Programme. It has been processed to provide a basis for modelling change in acreage of corn planted at the county level. It factors in a county's suitability for growing corn, competition from other commodities, GM uptake and commodity price over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070285.v2
title: 1f Soybean Intensification.csv

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and NOAA climate data. It has been processed to provide a basis for modelling intensification in Corn production. It factors in a county's suitability for growing soybean, GM uptake, irrigation and other intensive inputs over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070255.v1
title: 2a Corn Extensification

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and Conservation Reserve Programme. It has been processed to provide a basis for modelling change in acreage of corn planted at the county level. It factors in a county's suitability for growing corn, competition from other commodities, GM uptake and commodity price over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070264.v1
title: 2b Soy Extensification.csv

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and Conservation Reserve Programme. It has been processed to provide a basis for modelling change in acreage of soybean planted at the county level. It factors in a county's suitability for growing soybean, competition from other commodities, GM uptake and commodity price over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070285.v4
title: 2f Soybean Intensification.csv

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and NOAA climate data. It has been processed to provide a basis for modelling intensification in Corn production. It factors in a county's suitability for growing soybean, GM uptake, irrigation and other intensive inputs over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070285.v3
title: 2f Soybean Intensification.csv

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and NOAA climate data. It has been processed to provide a basis for modelling intensification in Corn production. It factors in a county's suitability for growing soybean, GM uptake, irrigation and other intensive inputs over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070249
title: 2b Soybean 3year Expectation.csv

description: This dataset is a combination of data from the USDA National Statistics Service and Economic Research Service. It has been processed to create a rolling 3-year expectation of return per acre for growing soybean. It factors in yield, commodity price, wastage, costs and subsidies over the period 1990-2017.<br>
DOI: 10.6084/m9.figshare.10070255.v2
title: 1c Corn Extensification

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and Conservation Reserve Programme. It has been processed to provide a basis for modelling change in acreage of corn planted at the county level. It factors in a county's suitability for growing corn, competition from other commodities, GM uptake and commodity price over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070246
title: ToF depth sensor characterization data (VL53L1X)

description: Experimental characterization of the VL53L1X ToF depth sensor and the HC-SR04 (ultrasonic).<br>Data files have descriptive headers that explain the experimental setup. <br><b>Ultrasonic Sensor Range (mm): </b>measured distance by ultrasonic sensor <b>Optical Sensor Range (mm): </b>measured distance<b>Range Status:</b> a flag from the VL53L1X determining if ranging was successful<b>Signal Rate Return Mega cps: </b>signal rate, best to normalize by the number of enabled SPADs.<b>Ambient Rate Return Mega cps: </b>ambient rate, best to normalize by the number of enabled SPADs.<b>Stream Count: </b>ignore<b>Effective SPAD Return Count: </b>the number of enabled SPADs<b>Sigma mm: </b>the noise as determined by the sensor, our reported noise does not use this value but rather the standard deviation of multiple measurements<b>Time for Iteration:</b> time in seconds for the measurement<b>Setting Number: </b>describes timing budget and ranging mode, see lookup table in Python analysis code <br><br>ROI scans also have.<b>Block Location: </b>The row of a 4x4 ROI<b>Grid Location: </b>The index of a 4x4 ROI (row*column). Max is 13x13 = 169.<br>
DOI: 10.6084/m9.figshare.10070255.v4
title: 2c Corn Extensification

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and Conservation Reserve Programme. It has been processed to provide a basis for modelling change in acreage of corn planted at the county level. It factors in a county's suitability for growing corn, competition from other commodities, GM uptake and commodity price over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070264.v4
title: 2d Soy Extensification.csv

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and Conservation Reserve Programme. It has been processed to provide a basis for modelling change in acreage of soybean planted at the county level. It factors in a county's suitability for growing soybean, competition from other commodities, GM uptake and commodity price over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070255.v3
title: 2c Corn Extensification

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and Conservation Reserve Programme. It has been processed to provide a basis for modelling change in acreage of corn planted at the county level. It factors in a county's suitability for growing corn, competition from other commodities, GM uptake and commodity price over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070264.v3
title: 2d Soy Extensification.csv

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and Conservation Reserve Programme. It has been processed to provide a basis for modelling change in acreage of soybean planted at the county level. It factors in a county's suitability for growing soybean, competition from other commodities, GM uptake and commodity price over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070279.v3
title: 2e Corn Intensification.csv

description: This dataset is a combination of data from the USDA National Statistics Service, Economic Research Service and NOAA climate data. It has been processed to provide a basis for modelling intensification in Corn production. It factors in a county's suitability for growing corn, GM uptake, irrigation and other intensive inputs over the period 1990-2017.
DOI: 10.6084/m9.figshare.10070234
title: 2a Corn 3year Expectation.csv

description: This dataset is a combination of data from the USDA National Statistics Service and Economic Research Service. It has been processed to create a rolling 3-year expectation of return per acre for growing corn. It factors in yield, commodity price, wastage, costs and subsidies over the period 1990-2017. <br><br>
DOI: 10.6084/m9.figshare.10070249.v1
title: 1b Soybean 3year Expectation.csv

description: This dataset is a combination of data from the USDA National Statistics Service and Economic Research Service. It has been processed to create a rolling 3-year expectation of return per acre for growing soybean. It factors in yield, commodity price, wastage, costs and subsidies over the period 1990-2017.<br>
DOI: 10.6084/m9.figshare.10070249.v2
title: 2b Soybean 3year Expectation.csv

description: This dataset is a combination of data from the USDA National Statistics Service and Economic Research Service. It has been processed to create a rolling 3-year expectation of return per acre for growing soybean. It factors in yield, commodity price, wastage, costs and subsidies over the period 1990-2017.<br>
DOI: 10.6084/m9.figshare.10070249.v3
title: 2b Soybean 3year Expectation.csv

description: This dataset is a combination of data from the USDA National Statistics Service and Economic Research Service. It has been processed to create a rolling 3-year expectation of return per acre for growing soybean. It factors in yield, commodity price, wastage, costs and subsidies over the period 1990-2017.<br>
DOI: 10.6084/m9.figshare.10070111
title: Data for Figures - Wright & Elsden 2020

description: Data to reproduce figures from Wright and Elsden 2020 submitted to JGR Space Physics. IDL save file which can be restored.
DOI: 10.6084/m9.figshare.10070234.v1
title: 1a Corn 3year Expectation.csv

description: This dataset is a combination of data from the USDA National Statistics Service and Economic Research Service. It has been processed to create a rolling 3-year expectation of return per acre for growing corn. It factors in yield, commodity price, wastage, costs and subsidies over the period 1990-2017. <br><br>
DOI: 10.6084/m9.figshare.10070111.v1
title: Data for Figures - Wright & Elsden 2020

description: Data to reproduce figures from Wright and Elsden 2020 submitted to JGR Space Physics. IDL save file which can be restored.
DOI: 10.6084/m9.figshare.10070234.v2
title: 2a Corn 3year Expectation.csv

description: This dataset is a combination of data from the USDA National Statistics Service and Economic Research Service. It has been processed to create a rolling 3-year expectation of return per acre for growing corn. It factors in yield, commodity price, wastage, costs and subsidies over the period 1990-2017. <br><br>
DOI: 10.6084/m9.figshare.10069889
title: genotyperainbowtrout.txt

description: genotype of rainbow trout
DOI: 10.6084/m9.figshare.10069892
title: phenotyperainbowtrout.txt

description: phenotype of rainbow trout
DOI: 10.6084/m9.figshare.10069892.v1
title: phenotyperainbowtrout.txt

description: phenotype of rainbow trout
DOI: 10.6084/m9.figshare.10069889.v1
title: genotyperainbowtrout.txt

description: genotype of rainbow trout
DOI: 10.6084/m9.figshare.10069865
title: Supplementary Material for: Multimodal Wearable Sensors to Measure Gait and Voice

description: <b><i>Background:</i></b> Traditional measurement systems utilized in clinical trials are limited because they are episodic and thus cannot capture the day-to-day fluctuations and longitudinal changes that frequently affect patients across different therapeutic areas. <b><i>Objectives:</i></b> The aim of this study was to collect and evaluate data from multiple devices, including wearable sensors, and compare them to standard lab-based instruments across multiple domains of daily tasks. <b><i>Methods:</i></b> Healthy volunteers aged 18–65 years were recruited for a 1-h study to collect and assess data from wearable sensors. They performed walking tasks on a gait mat while instrumented with a watch, phone, and sensor insoles as well as several speech tasks on multiple recording devices. <b><i>Results:</i></b> Step count and temporal gait metrics derived from a single lumbar accelerometer are highly precise; spatial gait metrics are consistently 20% shorter than gait mat measurements. The insole’s algorithm only captures about 72% of steps but does have precision in measuring temporal gait metrics. Mobile device voice recordings provide similar results to traditional recorders for average signal pitch and sufficient signal-to-noise ratio for analysis when hand-held. Lossless compression techniques are advised for signal processing. <b><i>Conclusions:</i></b> Gait metrics from a single lumbar accelerometer sensor are in reasonable concordance with standard measurements, with some variation between devices and across individual metrics. Finally, participants in this study were familiar with mobile devices and had high acceptance of potential future continuous wear for clinical trials.
DOI: 10.6084/m9.figshare.10069784
title: Video of iEMG movements

description: Video of iEMG movements with codes
DOI: 10.6084/m9.figshare.10069742
title: A ChIP-seq (H4 and H4K12Ac) dataset of heat-shock response in the yeast RNA binding protein Mip6

description: H4 and H4K12Ac ChIP-seq data of yeast WT and mip6delta strains. Cultures were maintained at 30 ºC, or at 39 ºC for either 20 and 120 minutes.
DOI: 10.6084/m9.figshare.10069742.v1
title: A ChIP-seq (H4 and H4K12Ac) dataset of heat-shock response in the yeast RNA binding protein Mip6

description: H4 and H4K12Ac ChIP-seq data of yeast WT and mip6delta strains. Cultures were maintained at 30 ºC, or at 39 ºC for either 20 and 120 minutes.
DOI: 10.6084/m9.figshare.10069703
title: Metabolomics dataset of heat-shock response in the yeast RNA binding protein Mip6

description: NMR metabolomics data of yeast WT and mip6delta strains. Cultures were maintained at 30 C, or at 39 C for either 20 and 120 minutes.
DOI: 10.6084/m9.figshare.10069700
title: RNA-seq dataset of heat-shock response in the yeast RNA binding protein Mip6

description: mRNA-seq data of yeast WT and mip6delta strains. Cultures were maintained at 30 C, or at 39 C for either 20 and 120 minutes.
DOI: 10.6084/m9.figshare.10069700.v1
title: RNA-seq dataset of heat-shock response in the yeast RNA binding protein Mip6

description: mRNA-seq data of yeast WT and mip6delta strains. Cultures were maintained at 30 C, or at 39 C for either 20 and 120 minutes.
DOI: 10.6084/m9.figshare.10069289
title: Supplementary Material for: Ultrafiltration Rate Effects Declines in Residual Kidney Function in Hemodialysis Patients

description: <b><i>Background:</i></b> High ultrafiltration rate (UFR) has been associated with increased mortality in hemodialysis (HD) patients. However, the impact of UFR on decline of residual kidney function (RKF) has not been elucidated among patients receiving conventional HD. <b><i>Methods:</i></b> We performed a retrospective cohort study of 7,753 patients who initiated conventional HD from 2007 to 2011 and survived the first year of dialysis with baseline UFR and renal urea clearance (KRU) data at baseline and 1 year (5th patient-quarter). The primary exposure was average UFR at the 1st patient-quarter from dialysis initiation (&lt;4, 4 to &lt;6, 6 to &lt;9, 9 to &lt;13, and ≥13 mL/h/kg). Decline in RKF was defined as the percent change in KRU and decline in urine output during the first year after initiation of dialysis. We used a logistic regression model for rapid decline in RKF and a linear regression model for change in urine volume. <b><i>Results:</i></b> In our HD cohort, mean baseline UFR was 7.0 ± 3.1 mL/h/kg, and median (interquartile range) baseline KRU was 3.5 (2.1–5.3) mL/min/1.73 m<sup>2</sup>. There was a graded association between UFR and a rapid decline in RKF; the expanded case mix-adjusted ORs and 95% CIs were 1.21 (1.04–1.40), 1.34 (1.16–1.55), 1.73 (1.46–2.04), and 1.93 (1.48–2.52) for baseline UFR 4 to &lt;6, 6 to &lt;9, 9 to &lt;13, and ≥13 mL/h/kg, respectively (reference: &lt;4 mL/h/kg). KRU trajectories showed a greater KRU decline over time in higher UFR categories. Higher UFR was also associated with a greater decline in urine output after 1 year. <b><i>Conclusion:</i></b> Higher UFR was associated with a rapid decline in RKF among conventional HD patients. Further clinical trials are needed to elucidate a causal effect of UFR on RKF among HD patients.
DOI: 10.6084/m9.figshare.10069289.v1
title: Supplementary Material for: Ultrafiltration Rate Effects Declines in Residual Kidney Function in Hemodialysis Patients

description: <b><i>Background:</i></b> High ultrafiltration rate (UFR) has been associated with increased mortality in hemodialysis (HD) patients. However, the impact of UFR on decline of residual kidney function (RKF) has not been elucidated among patients receiving conventional HD. <b><i>Methods:</i></b> We performed a retrospective cohort study of 7,753 patients who initiated conventional HD from 2007 to 2011 and survived the first year of dialysis with baseline UFR and renal urea clearance (KRU) data at baseline and 1 year (5th patient-quarter). The primary exposure was average UFR at the 1st patient-quarter from dialysis initiation (&lt;4, 4 to &lt;6, 6 to &lt;9, 9 to &lt;13, and ≥13 mL/h/kg). Decline in RKF was defined as the percent change in KRU and decline in urine output during the first year after initiation of dialysis. We used a logistic regression model for rapid decline in RKF and a linear regression model for change in urine volume. <b><i>Results:</i></b> In our HD cohort, mean baseline UFR was 7.0 ± 3.1 mL/h/kg, and median (interquartile range) baseline KRU was 3.5 (2.1–5.3) mL/min/1.73 m<sup>2</sup>. There was a graded association between UFR and a rapid decline in RKF; the expanded case mix-adjusted ORs and 95% CIs were 1.21 (1.04–1.40), 1.34 (1.16–1.55), 1.73 (1.46–2.04), and 1.93 (1.48–2.52) for baseline UFR 4 to &lt;6, 6 to &lt;9, 9 to &lt;13, and ≥13 mL/h/kg, respectively (reference: &lt;4 mL/h/kg). KRU trajectories showed a greater KRU decline over time in higher UFR categories. Higher UFR was also associated with a greater decline in urine output after 1 year. <b><i>Conclusion:</i></b> Higher UFR was associated with a rapid decline in RKF among conventional HD patients. Further clinical trials are needed to elucidate a causal effect of UFR on RKF among HD patients.