cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13140176.v1
title: Database: Systematic review of preclinical studies on Neu

description: Database: Systematic review of preclinical studies on the neutrophils
DOI: 10.6084/m9.figshare.12490607
title: A strathclyde cluster model for gait kinematic measurement using functional methods: a study of inter-assessor reliability analysis with comparison to anatomical models

description: A major source of error in reliability of gait analysis arises from the palpation of anatomical landmarks (ALs). The purpose of this study was to investigate whether less reliance on manually identifying ALs could improve inter-assessor reliability of joint kinematics compared to two anatomical models. It was hypothesised that the Strathclyde functional cluster model (SFCM), in which the hip, knee and ankle joint centres and knee and ankle flexion axes were determined by functional methods, would obtain greater inter-assessor reliability. Ten able-bodied participants and seven assessors were recruited. Each participant completed three trials conducted by different assessors on non-consecutive days. Agreement and inter-assessor reliability between the models were compared and analysed, whilst factor effects of assessor experience and body mass index (BMI) were investigated. The SFCM obtained excellent agreement with anatomical models for all sagittal angles and hip ab/adduction angle, and it showed slightly higher inter-assessor reliability with smaller variations in the knee and ankle. The assessor experience was not a significant factor, but the BMI had a significant effect on the inter-assessor reliability. The results demonstrate that the SFCM may be more beneficial for less experienced assessors.
DOI: 10.6084/m9.figshare.12490607.v1
title: A strathclyde cluster model for gait kinematic measurement using functional methods: a study of inter-assessor reliability analysis with comparison to anatomical models

description: A major source of error in reliability of gait analysis arises from the palpation of anatomical landmarks (ALs). The purpose of this study was to investigate whether less reliance on manually identifying ALs could improve inter-assessor reliability of joint kinematics compared to two anatomical models. It was hypothesised that the Strathclyde functional cluster model (SFCM), in which the hip, knee and ankle joint centres and knee and ankle flexion axes were determined by functional methods, would obtain greater inter-assessor reliability. Ten able-bodied participants and seven assessors were recruited. Each participant completed three trials conducted by different assessors on non-consecutive days. Agreement and inter-assessor reliability between the models were compared and analysed, whilst factor effects of assessor experience and body mass index (BMI) were investigated. The SFCM obtained excellent agreement with anatomical models for all sagittal angles and hip ab/adduction angle, and it showed slightly higher inter-assessor reliability with smaller variations in the knee and ankle. The assessor experience was not a significant factor, but the BMI had a significant effect on the inter-assessor reliability. The results demonstrate that the SFCM may be more beneficial for less experienced assessors.
DOI: 10.5281/zenodo.4128587
title: Wikidata Dump Kannan_indus2_en

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4128586
title: Wikidata Dump Kannan_indus2_en

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.6084/m9.figshare.13140215
title: methylation_blood

description: blood methylation
DOI: 10.6084/m9.figshare.13140215.v1
title: methylation_blood

description: blood methylation
DOI: 10.6084/m9.figshare.13140137
title: 2019 12 27 Field Notes

description: Field notes including image and/or video records.
DOI: 10.6084/m9.figshare.13140092
title: JVASP-68563 to JVASP-69340

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13139402.v1
title: Arctic COSMO-CLM Reanalysis 2010

description: COSMO-CLM Russian Arctic climate dataset
DOI: 10.6084/m9.figshare.13140095
title: JVASP-69341 to JVASP-70669

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140095.v1
title: JVASP-69341 to JVASP-70669

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140098
title: JVASP-70672 to JVASP-71303

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140092.v1
title: JVASP-68563 to JVASP-69340

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140098.v1
title: JVASP-70672 to JVASP-71303

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140137.v1
title: 2019 12 27 Field Notes

description: Field notes including image and/or video records.
DOI: 10.5281/zenodo.4009546
title: Analysis and Figures from "Causal network inference from gene transcriptional time-series response to glucocorticoids"

description: Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of 2,768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2,768 genes and 31,945 directed edges (FDR &lt;= 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS This upload documents the analysis and figure files that support each numerical claim of the manuscript. Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript, assuming the home folder of port-from-della
description: Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript. The home folder is port-from-della
DOI: 10.5281/zenodo.3945081
title: Analysis and Figures from "Causal network inference from gene transcriptional time-series response to glucocorticoids"

description: Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of 2,768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2,768 genes and 31,945 directed edges (FDR &lt;= 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS This upload documents the analysis and figure files that support each numerical claim of the manuscript. Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript, assuming the home folder of port-from-della
description: Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript. The home folder is port-from-della
DOI: 10.5281/zenodo.4130594
title: AD and Diet Data

description: 2019 data on AD and diet in the UK with demogrraphics
DOI: 10.5281/zenodo.4130593
title: AD and Diet Data

description: 2019 data on AD and diet in the UK with demogrraphics
DOI: 10.21227/f7p4-k621
title: Model 2

description: Model 2 Clustered-meter average monthly ratios Monthly (kWh) consumption clustering: 1. Twelve clustering models (one per month of signup): Spectral Clustering with nearest neighbors.2. Clustering using (daily kWh) consumption extracted features. Cluster count set to two.3. Meters not in the training clustering data, are classified to a specific cluster using a Gaussian Process Classifier (GPC) on the same dataset (12 classifiers total). Features from weekend/weekday and full series:- Statistical: median, variance, quantiles, ...- Time-series: autocorrelations, trends, seasonalities, ... Forecasting for a meter: Using the mean montly consumption + in-cluster month-to-month ratios.
DOI: 10.6084/m9.figshare.12937433
title: Supplementary Table 1

description: <b>Supplementary Table 1. Baseline MAP, HR and RSNA in sham and CIH-exposed rats (cohort 2) </b>Data were collected from sham (n=13) and CIH-exposed rats (n=10) and are expressed as mean±SD. Baseline values over a 2-minute period were averaged before and after the infusion of saline or CPZ and were analysed using repeated-measures two-way ANOVA. * p&lt;0.05 vs. corresponding value after intra-renal infusion of drug. MAP, mean arterial blood pressure; HR, heart rate; VE, volume expansion; CPZ, capsaizepine; normalized RSNA, renal sympathetic nerve activity normalized to maximum activity recorded during baroreflex activation during euthanasia.<b></b><br>
DOI: 10.6084/m9.figshare.12937433.v1
title: Supplementary Table 1

description: <b>Supplementary Table 1. Baseline MAP, HR and RSNA in sham and CIH-exposed rats (cohort 2) </b>Data were collected from sham (n=13) and CIH-exposed rats (n=10) and are expressed as mean±SD. Baseline values over a 2-minute period were averaged before and after the infusion of saline or CPZ and were analysed using repeated-measures two-way ANOVA. * p&lt;0.05 vs. corresponding value after intra-renal infusion of drug. MAP, mean arterial blood pressure; HR, heart rate; VE, volume expansion; CPZ, capsaizepine; normalized RSNA, renal sympathetic nerve activity normalized to maximum activity recorded during baroreflex activation during euthanasia.<b></b><br>
DOI: 10.6084/m9.figshare.12937571
title: Supplementary Table 2

description: <b>Supplementary Table 2: Baseline renal excretory parameters in sham and CIH-exposed rats. </b>Data are expressed as mean±SD in sham (n=9) and CIH-exposed rats (n=9). Two basal urine samples were averaged and data were compared between CIH-exposed and sham rats during saline and CPZ phases by repeated-measures two-way ANOVA. * p&lt;0.05 vs. corresponding value during intra-renal infusion. GFR, glomerular filtration rate; UF, urine flow; CPZ, capsaizepine.<br>
DOI: 10.6084/m9.figshare.12937571.v1
title: Supplementary Table 2

description: <b>Supplementary Table 2: Baseline renal excretory parameters in sham and CIH-exposed rats. </b>Data are expressed as mean±SD in sham (n=9) and CIH-exposed rats (n=9). Two basal urine samples were averaged and data were compared between CIH-exposed and sham rats during saline and CPZ phases by repeated-measures two-way ANOVA. * p&lt;0.05 vs. corresponding value during intra-renal infusion. GFR, glomerular filtration rate; UF, urine flow; CPZ, capsaizepine.<br>
DOI: 10.5281/zenodo.4130559
title: Expert AR Detector Counts

description: Global atmospheric river (AR) counts, contributed by a set of 8 experts in atmospheric science. Each contributor was presented with meteorological information in a graphical user interface and were asked to manually identify AR locations. Contributors counted ARs in at least 30 independent meteorological fields. Information from each contributor is stored in a separate netCDF file. The information includes: AR counts, approximate AR location, the corresponding integrated vapor transport field, and the associated timestamp. Each contributor is assigned a number, following the convention described by O'Brien et al., (2020, GMD). This dataset was used by O'Brien et al. (2020, GMD) to train a Bayesian AR Detector. O'Brien, T. A., Risser, M. D., Loring, B., Elbashandy, A. A., Krishnan, H., Johnson, J., Patricola, C. M., O'Brien, J. P., Mahesh, A., Prabhat, Arriaga Ramirez, S., Rhoades, A. M., Charn, A., Inda Díaz, H., and Collins, W. D.: Detection of Atmospheric Rivers with Inline Uncertainty Quantification: TECA-BARD v1.0, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-55, Accepted, 2020.
description: {"references": ["O'Brien, T. A., Risser, M. D., Loring, B., Elbashandy, A. A., Krishnan, H., Johnson, J., Patricola, C. M., O'Brien, J. P., Mahesh, A., Prabhat, Arriaga Ramirez, S., Rhoades, A. M., Charn, A., Inda D\u00edaz, H., and Collins, W. D.: Detection of Atmospheric Rivers with Inline Uncertainty Quantification: TECA-BARD v1.0, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-55, Accepted, 2020."]}
DOI: 10.5281/zenodo.4130558
title: Expert AR Detector Counts

description: Global atmospheric river (AR) counts, contributed by a set of 8 experts in atmospheric science. Each contributor was presented with meteorological information in a graphical user interface and were asked to manually identify AR locations. Contributors counted ARs in at least 30 independent meteorological fields. Information from each contributor is stored in a separate netCDF file. The information includes: AR counts, approximate AR location, the corresponding integrated vapor transport field, and the associated timestamp. Each contributor is assigned a number, following the convention described by O'Brien et al., (2020, GMD). This dataset was used by O'Brien et al. (2020, GMD) to train a Bayesian AR Detector. O'Brien, T. A., Risser, M. D., Loring, B., Elbashandy, A. A., Krishnan, H., Johnson, J., Patricola, C. M., O'Brien, J. P., Mahesh, A., Prabhat, Arriaga Ramirez, S., Rhoades, A. M., Charn, A., Inda Díaz, H., and Collins, W. D.: Detection of Atmospheric Rivers with Inline Uncertainty Quantification: TECA-BARD v1.0, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-55, Accepted, 2020.
description: {"references": ["O'Brien, T. A., Risser, M. D., Loring, B., Elbashandy, A. A., Krishnan, H., Johnson, J., Patricola, C. M., O'Brien, J. P., Mahesh, A., Prabhat, Arriaga Ramirez, S., Rhoades, A. M., Charn, A., Inda D\u00edaz, H., and Collins, W. D.: Detection of Atmospheric Rivers with Inline Uncertainty Quantification: TECA-BARD v1.0, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-55, Accepted, 2020."]}
DOI: 10.6084/m9.figshare.12937580.v1
title: Supplementary Table 3

description: <b>Supplementary Table 3. </b><b>High-pressure baroreflex parameters during intra-renal infusion of capsaizepine in sham and CIH-exposed rats (cohort 2). </b>Data are expressed as mean±SD from sham (n=10) and CIH-exposed (n=10) groups and were analysed using independent sample <i>t</i>-tests. CIH, chronic intermittent hypoxia; RSNA, renal sympathetic nerve activity; HR, heart rate; A1, response range of RSNA/HR; A2, the gain coefficient; A3, midpoint pressure of input; A4, the minimum response.
DOI: 10.21227/sexg-m833
title: Model 2.1

description: Model 2.1 Clustered-meter average monthly ratios Monthly (kWh) consumption clustering: 1. Twelve clustering models (one per month of signup): Spectral Clustering with nearest neighbors.2. Clustering using (daily kWh) consumption extracted features. Cluster count set to 2-4: final count is decided by maximum silhouette score of manhattan distances.3. Meters not in the training clustering data, are classified to a specific cluster using a Gaussian Process Classifier (GPC) on the same dataset (12 classifiers total). Features (170) from weekend/weekday and full series:- Statistical: median, variance, quantiles, ...- Time-series: autocorrelations, trends, seasonalities, ... Forecasting for a meter: Using the mean montly consumption + in-cluster month-to-month ratios.
DOI: 10.6084/m9.figshare.12937580
title: Supplementary Table 3

description: <b>Supplementary Table 3. </b><b>High-pressure baroreflex parameters during intra-renal infusion of capsaizepine in sham and CIH-exposed rats (cohort 2). </b>Data are expressed as mean±SD from sham (n=10) and CIH-exposed (n=10) groups and were analysed using independent sample <i>t</i>-tests. CIH, chronic intermittent hypoxia; RSNA, renal sympathetic nerve activity; HR, heart rate; A1, response range of RSNA/HR; A2, the gain coefficient; A3, midpoint pressure of input; A4, the minimum response.
DOI: 10.6084/m9.figshare.13140242
title: JVASP-77461 to JVASP-78863

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140242.v1
title: JVASP-77461 to JVASP-78863

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140239
title: JVASP-75601 to JVASP-77455

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140278
title: H II Regions: Witnesses to Massive Star Formation

description: Run B from Peters et al., The Astrophysical Journal, Volume 711, Issue 2, pp. 1017-1028 (2010)
DOI: 10.6084/m9.figshare.13140278.v1
title: H II Regions: Witnesses to Massive Star Formation

description: Run B from Peters et al., The Astrophysical Journal, Volume 711, Issue 2, pp. 1017-1028 (2010)
DOI: 10.6084/m9.figshare.13140236
title: JVASP-74640 to JVASP-75600

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140233
title: JVASP-72164 to JVASP-74639

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140230
title: JVASP-71304 to JVASP-72160

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140230.v1
title: JVASP-71304 to JVASP-72160

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13140263
title: ClinicalTrials.gov and PubMed.gov study databases of "Immunological biomarkers of psychotherapeutic interventions: A systematic review and meta-analysis"

description: ClinicalTrials.gov and PubMed.gov (25/10/2020): <br>(psychotherapy OR "cognitive behavioral therapy" OR psychology OR endocrinology OR psychiatry OR meditation OR mindfulness OR yoga OR psychoanalysis) AND (cortisol OR cytokine OR cytokines OR interleukin OR lymphocyte OR TNF OR inflammation). <br>PubMed.gov filters applied: Clinical Study, Clinical Trial, Clinical Trial Protocol, Clinical Trial, Phase I, Clinical Trial, Phase II, Clinical Trial, Phase III, Clinical Trial, Phase IV, Controlled Clinical Trial, Meta-Analysis, Multicenter Study, Observational Study, Pragmatic Clinical Trial, Randomized Controlled Trial, Systematic Review.
DOI: 10.6084/m9.figshare.13140263.v1
title: ClinicalTrials.gov and PubMed.gov study databases of "Immunological biomarkers of psychotherapeutic interventions: A systematic review and meta-analysis"

description: ClinicalTrials.gov and PubMed.gov (25/10/2020): <br>(psychotherapy OR "cognitive behavioral therapy" OR psychology OR endocrinology OR psychiatry OR meditation OR mindfulness OR yoga OR psychoanalysis) AND (cortisol OR cytokine OR cytokines OR interleukin OR lymphocyte OR TNF OR inflammation). <br>PubMed.gov filters applied: Clinical Study, Clinical Trial, Clinical Trial Protocol, Clinical Trial, Phase I, Clinical Trial, Phase II, Clinical Trial, Phase III, Clinical Trial, Phase IV, Controlled Clinical Trial, Meta-Analysis, Multicenter Study, Observational Study, Pragmatic Clinical Trial, Randomized Controlled Trial, Systematic Review.
DOI: 10.6084/m9.figshare.13140044
title: R0311_20191126a_videos

description: Rat 0311, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13140044.v1
title: R0311_20191126a_videos

description: Rat 0311, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.5281/zenodo.4098386
title: Individualized Nutritional Support versus Usual Care in Medical Inpatients at Risk of Malnutrition: Randomized Trial

description: Dataset for analysis of the trial "Individualized Nutritional Support versus Usual Care in Medical Inpatients at Risk of Malnutrition"
DOI: 10.5281/zenodo.4098385
title: Individualized Nutritional Support versus Usual Care in Medical Inpatients at Risk of Malnutrition: Randomized Trial

description: Dataset for analysis of the trial "Individualized Nutritional Support versus Usual Care in Medical Inpatients at Risk of Malnutrition"
DOI: 10.5281/zenodo.4130777
title: Orthopedic treatment for Class II malocclusion with functional appliances and its effect on upper airways: a systematic review with meta-analysis

description: Dataset for all analyses in the paper.
DOI: 10.5281/zenodo.4130776
title: Orthopedic treatment for Class II malocclusion with functional appliances and its effect on upper airways: a systematic review with meta-analysis

description: Dataset for all analyses in the paper.
DOI: 10.21227/qhzd-tm46
title: Model 2.2

description: Smart Meters IEEE contest Model 2.2 Clustered-meter average monthly ratios Monthly (kWh) consumption clustering: 1. Twelve clustering models (one per month of signup): Spectral Clustering with nearest neighbors.2. Clustering using (daily kWh) consumption extracted features. Cluster count set to 2-23: final count is decided by maximum silhouette score of manhattan distances. Features (170) from weekend/weekday and full series:- Statistical: median, variance, quantiles, ...- Time-series: autocorrelations, trends, seasonalities, ... Forecasting for a meter: Using the mean montly consumption + in-cluster month-to-month ratios.
DOI: 10.6084/m9.figshare.9864146
title: Punctuational mode of evolution for North American Martes

description: This dataset contains raw 3D geometric morphometric landmark data for the humerus, radius, ulna, femur, tibia, and fibula of Martes americana and Martes caurina. It also contains the centroid sizes calculated for each limb element. The associated R scripts for assessing sexual dimorphism and evolutionary tempo and mode are also included.
DOI: 10.6084/m9.figshare.9864146.v1
title: Punctuational mode of evolution for North American Martes

description: This dataset contains raw 3D geometric morphometric landmark data for the humerus, radius, ulna, femur, tibia, and fibula of Martes americana and Martes caurina. It also contains the centroid sizes calculated for each limb element. The associated R scripts for assessing sexual dimorphism and evolutionary tempo and mode are also included.
DOI: 10.6084/m9.figshare.12948842
title: Data for "Cognition of feedback loops in a fire-prone social-ecological system"

description: Data for "Cognition of feedback loops in a fire-prone social-ecological system"<br>
DOI: 10.6084/m9.figshare.12948842.v1
title: Data for "Cognition of feedback loops in a fire-prone social-ecological system"

description: Data for "Cognition of feedback loops in a fire-prone social-ecological system"<br>
DOI: 10.6084/m9.figshare.13139324.v1
title: Arctic COSMO-CLM Reanalysis 2007

description: COSMO-CLM Russian Arctic climate dataset