cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5044558
title: Dataset 1: Data on ciHHV-6 presence and HIV disease progression in 439 patients at the Infectious Diseases Clinic at Strong Memorial Hospital in Rochester, NY

description: PID: Subject identification; Last_VL: Subject’s most recent viral load; Last_CD4: Subject’s most recent CD4+ T-cell count;Last_CD4_%: Subject’s most recent CD4+ T-cell count percent; Time_to_progression: time (in years) between HIV diagnosis and AIDS diagnosis; Rapid Progressor: Less than two years to between HIV and AIDS diagnosis; ART Naïve: If subject is naïve to antiretroviral therapy – ‘yes’; CIHHV6Run2: Subject positive for qualitative PCR results – ‘1’; QuantPCR: Subject positive for quantitative PCR results – ‘1’.
DOI: 10.6084/m9.figshare.5044558.v1
title: Dataset 1: Data on ciHHV-6 presence and HIV disease progression in 439 patients at the Infectious Diseases Clinic at Strong Memorial Hospital in Rochester, NY

description: PID: Subject identification; Last_VL: Subject’s most recent viral load; Last_CD4: Subject’s most recent CD4+ T-cell count;Last_CD4_%: Subject’s most recent CD4+ T-cell count percent; Time_to_progression: time (in years) between HIV diagnosis and AIDS diagnosis; Rapid Progressor: Less than two years to between HIV and AIDS diagnosis; ART Naïve: If subject is naïve to antiretroviral therapy – ‘yes’; CIHHV6Run2: Subject positive for qualitative PCR results – ‘1’; QuantPCR: Subject positive for quantitative PCR results – ‘1’.
DOI: 10.6084/m9.figshare.5044519
title: Appendix E

description: Gene functional classification for genes in drug-target data that are not found in human tissue expression data
DOI: 10.6084/m9.figshare.5044537
title: Appendix F

description: Gene functional classification for genes in drug-target data zero rows in human tissue expression data
DOI: 10.6084/m9.figshare.5044519.v1
title: Appendix E

description: Gene functional classification for genes in drug-target data that are not found in human tissue expression data
DOI: 10.6084/m9.figshare.5044513
title: Appendix D

description: Gene functional classification for group enriched genes expressed in appendix, spleen, tonsil, bone marrow and lymph node
DOI: 10.6084/m9.figshare.5044537.v1
title: Appendix F

description: Gene functional classification for genes in drug-target data zero rows in human tissue expression data
DOI: 10.6084/m9.figshare.5044507
title: Appendix B

description: Gene functional classification for tissue enhanced genes expressed in brain and testis
DOI: 10.6084/m9.figshare.5044510
title: Appendix C

description: Gene functional classification for group enriched genes expressed in appendix, duodenum, small intestine, colon and rectum
DOI: 10.6084/m9.figshare.5044513.v1
title: Appendix D

description: Gene functional classification for group enriched genes expressed in appendix, spleen, tonsil, bone marrow and lymph node
DOI: 10.6084/m9.figshare.5044510.v1
title: Appendix C

description: Gene functional classification for group enriched genes expressed in appendix, duodenum, small intestine, colon and rectum
DOI: 10.6084/m9.figshare.5044495
title: Appendix A

description: Gene functional classification for genes with all zero values in human tissue expression data
DOI: 10.6084/m9.figshare.5044495.v1
title: Appendix A

description: Gene functional classification for genes with all zero values in human tissue expression data
DOI: 10.6084/m9.figshare.5044507.v1
title: Appendix B

description: Gene functional classification for tissue enhanced genes expressed in brain and testis
DOI: 10.6084/m9.figshare.5044453
title: EEG phase decoding for alphabet

description: 14 subjects' EEG data recorded in Fudan University
DOI: 10.5281/zenodo.4011195
title: NoSta-D Named Entity Annotation for German

description: Dataset of the GermEval2014 Named Entity Recognition task.
description: {"references": ["Germeval 2014 named entity recognition: Companion paper Darina Benikova, Chris Biemann, Max Kisselew and Sebastian Pad\u00f3, In Proceedings of the KONVENS GermEval Shared Task on Named Entity Recognition, Hildesheim, Germany, 2014."]}
DOI: 10.5281/zenodo.4011194
title: NoSta-D Named Entity Annotation for German

description: Dataset of the GermEval2014 Named Entity Recognition task.
description: {"references": ["Germeval 2014 named entity recognition: Companion paper Darina Benikova, Chris Biemann, Max Kisselew and Sebastian Pad\u00f3, In Proceedings of the KONVENS GermEval Shared Task on Named Entity Recognition, Hildesheim, Germany, 2014."]}
DOI: 10.5281/zenodo.3653402
title: wine recommendations dataset crafted by human wine experts

description: wine recommendation dataset crafted by human wine experts
DOI: 10.5281/zenodo.4011691
title: Replication package for: "What Would You Do with $500? Spending Responses to Gains, Losses, News and Loans"

description: The package contains all the code and data necessary to reproduce the tables and figures in Fuster, Kaplan and Zafar, "What Would You Do with $500? Spending Responses to Gains, Losses, News and Loans", Review of Economic Studies.
DOI: 10.6084/m9.figshare.5043280
title: Supporting Information.xlsx

description: The data of patients enrolled.
DOI: 10.6084/m9.figshare.5043256
title: Dataset for: The thermodynamic basis of glucose stimulated insulin release: a model of the core mechanism.

description: A model for glucose sensing by pancreatic β-cells is developed and compared to the available experimental data. The model brings together mathematical representations for the activities of the glucose sensor, glucokinase, and oxidative phosphorylation. Glucokinase produces glucose 6-phosphate (G-6-P) in an irreversible reaction that determines glycolytic flux. The primary products of glycolysis are NADH and pyruvate. The NADH is reoxidized and the reducing equivalents transferred to oxidative phosphorylation by the glycerol phosphate shuttle and some of the pyruvate is oxidized by pyruvate dehydrogenase and enters the citric acid cycle. These reactions are irreversible, and result in a glucose concentration dependent reduction of the intramitochondrial NAD pool. This increases the electrochemical energy coupled to ATP synthesis, and thereby the cellular energy state ([ATP]/[ADP][Pi]). [ATP] and [Pi] are 10 to 100 times greater than [ADP], so the increase in energy state is primarily through decrease in [ADP]. The decrease in [ADP] is considered responsible for altering ion channel conductance and releasing insulin. Applied to the reported glucose concentration dependent release of insulin by perifused islet preparations (4), the model predicts that the dependence of insulin release on [ADP] is strongly cooperative with a threshold of about 30 µM and a negative Hill coefficient near -5.5. The predicted cellular energy state, [ADP], creatine phosphate/creatine ratio, and cytochrome c reduction, including their dependence on glucose concentration, are consistent with experimental data. The ability of the model to predict behavior consistent with experiment is an invaluable resource for understanding glucose sensing and planning experiments.
DOI: 10.6084/m9.figshare.5043265
title: DingXiang

description: This is the DingXiang forum partial data.
DOI: 10.6084/m9.figshare.5043280.v1
title: Supporting Information.xlsx

description: The data of patients enrolled.
DOI: 10.6084/m9.figshare.5043265.v1
title: DingXiang

description: This is the DingXiang forum partial data.
DOI: 10.6084/m9.figshare.5043214
title: main time stepping code and the data it generates

description: main time stepping code and the data it generates
DOI: 10.6084/m9.figshare.5043256.v1
title: Dataset for: The thermodynamic basis of glucose stimulated insulin release: a model of the core mechanism.

description: A model for glucose sensing by pancreatic β-cells is developed and compared to the available experimental data. The model brings together mathematical representations for the activities of the glucose sensor, glucokinase, and oxidative phosphorylation. Glucokinase produces glucose 6-phosphate (G-6-P) in an irreversible reaction that determines glycolytic flux. The primary products of glycolysis are NADH and pyruvate. The NADH is reoxidized and the reducing equivalents transferred to oxidative phosphorylation by the glycerol phosphate shuttle and some of the pyruvate is oxidized by pyruvate dehydrogenase and enters the citric acid cycle. These reactions are irreversible, and result in a glucose concentration dependent reduction of the intramitochondrial NAD pool. This increases the electrochemical energy coupled to ATP synthesis, and thereby the cellular energy state ([ATP]/[ADP][Pi]). [ATP] and [Pi] are 10 to 100 times greater than [ADP], so the increase in energy state is primarily through decrease in [ADP]. The decrease in [ADP] is considered responsible for altering ion channel conductance and releasing insulin. Applied to the reported glucose concentration dependent release of insulin by perifused islet preparations (4), the model predicts that the dependence of insulin release on [ADP] is strongly cooperative with a threshold of about 30 µM and a negative Hill coefficient near -5.5. The predicted cellular energy state, [ADP], creatine phosphate/creatine ratio, and cytochrome c reduction, including their dependence on glucose concentration, are consistent with experimental data. The ability of the model to predict behavior consistent with experiment is an invaluable resource for understanding glucose sensing and planning experiments.
DOI: 10.6084/m9.figshare.5043196
title: Portal Rodent Trapping Forecasts for May 2017

description: Predictions and 90% prediction intervals on the Portal rodent trapping results for 5/26 - 5/28/2017. Predictions are made for the next 12 months, by species and for total of all rodents, on all plots and on only the long-term controls. Details of the models used can be found at https://github.com/weecology/portalPredictions .
DOI: 10.6084/m9.figshare.5043196.v1
title: Portal Rodent Trapping Forecasts for May 2017

description: Predictions and 90% prediction intervals on the Portal rodent trapping results for 5/26 - 5/28/2017. Predictions are made for the next 12 months, by species and for total of all rodents, on all plots and on only the long-term controls. Details of the models used can be found at https://github.com/weecology/portalPredictions .
DOI: 10.6084/m9.figshare.5043139
title: Introduction to Dynamic Modeling of Infectious Diseases

description: An introduction to infectious disease transmission modeling. Goals are for audience to be able to:<br>1. Understand the natural history of infection <br>2. Explain SIR model formulation Define the basic reproduction number (R0) <br>3. Calculate vaccinated proportion needed for pathogen elimination <br>4.Explain the effect of demography on epidemics<br><br>Latest slide set with video, MMED 2017:- 'BellanIntrotoDisDynamics.pdf'- 'Bellan-Intro to Inf Dis Modeling.mp4'<br>Latest slide set, MMED 2018:'PulliamIntrotoDisDynamics2018.pdf'
DOI: 10.6084/m9.figshare.5043142
title: Sample size for comparing negative binomial rates in noninferiority and equivalence trials with unequal follow-up times

description: We derive the sample size formulae for comparing two negative binomial rates based on both the relative and absolute rate difference metrics in noninferiority and equivalence trials with unequal follow-up times, and establish an approximate relationship between the sample sizes required for the treatment comparison based on the two treatment effect metrics. The proposed method allows the dispersion parameter to vary by treatment groups. The accuracy of these methods is assessed by simulations. It is demonstrated that ignoring the between-subject variation in the follow-up time by setting the follow-up time for all individuals to be the mean follow-up time may greatly underestimate the required size, resulting in underpowered studies. Methods are provided for back-calculating the dispersion parameter based on the published summary results.
DOI: 10.6084/m9.figshare.5043136
title: Introduction to Thinking About Data I

description: An introductory lecture (slide set and video) on how to identify patterns in data and use them to generate and test hypotheses.<br><br>Slide set and video, MMED 2017: - 'Williams-Thinking About Data.pdf'- 'Hargrove-Intro to Thinking Abouta Data.mp4'<br>Most recent slide set, MMED 2018:- 'WilliamsThinkingAboutDataMMED2018.pdf'
DOI: 10.6084/m9.figshare.5043142.v2
title: Sample size for comparing negative binomial rates in noninferiority and equivalence trials with unequal follow-up times

description: We derive the sample size formulae for comparing two negative binomial rates based on both the relative and absolute rate difference metrics in noninferiority and equivalence trials with unequal follow-up times, and establish an approximate relationship between the sample sizes required for the treatment comparison based on the two treatment effect metrics. The proposed method allows the dispersion parameter to vary by treatment groups. The accuracy of these methods is assessed by simulations. It is demonstrated that ignoring the between-subject variation in the follow-up time by setting the follow-up time for all individuals to be the mean follow-up time may greatly underestimate the required size, resulting in underpowered studies. Methods are provided for back-calculating the dispersion parameter based on the published summary results.
DOI: 10.6084/m9.figshare.5042998
title: Research Data Management Teaching Toolkit

description: This teaching toolkit is designed to be used for a one hour introductory data management class for biomedical researchers. It consists of an instructional guide for teaching the material, a PowerPoint presentation with a script in the accompanying notes to each slide, and a separate evaluation form. This material is built on training material provided in the BD2K funded online research data management educational modules that are freely available here: http://bit.ly/RDM_Modules
DOI: 10.6084/m9.figshare.5042974
title: Foundations of Dynamical Modeling

description: An introduction to the susceptible-infected-recovered compartmental model of infectious disease transmission. This video lecture includes a demonstration of how to build an SIR model using spreadsheet software (Google Sheets), including a capture of Jonathan's screen as he builds the model.<br>Slide set with video, MMED 2017:- 'Dushoff-TheSIRModelFamily.pdf'- 'Dushoff-Foundations of Dynamic Modelling.mp4'<br>Latest slide set, MMED 2019:'Dushoff_FoundationsOfDynamicModelling_2019.pdf'<br>
DOI: 10.6084/m9.figshare.5042998.v1
title: Research Data Management Teaching Toolkit

description: This teaching toolkit is designed to be used for a one hour introductory data management class for biomedical researchers. It consists of an instructional guide for teaching the material, a PowerPoint presentation with a script in the accompanying notes to each slide, and a separate evaluation form. This material is built on training material provided in the BD2K funded online research data management educational modules that are freely available here: http://bit.ly/RDM_Modules
DOI: 10.6084/m9.figshare.5043136.v5
title: Introduction to Thinking About Data I

description: An introductory lecture (slide set and video) on how to identify patterns in data and use them to generate and test hypotheses.<br><br>Slide set and video, MMED 2017: - 'Williams-Thinking About Data.pdf'- 'Hargrove-Intro to Thinking Abouta Data.mp4'<br>Most recent slide set, MMED 2018:- 'WilliamsThinkingAboutDataMMED2018.pdf'
DOI: 10.6084/m9.figshare.5043139.v4
title: Introduction to Dynamic Modeling of Infectious Diseases

description: An introduction to infectious disease transmission modeling. Goals are for audience to be able to:<br>1. Understand the natural history of infection <br>2. Explain SIR model formulation Define the basic reproduction number (R0) <br>3. Calculate vaccinated proportion needed for pathogen elimination <br>4.Explain the effect of demography on epidemics<br><br>Latest slide set with video, MMED 2017:- 'BellanIntrotoDisDynamics.pdf'- 'Bellan-Intro to Inf Dis Modeling.mp4'<br>Latest slide set, MMED 2018:'PulliamIntrotoDisDynamics2018.pdf'
DOI: 10.6084/m9.figshare.5042998.v4
title: Research Data Management Teaching Toolkit

description: This teaching toolkit is designed to be used for a one hour introductory data management class for biomedical researchers. It consists of an instructional guide for teaching the material, a PowerPoint presentation with a script in the accompanying notes to each slide, and a separate evaluation form. This material is built on training material provided in the BD2K funded online research data management educational modules that are freely available here: http://bit.ly/RDM_Modules
DOI: 10.6084/m9.figshare.5042998.v5
title: Research Data Management Teaching Toolkit

description: This teaching toolkit is designed to be used for a one hour introductory data management class for biomedical researchers. It consists of an instructional guide for teaching the material, a PowerPoint presentation with a script in the accompanying notes to each slide, and a separate evaluation form. This material is built on training material provided in the BD2K funded online research data management educational modules that are freely available here: http://bit.ly/RDM_Modules
DOI: 10.6084/m9.figshare.5042974.v6
title: Foundations of Dynamical Modeling

description: An introduction to the susceptible-infected-recovered compartmental model of infectious disease transmission. This video lecture includes a demonstration of how to build an SIR model using spreadsheet software (Google Sheets), including a capture of Jonathan's screen as he builds the model.
DOI: 10.6084/m9.figshare.5042974.v7
title: Foundations of Dynamical Modeling

description: An introduction to the susceptible-infected-recovered compartmental model of infectious disease transmission. This video lecture includes a demonstration of how to build an SIR model using spreadsheet software (Google Sheets), including a capture of Jonathan's screen as he builds the model.<br>Two versions of the slides are available, one from each of the MMED & DAIDD Clinics.
DOI: 10.6084/m9.figshare.5039713
title: Cleaner wrasses indirectly affect the cognitive performance of a damselfish through ectoparasite removal

description: Data and script for "Binning SA, Roche DG, Grutter A, Colosio S, Sun D, Miest J, Bshary R (2018) Cleaner wrasse indirectly affect the cognitive performance of a damselfish through ectoparasite removal. Proc. R. Soc. B. 20172447. http://dx.doi.org/10.1098/rspb.2017.2447"<br>Please see readMe.txt
DOI: 10.6084/m9.figshare.5042974.v8
title: Foundations of Dynamical Modeling

description: An introduction to the susceptible-infected-recovered compartmental model of infectious disease transmission. This video lecture includes a demonstration of how to build an SIR model using spreadsheet software (Google Sheets), including a capture of Jonathan's screen as he builds the model.<br>Latest slide set with video, MMED 2017:- 'Dushoff-TheSIRModelFamily.pdf'- 'Dushoff-Foundations of Dynamic Modelling.mp4'<br>Latest slide set, MMED 2018:'DushoffFoundationsDynamicalModellingMMED2018.pdf'<br>
DOI: 10.6084/m9.figshare.5039713.v1
title: Cleaner wrasses indirectly affect the cognitive performance of a damselfish through ectoparasite removal

description: Data and script for "Binning SA, Roche DG, Grutter A, Colosio S, Sun D, Miest J, Bshary R (2018) Cleaner wrasse indirectly affect the cognitive performance of a damselfish through ectoparasite removal. Proc. R. Soc. B. 20172447. http://dx.doi.org/10.1098/rspb.2017.2447"<br>Please see readMe.txt
DOI: 10.6084/m9.figshare.5039731.v1
title: Redescription of <i>Szelepis</i> Liu, 1981 (Placodermi, Arthrodira) from the Lower Devonian of China

description: The skull roof of the species <i>Szelepis yunnanensis</i> (Liu, 1979) from the Early Devonian of Yunnan is redescribed, as well as other <i>Szelepis</i> material previously published. It appears that only the holotype of <i>S. yunnanensis</i> can be confidently assigned to this genus. The fusion of the preorbital plates into one plate strongly suggests the assignment of <i>Szelepis yunnanensis</i> to the monophyletic family Actinolepididae Gross, 1940, sensu stricto (comprising <i>Actinolepis</i> Agassiz, 1884, and <i>Bollandaspis</i> Schmidt, 1979). This is confirmed by a phylogenetic analysis. Hence, <i>Szelepis</i> constitutes the most ancient occurrence of the family but not the sister group to the remaining members. SUPPLEMENTAL DATA—Supplemental materials are available for this article for free at www.tandfonline.com/UJVP Citation for this article: Dupret, V., M. Zhu, and J.-Q. Wang. 2017. Redescription of <i>Szelepis</i> Liu, 1981 (Placodermi, Arthrodira) from the Lower Devonian of China. Journal of Vertebrate Paleontology. DOI: 10.1080/02724634.2017.1312422.
DOI: 10.5519/qd.z1uivaog
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (15 records)
DOI: 10.6084/m9.figshare.5039671
title: Dataset for: Phosphorus accumulates faster than nitrogen globally in freshwater ecosystems under anthropogenic impacts

description: Combined effects of cumulative nutrient inputs and biogeochemical processes that occur in freshwater under anthropogenic eutrophication could lead to myriad shifts in nitrogen (N):phosphorus (P) stoichiometry in global freshwater ecosystems, but this is not yet well-assessed. Here we evaluated the characteristics of N and P stoichiometries in bodies of freshwater and their herbaceous macrophytes across human-impact levels, regions and periods. Freshwater and its macrophytes had higher N and P concentrations and lower N:P ratios in heavily than lightly human-impacted environments, further evidenced by spatiotemporal comparisons across eutrophication gradients. N and P concentrations in freshwater ecosystems were positively correlated and N:P was negatively correlated with population density in China. These results indicate a faster accumulation of P than N in human-impacted freshwater ecosystems, which could have large effects on the trophic webs and biogeochemical cycles of estuaries and coastal areas by freshwater loadings, and reinforce the importance of rehabilitating these ecosystems.
DOI: 10.17028/rd.lboro.5039605
title: Individual, employment and psychosocial factors influencing walking to work: Implications for intervention design - dataset

description: This dataset contains baseline data for 1,544 participants in the Walking Works study and was used for analysis in the paper: Individual, employment and psychosocial factors influencing walking to work: implications for intervention design (Adams et al., 2017) doi:10.1371/journal.pone.0171374. <br>The data was collected as part of the Walking Works project which aimed to increase walking on the journey to and from work and during the working day. The project engaged with five employers in England based in the North East, Yorkshire, East Midlands, West Midlands and London. Walking initiatives were developed through recruiting employees as volunteer ‘walking champions’ and providing resources (such as materials to promote a national walk to work campaign and pedometer challenges) to encourage people to walk for all or some of their journey to work, as well as to walk more during the working day. <br>Consent was given to take part in the study on the basis that only researchers at Loughborough University would see the data provided, therefore we are not able to make the files openly available.<br>