cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7585853
title: Raw MS data for "Ligand-specific changes in conformational flexibility mediate long-range allostery in the lac repressor"

description: These are the raw HDX/MS data for our paper: "Ligand-specific changes in conformational flexibility mediate long-range allostery in the lac repressor."
description: Funding: This work was supported by grants from the National Institutes of Health (K99GM135529 and R00GM135529 to AG and R01GM050945 to SM), and fellowships from the UCSF Institutional Research and Academic Career Development Award (IRACDA K12) program (K12GM081266) and the UC Office of the President through the UCSF Chancellor's Fellowship to AG. ZRP was supported by the Rosetta Commons Research Experience for Undergraduates Summer Internship program (NSF Award 1950697). SM and TK are Chan Zuckerberg Biohub Investigators.
DOI: 10.6073/pasta/888f1d59a0ee4e3190a4ccef94e454b4
title: PBG03 Disk pasture meter measurements to estimate plant standing biomass in the Patch-Burn Grazing experiment at Konza Prairie

description: "PBG" datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This dataset includes both the annual calibration data for the disk pasture meter measurements (PBG031) and the actual disk pasture meter measurements in the PBG experiment (PBG032). Measurements were taken at a total of 64 transects (8 watersheds x 4 sites per watershed x 2 pasture meter transects per site). Each cattle-grazed watershed (designated as of May 2011 C3A, C3B, C3C, and C1A) and the four Shane cattle-grazed watersheds (C1B, C3SA, C3SB, and C3SC) includes 4 plant composition sampling transects (A-D). Pasture meter measurements were taken along two transects adjacent and parallel to the plant composition transects in each of these watersheds. Standing biomass samples for pasture meter calibration were also collected near the plant composition transects in the same 8 watersheds.
DOI: 10.6084/m9.figshare.18613633
title: Bayesian Testing of Linear Versus Nonlinear Effects Using Gaussian Process Priors

description: A Bayes factor is proposed for testing whether the effect of a key predictor variable on a dependent variable is linear or nonlinear, possibly while controlling for certain covariates. The test can be used (i) in substantive research for assessing the nature of the relationship between certain variables based on scientific expectations, and (ii) for statistical model building to infer whether a (transformed) variable should be added as a linear or nonlinear predictor in a regression model. Under the nonlinear model, a Gaussian process prior is employed using a parameterization similar to Zellner’s <i>g</i> prior resulting in a scale-invariant test. Unlike existing <i>p</i>-values, the proposed Bayes factor can be used for quantifying the relative evidence in the data in favor of linearity. Furthermore the Bayes factor does not overestimate the evidence against the linear null model resulting in more parsimonious models. An extension is proposed for Bayesian one-sided testing of whether a nonlinear effect is consistently positive, consistently negative, or neither. Applications are provided from various fields including social network research and education.
DOI: 10.6084/m9.figshare.18613633.v2
title: Bayesian Testing of Linear Versus Nonlinear Effects Using Gaussian Process Priors

description: A Bayes factor is proposed for testing whether the effect of a key predictor variable on a dependent variable is linear or nonlinear, possibly while controlling for certain covariates. The test can be used (i) in substantive research for assessing the nature of the relationship between certain variables based on scientific expectations, and (ii) for statistical model building to infer whether a (transformed) variable should be added as a linear or nonlinear predictor in a regression model. Under the nonlinear model, a Gaussian process prior is employed using a parameterization similar to Zellner’s <i>g</i> prior resulting in a scale-invariant test. Unlike existing <i>p</i>-values, the proposed Bayes factor can be used for quantifying the relative evidence in the data in favor of linearity. Furthermore the Bayes factor does not overestimate the evidence against the linear null model resulting in more parsimonious models. An extension is proposed for Bayesian one-sided testing of whether a nonlinear effect is consistently positive, consistently negative, or neither. Applications are provided from various fields including social network research and education.
DOI: 10.6084/m9.figshare.19341844
title: Assignment-Control Plots: A Visual Companion for Causal Inference Study Design

description: An important step for any causal inference study design is understanding the distribution of the subjects in terms of measured baseline covariates. However, not all baseline variation is equally important. We propose a set of visualizations that reduce the space of measured covariates into two components of baseline variation important to the design of an observational causal inference study: a propensity score summarizing baseline variation associated with treatment assignment and a prognostic score summarizing baseline variation associated with the untreated potential outcome. These <i>assignment-control plots</i> and variations thereof visualize study design tradeoffs and illustrate core methodological concepts in causal inference. As a practical demonstration, we apply assignment-control plots to a hypothetical study of cardiothoracic surgery. To demonstrate how these plots can be used to illustrate nuanced concepts, we use them to visualize unmeasured confounding and to consider the relationship between propensity scores and instrumental variables. While the family of visualization tools for studies of causality is relatively sparse, simple visual tools can be an asset to education, application, and methods development.
DOI: 10.6084/m9.figshare.19341844.v1
title: Assignment-Control Plots: A Visual Companion for Causal Inference Study Design

description: An important step for any causal inference study design is understanding the distribution of the subjects in terms of measured baseline covariates. However, not all baseline variation is equally important. We propose a set of visualizations that reduce the space of measured covariates into two components of baseline variation important to the design of an observational causal inference study: a propensity score summarizing baseline variation associated with treatment assignment and a prognostic score summarizing baseline variation associated with the untreated potential outcome. These <i>assignment-control plots</i> and variations thereof visualize study design tradeoffs and illustrate core methodological concepts in causal inference. As a practical demonstration, we apply assignment-control plots to a hypothetical study of cardiothoracic surgery. To demonstrate how these plots can be used to illustrate nuanced concepts, we use them to visualize unmeasured confounding and to consider the relationship between propensity scores and instrumental variables. While the family of visualization tools for studies of causality is relatively sparse, simple visual tools can be an asset to education, application, and methods development.
DOI: 10.6084/m9.figshare.19469479
title: “Two Truths and a Lie” as a Class-Participation Activity

description: We adapt the social game “Two truths and a lie” to a classroom setting to give an activity that introduces principles of statistical measurement, uncertainty, prediction, and calibration, while giving students an opportunity to meet each other. We discuss how this activity can be used in a range of different statistics courses.
DOI: 10.6084/m9.figshare.19469479.v2
title: “Two Truths and a Lie” as a Class-Participation Activity

description: We adapt the social game “Two truths and a lie” to a classroom setting to give an activity that introduces principles of statistical measurement, uncertainty, prediction, and calibration, while giving students an opportunity to meet each other. We discuss how this activity can be used in a range of different statistics courses.
DOI: 10.6084/m9.figshare.18613633.v1
title: Bayesian testing of linear versus nonlinear effects using Gaussian process priors

description: A Bayes factor is proposed for testing whether the effect of a key predictor variable on a dependent variable is linear or nonlinear, possibly while controlling for certain covariates. The test can be used (i) in substantive research for assessing the nature of the relationship between certain variables based on scientific expectations, and (ii) for statistical model building to infer whether a (transformed) variable should be added as a linear or nonlinear predictor in a regression model. Under the nonlinear model, a Gaussian process prior is employed using a parameterization similar to Zellner’s <i>g</i> prior resulting in a scale-invariant test. Unlike existing <i>p</i> values, the proposed Bayes factor can be used for quantifying the relative evidence in the data in favor of linearity. Furthermore the Bayes factor does not overestimate the evidence against the linear null model resulting in more parsimonious models. An extension is proposed for Bayesian one-sided testing of whether a nonlinear effect is consistently positive, consistently negative, or neither. Applications are provided from various fields including social network research and education.
DOI: 10.6084/m9.figshare.19324099
title: Coherent Tests for Interval Null Hypotheses

description: In a celebrated 1996 article, Schervish showed that, for testing interval null hypotheses, tests typically viewed as optimal can be logically incoherent. Specifically, one may fail to reject a specific interval null, but nevertheless—testing at the same level with the same data—reject a larger null, in which the original one is nested. This result has been used to argue against the widespread practice of viewing <i>p</i>-values as measures of evidence. In the current work we approach tests of interval nulls using simple Bayesian decision theory, and establish straightforward conditions that ensure coherence in Schervish’s sense. From these, we go on to establish novel frequentist criteria—different to Type I error rate—that, when controlled at fixed levels, give tests that are coherent in Schervish’s sense. The results suggest that exploring frequentist properties beyond the familiar Neyman–Pearson framework may ameliorate some of statistical testing’s well-known problems.
DOI: 10.6084/m9.figshare.19324099.v1
title: Coherent Tests for Interval Null Hypotheses

description: In a celebrated 1996 article, Schervish showed that, for testing interval null hypotheses, tests typically viewed as optimal can be logically incoherent. Specifically, one may fail to reject a specific interval null, but nevertheless—testing at the same level with the same data—reject a larger null, in which the original one is nested. This result has been used to argue against the widespread practice of viewing <i>p</i>-values as measures of evidence. In the current work we approach tests of interval nulls using simple Bayesian decision theory, and establish straightforward conditions that ensure coherence in Schervish’s sense. From these, we go on to establish novel frequentist criteria—different to Type I error rate—that, when controlled at fixed levels, give tests that are coherent in Schervish’s sense. The results suggest that exploring frequentist properties beyond the familiar Neyman–Pearson framework may ameliorate some of statistical testing’s well-known problems.
DOI: 10.6084/m9.figshare.20449072.v2
title: Using the Lambert Function to Estimate Shared Frailty Models with a Normally Distributed Random Intercept

description: Shared frailty models, that is, hazard regression models for censored data including random effects acting multiplicatively on the hazard, are commonly used to analyze time-to-event data possessing a hierarchical structure. When the random effects are assumed to be normally distributed, the cluster-specific marginal likelihood has no closed-form expression. A powerful method for approximating such integrals is the adaptive Gauss-Hermite quadrature (AGHQ). However, this method requires the estimation of the mode of the integrand in the expression defining the cluster-specific marginal likelihood: it is generally obtained through a nested optimization at the cluster level for each evaluation of the likelihood function. In this work, we show that in the case of a parametric shared frailty model including a normal random intercept, the cluster-specific modes can be determined analytically by using the principal branch of the Lambert function, W0. Besides removing the need for the nested optimization procedure, it provides closed-form formulas for the gradient and Hessian of the approximated likelihood making its maximization by Newton-type algorithms convenient and efficient. The Lambert-based AGHQ (LAGHQ) might be applied to other problems involving similar integrals, such as the normally distributed random intercept Poisson model and the computation of probabilities from a Poisson lognormal distribution.
DOI: 10.6084/m9.figshare.19469479.v1
title: “Two truths and a lie” as a class-participation activity*

description: We adapt the social game “Two truths and a lie” to a classroom setting to give an activity that introduces principles of statistical measurement, uncertainty, prediction, and calibration, while giving students an opportunity to meet each other. We discuss how this activity can be used in a range of different statistics courses.
DOI: 10.6084/m9.figshare.20449072
title: Using the Lambert Function to Estimate Shared Frailty Models with a Normally Distributed Random Intercept

description: Shared frailty models, that is, hazard regression models for censored data including random effects acting multiplicatively on the hazard, are commonly used to analyze time-to-event data possessing a hierarchical structure. When the random effects are assumed to be normally distributed, the cluster-specific marginal likelihood has no closed-form expression. A powerful method for approximating such integrals is the adaptive Gauss-Hermite quadrature (AGHQ). However, this method requires the estimation of the mode of the integrand in the expression defining the cluster-specific marginal likelihood: it is generally obtained through a nested optimization at the cluster level for each evaluation of the likelihood function. In this work, we show that in the case of a parametric shared frailty model including a normal random intercept, the cluster-specific modes can be determined analytically by using the principal branch of the Lambert function, W0. Besides removing the need for the nested optimization procedure, it provides closed-form formulas for the gradient and Hessian of the approximated likelihood making its maximization by Newton-type algorithms convenient and efficient. The Lambert-based AGHQ (LAGHQ) might be applied to other problems involving similar integrals, such as the normally distributed random intercept Poisson model and the computation of probabilities from a Poisson lognormal distribution.
DOI: 10.5281/zenodo.7586313
title: Dataset for "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark"

description: Dataset containing the results shown in Fig. 6 of "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark".
DOI: 10.5281/zenodo.7586312
title: Dataset for "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark"

description: Dataset containing the results shown in Fig. 6 of "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark".
DOI: 10.6073/pasta/b19fa687678b91fafec18007119cad14
title: PBG04 Reproductive effort of Big Bluestem, Indiangrass and Little Bluestem in the Patch-Burn Grazing Experiment at Konza Prairie

description: 'PBG' datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. PBG041 includes data on flowering stem height (m) of three dominant prairie grasses: Andropogon gerardii (ANGE), Sorghastrum nutans (SONU), and Schizachyrium scoparium (ANSC). PBG042 includes flowering stem density (no. per sq. m) and mass (grams per sq. m) for the same grass species.
DOI: 10.7303/syn51015750
title: Mathys et al 2023 - ROSMAP TBD
DOI: 10.6084/m9.figshare.21977744
title: Anxiety ASD

description: Anxiety in ASD at early ages
DOI: 10.6084/m9.figshare.21913023.v1
title: MalishEtAl2023.zip

description: Data: Files ending in _current of _future contain hydrological model output, used to caclulate continuity and connectivity metrics. File in map_stats folder contain continuity metric values. blue_fixed.rda and connectivity_wts.csv are used to calculate DCI. <br> Code: ConnectivityMetrics.R contains code to calculate percent wet length, number of dry fragments, and average length of dry fragments. DCI.R contains code to calculate DCI. change_dryperiodlength.R, change_dryperiods.R, and change_zeroflowdays.R contain code to calculate change in continuity metrics.
DOI: 10.6084/m9.figshare.21789092
title: Effects of joint mobilization combined with acupuncture on pain in stroke patients with chronic neuropathic pain: a randomized controlled trial

description: Pain, physical function, and depression in stroke patients before and after the intervention.
DOI: 10.6084/m9.figshare.21789092.v5
title: Effects of joint mobilization combined with traditional Korean medicine treatment on pain, physical function, and depression in stroke patients with chronic neuropathic pain: a randomized controlled trial

description: Pain, physical function, and depression in stroke patients before and after the intervention.
DOI: 10.6084/m9.figshare.19601653
title: Concatenated matrix for "The evolution of the venom delivery system in dipsadines"

description: Concatenated matrix composed of 12S, 116S, cytb, nd4, bdnf, c-mos and nt-3.
DOI: 10.6084/m9.figshare.19601653.v1
title: Concatenated matrix for "The evolution of the venom delivery system in dipsadines"

description: Concatenated matrix composed of 12S, 116S, cytb, nd4, bdnf, c-mos and nt-3.
DOI: 10.5517/ccdc.csd.cc1pbjp7
title: CCDC 1559259: Experimental Crystal Structure Determination

description: Related Article: Philippe Weis, Daniel Kratzert, Ingo Krossing|2018|Eur.J.Inorg.Chem.|2018|3203|doi:10.1002/ejic.201800430
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1pbjq8
title: CSD 1559260: Experimental Crystal Structure Determination

description: Related Article: Philippe Weis, Daniel Kratzert, Ingo Krossing|2018|Eur.J.Inorg.Chem.|2018|3203|doi:10.1002/ejic.201800430
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1q24yw
title: CCDC 1580998: Experimental Crystal Structure Determination

description: Related Article: Philippe Weis, Daniel Kratzert, Ingo Krossing|2018|Eur.J.Inorg.Chem.|2018|3203|doi:10.1002/ejic.201800430
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1q7ds4
title: CCDC 1586046: Experimental Crystal Structure Determination

description: Related Article: Philippe Weis, Daniel Kratzert, Ingo Krossing|2018|Eur.J.Inorg.Chem.|2018|3203|doi:10.1002/ejic.201800430
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1pz3lc
title: CCDC 1578073: Experimental Crystal Structure Determination

description: Related Article: Philippe Weis, Daniel Kratzert, Ingo Krossing|2018|Eur.J.Inorg.Chem.|2018|3203|doi:10.1002/ejic.201800430
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.