cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22941740
title: SourceData_MainFigure1_EDFigure1_EDFigure2

description: Source Data for Main Figure 1 and Extended Data Figures 1 and 2
DOI: 10.3886/8s65-p687
title: Indonesia Compact - Green Prosperity: Participatory Land Use Planning: Design Report (Revised)
DOI: 10.3886/fzff-4h97
title: Indonesia Compact - Green Prosperity: Participatory Land Use Planning: Design Report (Revised)
DOI: 10.6084/m9.figshare.23153555
title: Condensation Heat Transfer and Pressure Drop Data for R-454B, R-32, and R-454C in Smooth Aluminum Tubes

description: The data presented in the following tabs are for condensation of R-32, R-454B, and R-454C in a smooth aluminum tube. The tube outer diameter is 7.2 mm and its internal diameter is 5.94 mm. These data were obtained in a pumped refrigerant loop. The condensation heat transfer coefficient and pressure drop measurement for these refrigerants was measured in a water coupled test section. More details about this can be found in the publication accompanying this data set.
DOI: 10.3886/jbz6-9622
title: Indonesia Compact - Green Prosperity: Participatory Land Use Planning: Design Report (Original)
DOI: 10.3886/2wp2-4t08
title: Indonesia Compact - Green Prosperity: Participatory Land Use Planning: Design Report (Original)
DOI: 10.5281/zenodo.7967135
title: Simulation Datasets of 5HT3AR Bound to Two Partial Agonists

description: Simulation Datasets Reported in the manuscript <strong>Structural Basis for Partial Agonism in 5HT3AR Receptors:</strong> 1. SMP100 PDB and MOL2 files: SMP.pdb (4.7 KB) and SMP.mol2 (4.7 KB)<br> 2. SMP100 topology and parameter files: SMP.top (2.8 KB) and SMP.prm (4.7 KB)<br> 3. SMP-bound 5-HT3AR initial conformation from cryo-EM and prepared in Maestro: 5HT3AR-SMP_init.pdb (2.7 MB)<br> 4. SMP-bound 5-HT3AR system PDB file and trajectory: 5HT3AR-SMP.pdb (14.4 MB) and 5HT3AR-SMP.xtc (135.7 MB)<br> 5. ALB148471 PDB and MOL2 files: ALB.pdb (4.7 KB) and ALB.mol2 (4.7 KB)<br> 6. ALB148471 topology and parameter files: ALB.top (2.8 KB) and ALB.prm (4.7 KB)<br> 7. ALB-bound 5-HT3AR initial conformation from cryo-EM and prepared in Maestro: 5HT3AR-ALB_init.pdb (2.7 MB)<br> 8. ALB-bound 5-HT3AR system PDB file and trajectory: 5HT3AR-ALB.pdb (14.4 MB) and 5HT3AR-ALB.xtc (135.7 MB)
DOI: 10.5281/zenodo.7967134
title: Simulation Datasets of 5HT3AR Bound to Two Partial Agonists

description: Simulation Datasets Reported in the manuscript <strong>Structural Basis for Partial Agonism in 5HT3AR Receptors:</strong> 1. SMP100 PDB and MOL2 files: SMP.pdb (4.7 KB) and SMP.mol2 (4.7 KB)<br> 2. SMP100 topology and parameter files: SMP.top (2.8 KB) and SMP.prm (4.7 KB)<br> 3. SMP-bound 5-HT3AR initial conformation from cryo-EM and prepared in Maestro: 5HT3AR-SMP_init.pdb (2.7 MB)<br> 4. SMP-bound 5-HT3AR system PDB file and trajectory: 5HT3AR-SMP.pdb (14.4 MB) and 5HT3AR-SMP.xtc (135.7 MB)<br> 5. ALB148471 PDB and MOL2 files: ALB.pdb (4.7 KB) and ALB.mol2 (4.7 KB)<br> 6. ALB148471 topology and parameter files: ALB.top (2.8 KB) and ALB.prm (4.7 KB)<br> 7. ALB-bound 5-HT3AR initial conformation from cryo-EM and prepared in Maestro: 5HT3AR-ALB_init.pdb (2.7 MB)<br> 8. ALB-bound 5-HT3AR system PDB file and trajectory: 5HT3AR-ALB.pdb (14.4 MB) and 5HT3AR-ALB.xtc (135.7 MB)
DOI: 10.18112/openneuro.ds003020.v2.0.1
title: An fMRI dataset during a passive natural language listening task
DOI: 10.5281/zenodo.7711952
title: MISATO - Machine learning dataset for structure-based drug discovery

description: Developments in Artificial Intelligence (AI) have had an enormous impact on scientific research in recent years. Yet, relatively few robust methods have been reported in the field of structure-based drug discovery. To train AI models to abstract from structural data, highly curated and precise biomolecule-ligand interaction datasets are urgently needed. We present MISATO, a curated dataset of almost 20000 experimental structures of protein-ligand complexes, associated molecular dynamics traces, and electronic properties. Semi-empirical quantum mechanics was used to systematically refine protonation states of proteins and small molecule ligands. Molecular dynamics traces for protein-ligand complexes were obtained in explicit water. The dataset is made readily available to the scientific community via simple python data-loaders. AI baseline models are provided for dynamical and electronic properties. This highly curated dataset is expected to enable the next-generation of AI models for structure-based drug discovery. Our vision is to make MISATO the first step of a vibrant community project for the development of powerful AI-based drug discovery tools.
description: Funding: BMWi ZIM. KK 5197901TS0. BMBF, SUPREME, 031L0268.
DOI: 10.5281/zenodo.7711953
title: MISATO - Machine learning dataset for structure-based drug discovery

description: Developments in Artificial Intelligence (AI) have had an enormous impact on scientific research in recent years. Yet, relatively few robust methods have been reported in the field of structure-based drug discovery. To train AI models to abstract from structural data, highly curated and precise biomolecule-ligand interaction datasets are urgently needed. We present MISATO, a curated dataset of almost 20000 experimental structures of protein-ligand complexes, associated molecular dynamics traces, and electronic properties. Semi-empirical quantum mechanics was used to systematically refine protonation states of proteins and small molecule ligands. Molecular dynamics traces for protein-ligand complexes were obtained in explicit water. The dataset is made readily available to the scientific community via simple python data-loaders. AI baseline models are provided for dynamical and electronic properties. This highly curated dataset is expected to enable the next-generation of AI models for structure-based drug discovery. Our vision is to make MISATO the first step of a vibrant community project for the development of powerful AI-based drug discovery tools.
description: Funding: BMWi ZIM. KK 5197901TS0. BMBF, SUPREME, 031L0268.
DOI: 10.3886/e138481v1-139134
title: Data and Code for: Re-Examining the Contribution of Public Health Efforts to the Decline in Urban Mortality: Reply to Cutler and Miller

description: Data and code to replicate Figure 1 in "Re-Examining the Contribution of Public Health Efforts to the Decline in Urban Mortality: Reply to Cutler and Miller."
DOI: 10.21430/m3bsi1oe6t
title: COVID19 SARS Vaccinations: Systemic Allergic Reactions to SARS-CoV-2 Vaccinations

description: Allergic reactions have been reported to occur after vaccination with both the Pfizer-BioNTech COVID-19 Vaccine and Moderna COVID-19 Vaccine. The reactions vary from mild to severe, including life-threatening anaphylaxis, but no deaths have been reported with either vaccine. This study is designed with two principal aims: To estimate the proportions of systemic allergic reactions to the Pfizer-BioNTech COVID-19 Vaccine and the Moderna COVID-19 Vaccine in a High-Allergy/Mast Cell Disorder (HA/MCD) population, and If the risk in the HA/MCD is demonstrable, to determine whether the proportions are higher in the HA/MCD in comparison to a representative population without severe allergies or mast cell disorders
DOI: 10.21430/m3u7ue12vc
title: CALIBRATE ITN055AI: Rituximab Plus Cyclophosphamide Followed by Belimumab for the Treatment of Lupus Nephritis

description: In this experimental study, researchers will try to find out if treatment of lupus nephritis with a combination of rituximab and cyclophosphamide (CTX), or a combination of rituximab and CTX followed by treatment with belimumab is safe and if this drug combination can block the immune system attack
DOI: 10.5281/zenodo.7764083
title: Transcription factor dataset in the pea genome

description: <strong>Table 1:</strong> TF dataset identified in the pea genome. The 1807 TF dataset is shown with TF family prediction (from PlantTFDB) and the gene description available from the <em>Pisum sativum</em> genome (v1a). The best BlastP hit against <em>M. truncatula </em>(v4) and <em>A. thaliana</em> (TAIR10) proteomes is presented with both coverage (%) and e-value. <strong>Table 2:</strong> Pea, Medicago and Arabidopsis TF datasets with unique gene identifier, predicted TF family (from PlantTFDB) and gene description (retrieved from respective genome annotation) is displayed on a second sheet.
DOI: 10.5281/zenodo.7764084
title: Transcription factor dataset in the pea genome

description: TF dataset identified in the pea genome. The 1807 TF dataset is shown with TF family prediction (from PlantTFDB) and the gene description available from the <em>Pisum sativum</em> genome (v1a). The best BlastP hit against <em>M. truncatula </em>(v4) and <em>A. thaliana</em> (TAIR10) proteomes is presented with both coverage (%) and e-value. Pea, Medicago and Arabidopsis TF datasets with unique gene identifier, predicted TF family (from PlantTFDB) and gene description (retrieved from respective genome annotation) is displayed on a second sheet.
DOI: 10.5281/zenodo.7808819
title: Transcription factor dataset in the pea genome

description: Table 1: TF dataset identified in the pea genome. The 1807 TF dataset is shown with TF family prediction (from PlantTFDB) and the gene description available from the <em>Pisum sativum</em> genome (v1a). The best BlastP hit against <em>M. truncatula </em>(v4) and <em>A. thaliana</em> (TAIR10) proteomes is presented with both coverage (%) and e-value. Table 2: Pea, Medicago and Arabidopsis TF datasets with unique gene identifier, predicted TF family (from PlantTFDB) and gene description (retrieved from respective genome annotation) is displayed on a second sheet. Figure 2: Comparison of the TF family distribution between TF datasets of <em>P. sativum</em>, <em>M. truncatula</em> and <em>A. thaliana</em>.
DOI: 10.5281/zenodo.7808842
title: Transcription factor dataset in the pea genome

description: Table 1: TF dataset identified in the pea genome. The 1807 TF dataset is shown with TF family prediction (from PlantTFDB) and the gene description available from the <em>Pisum sativum</em> genome (v1a). The best BlastP hit against <em>M. truncatula </em>(v4) and <em>A. thaliana</em> (TAIR10) proteomes is presented with both coverage (%) and e-value. Table 2: Pea, Medicago and Arabidopsis TF datasets with unique gene identifier, predicted TF family (from PlantTFDB) and gene description (retrieved from respective genome annotation) is displayed on a second sheet. Figure 2: Comparison of the TF family distribution between TF datasets of <em>P. sativum</em>, <em>M. truncatula</em> and <em>A. thaliana</em>.
DOI: 10.5281/zenodo.7941132
title: Transcription factor dataset in the pea genome

description: <strong>Figure 1</strong>: A. Comparison between genome-wide TF datasets of <em>P. sativum</em> (1807 TFs), <em>M. truncatula</em> (2140 TFs) and <em>A. thaliana</em> (1709 TFs). B. Top 10 TF families identified in the pea genome (percentage proportion is given in brackets). C. Proportion of TFs reported to the total number of genes in respective plant genomes (<em>P. sativum</em> genomes v1a: 44,756 genes, <em>M. truncatula</em> v4.0: 50,894 genes, <em>A. thaliana</em> TAIR10: 27,382 genes). <strong>Figure 2:</strong> Comparison of the TF family distribution between TF datasets of <em>P. sativum</em>, <em>M. truncatula</em> and <em>A. thaliana</em>. <strong>Table 1:</strong> TF dataset identified in the pea genome. The 1807 TF dataset is shown with TF family prediction (from PlantTFDB) and the gene description available from the <em>Pisum sativum</em> genome (v1a). The best BlastP hit against <em>M. truncatula </em>(v4) and <em>A. thaliana</em> (TAIR10) proteomes is presented with both coverage (%) and e-value. <strong>Table 2:</strong> Pea, Medicago and Arabidopsis TF datasets with unique gene identifier, predicted TF family (from PlantTFDB) and gene description (retrieved from respective genome annotation) is displayed on a second sheet.
DOI: 10.5281/zenodo.7968044
title: Transcription factor dataset in the pea genome

description: <strong>Table 1:</strong> TF dataset identified in the pea genome. The 1807 TF dataset is shown with TF family prediction (from PlantTFDB) and the gene description available from the <em>Pisum sativum</em> genome (v1a). The best BlastP hit against <em>M. truncatula </em>(v4) and <em>A. thaliana</em> (TAIR10) proteomes is presented with both coverage (%) and e-value. <strong>Table 2:</strong> Pea, Medicago and Arabidopsis TF datasets with unique gene identifier, predicted TF family (from PlantTFDB) and gene description (retrieved from respective genome annotation) is displayed on a second sheet.
DOI: 10.5066/p9g60nj0
title: WARMER-2 Model Inputs and Projections for Three Tidal Wetland Sites Across San Francisco Bay Estuary

description: Understanding the rates and patterns of tidal wetland elevation changes relative to sea-level is essential for understanding the extent of potential wetland loss over the coming years. Using an enhanced and more flexible modeling framework of an ecosystem model (WARMER-2), we explored sea-level rise (SLR) impacts on wetland elevations and carbon sequestration rates through 2100 by considering plant community transitions, salinity effects on productivity, and changes in sediment availability. We incorporated local experimental results for plant productivity relative to inundation and salinity into a species transition model, as well as site-level estimates of organic matter decomposition. The revised modeling framework includes an improved calibration scheme that more accurately reconstructs soil profiles and incorporates parameter uncertainty through Monte Carlo simulations. Using WARMER-2, we evaluated elevation change in three tidal wetlands in the San Francisco Bay Estuary, CA, USA along an estuarine tidal and salinity gradient with varying scenarios of SLR, salinization, and changes in sediment availability. We also tested the sensitivity of marsh elevation and carbon accumulation rates to different plant productivity functions. Wetland elevation at all three sites was sensitive to changes in sediment availability, but sites with greater initial elevations or space for upland transgression persisted longer under higher SLR rates than sites at lower elevations. Using a multi-species wetland vegetation transition model for organic matter contribution to accretion, WARMER-2 projected increased elevations relative to sea levels (resilience) and higher rates of carbon accumulation when compared with projections assuming no future change in vegetation with SLR. The new WARMER-2 modeling framework is widely applicable to other tidal wetland ecosystems and can assist in teasing apart important drivers of wetland elevation change under SLR. These data support the following publication:
DOI: 10.5281/zenodo.7967802
title: Instrumento para avaliação de sinais e sintomas dos pacientes oncológicos em cuidados paliativos domiciliares:

description: O instrumento utiliza dados da Escala de avaliação de sintomas associados a outros sinais e sintomas frequentemente relatados por pacientes com câncer avançado. Produto técnico de dissertação de mestrado intitulada " PROTOCOLO DE AVALIAÇÃO E CLASSIFICAÇÃO DE RISCO DE PACIENTES ONCOLÓGICOS EM CUIDADOS PALIATIVOS DOMICILIARES: triagem baseada em sinais e sintomas de alerta. PPGSTEH/UNIRIO, 2022.
DOI: 10.5281/zenodo.7967801
title: Instrumento para avaliação de sinais e sintomas dos pacientes oncológicos em cuidados paliativos domiciliares:

description: O instrumento utiliza dados da Escala de avaliação de sintomas associados a outros sinais e sintomas frequentemente relatados por pacientes com câncer avançado. Produto técnico de dissertação de mestrado intitulada " PROTOCOLO DE AVALIAÇÃO E CLASSIFICAÇÃO DE RISCO DE PACIENTES ONCOLÓGICOS EM CUIDADOS PALIATIVOS DOMICILIARES: triagem baseada em sinais e sintomas de alerta. PPGSTEH/UNIRIO, 2022.
DOI: 10.6084/m9.figshare.19692788
title: A General Method for Deriving Tight Symbolic Bounds on Causal Effects

description: A causal query will commonly not be identifiable from observed data, in which case no estimator of the query can be contrived without further assumptions or measured variables, regardless of the amount or precision of the measurements of observed variables. However, it may still be possible to derive symbolic bounds on the query in terms of the distribution of observed variables. Bounds, numeric or symbolic, can often be more valuable than a statistical estimator derived under implausible assumptions. Symbolic bounds, however, provide a measure of uncertainty and information loss due to the lack of an identifiable estimand even in the absence of data. We develop and describe a general approach for computation of symbolic bounds and characterize a class of settings in which our method is guaranteed to provide tight valid bounds. This expands the known settings in which tight causal bounds are solutions to linear programs. We also prove that our method can provide valid and possibly informative symbolic bounds that are not guaranteed to be tight in a larger class of problems. We illustrate the use and interpretation of our algorithms in three examples in which we derive novel symbolic bounds. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19692788.v1
title: A General Method for Deriving Tight Symbolic Bounds on Causal Effects

description: A causal query will commonly not be identifiable from observed data, in which case no estimator of the query can be contrived without further assumptions or measured variables, regardless of the amount or precision of the measurements of observed variables. However, it may still be possible to derive symbolic bounds on the query in terms of the distribution of observed variables. Bounds, numeric or symbolic, can often be more valuable than a statistical estimator derived under implausible assumptions. Symbolic bounds, however, provide a measure of uncertainty and information loss due to the lack of an identifiable estimand even in the absence of data. We develop and describe a general approach for computation of symbolic bounds and characterize a class of settings in which our method is guaranteed to provide tight valid bounds. This expands the known settings in which tight causal bounds are solutions to linear programs. We also prove that our method can provide valid and possibly informative symbolic bounds that are not guaranteed to be tight in a larger class of problems. We illustrate the use and interpretation of our algorithms in three examples in which we derive novel symbolic bounds. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20822251
title: Pseudo-Marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations

description: Bayesian inference for Continuous-Time Markov chains (CTMCs) on countably infinite spaces is notoriously difficult because evaluating the likelihood exactly is intractable. One way to address this challenge is to first build a nonnegative and unbiased estimate of the likelihood—involving the matrix exponential of finite truncations of the true rate matrix—and then to use the estimates in a pseudo-marginal inference method. In this work, we show that we can dramatically increase the efficiency of this approach by avoiding the computation of exact matrix exponentials. In particular, we develop a general methodology for constructing an unbiased, nonnegative estimate of the likelihood using doubly-monotone matrix exponential approximations. We further develop a novel approximation in this family—the skeletoid—as well as theory regarding its approximation error and how that relates to the variance of the estimates used in pseudo-marginal inference. Experimental results show that our approach yields more efficient posterior inference for a wide variety of CTMCs. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20822251.v2
title: Pseudo-Marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations

description: Bayesian inference for Continuous-Time Markov chains (CTMCs) on countably infinite spaces is notoriously difficult because evaluating the likelihood exactly is intractable. One way to address this challenge is to first build a nonnegative and unbiased estimate of the likelihood—involving the matrix exponential of finite truncations of the true rate matrix—and then to use the estimates in a pseudo-marginal inference method. In this work, we show that we can dramatically increase the efficiency of this approach by avoiding the computation of exact matrix exponentials. In particular, we develop a general methodology for constructing an unbiased, nonnegative estimate of the likelihood using doubly-monotone matrix exponential approximations. We further develop a novel approximation in this family—the skeletoid—as well as theory regarding its approximation error and how that relates to the variance of the estimates used in pseudo-marginal inference. Experimental results show that our approach yields more efficient posterior inference for a wide variety of CTMCs. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20822251.v1
title: Pseudo-marginal inference for CTMCs on infinite spaces via monotonic likelihood approximations

description: Bayesian inference for Continuous-Time Markov Chains (CTMCs) on countably infinite spaces is notoriously difficult because evaluating the likelihood exactly is intractable. One way to address this challenge is to first build a non-negative and unbiased estimate of the likelihood—involving the matrix exponential of finite truncations of the true rate matrix—and then to use the estimates in a pseudo-marginal inference method. In this work, we show that we can dramatically increase the efficiency of this approach by avoiding the computation of exact matrix exponentials. In particular, we develop a general methodology for constructing an unbiased, non-negative estimate of the likelihood using doubly-monotone matrix exponential approximations. We further develop a novel approximation in this family—the skeletoid—as well as theory regarding its approximation error and how that relates to the variance of the estimates used in pseudo-marginal inference. Experimental results show that our approach yields more efficient posterior inference for a wide variety of CTMCs.
DOI: 10.5066/p9cwogxv
title: Post-fire conifer regeneration observations for National Forest land in California (2009 - 2017)

description: This data consists of presence/absence observations for post-fire conifer regeneration. The data also includes estimates of plot-level topography (slope, aspect), relativized differenced normalized burn ratio (RdNBR), post-fire climate, live basal area, and seed rain. These data support the following publication: