cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7284/110134
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise MV1208 using a Sea-Bird SBE-911+ instrument system onboard the platform RV Melville

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7445657
title: Tower- and chamber-based greenhouse gas flux measurements from Howland Forest, Maine (2012-2018)

description: Carbon dioxide (CO<sub>2</sub>), methane (CH<sub>4</sub>), and nitrous oxide (N<sub>2</sub>O) are the greenhouse gases largely responsible for anthropogenic climate change. Natural plant and microbial metabolic processes play a major role in the global atmospheric budget of each. We have been studying ecosystem-atmosphere trace gas exchange at a sub-boreal forest in the northeastern United States for over two decades. Historically our emphasis was on turbulent fluxes of CO<sub>2</sub> and water vapor. In 2012 we embarked on an expanded campaign to also measure CH<sub>4</sub> and N<sub>2</sub>O. Here we present continuous tower-based measurements of the ecosystem-atmosphere exchange of CO<sub>2</sub> and CH<sub>4</sub>, recorded over the period 2012-2018 and reported at a 30-minute time step. Additionally, we describe a five-year (2012-2016) dataset of chamber-based measurements of soil fluxes of CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O (2013-2016 only), conducted each year from May to November. These data can be used for process studies, for biogeochemical and land surface model validation and benchmarking, and for regional-to-global upscaling and budgeting analyses.
DOI: 10.6084/m9.figshare.7445660
title: NicholsonEtal2019_CPF_validation.csv

description: For variable descriptions, Please see corresponding metadata document ("NicholsonEtal2019_validation_metadata.csv"). Two types of data are contained: <i>observational data and pollination model-predicted data</i>. <br>OBSERVATIONAL DATA are from a 2 years of pollinator observation on Vermont, USA highbush blueberry (<i>Vaccinium corymbosum</i> L.) farms (2016 & 2017, N = 8). Pollinator observations occurred in 2 habitat types blueberry patches ("BLU") and open scrub patches ("OS"). Visitation rates are presented as yearly averages for each habitat type. These habitat types differed in the density of floral units m-1 <br>MODEL PREDICTIONS are visitation rates predicted by the <i>Central Place Foraging model (CPF)</i> using land-use maps for each farm (Crop Data Layer (CDL, NASS 2008) at 9 m resolution. <br>These data will produce Figures 3 and 4 in Nicholson et al. 2019.
DOI: 10.6084/m9.figshare.7445666.v1
title: Workflows prediction of activity (ALEX-UFPB)

description: This file consists of a workflow of a prediction model for biological activity used in chemoinformatics, it consists of a simple but quite robust map for those who are initiating studies on rational drug planning.<br>
DOI: 10.7284/110136
title: Expendable Probe data collected during research cruise MV1208 using a Sippican MK21 instrument system onboard the platform RV Melville

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7445654
title: Segfault_sup

description: Extra info on bcftools segmentation fault error replication
DOI: 10.6084/m9.figshare.7445654.v1
title: Segfault_sup

description: Extra info on bcftools segmentation fault error replication
DOI: 10.7284/110131
title: GNSS (Global Navigation Satellite System) data collected during research cruise MV1208 using a Furuno GP-150 instrument system onboard the platform RV Melville

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7445603
title: Headings used by articles and documents in the PMC Open Access collection

description: Section headings of articles in the PubMed Central Open Access subset, counted by field classification. Extra data for the article: "The Rhetorical Structure of Science? A Multidisciplinary Analysis of Article Headings".
DOI: 10.6084/m9.figshare.7445603.v1
title: Headings used by articles and documents in the PMC Open Access collection

description: Section headings of articles in the PubMed Central Open Access subset, counted by field classification. Extra data for the article: "The Rhetorical Structure of Science? A Multidisciplinary Analysis of Article Headings".
DOI: 10.6084/m9.figshare.7445612.v1
title: Tree, xml and log files for the calibrated tests (Fig. S7) in Cole et al. 2019.

description: This file contains the tree, log and xml files for the calibrated test files (Birth death and calibrated yule process model) in Cole et al. (2019) (Fig. S7), with <i>Phoebastria albatrus</i> as the outgroup.
DOI: 10.7284/110137
title: GNSS (Global Navigation Satellite System) data collected during research cruise MV1208 using a Ashtech ADU2 instrument system onboard the platform RV Melville

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7445648.v1
title: Estimating and Testing Vaccine Sieve Effects Using Machine Learning

description: When available, vaccines are an effective means of disease prevention. Unfortunately, efficacious vaccines have not yet been developed for several major infectious diseases, including HIV and malaria. Vaccine sieve analysis studies whether and how the efficacy of a vaccine varies with the genetics of the pathogen of interest, which can guide subsequent vaccine development and deployment. In sieve analyses, the effect of the vaccine on the cumulative incidence corresponding to each of several possible genotypes is often assessed within a competing risks framework. In the context of clinical trials, the estimators employed in these analyses generally do not account for covariates, even though the latter may be predictive of the study endpoint or censoring. Motivated by two recent preventive vaccine efficacy trials for HIV and malaria, we develop new methodology for vaccine sieve analysis. Our approach offers improved validity and efficiency relative to existing approaches by allowing covariate adjustment through ensemble machine learning. We derive results that indicate how to perform statistical inference using our estimators. Our analysis of the HIV and malaria trials shows markedly increased precision—up to doubled efficiency in both trials—under more plausible assumptions compared with standard methodology. Our findings provide greater evidence for vaccine sieve effects in both trials. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.7445594
title: NicholsonEtal2019_LEM_validation.csv

description: For variable descriptions, Please see corresponding metadata document ("NicholsonEtal2019_validation_metadata.csv"). Two types of data are contained: o<i>bservational data and pollination model-predicted data</i>.OBSERVATIONAL DATA are from a 2 years of pollinator observation on Vermont, USA highbush blueberry (<i>Vaccinium corymbosum</i> L.) farms (2016 & 2017, N = 8). Pollinator observations occurred in 2 habitat types blueberry patches ("BLU") and open scrub patches ("OS"). Visitation rates are presented as yearly averages for each habitat type. These habitat types differed in the density of floral units m<sup>-1</sup>MODEL PREDICTIONS are visitation rates predicted by the <i>Lonsdorf et al model (LEM)</i> using land-use maps for each farm (Crop Data Layer (CDL, NASS 2008) at 9 m resolution.<sup><br></sup>These data will produce Figures 3 and 4 in Nicholson et al. 2019.<br>
DOI: 10.7284/110139
title: Gravimeter data collected during research cruise MV1208 using a Bell BGM-3 instrument system onboard the platform RV Melville

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7445600.v1
title: Tree, xml, and log files for Fig. S8 [B] in Cole et al. (2019).

description: This file contains the tree, log and xml files for the calibrated Yule penguin phylogeny (Fig S8 [B]) in Cole et al. (2019).
DOI: 10.6084/m9.figshare.7445648.v2
title: Estimating and Testing Vaccine Sieve Effects Using Machine Learning

description: When available, vaccines are an effective means of disease prevention. Unfortunately, efficacious vaccines have not yet been developed for several major infectious diseases, including HIV and malaria. Vaccine sieve analysis studies whether and how the efficacy of a vaccine varies with the genetics of the pathogen of interest, which can guide subsequent vaccine development and deployment. In sieve analyses, the effect of the vaccine on the cumulative incidence corresponding to each of several possible genotypes is often assessed within a competing risks framework. In the context of clinical trials, the estimators employed in these analyses generally do not account for covariates, even though the latter may be predictive of the study endpoint or censoring. Motivated by two recent preventive vaccine efficacy trials for HIV and malaria, we develop new methodology for vaccine sieve analysis. Our approach offers improved validity and efficiency relative to existing approaches by allowing covariate adjustment through ensemble machine learning. We derive results that indicate how to perform statistical inference using our estimators. Our analysis of the HIV and malaria trials shows markedly increased precision—up to doubled efficiency in both trials—under more plausible assumptions compared with standard methodology. Our findings provide greater evidence for vaccine sieve effects in both trials. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.7445582
title: STEM_birth_death_trees

description: This file contains the tree files (birth death) for the calibrated test using only the stem penguin calibration point, using Phoebastria albatrus as the outgroup.
DOI: 10.6084/m9.figshare.7445591
title: STEM_yule_Trees

description: This file contains the tree files (calibrated yule process) for the calibrated test using only the stem penguin calibration point, using Phoebastria albatrus as the outgroup.
DOI: 10.7284/110191
title: Gyrocompass data collected during research cruise MV1208 using a SGB Meridian instrument system onboard the platform RV Melville

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7445591.v1
title: STEM_yule_Trees

description: This file contains the tree files (calibrated yule process) for the calibrated test using only the stem penguin calibration point, using Phoebastria albatrus as the outgroup.
DOI: 10.6084/m9.figshare.7445549
title: VP3_raw

description: Dataset from Ehinger, Groß et. al 2019A test battery for eye tracking comparisons. Concurrent recording of Eyelink 1000 & Pupil Labs Glasses in 15 subject in many different tasks<br>
DOI: 10.6084/m9.figshare.7445555
title: Full_yule

description: This file contains the tree files (calibrated yule process) for the calibrated test using all the calibration points, using Phoebastria albatrus as the outgroup.
DOI: 10.6084/m9.figshare.7445561
title: Full_birth_death_trees

description: This file contains the tree files (birth death) for the calibrated test using all the crown penguin calibration points, using Phoebastria albatrus as the outgroup.
DOI: 10.6084/m9.figshare.7445549.v1
title: VP3_raw

description: Dataset from Ehinger, Groß et. al 2019A test battery for eye tracking comparisons. Concurrent recording of Eyelink 1000 & Pupil Labs Glasses in 15 subject in many different tasks<br>
DOI: 10.6084/m9.figshare.7445555.v1
title: Full_yule

description: This file contains the tree files (calibrated yule process) for the calibrated test using all the calibration points, using Phoebastria albatrus as the outgroup.
DOI: 10.7284/110190
title: INS (Inertial Navigation System) data collected during research cruise MV1208 using a TSS TSS Orion instrument system onboard the platform RV Melville

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7445525
title: Crown_yule_trees

description: This file contains the tree files (calibrated yule process) for the calibrated test using only the crown penguin calibration points, using Phoebastria albatrus as the outgroup.
DOI: 10.6084/m9.figshare.7445561.v1
title: Full_birth_death_trees

description: This file contains the tree files (birth death) for the calibrated test using all the crown penguin calibration points, using Phoebastria albatrus as the outgroup.
DOI: 10.6084/m9.figshare.7445525.v1
title: Crown_yule_trees

description: This file contains the tree files (calibrated yule process) for the calibrated test using only the crown penguin calibration points, using Phoebastria albatrus as the outgroup.
DOI: 10.6084/m9.figshare.7445540
title: Crown_BirthDeath_Trees

description: This file contains the tree files (birth death model) for the calibrated test using only the crown penguin calibration points, using Phoebastria albatrus as the outgroup.
DOI: 10.7284/114663
title: INS (Inertial Navigation System) data collected during research cruise MV1208 using a CodaOctopus F-185 instrument system onboard the platform RV Melville

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7445540.v1
title: Crown_BirthDeath_Trees

description: This file contains the tree files (birth death model) for the calibrated test using only the crown penguin calibration points, using Phoebastria albatrus as the outgroup.
DOI: 10.6084/m9.figshare.7445423
title: Hierarchical Testing of a Primary and a Secondary Endpoint in a Group Sequential Design With Different Information Times

description: We consider a confirmatory clinical trial where a primary and a secondary endpoints are tested hierarchically to control the family-wise error rate at level <i>α</i>. The trial uses a group sequential design with an interim and a final analysis. When the information times at the interim analysis are the same for the primary and the secondary hypotheses, it has been shown in the literature that the secondary hypothesis has to be tested using a group sequential boundary at a level no more than <i>α</i>. In many event-driven trials, however, the information times are usually different because of different event rates for the two endpoints. The information times may also be different for a noninferiority hypothesis and a superiority hypothesis due to different analysis sets. We consider this general setup and derive a sharp upper bound on the probability of rejecting the secondary hypothesis at an interim and a final analysis. This bound suggests that the secondary boundary can be refined so that the group sequential design can be tested at a significance level greater than <i>α</i> while still controlling the family-wise error rate at level <i>α</i>. We carry out a simulation study to illustrate the power gain by using the refined boundary for different choices of boundaries for the two hypotheses. The proposed approach is illustrated in two oncology clinical trials with more than two analyses.
DOI: 10.6084/m9.figshare.7445429
title: raw.zip

description: These are the raw datasets that generated Fig. 3 - Fig. 7, Supplementary Fig. 1 - Fig. 3 in the paper entitled "Characterization of the hemodynamic response function in white matter tracts for event-related fMRI."
DOI: 10.7284/115496
title: Meteorological Station data collected during research cruise MV1208 using a SIO MET-System instrument system onboard the platform RV Melville

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7445429.v1
title: raw.zip

description: These are the raw datasets that generated Fig. 3 - Fig. 7, Supplementary Fig. 1 - Fig. 3 in the paper entitled "Characterization of the hemodynamic response function in white matter tracts for event-related fMRI."
DOI: 10.6084/m9.figshare.7445423.v2
title: Hierarchical Testing of a Primary and a Secondary Endpoint in a Group Sequential Design With Different Information Times

description: We consider a confirmatory clinical trial where a primary and a secondary endpoints are tested hierarchically to control the family-wise error rate at level <i>α</i>. The trial uses a group sequential design with an interim and a final analysis. When the information times at the interim analysis are the same for the primary and the secondary hypotheses, it has been shown in the literature that the secondary hypothesis has to be tested using a group sequential boundary at a level no more than <i>α</i>. In many event-driven trials, however, the information times are usually different because of different event rates for the two endpoints. The information times may also be different for a noninferiority hypothesis and a superiority hypothesis due to different analysis sets. We consider this general setup and derive a sharp upper bound on the probability of rejecting the secondary hypothesis at an interim and a final analysis. This bound suggests that the secondary boundary can be refined so that the group sequential design can be tested at a significance level greater than <i>α</i> while still controlling the family-wise error rate at level <i>α</i>. We carry out a simulation study to illustrate the power gain by using the refined boundary for different choices of boundaries for the two hypotheses. The proposed approach is illustrated in two oncology clinical trials with more than two analyses.
DOI: 10.6084/m9.figshare.7445423.v1
title: Hierarchical testing of a primary and a secondary endpoint in a group sequential design with different information times

description: We consider a confirmatory clinical trial where a primary and a secondary endpoints are tested hierarchically to control the familywise error rate at level <i>α</i>. The trial uses a group sequential design with an interim and a final analysis. When the information times at the interim analysis are the same for the primary and the secondary hypotheses, it has been shown in the literature that the secondary hypothesis has to be tested using a group sequential boundary at a level no more than <i>α</i>. In many event-driven trials, however, the information times are usually different because of different event rates for the two endpoints. The information times may also be different for a non-inferiority hypothesis and a superiority hypothesis due to different analysis sets. We consider this general setup and derive a sharp upper bound on the probability of rejecting the secondary hypothesis at an interim and a final analysis. This bound suggests that the secondary boundary can be refined so that the group sequential design can be tested at a significance level greater than <i>α</i> while still controlling the familywise error rate at level <i>α</i>. We carry out a simulation study to illustrate the power gain by using the refined boundary for different choices of boundaries for the two hypotheses. The proposed approach is illustrated in two oncology clinical trials with more than two analyses.
DOI: 10.7284/110133
title: Multibeam Echosounder data collected during research cruise MV1208 using a Kongsberg EM122 instrument system onboard the platform RV Melville

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7445378
title: Archetypal Analysis With Missing Data: See All Samples by Looking at a Few Based on Extreme Profiles

description: In this article, we propose several methodologies for handling missing or incomplete data in archetype analysis (AA) and archetypoid analysis (ADA). AA seeks to find archetypes, which are convex combinations of data points, and to approximate the samples as mixtures of those archetypes. In ADA, the representative archetypal data belong to the sample, that is, they are actual data points. With the proposed procedures, missing data are not discarded or previously filled by imputation and the theoretical properties regarding location of archetypes are guaranteed, unlike the previous approaches. The new procedures adapt the AA algorithm either by considering the missing values in the computation of the solution or by skipping them. In the first case, the solutions of previous approaches are modified to fulfill the theory and a new procedure is proposed, where the missing values are updated by the fitted values. In this second case, the procedure is based on the estimation of dissimilarities between samples and the projection of these dissimilarities in a new space, where AA or ADA is applied, and those results are used to provide a solution in the original space. A comparative analysis is carried out in a simulation study, with favorable results. The methodology is also applied to two real datasets: a well-known climate dataset and a global development dataset. We illustrate how these unsupervised methodologies allow complex data to be understood, even by nonexperts. Supplementary materials for this article are available online.
DOI: 10.7284/110132
title: TSG (Thermosalinograph) data collected during research cruise MV1208 using a Sea-Bird SBE-45 instrument system onboard the platform RV Melville

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7445378.v1
title: Archetypal analysis with missing data: see all samples by looking at a few based on extreme profiles

description: In this paper we propose several methodologies for handling missing or incomplete data in Archetype analysis (AA) and Archetypoid analysis (ADA). AA seeks to find archetypes, which are convex combinations of data points, and to approximate the samples as mixtures of those archetypes. In ADA, the representative archetypal data belong to the sample, i.e. they are actual data points. With the proposed procedures, missing data are not discarded or previously filled by imputation and the theoretical properties regarding location of archetypes are guaranteed, unlike the previous approaches. The new procedures adapt the AA algorithm either by considering the missing values in the computation of the solution or by skipping them. In the first case, the solutions of previous approaches are modified in order to fulfill the theory and a new procedure is proposed, where the missing values are updated by the fitted values. In this second case, the procedure is based on the estimation of dissimilarities between samples and the projection of these dissimilarities in a new space, where AA or ADA is applied, and those results are used to provide a solution in the original space. A comparative analysis is carried out in a simulation study, with favorable results. The methodology is also applied to two real data sets: a well-known climate data set and a global development data set. We illustrate how these unsupervised methodologies allow complex data to be understood, even by non-experts.