cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29120578
title: Adjacency Matrix Decomposition Clustering for Human Activity Data

description: Mobile apps and wearable devices accurately and continuously measure human activity; patterns within this data can provide a wealth of information applicable to fields such as transportation and health. Despite the potential utility of this data, there has been limited development of analysis methods for sequences of daily activities. In this article, we propose a novel clustering method and cluster evaluation metric for human activity data that leverages an adjacency matrix representation to cluster the data without the calculation of a distance matrix. Our technique is substantially faster than conventional methods based on computing pairwise distances via sequence alignment algorithms and also enhances interpretability of results. We compare our method to distance-based hierarchical clustering and nTreeClus through simulation studies and an application to data collected by Daynamica, an app that turns sensor data into a daily summary of a user’s activities. Among days that contain a large portion of time spent at home, our method distinguishes days that also contain multiple hours of travel or other activities, while both comparison methods fail to identify these patterns. We further identify which day patterns classified by our method are associated with higher concern for contracting COVID-19 with implications for public health messaging. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29120578.v1
title: Adjacency Matrix Decomposition Clustering for Human Activity Data

description: Mobile apps and wearable devices accurately and continuously measure human activity; patterns within this data can provide a wealth of information applicable to fields such as transportation and health. Despite the potential utility of this data, there has been limited development of analysis methods for sequences of daily activities. In this article, we propose a novel clustering method and cluster evaluation metric for human activity data that leverages an adjacency matrix representation to cluster the data without the calculation of a distance matrix. Our technique is substantially faster than conventional methods based on computing pairwise distances via sequence alignment algorithms and also enhances interpretability of results. We compare our method to distance-based hierarchical clustering and nTreeClus through simulation studies and an application to data collected by Daynamica, an app that turns sensor data into a daily summary of a user’s activities. Among days that contain a large portion of time spent at home, our method distinguishes days that also contain multiple hours of travel or other activities, while both comparison methods fail to identify these patterns. We further identify which day patterns classified by our method are associated with higher concern for contracting COVID-19 with implications for public health messaging. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29307005
title: Mutually Exciting Point Processes with Latency

description: A novel statistical approach to estimating latency, defined as the time it takes to learn about an event and generate response to this event, is proposed. Our approach only requires a multidimensional point process describing event times, which circumvents the use of more detailed datasets which may not even be available. We consider the class of parametric Hawkes models capturing clustering effects and define latency as a known function of kernel parameters, typically the mode of kernel function. Since latency is not well-defined when the kernel is exponential, we consider maximum likelihood estimation in the mixture of generalized gamma kernels case and derive the feasible central limit theory with in-fill asymptotics. As a byproduct, central limit theory for a latency estimator and related tests are provided. Our numerical study corroborates the theory. An empirical application on high frequency data transactions from the New York Stock Exchange and Toronto Stock Exchange shows that latency estimates for the United States and Canadian stock exchanges vary between 1 and 6 milliseconds from 2020 to 2021. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29307005.v2
title: Mutually Exciting Point Processes with Latency

description: A novel statistical approach to estimating latency, defined as the time it takes to learn about an event and generate response to this event, is proposed. Our approach only requires a multidimensional point process describing event times, which circumvents the use of more detailed datasets which may not even be available. We consider the class of parametric Hawkes models capturing clustering effects and define latency as a known function of kernel parameters, typically the mode of kernel function. Since latency is not well-defined when the kernel is exponential, we consider maximum likelihood estimation in the mixture of generalized gamma kernels case and derive the feasible central limit theory with in-fill asymptotics. As a byproduct, central limit theory for a latency estimator and related tests are provided. Our numerical study corroborates the theory. An empirical application on high frequency data transactions from the New York Stock Exchange and Toronto Stock Exchange shows that latency estimates for the United States and Canadian stock exchanges vary between 1 and 6 milliseconds from 2020 to 2021. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29307005.v1
title: Mutually exciting point processes with latency*

description: A novel statistical approach to estimating latency, defined as the time it takes to learn about an event and generate response to this event, is proposed. Our approach only requires a multidimensional point process describing event times, which circumvents the use of more detailed datasets which may not even be available. We consider the class of parametric Hawkes models capturing clustering effects and define latency as a known function of kernel parameters, typically the mode of kernel function. Since latency is not well-defined when the kernel is exponential, we consider maximum likelihood estimation in the mixture of generalized gamma kernels case and derive the feasible central limit theory with in-fill asymptotics. As a byproduct, central limit theory for a latency estimator and related tests are provided. Our numerical study corroborates the theory. An empirical application on high frequency data transactions from the New York Stock Exchange and Toronto Stock Exchange shows that latency estimates for the US and Canadian stock exchanges vary between 1 and 6 milliseconds from 2020 to 2021.
DOI: 10.15468/dl.pfxhpy
title: Occurrence Download

description: A dataset containing 796 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Juncus effusus",
 "Country is New Zealand",
 "HasCoordinate is true"
 ]
}

 The dataset includes 796 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002802-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29214611
title: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review

description: We conducted an experiment during the review process of the 2023 International Conference on Machine Learning (ICML), asking authors with multiple submissions to rank their papers based on perceived quality. In total, we received 1342 rankings, each from a different author, covering 2592 submissions. In this article, we present an empirical analysis of how author-provided rankings could be leveraged to improve peer review processes at machine learning conferences. We focus on the Isotonic Mechanism, which calibrates raw review scores using the author-provided rankings. Our analysis shows that these ranking-calibrated scores outperform the raw review scores in estimating the ground truth “expected review scores” in terms of both squared and absolute error metrics. Furthermore, we propose several cautious, low-risk applications of the Isotonic Mechanism and author-provided rankings in peer review, including supporting senior area chairs in overseeing area chairs’ recommendations, assisting in the selection of paper awards, and guiding the recruitment of emergency reviewers. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29214611.v1
title: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review

description: We conducted an experiment during the review process of the 2023 International Conference on Machine Learning (ICML), asking authors with multiple submissions to rank their papers based on perceived quality. In total, we received 1342 rankings, each from a different author, covering 2592 submissions. In this article, we present an empirical analysis of how author-provided rankings could be leveraged to improve peer review processes at machine learning conferences. We focus on the Isotonic Mechanism, which calibrates raw review scores using the author-provided rankings. Our analysis shows that these ranking-calibrated scores outperform the raw review scores in estimating the ground truth “expected review scores” in terms of both squared and absolute error metrics. Furthermore, we propose several cautious, low-risk applications of the Isotonic Mechanism and author-provided rankings in peer review, including supporting senior area chairs in overseeing area chairs’ recommendations, assisting in the selection of paper awards, and guiding the recruitment of emergency reviewers. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29203435
title: Deep Fréchet Regression

description: Advancements in modern science have led to the increasing availability of non-Euclidean data in metric spaces. This article addresses the challenge of modeling relationships between non-Euclidean responses and multivariate Euclidean predictors. We propose a flexible regression model capable of handling high-dimensional predictors without imposing parametric assumptions. Two primary challenges are addressed: the curse of dimensionality in nonparametric regression and the absence of linear structure in general metric spaces. The former is tackled using deep neural networks, while for the latter we demonstrate the feasibility of mapping the metric space where responses reside to a low-dimensional Euclidean space using manifold learning. We introduce a reverse mapping approach, employing local Fréchet regression, to map the low-dimensional manifold representations back to objects in the original metric space. We develop a theoretical framework, investigating the convergence rate of deep neural networks under dependent sub-Gaussian noise with bias. The convergence rate of the proposed regression model is then obtained by expanding the scope of local Fréchet regression to accommodate multivariate predictors in the presence of errors in predictors. Simulations and case studies show that the proposed model outperforms existing methods for non-Euclidean responses, focusing on the special cases of probability distributions and networks. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29203435.v1
title: Deep Fréchet Regression

description: Advancements in modern science have led to the increasing availability of non-Euclidean data in metric spaces. This article addresses the challenge of modeling relationships between non-Euclidean responses and multivariate Euclidean predictors. We propose a flexible regression model capable of handling high-dimensional predictors without imposing parametric assumptions. Two primary challenges are addressed: the curse of dimensionality in nonparametric regression and the absence of linear structure in general metric spaces. The former is tackled using deep neural networks, while for the latter we demonstrate the feasibility of mapping the metric space where responses reside to a low-dimensional Euclidean space using manifold learning. We introduce a reverse mapping approach, employing local Fréchet regression, to map the low-dimensional manifold representations back to objects in the original metric space. We develop a theoretical framework, investigating the convergence rate of deep neural networks under dependent sub-Gaussian noise with bias. The convergence rate of the proposed regression model is then obtained by expanding the scope of local Fréchet regression to accommodate multivariate predictors in the presence of errors in predictors. Simulations and case studies show that the proposed model outperforms existing methods for non-Euclidean responses, focusing on the special cases of probability distributions and networks. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.kkgttd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Buteo platypterus)",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002804-250717081556266/datasets/export for details.
DOI: 10.15468/dl.upkn6e
title: Occurrence Download

description: A dataset containing 35564 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Ambystoma maculatum)"
 ]
}

 The dataset includes 35564 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002803-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fapnm5
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Helianthus tuberosus",
 "Country is Liechtenstein",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002806-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c3g9hd
title: Occurrence Download

description: A dataset containing 1181 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Artemisia verlotiorum",
 "Country is Germany",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1181 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002805-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.22tg3f
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lapathum crispum, Lapathum crispum, Rheum crispum, Rumex coreanus, Rumex crispus integerrimus, Rumex crispus subcordatus, Rumex crispus tricallosus, Rumex crispus integerrimus, Rumex crispus littoralis, Rumex crispus oelandicus)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 30 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002807-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6srm47
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Isatis harsukhii"
 ]
}

 The dataset includes 9 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002808-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16048795
title: GC Wealth Project Data Warehouse

description: The GC Wealth Project, a central project of the Graduate Center's Stone Center on Socio-Economic Inequality, is an effort aimed at expanding and consolidating access to the most up-to-date research and information on wealth, wealth inequalities, and wealth transfers and related tax policies, across countries and over time. The dataset consolidates most existing evidence on private wealth and undertakes a significant data harmonization effort. We supplement each data point with extensive metadata on methodology and add new data. The data warehouse features an extensive collection of information on the household wealth levels and balance sheets, along with distributional statistics from a wide range of sources. Moreover, it draws together data on wealth transfer tax revenues, and tax features such as rates, exemption thresholds, and tax schedules. The broad range of data sources in the warehouse allows users to assess the degree of heterogeneity of estimates, and how methodological choices affect measurement outcomes. The new data points and policy indicators also allow extending quantitative analysis of wealth and public policy.
DOI: 10.26030/1gsg-7p67
title: Effects of 5-Ion Beam Irradiation and Hindlimb Unloading on Metabolic Pathways in Plasma and Brain of Behaviorally Tested WAG/Rij Rats - Cortex data

description: A limitation of simulated space radiation studies is that radiation exposure is not the only environmental challenge astronauts face during missions. Therefore, we characterized behavioral and cognitive performance of male WAG/Rij rats 3 months after sham irradiation or total body irradiation with a simplified 5-ion mixed beam exposure in the absence or presence of simulated weightlessness using hindlimb unloading (HU) alone. Six months following behavioral and cognitive testing or 9 months following sham-irradiation or total body irradiation, plasma and brain tissues (hippocampus and cortex) were processed to determine whether the behavioral and cognitive effects were associated with long-term alterations in metabolic pathways in plasma and brain. Sham HU, but not irradiated HU, rats were impaired in spatial habituation learning. Rats irradiated with 1.5 Gy showed increased depressive-like behaviors. This was seen in the absence but not presence of HU. Thus, HU has differential effects in sham irradiated and irradiated animals and specific behavioral measures are associated with plasma levels of distinct metabolites 6 months later. The combined effects of HU and radiation on metabolic pathways in plasma and brain illustrate the complex interaction of environmental stressors and highlights the importance of assessing these interactions. This study contains cortex metabolomics data. The plasma metabolomics data are deposited in OSD-832, the hippocampus metabolomics data in OSD-833, and the behavioral assay data in OSD-835.
DOI: 10.5281/zenodo.15619844
title: SEEA-Water-England

description: Results of SEEA-Water Framework for England. Contact the author for accessibility.
DOI: 10.15468/dl.nrf69g
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Artemisia verlotiorum",
 "Country is Greece",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002811-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14262304
title: SEEA-Water-England

description: Results of SEEA-Water Framework for England. Contact the author for accessibility.
DOI: 10.15468/dl.h6bpd2
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Juncus gerardi",
 "Country is New Zealand",
 "HasCoordinate is true"
 ]
}

 The dataset includes 64 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002809-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t38ntf
title: Occurrence Download

description: A dataset containing 73 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Helianthus tuberosus",
 "Country is Lithuania",
 "HasCoordinate is true"
 ]
}

 The dataset includes 73 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002812-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28399514
title: MindLab Pro project

description: This data supports the study "<b>Effect of a plant-based nootropic supplement on perceptual decision-making and brain network interdependencies: a randomised, double-blinded and placebo-controlled study</b>".It includes EEG data and demographic details for 37 participants who were recorded pre- and post- 2 months of supplementation. These recordings were made while participants performed a perceptual decision-making task and three different working memory tasks.Further details can be provided at: d.oreilly@leeds.ac.uk
DOI: 10.6084/m9.figshare.28399514.v1
title: MindLab Pro project

description: This data supports the study "<b>Effect of a plant-based nootropic supplement on perceptual decision-making and brain network interdependencies: a randomised, double-blinded and placebo-controlled study</b>".It includes EEG data and demographic details for 37 participants who were recorded pre- and post- 2 months of supplementation. These recordings were made while participants performed a perceptual decision-making task and three different working memory tasks.Further details can be provided at: d.oreilly@leeds.ac.uk