cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26093211
title: Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors

description: This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an effective way to bridge the gap between predictors and the target variable of interest by scaling and combining the predictors and their lagged values, resulting in an effective dynamic forecasting. Unlike the traditional diffusion-index approach, which does not learn the relationships between the predictors and the target variable before conducting PCA, we first rescale each predictor according to their significance in forecasting the targeted variable in a dynamic fashion, and a PCA is then applied to a rescaled and additive panel, which establishes a connection between the predictability of the PCA factors and the target variable. We also propose to use penalized methods such as the LASSO to select the significant factors that have superior predictive power over the others. Theoretically, we show that our estimators are consistent and outperform the traditional methods in prediction under some mild conditions. We conduct extensive simulations to verify that the proposed method produces satisfactory forecasting results and outperforms most of the existing methods using the traditional PCA. An example of predicting U.S. macroeconomic variables using a large number of predictors showcases that our method fares better than most of the existing ones in applications. 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.26093211.v1
title: Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors

description: This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an effective way to bridge the gap between predictors and the target variable of interest by scaling and combining the predictors and their lagged values, resulting in an effective dynamic forecasting. Unlike the traditional diffusion-index approach, which does not learn the relationships between the predictors and the target variable before conducting PCA, we first rescale each predictor according to their significance in forecasting the targeted variable in a dynamic fashion, and a PCA is then applied to a rescaled and additive panel, which establishes a connection between the predictability of the PCA factors and the target variable. We also propose to use penalized methods such as the LASSO to select the significant factors that have superior predictive power over the others. Theoretically, we show that our estimators are consistent and outperform the traditional methods in prediction under some mild conditions. We conduct extensive simulations to verify that the proposed method produces satisfactory forecasting results and outperforms most of the existing methods using the traditional PCA. An example of predicting U.S. macroeconomic variables using a large number of predictors showcases that our method fares better than most of the existing ones in applications. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.5281/zenodo.13645431
title: The effect of ice damage on Amundsen Sea Embayment projections

description: ==============================================================The effect of ice damage on Amundsen Sea Embayment projections==============================================================

------------------------------INTRODUCTION------------------------------

This dataset contains the data and scripts required to reproduce the figures and tables presented in the study:"The effect of ice damage on Amundsen Sea Embayment projections".

------------------------------PROVIDED SCRIPTS: ------------------------------

Kori-ULB contains all the necessary script to reproduce this study. Script are sorted by subfolder (ASE_scripts, MISMIP+_scripts, subroutines). The main scripts are:

    - 'KoriModelAll.m' and 'KoriInputParams.m': Kori-ULB ice flow model (more info at https://github.com/FrankPat/Kori-ULB)    - 'Init.m':         Initialization script    - 'DamageForcing.m':     Forcing script    - 'LHSensemble.mat':     Parameter Ensemble. In this study only u0.    - 'MISMIPplus.m':    MISMIP= script.    ------------------------------PROVIDED DATA: ------------------------------

Data are stored in the ASE and AIS folder. 1D fields are stored in 1D_DATA. 2D fields are stored in 2D_DATA. 

1D files:    law:    - 'gln'    refers to glen flow law.    - 'bye' refers to Nye damage law.    - 'wrt' refers to Weertman damage law.

    scenario:        - 'pd'        present-day forcing    - 'rcp26'    RCP2.6 forcing    - 'rcp85'    RCP8.5 forcing        variables with time dimension:    - 'SLC'    sea-level contribution in meters    - 'Ag'    grounded ice extension in m^2    - 'n'    number of simulations which are still running         variables without time dimension:    - 'dBMB_2100-2015'    Basal-mass balance between year 2100-2015 (in Gt)    - 'ddynC_2100-2015'    Dynamical mass loss between year 2100-2015 (in mm)    2D files:    We selected only the snapshot for RCP8.5 scenario which are presented in the main manuscript since they are too heavy. We can provide the rest of the data upon request.    ------------------------------FIGURES: ------------------------------

'FigureScript' contains the python script to reproduce the presented figures. The path to the data needs to be changed accordingly.
DOI: 10.6084/m9.figshare.26018398
title: Distance-based Clustering of Functional Data with Derivative Principal Component Analysis

description: Functional data analysis (FDA) is an important modern paradigm for handling infinite-dimensional data. An important task in FDA is clustering, which identifies subgroups based on the shapes of measured curves. Considering that derivatives can provide additional useful information about the shapes of functionals, we propose a novel <i>L</i><sup>2</sup> distance between two random functions by incorporating the functions and their derivative information to determine the dissimilarity of curves under a unified scheme for dense observations. The Karhunen–Loève expansion is used to approximate the curves and their derivatives. Cluster membership prediction for each curve intends to minimize the new distances between the observed and predicted curves through subspace projection among all possible clusters. We provide consistent estimators for the curves, curve derivatives, and the proposed distance. Identifiability issues of the clustering procedure are also discussed. The utility of the proposed method is illustrated via simulation studies and applications to two real datasets. The proposed method can considerably improve cluster performance compared with existing functional clustering methods. Supplementary materials for the article are available online.
DOI: 10.6084/m9.figshare.26018398.v2
title: Distance-based Clustering of Functional Data with Derivative Principal Component Analysis

description: Functional data analysis (FDA) is an important modern paradigm for handling infinite-dimensional data. An important task in FDA is clustering, which identifies subgroups based on the shapes of measured curves. Considering that derivatives can provide additional useful information about the shapes of functionals, we propose a novel <i>L</i><sup>2</sup> distance between two random functions by incorporating the functions and their derivative information to determine the dissimilarity of curves under a unified scheme for dense observations. The Karhunen–Loève expansion is used to approximate the curves and their derivatives. Cluster membership prediction for each curve intends to minimize the new distances between the observed and predicted curves through subspace projection among all possible clusters. We provide consistent estimators for the curves, curve derivatives, and the proposed distance. Identifiability issues of the clustering procedure are also discussed. The utility of the proposed method is illustrated via simulation studies and applications to two real datasets. The proposed method can considerably improve cluster performance compared with existing functional clustering methods. Supplementary materials for the article are available online.
DOI: 10.6084/m9.figshare.26150484
title: Dynamic Prediction Using Landmark Historical Functional Cox Regression

description: Dynamic prediction of survival data in the presence of time-varying covariates is an area of active research. Two common analytic approaches for this type of data are joint modeling of the longitudinal and survival processes and landmarking. However, there has been little work dedicated to densely measured time-varying covariates using either approach. Moreover, the software for joint modeling is slow, especially for large datasets, and rather limited for landmarking. We propose a landmark approach for dynamic prediction of survival outcomes using densely measured longitudinal predictors, which treats the past of the time-varying covariate at each landmark point as a functional predictor. This approach is orders of magnitude faster than existing software for simpler joint models. Our extensive comparative simulation study required 8.4 computation-years, over 99% of which was devoted to fitting and predicting from two joint models. Our landmark approach performs similarly to joint modeling when the joint model is correctly specified and substantially out-performs it when it is not. Methods are motivated by an application predicting time to recovery of Multiple Sclerosis lesions in a large neuroimaging dataset. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.26018398.v1
title: Distance-based clustering of functional data with derivative principal component analysis

description: Functional data analysis (FDA) is an important modern paradigm for handling infinite-dimensional data. An important task in FDA is clustering, which identifies subgroups based on the shapes of measured curves. Considering that derivatives can provide additional useful information about the shapes of functionals, we propose a novel <i>L</i><sup>2</sup> distance between two random functions by incorporating the functions and their derivative information to determine the dissimilarity of curves under a unified scheme for dense observations. The Karhunen–Loève expansion is utilized to approximate the curves and their derivatives. Cluster membership prediction for each curve intends to minimize the new distances between the observed and predicted curves through subspace projection among all possible clusters. We provide consistent estimators for the curves, curve derivatives, and the proposed distance. Identifiability issues of the clustering procedure are also discussed. The utility of the proposed method is illustrated via simulation studies and applications to two real datasets. The proposed method can considerably improve cluster performance compared with existing functional clustering methods. Supplementary materials for the article are available online.
DOI: 10.6084/m9.figshare.26150484.v2
title: Dynamic Prediction Using Landmark Historical Functional Cox Regression

description: Dynamic prediction of survival data in the presence of time-varying covariates is an area of active research. Two common analytic approaches for this type of data are joint modeling of the longitudinal and survival processes and landmarking. However, there has been little work dedicated to densely measured time-varying covariates using either approach. Moreover, the software for joint modeling is slow, especially for large datasets, and rather limited for landmarking. We propose a landmark approach for dynamic prediction of survival outcomes using densely measured longitudinal predictors, which treats the past of the time-varying covariate at each landmark point as a functional predictor. This approach is orders of magnitude faster than existing software for simpler joint models. Our extensive comparative simulation study required 8.4 computation-years, over 99% of which was devoted to fitting and predicting from two joint models. Our landmark approach performs similarly to joint modeling when the joint model is correctly specified and substantially out-performs it when it is not. Methods are motivated by an application predicting time to recovery of Multiple Sclerosis lesions in a large neuroimaging dataset. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.26150484.v1
title: Dynamic prediction using landmark historical functional Cox regression

description: Dynamic prediction of survival data in the presence of time-varying covariates is an area of active research. Two common analytic approaches for this type of data are joint modeling of the longitudinal and survival processes and landmarking. However, there has been little work dedicated to densely measured time-varying covariates using either approach. Moreover, the software for joint modeling is slow, especially for large datasets, and rather limited for landmarking. We propose a landmark approach for dynamic prediction of survival outcomes using densely measured longitudinal predictors, which treats the past of the time-varying covariate at each landmark point as a functional predictor. This approach is orders of magnitude faster than existing software for simpler joint models. Our extensive comparative simulation study required 8.4 computation-years, over 99% of which was devoted to fitting and predicting from two joint models. Our landmark approach performs similarly to joint modeling when the joint model is correctly specified and substantially out-performs it when it is not. Methods are motivated by an application predicting time to recovery of Multiple Sclerosis lesions in a large neuroimaging dataset. The supplemental materials associated with this manuscript are available online.
DOI: 10.6084/m9.figshare.26107764
title: Contextual Dynamic Pricing with Strategic Buyers

description: Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. Such strategic behavior can hinder firms from maximizing their profits. In this article, we study the contextual dynamic pricing problem with strategic buyers. The seller does not observe the buyer’s true feature, but a manipulated feature according to buyers’ strategic behavior. In addition, the seller does not observe the buyers’ valuation of the product, but only a binary response indicating whether a sale happens or not. Recognizing these challenges, we propose a strategic dynamic pricing policy that incorporates the buyers’ strategic behavior into the online learning to maximize the seller’s cumulative revenue. We first prove that existing nonstrategic pricing policies that neglect the buyers’ strategic behavior result in a linear Ω(T) regret with T the total time horizon, indicating that these policies are not better than a random pricing policy. We then establish an O(T) regret upper bound of our proposed policy and an Ω(T) regret lower bound for any pricing policy within our problem setting. This underscores the rate optimality of our policy. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. Our policy can also accommodate the scenario when the marginal cost of manipulation is unknown in advance. To account for it, we simultaneously estimate the valuation parameter and the cost parameter in the online pricing policy, which is shown to also achieve an O(T) regret bound. Extensive experiments support our theoretical developments and demonstrate the superior performance of our policy compared to other pricing policies that are unaware of the strategic behaviors. 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.26107764.v1
title: Contextual Dynamic Pricing with Strategic Buyers

description: Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. Such strategic behavior can hinder firms from maximizing their profits. In this article, we study the contextual dynamic pricing problem with strategic buyers. The seller does not observe the buyer’s true feature, but a manipulated feature according to buyers’ strategic behavior. In addition, the seller does not observe the buyers’ valuation of the product, but only a binary response indicating whether a sale happens or not. Recognizing these challenges, we propose a strategic dynamic pricing policy that incorporates the buyers’ strategic behavior into the online learning to maximize the seller’s cumulative revenue. We first prove that existing nonstrategic pricing policies that neglect the buyers’ strategic behavior result in a linear Ω(T) regret with T the total time horizon, indicating that these policies are not better than a random pricing policy. We then establish an O(T) regret upper bound of our proposed policy and an Ω(T) regret lower bound for any pricing policy within our problem setting. This underscores the rate optimality of our policy. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. Our policy can also accommodate the scenario when the marginal cost of manipulation is unknown in advance. To account for it, we simultaneously estimate the valuation parameter and the cost parameter in the online pricing policy, which is shown to also achieve an O(T) regret bound. Extensive experiments support our theoretical developments and demonstrate the superior performance of our policy compared to other pricing policies that are unaware of the strategic behaviors. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.pugrs4
title: Occurrence Download

description: A dataset containing 977 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Culex modestus Ficalbi, 1947"
 ]
}

 The dataset includes 977 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0114925-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34622/datarepositorium/s2o9fa
title: Motivo VRM003-011

description: Motivo (VRM003-011) figurativo de tipo arma de um escudo tipo "Caetra" utilizado por um guerreiro em combate. Foi gravado com incisões simples e encontra-se no esquerdo centro do suporte. Este motivo encontra-se em bom estado de conservação.
DOI: 10.15468/dl.4kv74v
title: Occurrence Download

description: A dataset containing 105405 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Conyza canadensis (L.) Cronquist",
 "Year 2000-2024"
 ]
}

 The dataset includes 105405 records from 615 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0114896-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.azngaf
title: Occurrence Download

description: A dataset containing 124 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Strix hylophila Temminck, 1825"
 ]
}

 The dataset includes 124 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002874-150811131857512/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.28axzz
title: Occurrence Download

description: A dataset listing the 3 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Costa Rica",
 "DatasetKey is iNaturalist Research-grade Observations",
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Ara macao (Linnaeus, 1758)"
 ]
}

 The dataset's 3 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049951-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.amv9gf
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dendrocopos major (Linnaeus, 1758)",
 "BasisOfRecord is Specimen",
 "Country is one of (Russian Federation)",
 "DecimalLongitude is greater than 136",
 "DecimalLatitude is greater than 54"
 ]
}

 The dataset includes 10 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049940-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wuxkpq
title: Occurrence Download

description: A dataset containing 11209 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vicia tenuifolia Roth",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 11209 records from 118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002874-150831095807960/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mv9cbn
title: Occurrence Download

description: A dataset containing 1195 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Paracentropogon rubripinnis (Temminck & Schlegel, 1843)"
 ]
}

 The dataset includes 1195 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035142-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc27y1py
title: CCDC 2083500: Experimental Crystal Structure Determination

description: Related Article: Anna A. Hoser, Marcin Sztylko, Damian Trzybiński, Anders Ø. Madsen|2021|Chem.Commun.|57|9370|doi:10.1039/D1CC02608A
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.