cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25632134
title: Selection and Aggregation of Conformal Prediction Sets

description: Conformal prediction is a generic methodology for finite-sample valid distribution-free prediction. This technique has garnered a lot of attention in the literature partly because it can be applied with any machine learning algorithm that provides point predictions to yield valid prediction regions. Of course, the efficiency (width/volume) of the resulting prediction region depends on the performance of the machine learning algorithm. In the context of point prediction, several techniques (such as cross-validation) exist to select one of many machine learning algorithms for better performance. In contrast, such selection techniques are seldom discussed in the context of set prediction (or prediction regions). In this article, we consider the problem of obtaining the smallest conformal prediction region given a family of machine learning algorithms. We provide two general-purpose selection algorithms and consider coverage as well as width properties of the final prediction region. The first selection method yields the smallest width prediction region among the family of conformal prediction regions for all sample sizes but only has an approximate coverage guarantee. The second selection method has a finite sample coverage guarantee but only attains close to the smallest width. The approximate optimal width property of the second method is quantified via an oracle inequality. As an illustration, we consider the use of aggregation of nonparametric regression estimators in the split conformal method with the absolute residual conformal score. 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.25632134.v1
title: Selection and Aggregation of Conformal Prediction Sets

description: Conformal prediction is a generic methodology for finite-sample valid distribution-free prediction. This technique has garnered a lot of attention in the literature partly because it can be applied with any machine learning algorithm that provides point predictions to yield valid prediction regions. Of course, the efficiency (width/volume) of the resulting prediction region depends on the performance of the machine learning algorithm. In the context of point prediction, several techniques (such as cross-validation) exist to select one of many machine learning algorithms for better performance. In contrast, such selection techniques are seldom discussed in the context of set prediction (or prediction regions). In this article, we consider the problem of obtaining the smallest conformal prediction region given a family of machine learning algorithms. We provide two general-purpose selection algorithms and consider coverage as well as width properties of the final prediction region. The first selection method yields the smallest width prediction region among the family of conformal prediction regions for all sample sizes but only has an approximate coverage guarantee. The second selection method has a finite sample coverage guarantee but only attains close to the smallest width. The approximate optimal width property of the second method is quantified via an oracle inequality. As an illustration, we consider the use of aggregation of nonparametric regression estimators in the split conformal method with the absolute residual conformal score. 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.25669035.v2
title: Testing the Number of Common Factors by Bootstrapped Sample Covariance Matrix in High-Dimensional Factor Models

description: This article studies the impact of bootstrap procedure on the eigenvalue distributions of the sample covariance matrix under a high-dimensional factor structure. We provide asymptotic distributions for the top eigenvalues of bootstrapped sample covariance matrix under mild conditions. After bootstrap, the spiked eigenvalues which are driven by common factors will converge weakly to Gaussian limits after proper scaling and centralization. However, the largest non-spiked eigenvalue is mainly determined by the order statistics of the bootstrap resampling weights, and follows extreme value distribution. Based on the disparate behavior of the spiked and non-spiked eigenvalues, we propose innovative methods to test the number of common factors. Indicated by extensive numerical and empirical studies, the proposed methods perform reliably and convincingly under the existence of both weak factors and cross-sectionally correlated errors. Our technical details contribute to random matrix theory on spiked covariance model with convexly decaying density and unbounded support, or with general elliptical distributions. 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.25669035.v1
title: Testing the number of common factors by bootstrapped sample covariance matrix in high-dimensional factor models

description: This paper studies the impact of bootstrap procedure on the eigenvalue distributions of the sample covariance matrix under a high-dimensional factor structure. We provide asymptotic distributions for the top eigenvalues of bootstrapped sample covariance matrix under mild conditions. After bootstrap, the spiked eigenvalues which are driven by common factors will converge weakly to Gaussian limits after proper scaling and centralization. However, the largest non-spiked eigenvalue is mainly determined by the order statistics of the bootstrap resampling weights, and follows extreme value distribution. Based on the disparate behavior of the spiked and non-spiked eigenvalues, we propose innovative methods to test the number of common factors. Indicated by extensive numerical and empirical studies, the proposed methods perform reliably and convincingly under the existence of both weak factors and cross-sectionally correlated errors. Our technical details contribute to random matrix theory on spiked covariance model with convexly decaying density and unbounded support, or with general elliptical distributions.
DOI: 10.6084/m9.figshare.25594003
title: Sharp-SSL: Selective High-Dimensional Axis-Aligned Random Projections for Semi-Supervised Learning

description: We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random projections of the data. Our primary goal is to identify important variables for distinguishing between the classes; existing low-dimensional methods can then be applied for final class assignment. To this end, we score projections according to their class-distinguishing ability; for instance, motivated by a generalized Rayleigh quotient, we can compute the traces of estimated whitened between-class covariance matrices on the projected data. This enables us to assign an importance weight to each variable for a given projection, and to select our signal variables by aggregating these weights over high-scoring projections. Our theory shows that the resulting Sharp-SSL algorithm is able to recover the signal coordinates with high probability when we aggregate over sufficiently many random projections and when the base procedure estimates the diagonal entries of the whitened between-class covariance matrix sufficiently well. For the Gaussian EM base procedure, we provide a new analysis of its performance in semi-supervised settings that controls the parameter estimation error in terms of the proportion of labeled data in the sample. Numerical results on both simulated data and a real colon tumor dataset support the excellent empirical performance of the method. 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.25594003.v1
title: Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning

description: We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random projections of the data. Our primary goal is to identify important variables for distinguishing between the classes; existing low-dimensional methods can then be applied for final class assignment. To this end, we score projections according to their class-distinguishing ability; for instance, motivated by a generalized Rayleigh quotient, we can compute the traces of estimated whitened between-class covariance matrices on the projected data. This enables us to assign an importance weight to each variable for a given projection, and to select our signal variables by aggregating these weights over high-scoring projections. Our theory shows that the resulting Sharp-SSL algorithm is able to recover the signal coordinates with high probability when we aggregate over sufficiently many random projections and when the base procedure estimates the diagonal entries of the whitened between-class covariance matrix sufficiently well. For the Gaussian EM base procedure, we provide a new analysis of its performance in semi-supervised settings that controls the parameter estimation error in terms of the proportion of labeled data in the sample. Numerical results on both simulated data and a real colon tumor dataset support the excellent empirical performance of the method.
DOI: 10.6084/m9.figshare.25594003.v2
title: Sharp-SSL: Selective High-Dimensional Axis-Aligned Random Projections for Semi-Supervised Learning

description: We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random projections of the data. Our primary goal is to identify important variables for distinguishing between the classes; existing low-dimensional methods can then be applied for final class assignment. To this end, we score projections according to their class-distinguishing ability; for instance, motivated by a generalized Rayleigh quotient, we can compute the traces of estimated whitened between-class covariance matrices on the projected data. This enables us to assign an importance weight to each variable for a given projection, and to select our signal variables by aggregating these weights over high-scoring projections. Our theory shows that the resulting Sharp-SSL algorithm is able to recover the signal coordinates with high probability when we aggregate over sufficiently many random projections and when the base procedure estimates the diagonal entries of the whitened between-class covariance matrix sufficiently well. For the Gaussian EM base procedure, we provide a new analysis of its performance in semi-supervised settings that controls the parameter estimation error in terms of the proportion of labeled data in the sample. Numerical results on both simulated data and a real colon tumor dataset support the excellent empirical performance of the method. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.y37qpg
title: Occurrence Download

description: A dataset containing 2851 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Living Specimen)",
 "Geometry POLYGON((-120 -60,-25 -60,-25 30,-120 30,-120 -60))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Aeschynomene americana L."
 ]
}

 The dataset includes 2851 records from 187 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024609-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/8469343
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.15468/dl.7m2nqd
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (USA.24_1, USA.23_1, USA.50_1, USA.16_1, USA.15_1, USA.14_1)",
 "TaxonKey is Potamogeton oakesianus J.W.Robbins"
 ]
}

 The dataset includes 64 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024610-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7zdetm
title: Occurrence Download

description: A dataset containing 1599 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Elmidae"
 ]
}

 The dataset includes 1599 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024611-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f8etsm
title: Occurrence Download

description: A dataset containing 634 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cameraria gaultheriella (Walsingham, 1889))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 634 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024612-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k35jkc
title: Occurrence Download

description: A dataset containing 1637 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "TaxonKey is Sordaria Ces. & De Not., 1863",
 "Year is greater than or equal to 1991"
 ]
}

 The dataset includes 1637 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024613-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mubaer
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Psychotria danceri Urb."
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024614-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dqumac
title: Occurrence Download

description: A dataset containing 66978 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (Africa, Asia, Europe)",
 "Issue is one of (Zero coordinate, Coordinate rounded, Geodetic datum invalid, Geodetic datum assumed WGS84, Coordinate reprojected, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate accuracy invalid, Coordinate precision invalid, Coordinate uncertainty meters invalid, Coordinate precision/uncertainty mismatch, Footprint SRS invalid, Footprint WKT mismatch, Footprint WKT invalid, Country coordinate mismatch, Country mismatch, Country invalid, Country derived from coordinates, Continent coordinate mismatch, Continent country mismatch, Continent invalid, Continent derived from country, Continent derived from coordinates, Recorded date mismatch, Recorded date invalid, Recorded date unlikely, Taxon match fuzzy, Taxon match higher rank, Taxon match aggregate, The scientific name ID was not used in backbone matching, The taxon concept ID was not used in backbone matching, The taxon ID was not used in backbone matching, The scientific name ID was not found in the checklist, The taxon concept ID was not found in the checklist, The taxon ID was not found in the checklist, The scientific name differs on the record from that found in the checklist, Taxon match none, The backbone match found using the taxon IDs differs from what would be found if using the scientific names , Depth not metric, Depth unlikely, Depth min max swapped, Depth non numeric, Elevation unlikely, Elevation min max swapped, Elevation not metric, Elevation non numeric, Modified date invalid, Modified date unlikely, Identified date unlikely, Identified date invalid, Basis of record invalid, Type status invalid, Multimedia date invalid, Multimedia URI invalid, References URI invalid, Error during interpretation, Individual count invalid, Individual count conflicts with occurrence status, Occurrence status inferred from individual count, ccurrence status inferred from basis of record, Georeferenced date unlikely, Georeferenced date invalid, Ambiguous institution, Ambiguous collection, Institution match none, Collection match none, Institution match fuzzy, Collection match fuzzy, Institution collection mismatch, Possibly on loan, Different owner institution)",
 "OccurrenceStatus is Present",
 "TaxonKey is Viverridae"
 ]
}

 The dataset includes 66978 records from 725 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024598-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9pu4rr
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Mucropetraliella thenardii (Audouin, 1826))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 26 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024616-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nefsgj
title: Occurrence Download

description: A dataset containing 1986 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Caloptilia rufipennella (Hübner, 1796))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1986 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024615-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hf427u
title: Occurrence Download

description: A dataset containing 85 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (USA.24_1, USA.16_1, USA.15_1, USA.14_1, USA.23_1, USA.50_1)",
 "TaxonKey is Schoenoplectus heterochaetus (Chase) Soják"
 ]
}

 The dataset includes 85 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024618-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rsbf8y
title: Occurrence Download

description: A dataset containing 133 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Germany",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lynx lynx (Linnaeus, 1758)",
 "Year 1243-2024"
 ]
}

 The dataset includes 133 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024617-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sx9vsm
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Hexaplex Perry, 1810"
 ]
}

 The dataset includes 20 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024620-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cnc8jm
title: Occurrence Download

description: A dataset containing 537 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Living Specimen)",
 "Geometry POLYGON((-120 -60,-25 -60,-25 30,-120 30,-120 -60))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cecropia insignis Liebm."
 ]
}

 The dataset includes 537 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024619-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.958e9m
title: Occurrence Download

description: A dataset containing 4727 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "TaxonKey is Lasiodiplodia Ellis & Everh., 1896",
 "Year is greater than or equal to 1991"
 ]
}

 The dataset includes 4727 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024621-240506114902167/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.cc254q02
title: CCDC 2000523: Experimental Crystal Structure Determination

description: Related Article: Rafael Barbas, Lídia Bofill, Dafne de Sande, Mercè Font-Bardia, Rafel Prohens|2020|CrystEngComm|22|4210|doi:10.1039/D0CE00704H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc254q46
title: CCDC 2000527: Experimental Crystal Structure Determination

description: Related Article: Rafael Barbas, Lídia Bofill, Dafne de Sande, Mercè Font-Bardia, Rafel Prohens|2020|CrystEngComm|22|4210|doi:10.1039/D0CE00704H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc254pxy
title: CCDC 2000520: Experimental Crystal Structure Determination

description: Related Article: Rafael Barbas, Lídia Bofill, Dafne de Sande, Mercè Font-Bardia, Rafel Prohens|2020|CrystEngComm|22|4210|doi:10.1039/D0CE00704H
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.