cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.r5e2q2
title: Occurrence Download

description: A dataset containing 445 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((117.659 6.233135, 117.588323 6.227564, 117.51939 6.210989, 117.453903 6.183818, 117.393477 6.146722, 117.339602 6.100617, 117.293606 6.04664, 117.256619 5.986122, 117.229551 5.920555, 117.213064 5.851554, 117.207562 5.780821, 117.213175 5.710096, 117.229761 5.64112, 117.256909 5.575592, 117.293946 5.515123, 117.339961 5.4612, 117.393818 5.415149, 117.454193 5.378103, 117.519601 5.350971, 117.588433 5.33442, 117.659 5.328858, 117.729567 5.33442, 117.798399 5.350971, 117.863807 5.378103, 117.924182 5.415149, 117.978039 5.4612, 118.024054 5.515123, 118.061091 5.575592, 118.088239 5.64112, 118.104825 5.710096, 118.110438 5.780821, 118.104936 5.851554, 118.088449 5.920555, 118.061381 5.986122, 118.024394 6.04664, 117.978398 6.100617, 117.924523 6.146722, 117.864097 6.183818, 117.79861 6.210989, 117.729677 6.227564, 117.659 6.233135))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 445 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005716-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ta24xc
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-90.342 16.878812, -90.415403 16.873237, -90.486987 16.856649, -90.554976 16.82946, -90.617689 16.792343, -90.673575 16.746218, -90.721256 16.692227, -90.75956100000001 16.631704, -90.78755200000001 16.566146, -90.80455000000001 16.49717, -90.810146 16.426478, -90.804215 16.355811, -90.786917 16.286908, -90.758686 16.221463, -90.72022699999999 16.161083, -90.672493 16.10725, -90.61666 16.061284, -90.554101 16.02431, -90.486351 15.997234, -90.415069 15.980719, -90.342 15.975169, -90.26893099999999 15.980719, -90.197649 15.997234, -90.12989899999999 16.02431, -90.06734 16.061284, -90.01150699999999 16.10725, -89.963773 16.161083, -89.925314 16.221463, -89.89708299999999 16.286908, -89.879785 16.355811, -89.87385399999999 16.426478, -89.87945000000001 16.49717, -89.89644800000001 16.566146, -89.92443900000001 16.631704, -89.962744 16.692227, -90.010425 16.746218, -90.066311 16.792343, -90.129024 16.82946, -90.197013 16.856649, -90.268597 16.873237, -90.342 16.878812))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 15 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005718-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6ugtws
title: Occurrence Download

description: A dataset listing the 9 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cylas formicarius"
 ]
}

 The dataset's 9 records were derived from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005695-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.668kq8
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.89097 24.812449, 88.81361 24.806872, 88.738175 24.790279, 88.666541 24.763084, 88.600486 24.725963, 88.541645 24.679837, 88.491471 24.625852, 88.451194 24.565346, 88.421798 24.499816, 88.403992 24.430884, 88.398196 24.360249, 88.404532 24.289653, 88.422826 24.220832, 88.452609 24.155477, 88.493133 24.095191, 88.54339299999999 24.041449, 88.602149 23.995566, 88.667956 23.958664, 88.739203 23.931643, 88.81415 23.915163, 88.89097 23.909624, 88.96778999999999 23.915163, 89.042737 23.931643, 89.113984 23.958664, 89.17979099999999 23.995566, 89.238547 24.041449, 89.28880700000001 24.095191, 89.329331 24.155477, 89.35911400000001 24.220832, 89.377408 24.289653, 89.38374399999999 24.360249, 89.377948 24.430884, 89.360142 24.499816, 89.330746 24.565346, 89.290469 24.625852, 89.240295 24.679837, 89.181454 24.725963, 89.115399 24.763084, 89.04376499999999 24.790279, 88.96832999999999 24.806872, 88.89097 24.812449))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005719-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rmn782
title: Occurrence Download

description: A dataset containing 473 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((117.7 6.236135, 117.629322 6.230564, 117.560389 6.213988, 117.494902 6.186818, 117.434476 6.149722, 117.380601 6.103617, 117.334604 6.04964, 117.297617 5.989122, 117.270548 5.923554, 117.254062 5.854554, 117.248559 5.78382, 117.254173 5.713095, 117.270759 5.64412, 117.297907 5.578592, 117.334945 5.518123, 117.380959 5.4642, 117.434817 5.418149, 117.495192 5.381103, 117.5606 5.353971, 117.629433 5.33742, 117.7 5.331858, 117.770567 5.33742, 117.8394 5.353971, 117.904808 5.381103, 117.965183 5.418149, 118.019041 5.4642, 118.065055 5.518123, 118.102093 5.578592, 118.129241 5.64412, 118.145827 5.713095, 118.151441 5.78382, 118.145938 5.854554, 118.129452 5.923554, 118.102383 5.989122, 118.065396 6.04964, 118.019399 6.103617, 117.965524 6.149722, 117.905098 6.186818, 117.839611 6.213988, 117.770678 6.230564, 117.7 6.236135))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 473 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005721-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9zrn35
title: Occurrence Download

description: A dataset containing 1289 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phrynops geoffroanus"
 ]
}

 The dataset includes 1289 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005722-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3fdh4a
title: Occurrence Download

description: A dataset containing 155 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-89.863 16.987807, -89.936446 16.982232, -90.00807 16.965644, -90.076098 16.938455, -90.138847 16.901338, -90.19476400000001 16.855213, -90.24247200000001 16.801222, -90.280798 16.740699, -90.30880399999999 16.675141, -90.32581 16.606166, -90.331408 16.535474, -90.325473 16.464808, -90.30816299999999 16.395906, -90.279916 16.330462, -90.241435 16.270083, -90.193674 16.216252, -90.13781 16.170286, -90.07521699999999 16.133313, -90.007429 16.106238, -89.936109 16.089723, -89.863 16.084173, -89.789891 16.089723, -89.718571 16.106238, -89.650783 16.133313, -89.58819 16.170286, -89.532326 16.216252, -89.484565 16.270083, -89.446084 16.330462, -89.41783700000001 16.395906, -89.400527 16.464808, -89.394592 16.535474, -89.40018999999999 16.606166, -89.417196 16.675141, -89.44520199999999 16.740699, -89.48352800000001 16.801222, -89.53123600000001 16.855213, -89.587153 16.901338, -89.649902 16.938455, -89.71793 16.965644, -89.789554 16.982232, -89.863 16.987807))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 155 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005723-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18344263
title: Research on Decoupling Measurement Technology for 2-DOF Angular Signals Based on Spherical Capacitive Sensors
DOI: 10.5281/zenodo.18344264
title: Research on Decoupling Measurement Technology for 2-DOF Angular Signals Based on Spherical Capacitive Sensors
DOI: 10.15468/dl.euvy68
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mesoclemmys heliostemma"
 ]
}

 The dataset includes 7 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005724-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.frjyeg
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.89097 24.812449, 88.81361 24.806872, 88.738175 24.790279, 88.666541 24.763084, 88.600486 24.725963, 88.541645 24.679837, 88.491471 24.625852, 88.451194 24.565346, 88.421798 24.499816, 88.403992 24.430884, 88.398196 24.360249, 88.404532 24.289653, 88.422826 24.220832, 88.452609 24.155477, 88.493133 24.095191, 88.54339299999999 24.041449, 88.602149 23.995566, 88.667956 23.958664, 88.739203 23.931643, 88.81415 23.915163, 88.89097 23.909624, 88.96778999999999 23.915163, 89.042737 23.931643, 89.113984 23.958664, 89.17979099999999 23.995566, 89.238547 24.041449, 89.28880700000001 24.095191, 89.329331 24.155477, 89.35911400000001 24.220832, 89.377408 24.289653, 89.38374399999999 24.360249, 89.377948 24.430884, 89.360142 24.499816, 89.330746 24.565346, 89.290469 24.625852, 89.240295 24.679837, 89.181454 24.725963, 89.115399 24.763084, 89.04376499999999 24.790279, 88.96832999999999 24.806872, 88.89097 24.812449))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005725-260120142942310/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.26880657
title: Model-Based Causal Feature Selection for General Response Types

description: Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters, Bühlmann, and Meinshausen) is a method for causal feature selection which requires data from heterogeneous settings and exploits that causal models are invariant. ICP has been extended to general additive noise models and to nonparametric settings using conditional independence tests. However, the latter often suffer from low power (or poor Type I error control) and additive noise models are not suitable for applications in which the response is not measured on a continuous scale, but reflects categories or counts. Here, we develop transformation-model (tram) based ICP, allowing for continuous, categorical, count-type, and uninformatively censored responses (these model classes, generally, do not allow for identifiability when there is no exogenous heterogeneity). As an invariance test, we propose tram-GCM based on the expected conditional covariance between environments and score residuals with uniform asymptotic level guarantees. For the special case of linear shift trams, we also consider tram-Wald, which tests invariance based on the Wald statistic. We provide an open-source R package <b>tramicp</b> and evaluate our approach on simulated data and in a case study investigating causal features of survival in critically ill patients. 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.26880657.v2
title: Model-Based Causal Feature Selection for General Response Types

description: Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters, Bühlmann, and Meinshausen) is a method for causal feature selection which requires data from heterogeneous settings and exploits that causal models are invariant. ICP has been extended to general additive noise models and to nonparametric settings using conditional independence tests. However, the latter often suffer from low power (or poor Type I error control) and additive noise models are not suitable for applications in which the response is not measured on a continuous scale, but reflects categories or counts. Here, we develop transformation-model (tram) based ICP, allowing for continuous, categorical, count-type, and uninformatively censored responses (these model classes, generally, do not allow for identifiability when there is no exogenous heterogeneity). As an invariance test, we propose tram-GCM based on the expected conditional covariance between environments and score residuals with uniform asymptotic level guarantees. For the special case of linear shift trams, we also consider tram-Wald, which tests invariance based on the Wald statistic. We provide an open-source R package <b>tramicp</b> and evaluate our approach on simulated data and in a case study investigating causal features of survival in critically ill patients. 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.26014760
title: Nonparametric Multiple-Output Center-Outward Quantile Regression

description: Building on recent measure-transportation-based concepts of multivariate quantiles, we are considering the problem of nonparametric multiple-output quantile regression. Our approach defines nested conditional center-outward quantile regression contours and regions with given conditional probability content, the graphs of which constitute nested center-outward <i>quantile regression tubes</i> with given unconditional probability content; these (conditional and unconditional) probability contents do not depend on the underlying distribution—an essential property of quantile concepts. Empirical counterparts of these concepts are constructed, yielding interpretable empirical contours, regions, and tubes which are shown to consistently reconstruct (in the Pompeiu-Hausdorff topology) their population versions. Our method is entirely nonparametric and performs well in simulations—with possible heteroscedasticity and nonlinear trends. Its potential as a data-analytic tool is illustrated on some real datasets. 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.26014760.v1
title: Nonparametric Multiple-Output Center-Outward Quantile Regression

description: Building on recent measure-transportation-based concepts of multivariate quantiles, we are considering the problem of nonparametric multiple-output quantile regression. Our approach defines nested conditional center-outward quantile regression contours and regions with given conditional probability content, the graphs of which constitute nested center-outward <i>quantile regression tubes</i> with given unconditional probability content; these (conditional and unconditional) probability contents do not depend on the underlying distribution—an essential property of quantile concepts. Empirical counterparts of these concepts are constructed, yielding interpretable empirical contours, regions, and tubes which are shown to consistently reconstruct (in the Pompeiu-Hausdorff topology) their population versions. Our method is entirely nonparametric and performs well in simulations—with possible heteroscedasticity and nonlinear trends. Its potential as a data-analytic tool is illustrated on some real datasets. 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.26880657.v1
title: Model-based causal feature selection for general response types

description: Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal feature selection which requires data from heterogeneous settings and exploits that causal models are invariant. ICP has been extended to general additive noise models and to nonparametric settings using conditional independence tests. However, the latter often suffer from low power (or poor type I error control) and additive noise models are not suitable for applications in which the response is not measured on a continuous scale, but reflects categories or counts. Here, we develop transformation-model (tram) based ICP, allowing for continuous, categorical, count-type, and uninformatively censored responses (these model classes, generally, do not allow for identifiability when there is no exogenous heterogeneity). As an invariance test, we propose tram-GCM based on the expected conditional covariance between environments and score residuals with uniform asymptotic level guarantees. For the special case of linear shift trams, we also consider tram-Wald, which tests invariance based on the Wald statistic. We provide an open-source R package <b>tramicp</b> and evaluate our approach on simulated data and in a case study investigating causal features of survival in critically ill patients.
DOI: 10.6084/m9.figshare.27074915
title: A Model-Agnostic Graph Neural Network for Integrating Local and Global Information

description: Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack of interpretability in their results due to their black-box nature, and an inability to learn representations of varying orders. To tackle these issues, we propose a novel Model-agnostic Graph Neural Network (MaGNet) framework, which is able to effectively integrate information of various orders, extract knowledge from high-order neighbors, and provide meaningful and interpretable results by identifying influential compact graph structures. In particular, MaGNet consists of two components: an estimation model for the latent representation of complex relationships under graph topology, and an interpretation model that identifies influential nodes, edges, and node features. Theoretically, we establish the generalization error bound for MaGNet via empirical Rademacher complexity, and demonstrate its power to represent layer-wise neighborhood mixing. We conduct comprehensive numerical studies using simulated data to demonstrate the superior performance of MaGNet in comparison to several state-of-the-art alternatives. Furthermore, we apply MaGNet to a real-world case study aimed at extracting task-critical information from brain activity data, thereby highlighting its effectiveness in advancing scientific research. 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.27074915.v1
title: A Model-Agnostic Graph Neural Network for Integrating Local and Global Information

description: Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack of interpretability in their results due to their black-box nature, and an inability to learn representations of varying orders. To tackle these issues, we propose a novel Model-agnostic Graph Neural Network (MaGNet) framework, which is able to effectively integrate information of various orders, extract knowledge from high-order neighbors, and provide meaningful and interpretable results by identifying influential compact graph structures. In particular, MaGNet consists of two components: an estimation model for the latent representation of complex relationships under graph topology, and an interpretation model that identifies influential nodes, edges, and node features. Theoretically, we establish the generalization error bound for MaGNet via empirical Rademacher complexity, and demonstrate its power to represent layer-wise neighborhood mixing. We conduct comprehensive numerical studies using simulated data to demonstrate the superior performance of MaGNet in comparison to several state-of-the-art alternatives. Furthermore, we apply MaGNet to a real-world case study aimed at extracting task-critical information from brain activity data, thereby highlighting its effectiveness in advancing scientific research. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.