cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/0tf-v0g-50w
title: Capillariidae Railliet, 1915

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/x4g-8ef-wkz
title: Girardia Ball, 1974

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/0l3-dxh-3j7
title: Bipalium Stimpson, 1857

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/zb7-tsb-0v9
title: Bipalium Stimpson, 1857

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/afa-0an-k6x
title: Pseudalius inflexus (Rudolphi, 1808) Schneider, 1866

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/myb-07t-k7e
title: Glossiphonia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/65g-ytw-d7g
title: Proteocephalus macrocephalus (Creplin, 1825) Nufer, 1905

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/bfv-m6w-8y0
title: Echinoparyphium Dietz, 1909

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/t2d-y9v-w5p
title: Baseodiscus rugosus (Punnett, 1900)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/alr-sep-bwe
title: Baseodiscus rugosus (Punnett, 1900)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/kxa-mzd-xj2
title: Spiculopteragia boehmi (Gebauer, 1932) Orloff, 1933

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/7an-abn-x0k
title: Cosmocephalus obvelatus (Creplin, 1825) Seurat, 1919

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/8dg-dwt-4ax
title: Dugesia japonica Ichikawa & Kawakatsu, 1964

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/8dt-j78-k7b
title: Dugesia ryukyuensis Kawakatsu, 1976

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/g11-3q1-73s
title: Hydroides norvegica Gunnerus, 1768

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/209-g6g-k5f
title: Lamellibrachia luymesi van der Land & Nørrevang, 1975

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/977-4jj-jjw
title: Dugesia gonocephala (Duges, 1830)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/f7k-7w8-l18
title: Tubificoides benedii (d'Udekem, 1855)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/1k2-n33-d7s
title: Heterakis gallinarum (Schrank, 1788) Madsen, 1949

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/as7-g6b-e97
title: Ancylostomatidae Looss, 1905

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/w6c-826-y6f
title: Himasthla leptosoma (Creplin, 1829) Dietz, 1909

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/xpr-6ry-cme
title: Spirobranchus triqueter (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/pzq-c5q-699
title: Spirobranchus triqueter (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/zdj-1pw-lde
title: Polycelis felina (Dalyell, 1814)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/c4f-f9q-zzm
title: Dugesia japonica Ichikawa & Kawakatsu, 1964

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/7d7-hb4-z3a
title: Harmothoe impar (Johnston, 1839)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/scs-56m-ct5
title: Syllis gracilis Grube, 1840

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/69l-7jq-vnf
title: Nephtys hombergii Savigny in Lamarck, 1818

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/m10-n38-gz4
title: Bdellocephala brunnea Ijima & Kaburaki, 1916

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25305032
title: <i>Max Share</i> Identification of Multiple Shocks: An Application to Uncertainty and Financial Conditions

description: We generalize the <i>Max Share</i> approach to allow for simultaneous identification of a multiplicity of shocks in a Structural Vector Autoregression. Our machinery therefore overcomes the well-known drawbacks that individually identified shocks (i) tend to be correlated to each other or (ii) can be separated under orthogonalizations with weak economic ground. We show that identification corresponds to solving a nontrivial optimization problem. We provide conditions for non-emptiness of solutions and point-identification, and Bayesian algorithms for estimation and inference. We use the approach to study the effects of uncertainty and financial shocks, allowing for the possibility that the former responds contemporaneously to other shocks, distinguishing macroeconomic from financial uncertainty and credit supply shocks. Using U.S. data we find that financial uncertainty mimics a demand shock, while the interpretation of macro uncertainty is more mixed. Furthermore, variation in uncertainty partially represents the endogenous response of uncertainty to other shocks.
DOI: 10.6084/m9.figshare.25305032.v1
title: <i>Max Share</i> Identification of Multiple Shocks: An Application to Uncertainty and Financial Conditions

description: We generalize the <i>Max Share</i> approach to allow for simultaneous identification of a multiplicity of shocks in a Structural Vector Autoregression. Our machinery therefore overcomes the well-known drawbacks that individually identified shocks (i) tend to be correlated to each other or (ii) can be separated under orthogonalizations with weak economic ground. We show that identification corresponds to solving a nontrivial optimization problem. We provide conditions for non-emptiness of solutions and point-identification, and Bayesian algorithms for estimation and inference. We use the approach to study the effects of uncertainty and financial shocks, allowing for the possibility that the former responds contemporaneously to other shocks, distinguishing macroeconomic from financial uncertainty and credit supply shocks. Using U.S. data we find that financial uncertainty mimics a demand shock, while the interpretation of macro uncertainty is more mixed. Furthermore, variation in uncertainty partially represents the endogenous response of uncertainty to other shocks.
DOI: 10.6084/m9.figshare.25386824
title: Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units: An Application to Share Data

description: This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n,T→∞, and the strength of the dominant units is equal to 1 when T→∞. For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities.
DOI: 10.6084/m9.figshare.25386824.v2
title: Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units: An Application to Share Data

description: This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n,T→∞, and the strength of the dominant units is equal to 1 when T→∞. For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities.
DOI: 10.3535/0a3-vyb-224
title: Nephtys longosetosa Örsted, 1842

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/aw3-665-lft
title: Paramphistomum Fischoeder, 1901

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25386824.v1
title: Nonlinear spatial dynamic panel data models with endogenous dominant units: An application to share data

description: This paper develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroskedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n,T→∞, and the strength of the dominant units is equal to 1 when T→∞. For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities.
DOI: 10.3535/khx-h8f-pmh
title: Glossiphonia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/a55-vny-9e6
title: Glossiphonia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qal-58l-8bz
title: Cyathostoma americanum Chapin, 1925

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25335259
title: Grouped Heterogeneity in Linear Panel Data Models with Heterogeneous Error Variances

description: We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introduce an objective function that avoids a singularity that arises in a pseudolikelihood approach. We provide theoretical and numerical evidence showing when allowing for variance groups improves classification. The developed procedure provides new evidence on the relation between firm-level research and development (R&D) investments and the business cycle. We find a well-defined group structure in the variances that ex-post can be related to firm size. Our estimates indicate stronger procyclical investment patterns at medium-size firms compared to large firms.
DOI: 10.6084/m9.figshare.25335259.v3
title: Grouped Heterogeneity in Linear Panel Data Models with Heterogeneous Error Variances

description: We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introduce an objective function that avoids a singularity that arises in a pseudolikelihood approach. We provide theoretical and numerical evidence showing when allowing for variance groups improves classification. The developed procedure provides new evidence on the relation between firm-level research and development (R&D) investments and the business cycle. We find a well-defined group structure in the variances that ex-post can be related to firm size. Our estimates indicate stronger procyclical investment patterns at medium-size firms compared to large firms.
DOI: 10.3535/85g-1lv-1le
title: Phagocata vivida (Ijima & Kaburaki, 1916)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wx0-t0s-74b
title: Phagocata vivida (Ijima & Kaburaki, 1916)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25335259.v2
title: Grouped heterogeneity in linear panel data models with heterogeneous error variances

description: We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introduce an objective function that avoids a singularity that arises in a pseudolikelihood approach. We provide theoretical and numerical evidence showing when allowing for variance groups improves classification. The developed procedure provides new evidence on the relation between firm-level research and development (R&D) investments and the business cycle. We find a well-defined group structure in the variances that ex-post can be related to firm size. Our estimates indicate stronger procyclical investment patterns at medium-size firms compared to large firms.
DOI: 10.6084/m9.figshare.25872449
title: Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

description: Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with many covariates and small sample size. In addition, the quantification of estimation uncertainty remains a challenge. We develop a general approach to statistical inference for heterogeneous treatment effects discovered by a generic ML algorithm. We apply the Neyman’s repeated sampling framework to a common setting, in which researchers use an ML algorithm to estimate the conditional average treatment effect and then divide the sample into several groups based on the magnitude of the estimated effects. We show how to estimate the average treatment effect within each of these groups, and construct a valid confidence interval. In addition, we develop nonparametric tests of treatment effect homogeneity across groups, and rank-consistency of within-group average treatment effects. The validity of our methodology does not rely on the properties of ML algorithms because it is solely based on the randomization of treatment assignment and random sampling of units. Finally, we generalize our methodology to the cross-fitting procedure by accounting for the additional uncertainty induced by the random splitting of data.
DOI: 10.6084/m9.figshare.25872449.v2
title: Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

description: Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with many covariates and small sample size. In addition, the quantification of estimation uncertainty remains a challenge. We develop a general approach to statistical inference for heterogeneous treatment effects discovered by a generic ML algorithm. We apply the Neyman’s repeated sampling framework to a common setting, in which researchers use an ML algorithm to estimate the conditional average treatment effect and then divide the sample into several groups based on the magnitude of the estimated effects. We show how to estimate the average treatment effect within each of these groups, and construct a valid confidence interval. In addition, we develop nonparametric tests of treatment effect homogeneity across groups, and rank-consistency of within-group average treatment effects. The validity of our methodology does not rely on the properties of ML algorithms because it is solely based on the randomization of treatment assignment and random sampling of units. Finally, we generalize our methodology to the cross-fitting procedure by accounting for the additional uncertainty induced by the random splitting of data.
DOI: 10.3535/7m9-qyp-nrj
title: Vejdovskyella (Machetna) intermedia (Bretscher, 1896)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25872449.v1
title: Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments*

description: Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with many covariates and small sample size. In addition, the quantification of estimation uncertainty remains a challenge. We develop a general approach to statistical inference for heterogeneous treatment effects discovered by a generic ML algorithm. We apply the Neyman’s repeated sampling framework to a common setting, in which researchers use an ML algorithm to estimate the conditional average treatment effect and then divide the sample into several groups based on the magnitude of the estimated effects. We show how to estimate the average treatment effect within each of these groups, and construct a valid confidence interval. In addition, we develop nonparametric tests of treatment effect homogeneity across groups, and rank-consistency of within-group average treatment effects. The validity of our methodology does not rely on the properties of ML algorithms because it is solely based on the randomization of treatment assignment and random sampling of units. Finally, we generalize our methodology to the cross-fitting procedure by accounting for the additional uncertainty induced by the random splitting of data.
DOI: 10.3535/by9-4lt-b7n
title: Glandiceps talaboti (Marion, 1876)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/sn7-jes-bxz
title: Vejdovskyella (Machetna) intermedia (Bretscher, 1896)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25342651
title: Reduced Rank Spatio-Temporal Models

description: To simultaneously model the cross-sectional dependency and dynamic time dependency among <i>n</i> units, most research in spatial econometrics parameterizes the coefficient matrices among the <i>n</i> units as functions of known weights matrices. This modeling framework is over-simplified and faces the risk of misspecification when constructing the weights matrices. In this article, we propose a novel reduced-rank spatio-temporal model by assuming the coefficient matrices follow a reduced-rank structure. This specification avoids construction of the weights matrices and provides a good interpretation, especially for financial data. To estimate the unknown parameters, a quasi-maximum likelihood estimator (QMLE) is proposed and obtained via the Gradient descent algorithm with Armijo line search. We establish the asymptotic properties of QMLE when the number of units and the number of time periods both diverge to infinity. To determine the rank, we propose a ridge-type ratio estimator and demonstrate its rank selection consistency. The proposed methodology is illustrated via extensive simulation studies. Finally, a Chinese stock dataset is analyzed to investigate the cross-sectional and temporal spillover effects among stock returns.