cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25378/janelia.24247201
title: Manual organelle segmentations (crop149) in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of mouse kidney (jrc_mus-kidney)

description: <b>This acquisition is part of the CellMap 2024 Segmentation Challenge</b><br><br>- <b>Challenge DOI:</b> https://doi.org/10.25378/janelia.c.7456966<br><br>- <b>Challenge Website:</b> https://cellmapchallenge.janelia.org/<br><br><b>Annotation description</b>: Dense segmentations of extracellular space, plasma membrane, vesicle membrane, endosome membrane, vesicle lumen, endosome lumen, cytosol, endosome, vesicle, cell in jrc_mus-kidney using Amira 3D 2021.1, Classic Segmentation Workroom and the 'Using Amira to manually segment organelles in vEM for machine learning V.3' annotation protocol.<br><br><b>Annotation ID</b>: crop149<br><br><b>Primary Annotator</b>: Woohyun Park<br><br><b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<br><br><b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<br><br><b>Annotated voxel size (nm)</b>: 4 x 4 x 4 (x, y, z)<br><br><b>Annotated data dimensions (µm)</b>: 0.8 x 0.8 x 0.8 (x, y, z)<br><br><b>Annotated data offset (nm)</b>: 11174 x 22006 x 96598 (x, y, z)<br><br><b>Classes annotated</b>: extracellular space, plasma membrane, vesicle membrane, endosome membrane, vesicle lumen, endosome lumen, cytosol, endosome, vesicle, cell<br><br><b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_mus-kidney/jrc_mus-kidney.zarr/recon-1/labels/groundtruth/crop149<br><br><b>Source (EM) dataset ID</b>: jrc_mus-kidney<br><br><b>Source (EM) voxel size (nm)</b>: 8 x 8 x 8 (x, y, z)<br><br><b>Source (EM) data dimensions (µm)</b>: 98.3 x 63.81 x 177.59 (x, y, z)<br><br><b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.16913035<br><br><b>Visualization website</b>: https://openorganelle.janelia.org/datasets/jrc_mus-kidney<br><br><b>Publication:</b> <i>CellMap Segmentation Challenge,</i> 2024.<br><br><b>The CellMap Project Team during this time consisted of:</b> David Ackerman, Davis Bennett, Marley Bryant, Hannah Nguyen, Grace Park, Alyson Petruncio, Alannah Post, Jacquelyn Price, Diana Ramirez, Jeff Rhoades, Rebecca Vorimo, Aubrey Weigel, Marwan Zouinkhi, Yurii Zubov.<br><br><b>The CellMap Project Team Steering Committee during this time consisted of:</b> Misha Ahrens, Christopher Beck, Teng-Leong Chew, Daniel Feliciano, Jan Funke, Harald Hess, Wyatt Korff, Jennifer Lippincott-Schwartz, Zhe J. Liu, Kayvon Pedram, Stephan Preibisch, Stephan Saalfeld, Ronald Vale, and Aubrey Weigel.
DOI: 10.25378/janelia.24247201.v1
title: Manual organelle segmentations (crop149) in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of mouse kidney (jrc_mus-kidney)

description: <b>This acquisition is part of the CellMap 2024 Segmentation Challenge</b><br><br>- <b>Challenge DOI:</b> https://doi.org/10.25378/janelia.c.7456966<br><br>- <b>Challenge Website:</b> https://cellmapchallenge.janelia.org/<br><br><b>Annotation description</b>: Dense segmentations of extracellular space, plasma membrane, vesicle membrane, endosome membrane, vesicle lumen, endosome lumen, cytosol, endosome, vesicle, cell in jrc_mus-kidney using Amira 3D 2021.1, Classic Segmentation Workroom and the 'Using Amira to manually segment organelles in vEM for machine learning V.3' annotation protocol.<br><br><b>Annotation ID</b>: crop149<br><br><b>Primary Annotator</b>: Woohyun Park<br><br><b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<br><br><b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<br><br><b>Annotated voxel size (nm)</b>: 4 x 4 x 4 (x, y, z)<br><br><b>Annotated data dimensions (µm)</b>: 0.8 x 0.8 x 0.8 (x, y, z)<br><br><b>Annotated data offset (nm)</b>: 11174 x 22006 x 96598 (x, y, z)<br><br><b>Classes annotated</b>: extracellular space, plasma membrane, vesicle membrane, endosome membrane, vesicle lumen, endosome lumen, cytosol, endosome, vesicle, cell<br><br><b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_mus-kidney/jrc_mus-kidney.zarr/recon-1/labels/groundtruth/crop149<br><br><b>Source (EM) dataset ID</b>: jrc_mus-kidney<br><br><b>Source (EM) voxel size (nm)</b>: 8 x 8 x 8 (x, y, z)<br><br><b>Source (EM) data dimensions (µm)</b>: 98.3 x 63.81 x 177.59 (x, y, z)<br><br><b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.16913035<br><br><b>Visualization website</b>: https://openorganelle.janelia.org/datasets/jrc_mus-kidney<br><br><b>Publication:</b> <i>CellMap Segmentation Challenge,</i> 2024.<br><br><b>The CellMap Project Team during this time consisted of:</b> David Ackerman, Davis Bennett, Marley Bryant, Hannah Nguyen, Grace Park, Alyson Petruncio, Alannah Post, Jacquelyn Price, Diana Ramirez, Jeff Rhoades, Rebecca Vorimo, Aubrey Weigel, Marwan Zouinkhi, Yurii Zubov.<br><br><b>The CellMap Project Team Steering Committee during this time consisted of:</b> Misha Ahrens, Christopher Beck, Teng-Leong Chew, Daniel Feliciano, Jan Funke, Harald Hess, Wyatt Korff, Jennifer Lippincott-Schwartz, Zhe J. Liu, Kayvon Pedram, Stephan Preibisch, Stephan Saalfeld, Ronald Vale, and Aubrey Weigel.
DOI: 10.3535/4k2-ac5-rlt
title: Paramphistomum Fischoeder, 1901

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/lrr-gqg-q01
title: Procerodes plebeius (Schmidt, 1861)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25298395
title: Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty

description: We develop a nonparametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values. The main building block of our model is a Gaussian process prior on the functional relationship that determines the conditional mean of the model, hence, the name of Gaussian process vector autoregression (GP-VAR). A flexible stochastic volatility specification is used to provide additional flexibility and control for heteroscedasticity. Markov chain Monte Carlo (MCMC) estimation is carried out through an efficient and scalable algorithm which can handle large models. The GP-VAR is used to analyze the effects of macroeconomic uncertainty, with a particular emphasis on time variation and asymmetries in the transmission mechanisms.
DOI: 10.6084/m9.figshare.25298395.v2
title: Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty

description: We develop a nonparametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values. The main building block of our model is a Gaussian process prior on the functional relationship that determines the conditional mean of the model, hence, the name of Gaussian process vector autoregression (GP-VAR). A flexible stochastic volatility specification is used to provide additional flexibility and control for heteroscedasticity. Markov chain Monte Carlo (MCMC) estimation is carried out through an efficient and scalable algorithm which can handle large models. The GP-VAR is used to analyze the effects of macroeconomic uncertainty, with a particular emphasis on time variation and asymmetries in the transmission mechanisms.
DOI: 10.6084/m9.figshare.25342651.v1
title: Reduced Rank Spatio-temporal Models

description: To simultaneously model the cross-sectional dependency and dynamic time dependency among n units, most research in spatial econometrics parameterizes the coefficient matrices among the n 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.
DOI: 10.3535/zse-hek-692
title: Cura pinguis (Weiss, 1909)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25298395.v1
title: Gaussian process vector autoregressions and macroeconomic uncertainty*

description: We develop a non-parametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values. The main building block of our model is a Gaussian process prior on the functional relationship that determines the conditional mean of the model, hence the name of Gaussian process vector autoregression (GP-VAR). A flexible stochastic volatility specification is used to provide additional flexibility and control for heteroskedasticity. Markov chain Monte Carlo (MCMC) estimation is carried out through an efficient and scalable algorithm which can handle large models. The GP-VAR is used to analyze the effects of macroeconomic uncertainty, with a particular emphasis on time variation and asymmetries in the transmission mechanisms.
DOI: 10.6084/m9.figshare.25438511
title: Abadie’s Kappa and Weighting Estimators of the Local Average Treatment Effect

description: Recent research has demonstrated the importance of flexibly controlling for covariates in instrumental variables estimation. In this article we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), motivated by Abadie’s kappa theorem and offering the requisite flexibility relative to standard practice. We argue that two of the estimators under consideration, which are weight normalized, are generally preferable. Several other estimators, which are unnormalized, do not satisfy the properties of scale invariance with respect to the natural logarithm and translation invariance, thereby exhibiting sensitivity to the units of measurement when estimating the LATE in logs and the centering of the outcome variable more generally. We also demonstrate that, when noncompliance is one sided, certain weighting estimators have the advantage of being based on a denominator that is strictly greater than zero by construction. This is the case for only one of the two normalized estimators, and we recommend this estimator for wider use. We illustrate our findings with a simulation study and three empirical applications, which clearly document the sensitivity of unnormalized estimators to how the outcome variable is coded. We implement the proposed estimators in the Stata package kappalate.
DOI: 10.6084/m9.figshare.25438511.v2
title: Abadie’s Kappa and Weighting Estimators of the Local Average Treatment Effect

description: Recent research has demonstrated the importance of flexibly controlling for covariates in instrumental variables estimation. In this article we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), motivated by Abadie’s kappa theorem and offering the requisite flexibility relative to standard practice. We argue that two of the estimators under consideration, which are weight normalized, are generally preferable. Several other estimators, which are unnormalized, do not satisfy the properties of scale invariance with respect to the natural logarithm and translation invariance, thereby exhibiting sensitivity to the units of measurement when estimating the LATE in logs and the centering of the outcome variable more generally. We also demonstrate that, when noncompliance is one sided, certain weighting estimators have the advantage of being based on a denominator that is strictly greater than zero by construction. This is the case for only one of the two normalized estimators, and we recommend this estimator for wider use. We illustrate our findings with a simulation study and three empirical applications, which clearly document the sensitivity of unnormalized estimators to how the outcome variable is coded. We implement the proposed estimators in the Stata package kappalate.
DOI: 10.3535/5j4-z13-t6t
title: Drepanophorella sebae Stiasny-Wijnhoff, 1936

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/dw5-x0v-tpe
title: Tripyla glomerans Bastian, 1865

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25266369
title: A Statistically Identified Structural Vector Autoregression with Endogenously Switching Volatility Regime

description: We introduce a structural vector autoregressive model with endogenously switching conditional covariance matrix. The structural shocks are identified by simultaneously diagonalizing the reduced form error covariance matrices. It is not, however, always clear whether the condition for the full statistical identification is satisfied, and its validity is difficult to justify formally. Therefore, we provide general sets of conditions, that allow to combine sign and zero restrictions on the impact matrix, for identifying a subset of the shocks when the condition for statistical identification of the model fails. In an empirical application to the effects of the U.S. monetary policy shock, we find that a contractionary monetary policy shock significantly decreases output in a persistent hump-shaped pattern. Prices decrease permanently, but there is short-run inertia in their response. The accompanying R package gmvarkit provides a comprehensive set of tools for numerical analysis of the model.
DOI: 10.6084/m9.figshare.25266369.v2
title: A Statistically Identified Structural Vector Autoregression with Endogenously Switching Volatility Regime

description: We introduce a structural vector autoregressive model with endogenously switching conditional covariance matrix. The structural shocks are identified by simultaneously diagonalizing the reduced form error covariance matrices. It is not, however, always clear whether the condition for the full statistical identification is satisfied, and its validity is difficult to justify formally. Therefore, we provide general sets of conditions, that allow to combine sign and zero restrictions on the impact matrix, for identifying a subset of the shocks when the condition for statistical identification of the model fails. In an empirical application to the effects of the U.S. monetary policy shock, we find that a contractionary monetary policy shock significantly decreases output in a persistent hump-shaped pattern. Prices decrease permanently, but there is short-run inertia in their response. The accompanying R package gmvarkit provides a comprehensive set of tools for numerical analysis of the model.
DOI: 10.6084/m9.figshare.25438511.v1
title: Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect*

description: Recent research has demonstrated the importance of flexibly controlling for covariates in instrumental variables estimation. In this paper we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), motivated by Abadie<i>’s</i> (2003) kappa theorem and offering the requisite flexibility relative to standard practice. We argue that two of the estimators under consideration, which are weight normalized, are generally preferable. Several other estimators, which are unnormalized, do not satisfy the properties of scale invariance with respect to the natural logarithm and translation invariance, thereby exhibiting sensitivity to the units of measurement when estimating the LATE in logs and the centering of the outcome variable more generally. We also demonstrate that, when noncompliance is one sided, certain weighting estimators have the advantage of being based on a denominator that is strictly greater than zero by construction. This is the case for only one of the two normalized estimators, and we recommend this estimator for wider use. We illustrate our findings with a simulation study and three empirical applications, which clearly document the sensitivity of unnormalized estimators to how the outcome variable is coded. We implement the proposed estimators in the Stata package kappalate.
DOI: 10.3535/l2v-1s6-ye8
title: Tripyla glomerans Bastian, 1865

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/mef-sge-t4p
title: Psilochasmus oxyurus (Creplin, 1825) Lühe, 1909

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25266369.v1
title: A statistically identified structural vector autoregression with endogenously switching volatility regime

description: We introduce a structural vector autoregressive model with endogenously switching conditional covariance matrix. The structural shocks are identified by simultaneously diagonalizing the reduced form error covariance matrices. It is not, however, always clear whether the condition for the full statistical identification is satisfied, and its validity is difficult to justify formally. Therefore, we provide general sets of conditions, that allow to combine sign and zero restrictions on the impact matrix, for identifying a subset of the shocks when the condition for statistical identification of the model fails. In an empirical application to the effects of the U.S. monetary policy shock, we find that a contractionary monetary policy shock significantly decreases output in a persistent hump-shaped pattern. Prices decrease permanently, but there is short-run inertia in their response. The accompanying R package gmvarkit provides a comprehensive set of tools for numerical analysis of the model.
DOI: 10.3535/kc8-2kv-50r
title: Mysta picta (Quatrefages, 1866)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25335247
title: Quantile Policy Effects: An Application to U.S. Macroprudential Policy

description: To assess the dynamic distributional impacts of macroeconomic policy, we propose quantile policy effects to quantify disparities between the quantiles of potential outcomes under different policies. We first identify quantile policy effects under the unconfoundedness assumption and propose an inverse probability weighting estimator. We then examine the asymptotic behavior of the proposed estimator in a time series framework and suggest a blockwise bootstrap method for inference. Applying this method, we investigate the effectiveness of U.S. macroprudential actions on bank credit growth from 1948 to 2019. Empirically, we find that the effects of macroprudential policy on credit growth are asymmetric and depend on the quantiles of credit growth. The tightening of macroprudential actions fails to rein in high credit growth, whereas easing policies do not effectively stimulate bank credit growth during low-growth periods. These findings suggest that U.S. macroprudential policies might not sufficiently address the challenges of soaring bank credit or ensure overarching financial stability.
DOI: 10.6084/m9.figshare.25335247.v3
title: Quantile Policy Effects: An Application to U.S. Macroprudential Policy

description: To assess the dynamic distributional impacts of macroeconomic policy, we propose quantile policy effects to quantify disparities between the quantiles of potential outcomes under different policies. We first identify quantile policy effects under the unconfoundedness assumption and propose an inverse probability weighting estimator. We then examine the asymptotic behavior of the proposed estimator in a time series framework and suggest a blockwise bootstrap method for inference. Applying this method, we investigate the effectiveness of U.S. macroprudential actions on bank credit growth from 1948 to 2019. Empirically, we find that the effects of macroprudential policy on credit growth are asymmetric and depend on the quantiles of credit growth. The tightening of macroprudential actions fails to rein in high credit growth, whereas easing policies do not effectively stimulate bank credit growth during low-growth periods. These findings suggest that U.S. macroprudential policies might not sufficiently address the challenges of soaring bank credit or ensure overarching financial stability.
DOI: 10.3535/xbd-csq-40j
title: Lamellibrachia luymesi van der Land & Nørrevang, 1975

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/yb0-en0-7kp
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.25335247.v2
title: Quantile Policy Effects: An Application to US Macroprudential Policy

description: To assess the dynamic distributional impacts of macroeconomic policy, we propose quantile policy effects to quantify disparities between the quantiles of potential outcomes under different policies. We first identify quantile policy effects under the unconfoundedness assumption and propose an inverse probability weighting estimator. We then examine the asymptotic behavior of the proposed estimator in a time series framework and suggest a blockwise bootstrap method for inference. Applying this method, we investigate the effectiveness of US macroprudential actions on bank credit growth from 1948 to 2019. Empirically, we find that the effects of macroprudential policy on credit growth are asymmetric and depend on the quantiles of credit growth. The tightening of macroprudential actions fails to rein in high credit growth, whereas easing policies do not effectively stimulate bank credit growth during low-growth periods. These findings suggest that US macroprudential policies might not sufficiently address the challenges of soaring bank credit or ensure overarching financial stability.
DOI: 10.6084/m9.figshare.25365593
title: Panel Data Cointegration Testing with Structural Instabilities

description: Spurious regression analysis in panel data when the time series are cross-section dependent is analyzed in the article. The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components. We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran’s common correlated effects approach. This result is used to design individual and panel cointegration test statistics that accommodate the presence of structural breaks that can induce parameter instabilities in the deterministic component, the cointegration vector and the common factor loadings.