cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25452/figshare.plus.20492769
title: THINGS-data: fMRI Regions of Interest

description: Subject specific category-selective and retinotopic regions of interest for the fMRI data. <br> Part of THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in brain and behavior. <br> See related materials in Collection at: https://doi.org/10.25452/figshare.plus.c.6161151
DOI: 10.25452/figshare.plus.20492775
title: THINGS-data: fMRI Noise Ceilings

description: Voxel-wise noise ceilings for the fMRI data which is part of the THINGS-data collection. Noise ceilings are provided for each subject and for different numbers of trials which may be averaged in a given analysis. <br> Part of THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in brain and behavior. <br> See related materials in Collection at: https://doi.org/10.25452/figshare.plus.c.6161151
DOI: 10.25452/figshare.plus.20496702
title: THINGS-data: fMRI cortical surface flat maps

description: Cortical flat maps for three subjects derived from the anatomical MRI images. Cortical surfaces were reconstructed from T1-weighted and T2-weighted anatomical images with freesurfer's reconall procedure. Relaxation cuts were placed manually to allow for flattening of each hemisphere's surface. Results of any analysis of the fMRI data can be viewed on these flat maps with pycortex. <br> Part of THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in brain and behavior <br> See related materials in Collection at: https://doi.org/10.25452/figshare.plus.c.6161151
DOI: 10.25452/figshare.plus.20552784
title: THINGS-data: Behavioral odd-one-out data and code

description: 4.7 million object odd-one-out judgements from human participants on Amazon Mechanical Turk. <br> Part of THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in brain and behavior. <br> See related materials in Collection at: https://doi.org/10.25452/figshare.plus.c.6161151
DOI: 10.5517/ccdc.csd.cc26prsg
title: CCDC 2046706: Experimental Crystal Structure Determination

description: Related Article: Vijay S. Parmar, Andreas M. Thiel, Rizwan Nabi, Gemma K. Gransbury, Marie S. Norre, Peter Evans, Sophie C. Corner, Jonathan M. Skelton, Nicholas F. Chilton, David P. Mills, Jacob Overgaard|2023|Chem.Commun.|59|2656|doi:10.1039/D2CC06722F
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.cc26przn
title: CCDC 2046712: Experimental Crystal Structure Determination

description: Related Article: Vijay S. Parmar, Andreas M. Thiel, Rizwan Nabi, Gemma K. Gransbury, Marie S. Norre, Peter Evans, Sophie C. Corner, Jonathan M. Skelton, Nicholas F. Chilton, David P. Mills, Jacob Overgaard|2023|Chem.Commun.|59|2656|doi:10.1039/D2CC06722F
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.cc26prym
title: CCDC 2046711: Experimental Crystal Structure Determination

description: Related Article: Vijay S. Parmar, Andreas M. Thiel, Rizwan Nabi, Gemma K. Gransbury, Marie S. Norre, Peter Evans, Sophie C. Corner, Jonathan M. Skelton, Nicholas F. Chilton, David P. Mills, Jacob Overgaard|2023|Chem.Commun.|59|2656|doi:10.1039/D2CC06722F
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.cc26ps0q
title: CCDC 2046713: Experimental Crystal Structure Determination

description: Related Article: Vijay S. Parmar, Andreas M. Thiel, Rizwan Nabi, Gemma K. Gransbury, Marie S. Norre, Peter Evans, Sophie C. Corner, Jonathan M. Skelton, Nicholas F. Chilton, David P. Mills, Jacob Overgaard|2023|Chem.Commun.|59|2656|doi:10.1039/D2CC06722F
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.cc26prth
title: CCDC 2046707: Experimental Crystal Structure Determination

description: Related Article: Vijay S. Parmar, Andreas M. Thiel, Rizwan Nabi, Gemma K. Gransbury, Marie S. Norre, Peter Evans, Sophie C. Corner, Jonathan M. Skelton, Nicholas F. Chilton, David P. Mills, Jacob Overgaard|2023|Chem.Commun.|59|2656|doi:10.1039/D2CC06722F
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.cc2d7zk6
title: CCDC 2212208: Experimental Crystal Structure Determination

description: Related Article: Manabu Yamada, Ruka Yoshizaki, Fumiya Uemura, Hiroshi Katagiri, Sumio Kato, Kazuhiko Akimoto, Fumio Hamada|2023|Chem.Commun.|59|2604|doi:10.1039/D2CC05647J
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.cc2d7zm8
title: CCDC 2212210: Experimental Crystal Structure Determination

description: Related Article: Manabu Yamada, Ruka Yoshizaki, Fumiya Uemura, Hiroshi Katagiri, Sumio Kato, Kazuhiko Akimoto, Fumio Hamada|2023|Chem.Commun.|59|2604|doi:10.1039/D2CC05647J
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.cc2djnp9
title: CCDC 2220551: Experimental Crystal Structure Determination

description: Related Article: Tingwei Wang, Zujia Lu, Zhenxin Yi, Baolong Kuang, Shu Bu, Zhiming Xie, Chao Zhang, Yan Li, Kun Wang, Jian-Guo Zhang|2023|Inorg.Chem.Front.|10|1475|doi:10.1039/D2QI02679A
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.cc2djnrc
title: CCDC 2220553: Experimental Crystal Structure Determination

description: Related Article: Raghunath Bag, Nagendra K. Sharma|2023|Org.Chem.Front.|10|1252|doi:10.1039/D2QO01876D
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.cc2djnsd
title: CCDC 2220554: Experimental Crystal Structure Determination

description: Related Article: Raghunath Bag, Nagendra K. Sharma|2023|Org.Chem.Front.|10|1252|doi:10.1039/D2QO01876D
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.cc2dng4p
title: CCDC 2224192: Experimental Crystal Structure Determination

description: Related Article: Yuji Liu, Mingjie Tang, Yuan Lai, Wei Huang, Yongxing Tang|2023|Org.Lett.|25|1061|doi:10.1021/acs.orglett.2c04242
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.cc2dng5q
title: CCDC 2224193: Experimental Crystal Structure Determination

description: Related Article: Yuji Liu, Mingjie Tang, Yuan Lai, Wei Huang, Yongxing Tang|2023|Org.Lett.|25|1061|doi:10.1021/acs.orglett.2c04242
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.cc20zk8v
title: CCDC 1876407: Experimental Crystal Structure Determination

description: Related Article: A. P. Prakasham, Sagar K. Patil, Chandrasekhar Nettem, Shreyata Dey, Gopalan Rajaraman, Prasenjit Ghosh|2023|ACS Omega|8|6439|doi:10.1021/acsomega.2c06729
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.cc20zk5r
title: CCDC 1876404: Experimental Crystal Structure Determination

description: Related Article: A. P. Prakasham, Sagar K. Patil, Chandrasekhar Nettem, Shreyata Dey, Gopalan Rajaraman, Prasenjit Ghosh|2023|ACS Omega|8|6439|doi:10.1021/acsomega.2c06729
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.cc215wlq
title: CCDC 1882493: Experimental Crystal Structure Determination

description: Related Article: A. P. Prakasham, Sagar K. Patil, Chandrasekhar Nettem, Shreyata Dey, Gopalan Rajaraman, Prasenjit Ghosh|2023|ACS Omega|8|6439|doi:10.1021/acsomega.2c06729
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.cc21bt3b
title: CCDC 1887221: Experimental Crystal Structure Determination

description: Related Article: A. P. Prakasham, Sagar K. Patil, Chandrasekhar Nettem, Shreyata Dey, Gopalan Rajaraman, Prasenjit Ghosh|2023|ACS Omega|8|6439|doi:10.1021/acsomega.2c06729
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.cc2bvfzm
title: CCDC 2170371: Experimental Crystal Structure Determination

description: Related Article: A. P. Prakasham, Sagar K. Patil, Chandrasekhar Nettem, Shreyata Dey, Gopalan Rajaraman, Prasenjit Ghosh|2023|ACS Omega|8|6439|doi:10.1021/acsomega.2c06729
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.cc2by9m7
title: CCDC 2173119: Experimental Crystal Structure Determination

description: Related Article: A. P. Prakasham, Sagar K. Patil, Chandrasekhar Nettem, Shreyata Dey, Gopalan Rajaraman, Prasenjit Ghosh|2023|ACS Omega|8|6439|doi:10.1021/acsomega.2c06729
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.cc2bhxd5
title: CCDC 2160247: Experimental Crystal Structure Determination

description: Related Article: A. P. Prakasham, Sagar K. Patil, Chandrasekhar Nettem, Shreyata Dey, Gopalan Rajaraman, Prasenjit Ghosh|2023|ACS Omega|8|6439|doi:10.1021/acsomega.2c06729
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: 10.15139/s3/d2in0f
title: Vision Zero San Antonio Action Plan (2016). San Antonio, Texas

description: Vision Zero is a strategy to reduce all traffic fatalities and severe injuries to zero, while increasing safe, equitable, and healthy mobility for all. Vision Zero Plans help guide municipalities, counties, and other areas to address these strategies considering the local context.
DOI: 10.15139/s3/ncnwgu
title: City of Bethlehem Vision Zero Action Plan (2016). Bethlehem, Pennsylvania

description: Vision Zero is a strategy to reduce all traffic fatalities and severe injuries to zero, while increasing safe, equitable, and healthy mobility for all. Vision Zero Plans help guide municipalities, counties, and other areas to address these strategies considering the local context.
DOI: 10.25376/hra.22191298
title: Figure 1.tiff

description: STAT3 is increased in fibrotic kidney tissues of human and mouse models. STAT3 phosphorylation occurs in pericytes, tubular cells, and other interstitial cells.
DOI: 10.25376/hra.22191298.v1
title: Figure 1.tiff

description: STAT3 is increased in fibrotic kidney tissues of human and mouse models. STAT3 phosphorylation occurs in pericytes, tubular cells, and other interstitial cells.
DOI: 10.25376/hra.22191310
title: Figure 2.tiff

description: STAT3 KO from pericytes protects mice from kidney fibrosis by reducing inflammation.
DOI: 10.25376/hra.22191310.v1
title: Figure 2.tiff

description: STAT3 KO from pericytes protects mice from kidney fibrosis by reducing inflammation.
DOI: 10.5281/zenodo.7385944
title: The effect of shear displacement and wear on fault stability: laboratory constraints

description: This is the ReadMe file corresponding to the study entitled:<br> "The effect of shear displacement and wear on fault stability: laboratory constraints"<br> By Noël C., Giorgetti c., Scuderi M.M., Collettini C. and Marone C. This study has been submited in JGR: Solid Earth This Read-Me file has been last edited in December 2022 This readme file describes the data repository and supplementary files accompanying the above publication. <br> For any further queries please contact corentin.noel@uniroma1.it
DOI: 10.5281/zenodo.7385943
title: The effect of shear displacement and wear on fault stability: laboratory constraints

description: This is the ReadMe file corresponding to the study entitled:<br> "The effect of shear displacement and wear on fault stability: laboratory constraints"<br> By Noël C., Giorgetti c., Scuderi M.M., Collettini C. and Marone C. This study has been submited in JGR: Solid Earth This Read-Me file has been last edited in December 2022 This readme file describes the data repository and supplementary files accompanying the above publication. <br> For any further queries please contact corentin.noel@uniroma1.it
DOI: 10.6084/m9.figshare.21948530.v1
title: On Semiparametrically Dynamic Functional-coefficient Autoregressive Spatio-Temporal Models with Irregular Location Wide Nonstationarity

description: Nonlinear dynamic modelling of spatio-temporal data is often a challenge, especially due to irregularly observed locations and location-wide non-stationarity. In this paper we propose a semiparametric family of Dynamic Functional-coefficient Autoregressive Spatio-Temporal (DyFAST) models to address the difficulties. We specify the autoregressive smoothing coefficients depending dynamically on both a concerned regime and location so that the models can characterise not only the dynamic regime-switching nature but also the location-wide non-stationarity in real data. Different smoothing schemes are then proposed to model the dynamic neighbouring-time interaction effects with irregular locations incorporated by (spatial) weight matrices. The first scheme popular in econometrics supposes that the weight matrix is pre-specified. We show that locally optimal bandwidths by a greedy idea popular in machine learning should be cautiously applied. Moreover, many weight matrices can be generated differently by data location features. Model selection is popular, but may suffer from loss of different candidate features. Our second scheme is thus to suggest a weight matrix fusion to let data combine or select the candidates with estimation done simultaneously. Both theoretical properties and Monte Carlo simulations are investigated. The empirical application to an EU energy market dataset further demonstrates the usefulness of our DyFAST models.
DOI: 10.6084/m9.figshare.22191383.v1
title: One Does Not Simply Correct for Serial Dependence

description: Serial dependence is present in most time series data sets collected in psychological research. This paper investigates the implications of various approaches for handling such serial dependence, when one is interested in the linear effect of a time-varying covariate on the time-varying criterion. Specifically, the serial dependence is either neglected, corrected for by specifying autocorrelated residuals, or modeled by including a lagged version of the criterion as an additional predictor. Using both empirical and simulated data, we showcase that the obtained results depend considerably on which approach is selected. We discuss how these differences can be explained by understanding the restrictions imposed under the various approaches. Based on the insight that all three approaches are restricted versions of an autoregressive distributed lag model, we demonstrate that accessible statistical tools, such as information criteria and likelihood-ratio tests can be used to justify a chosen approach empirically.
DOI: 10.6084/m9.figshare.22191383
title: One Does Not Simply Correct for Serial Dependence

description: Serial dependence is present in most time series data sets collected in psychological research. This paper investigates the implications of various approaches for handling such serial dependence, when one is interested in the linear effect of a time-varying covariate on the time-varying criterion. Specifically, the serial dependence is either neglected, corrected for by specifying autocorrelated residuals, or modeled by including a lagged version of the criterion as an additional predictor. Using both empirical and simulated data, we showcase that the obtained results depend considerably on which approach is selected. We discuss how these differences can be explained by understanding the restrictions imposed under the various approaches. Based on the insight that all three approaches are restricted versions of an autoregressive distributed lag model, we demonstrate that accessible statistical tools, such as information criteria and likelihood-ratio tests can be used to justify a chosen approach empirically.
DOI: 10.6084/m9.figshare.21834258.v1
title: A Correlated Network Scale-up Model: Finding the Connection Between Subpopulations

description: Aggregated relational data (ARD), formed from “How many X’s do you know?” questions, is a powerful tool for learning important network characteristics with incomplete network data. Compared to traditional survey methods, ARD is attractive as it does not require a sample from the target population and does not ask respondents to self-reveal their own status. This is helpful for studying hard-to-reach populations like female sex workers who may be hesitant to reveal their status. From December 2008 to February 2009, the Kiev International Institute of Sociology (KIIS) collected ARD from 10,866 respondents to estimate the size of HIV-related groups in Ukraine. To analyze this data, we propose a new ARD model which incorporates respondent and group covariates in a regression framework and includes a bias term that is correlated between groups. We also introduce a new scaling procedure utilizing the correlation structure to further reduce biases. The resulting size estimates of those most-at-risk of HIV infection can improve the HIV response efficiency in Ukraine. Additionally, the proposed model allows us to better understand two network features without the full network data: 1. What characteristics affect who respondents know, and 2. How is knowing someone from one group related to knowing people from other groups. These features can allow researchers to better recruit marginalized individuals into the prevention and treatment programs. Our proposed model and several existing NSUM models are implemented in the networkscaleup R package.