cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20009791
title: Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models

description: We study the problem of detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models, whose transition matrices exhibit low rank plus sparse structure. We first address the problem of detecting a single change point using an exhaustive search algorithm and establish a finite sample error bound for its accuracy. Next, we extend the results to the case of multiple change points that can grow as a function of the sample size. Their detection is based on a two-step algorithm, wherein the first step, an exhaustive search for a candidate change point is employed for overlapping windows, and subsequently a backward elimination procedure is used to screen out redundant candidates. The two-step strategy yields consistent estimates of the number and the locations of the change points. To reduce computation cost, we also investigate conditions under which a surrogate VAR model with a weakly sparse transition matrix can accurately estimate the change points and their locations for data generated by the original model. This work also addresses and resolves a number of novel technical challenges posed by the nature of the VAR models under consideration. The effectiveness of the proposed algorithms and methodology is illustrated on both synthetic and two real datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20009791.v1
title: Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models

description: We study the problem of detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models, whose transition matrices exhibit low rank plus sparse structure. We first address the problem of detecting a single change point using an exhaustive search algorithm and establish a finite sample error bound for its accuracy. Next, we extend the results to the case of multiple change points that can grow as a function of the sample size. Their detection is based on a two-step algorithm, wherein the first step, an exhaustive search for a candidate change point is employed for overlapping windows, and subsequently a backward elimination procedure is used to screen out redundant candidates. The two-step strategy yields consistent estimates of the number and the locations of the change points. To reduce computation cost, we also investigate conditions under which a surrogate VAR model with a weakly sparse transition matrix can accurately estimate the change points and their locations for data generated by the original model. This work also addresses and resolves a number of novel technical challenges posed by the nature of the VAR models under consideration. The effectiveness of the proposed algorithms and methodology is illustrated on both synthetic and two real datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3761562.v2511
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.22190185
title: NaneSoil

description: Soil samples were collected from plots located in the Irrigation District 023, San Juan del Rio, Queretaro during the period 2016-2018. NaneSoil contains most of the agronomic parameters necessary for the study of crop water requirements in different soils, and therefore, it is an excellent tool that can be used by farmers, academics, students, and people involved in crop production and research. The database includes 901 rows and 13 colums. The first row corresponds to the titles or attributes contained in the columns, as well as the units in which they are displayed. The thirteen columns are ordered as follows: sample identifier (A), UTM coordinates corresponding to zone 14 North (B and C), representative sample area (D), percentages of sand (E), silt (F) and clay (G), textural classification (H), bulk density (I), saturated moisture content (J), field capacity (K), permanent wilting point (L) and the saturated hydraulic conductivity (M). The USDA texture triangle was used for texture classification
DOI: 10.6084/m9.figshare.22190185.v1
title: NaneSoil

description: Soil samples were collected from plots located in the Irrigation District 023, San Juan del Rio, Queretaro during the period 2016-2018. NaneSoil contains most of the agronomic parameters necessary for the study of crop water requirements in different soils, and therefore, it is an excellent tool that can be used by farmers, academics, students, and people involved in crop production and research. <br> The database includes 901 rows and 13 colums. The first row corresponds to the titles or attributes contained in the columns, as well as the units in which they are displayed. The thirteen columns are ordered as follows: sample identifier (A), UTM coordinates corresponding to zone 14 North (B and C), representative sample area (D), percentages of sand (E), silt (F) and clay (G), textural classification (H), bulk density (I), saturated moisture content (J), field capacity (K), permanent wilting point (L) and the saturated hydraulic conductivity (M). The USDA texture triangle was used for texture classification
DOI: 10.6084/m9.figshare.22191364
title: Data set - Harmful consumption and alcohol dependence in Peruvian university students with body dissatisfaction.xlsx

description: The database presented is part of an observational cross-sectional study in university students. It has different sociodemographic variables; as well as scores obtained in the Body Satisfaction Questionnaire (BSQ) and the Alcohol Use Disorders Identification Test (AUDIT)
DOI: 10.6084/m9.figshare.22191364.v1
title: Data set - Harmful consumption and alcohol dependence in Peruvian university students with body dissatisfaction.xlsx

description: The database presented is part of an observational cross-sectional study in university students. It has different sociodemographic variables; as well as scores obtained in the Body Satisfaction Questionnaire (BSQ) and the Alcohol Use Disorders Identification Test (AUDIT)
DOI: 10.6084/m9.figshare.22191379
title: FenFit Tabular Data.xlsx

description: Raw data used to generate figures in FenFit paper
DOI: 10.6084/m9.figshare.22191379.v2
title: FenFit Tabular Data.xlsx

description: Raw data used to generate figures in FenFit paper
DOI: 10.6084/m9.figshare.22191379.v1
title: FenFit tabular data.xlsx

description: Raw data used to generate figures in FenFit paper
DOI: 10.6084/m9.figshare.22191400.v2
title: FenFit Aortic CT Segmentations

description: Raw de-identified patient geometry files used in FenFit paper
DOI: 10.5281/zenodo.7686343
title: Revised kinematic slip model of the 2019 Ridgecrest (California) earthquakes

description: The published slip model of Jin and Fialko (2020) features a complex rupture geometry in the shallow crust, including a “flower structure” formed by intersecting splay faults. Slip inversions were performed using the first-order Tikhonov regularization to moderate extreme variation in slip between the adjacent slip patches, including between slip patches that belonged to different sub-faults. This resulted in a highly peaked along-strike averaged normalized coseismic slip at the bottom of the inferred flower structure (3-4 km, Figure 15a in Jin and Fialko, 2020). However, in case of bifurcating faults, or “Y-shaped” fault intersections, a more appropriate regularization would be to preserve the slip budget between the bifurcated fault strands and a “parent” fault segment such that \(U_s^1 + U_s^2 = U_p\), where \(U_s^1, U_s^2\) are slip components that belong to two (or more) splay faults, and \(U_p \) is the respective slip component on the parent fault (Jin and Fialko, 2021). We re-run inversions of space geodetic data for the Ridgecrest earthquakes, as described in Jin and Fialko (2020), but with the imposed “slip budget” constraint for the intersecting faults. References: Jin, Z., & Fialko, Y. (2020). Finite slip models of the 2019 Ridgecrest earthquake sequence constrained by space geodetic data and aftershock locations. Bull. Seism. Soc. Am., 110, 1660–1679. Jin, Z., & Fialko, Y. (2021). Coseismic and early postseismic deformation due to the 2021 M7.4 Maduo (China) earthquake. Geophys. Res. Lett., 48, e2021GL095213.
DOI: 10.5281/zenodo.7686342
title: Revised kinematic slip model of the 2019 Ridgecrest (California) earthquakes

description: The published slip model of Jin and Fialko (2020) features a complex rupture geometry in the shallow crust, including a “flower structure” formed by intersecting splay faults. Slip inversions were performed using the first-order Tikhonov regularization to moderate extreme variation in slip between the adjacent slip patches, including between slip patches that belonged to different sub-faults. This resulted in a highly peaked along-strike averaged normalized coseismic slip at the bottom of the inferred flower structure (3-4 km, Figure 15a in Jin and Fialko, 2020). However, in case of bifurcating faults, or “Y-shaped” fault intersections, a more appropriate regularization would be to preserve the slip budget between the bifurcated fault strands and a “parent” fault segment such that \(U_s^1 + U_s^2 = U_p\), where \(U_s^1, U_s^2\) are slip components that belong to two (or more) splay faults, and \(U_p \) is the respective slip component on the parent fault (Jin and Fialko, 2021). We re-run inversions of space geodetic data for the Ridgecrest earthquakes, as described in Jin and Fialko (2020), but with the imposed “slip budget” constraint for the intersecting faults. References: Jin, Z., & Fialko, Y. (2020). Finite slip models of the 2019 Ridgecrest earthquake sequence constrained by space geodetic data and aftershock locations. Bull. Seism. Soc. Am., 110, 1660–1679. Jin, Z., & Fialko, Y. (2021). Coseismic and early postseismic deformation due to the 2021 M7.4 Maduo (China) earthquake. Geophys. Res. Lett., 48, e2021GL095213.
DOI: 10.26093/cds/vizier.19090176
title: SN2017hpa carbon-rich type Ia LCs and sp.

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'SN 2017hpa: a nearby carbon-rich type Ia supernova with a large velocity gradient.' (bibcode: 2021ApJ...909..176Z)
DOI: 10.26093/cds/vizier.21500001
title: VV29 and NGC 4676 HST photometry

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'Faint galaxies in Deep Advanced Camera for Surveys observations.' (bibcode: 2004ApJS..150....1B)
DOI: 10.5281/zenodo.7188726
title: Data and Code from "Structure-based prediction of Ras-effector binding affinities and design of 'branchegetic' interface mutations"

description: Data, data generation and data analysis for manuscript "Structure-based prediction of Ras-effector binding affinities and design of ‘branchegetic’ interface mutations", currently available as a preprint here. Contains the following directories: 01_models: Contains all scripts for model generation and selection, as well as some of the generated and selected models. 01_inputs: The different inputs for the homology modelling pipeline. This includes AlphaFold single and complex templates, PDB templates and sequence alignments. 02_validation: Model generation and initial selection for validation models, based on AF2 single models and PDB complex models. 03_production1: Model generation and initial selection for Ras effector complexes, based on AF2 single models and PDB complex models. 04_production2: Model generation and initial selection for Ras effector complexes, based on AF2 single models and AF2 complex models. 05_selection_optics: Code and analysis for selection by unsupervised learning using OPTICS. 02_selected_models: The three representative models selected for each complex. 03_affinity_prediction: Contains code and data for the prediction of binding affinities for Ras effector complexes. 04_branch_pruning: Contains code and data for branch pruning analysis. 05_systems_analysis: Contains code and data for the analysis of Ras effector systems based on affinities derived from affinity prediction and branch pruning analysis. 06_visualization: Information on where in the raw data the panels for the figures in the manuscript can be found.
DOI: 10.5281/zenodo.7188727
title: Data and Code from "Structure-based prediction of Ras-effector binding affinities and design of 'branchegetic' interface mutations"

description: Data, data generation and data analysis for manuscript "Structure-based prediction of Ras-effector binding affinities and design of ‘branchegetic’ interface mutations", currently available as a preprint here. Contains the following directories: 01_models: Contains all scripts for model generation and selection, as well as some of the generated and selected models. 01_inputs: The different inputs for the homology modelling pipeline. This includes AlphaFold single and complex templates, PDB templates and sequence alignments. 02_validation: Model generation and initial selection for validation models, based on AF2 single models and PDB complex models. 03_production1: Model generation and initial selection for Ras effector complexes, based on AF2 single models and PDB complex models. 04_production2: Model generation and initial selection for Ras effector complexes, based on AF2 single models and AF2 complex models. 05_selection_optics: Code and analysis for selection by unsupervised learning using OPTICS. 02_selected_models: The three representative models selected for each complex. 03_affinity_prediction: Contains code and data for the prediction of binding affinities for Ras effector complexes. 04_branch_pruning: Contains code and data for branch pruning analysis. 05_systems_analysis: Contains code and data for the analysis of Ras effector systems based on affinities derived from affinity prediction and branch pruning analysis. 06_visualization: Information on where in the raw data the panels for the figures in the manuscript can be found.
DOI: 10.5281/zenodo.7685617
title: Data and Code from "Structure-based prediction of Ras-effector binding affinities and design of 'branchegetic' interface mutations"

description: Data, data generation and data analysis for manuscript "Structure-based prediction of Ras-effector binding affinities and design of ‘branchegetic’ interface mutations", currently available as a preprint here. Contains the following directories: 01_models: Contains all scripts for model generation and selection, as well as some of the generated and selected models. 01_inputs: The different inputs for the homology modelling pipeline. This includes AlphaFold single and complex templates, PDB templates and sequence alignments. 02_validation: Model generation and initial selection for validation models, based on AF2 single models and PDB complex models. 03_production1: Model generation and initial selection for Ras effector complexes, based on AF2 single models and PDB complex models. 04_production2: Model generation and initial selection for Ras effector complexes, based on AF2 single models and AF2 complex models. 05_selection_optics: Code and analysis for selection by unsupervised learning using OPTICS. 02_selected_models: The three representative models selected for each complex. 03_affinity_prediction: Contains code and data for the prediction of binding affinities for Ras effector complexes. 04_branch_pruning: Contains code and data for branch pruning analysis. 05_systems_analysis: Contains code and data for the analysis of Ras effector systems based on affinities derived from affinity prediction and branch pruning analysis. 06_visualization: Information on where in the raw data the panels for the figures in the manuscript can be found.
DOI: 10.5281/zenodo.7686359
title: Prochlorococcus Cell Concentrations During the BiG-RAPA Expedition (Cruise MV1015) in the Peru Current and Eastern South Pacific Subtropical Gyre Between November and December of 2010

description: "These data include Prochlorococcus cell concentrations (total cell densities by flow cytometry and cell densities for specific ecotypes/clades determined by quantitative PCR). Samples were collected during the C-MORE Biogeochemical Gradients Role in Arranging Planktonic Assemblages (BiG-RAPA) expedition (Cruise MV1015) in the Peru Current and Eastern South Pacific Subtropical Gyre between dates 2010-11-19 and 2010-12-10 along a zonal transect from the northern coast of Chile to the island of Rapa Nui. Prochlorococcus is an important primary producer in the oligotrophic South Pacific Gyre and these data facilitate studies examining Prochlorococcus' ecology." https://www.bco-dmo.org/dataset/886299 Time is in UTC. Additional cruise information and methods can be found here: http://dmoserv3.bco-dmo.org/jg/info/BCO-DMO/C-MORE/prochlorococcus_data%7Bdir=dmoserv3.whoi.edu/jg/dir/BCO-DMO/C-MORE/,data=dmoserv3.bco-dmo.org:80/jg/serv/BCO-DMO/C-MORE/prochlorococcus_data.html0%7D?
DOI: 10.5281/zenodo.7686358
title: Prochlorococcus Cell Concentrations During the BiG-RAPA Expedition (Cruise MV1015) in the Peru Current and Eastern South Pacific Subtropical Gyre Between November and December of 2010

description: "These data include Prochlorococcus cell concentrations (total cell densities by flow cytometry and cell densities for specific ecotypes/clades determined by quantitative PCR). Samples were collected during the C-MORE Biogeochemical Gradients Role in Arranging Planktonic Assemblages (BiG-RAPA) expedition (Cruise MV1015) in the Peru Current and Eastern South Pacific Subtropical Gyre between dates 2010-11-19 and 2010-12-10 along a zonal transect from the northern coast of Chile to the island of Rapa Nui. Prochlorococcus is an important primary producer in the oligotrophic South Pacific Gyre and these data facilitate studies examining Prochlorococcus' ecology." https://www.bco-dmo.org/dataset/886299 Time is in UTC. Additional cruise information and methods can be found here: http://dmoserv3.bco-dmo.org/jg/info/BCO-DMO/C-MORE/prochlorococcus_data%7Bdir=dmoserv3.whoi.edu/jg/dir/BCO-DMO/C-MORE/,data=dmoserv3.bco-dmo.org:80/jg/serv/BCO-DMO/C-MORE/prochlorococcus_data.html0%7D?
DOI: 10.25345/c57659r26
title: MassIVE MSV000091380 - GNPS_U19_ADRC_Main_Study_Human_Fecal_Samples
title: Human fecal samples. Untargeted LC-MS/MS acquisition was performed on a Vanquish UHPLC system coupled to a Q-Exactive Hybrid Quadrupole-Orbitrap (Thermo Fisher Scientific, Bremen, Germany). Chromatographic separation was performed on a Kinetex 1.7 um 100 A pore size C18 reversed phase UHPLC column 50 x 2.1 mm (Phenomenex, Torrance, CA).
DOI: 10.15785/sbgrid/994
title: X-Ray Diffraction data from Mycobacterial glucosyl-3-phosphoglycerate synthase, source of 7PE4 structure

description: X-Ray Diffraction data from M. hassiacum GpgS, source of 7PE4 structure
DOI: 10.5517/ccdc.csd.cc2c306q
title: CCDC 2177632: 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.cc2dpxgh
title: CCDC 2225597: Experimental Crystal Structure Determination

description: Related Article: Chong Wang, Qing-Wen Zhu, Jian-Gang Yu, Xin Li, Hao Li, Li-Ying Sun, Ying-Feng Han|2023|Inorg.Chem.Front.|10|1457|doi:10.1039/D2QI02685F
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.cc2dpxjk
title: CCDC 2225599: Experimental Crystal Structure Determination

description: Related Article: Chong Wang, Qing-Wen Zhu, Jian-Gang Yu, Xin Li, Hao Li, Li-Ying Sun, Ying-Feng Han|2023|Inorg.Chem.Front.|10|1457|doi:10.1039/D2QI02685F
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.cc2dpxkl
title: CCDC 2225600: Experimental Crystal Structure Determination

description: Related Article: Chong Wang, Qing-Wen Zhu, Jian-Gang Yu, Xin Li, Hao Li, Li-Ying Sun, Ying-Feng Han|2023|Inorg.Chem.Front.|10|1457|doi:10.1039/D2QI02685F
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.cc2dpxlm
title: CCDC 2225601: Experimental Crystal Structure Determination

description: Related Article: Chong Wang, Qing-Wen Zhu, Jian-Gang Yu, Xin Li, Hao Li, Li-Ying Sun, Ying-Feng Han|2023|Inorg.Chem.Front.|10|1457|doi:10.1039/D2QI02685F
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.cc2dpxmn
title: CCDC 2225602: Experimental Crystal Structure Determination

description: Related Article: Chong Wang, Qing-Wen Zhu, Jian-Gang Yu, Xin Li, Hao Li, Li-Ying Sun, Ying-Feng Han|2023|Inorg.Chem.Front.|10|1457|doi:10.1039/D2QI02685F
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.cc2dpxnp
title: CCDC 2225603: Experimental Crystal Structure Determination

description: Related Article: Chong Wang, Qing-Wen Zhu, Jian-Gang Yu, Xin Li, Hao Li, Li-Ying Sun, Ying-Feng Han|2023|Inorg.Chem.Front.|10|1457|doi:10.1039/D2QI02685F
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.cc2dpxqr
title: CCDC 2225605: Experimental Crystal Structure Determination

description: Related Article: Chong Wang, Qing-Wen Zhu, Jian-Gang Yu, Xin Li, Hao Li, Li-Ying Sun, Ying-Feng Han|2023|Inorg.Chem.Front.|10|1457|doi:10.1039/D2QI02685F
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.