cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7504288
title: Mice infected with Low shedder S. mansoni parasites from cross B - cage 1 - Liver histopathology data.

description: The present dataset contains all the histopathology images used to quantify fibrotic areas, parasite egg counts and to quantify granuloma areas in liver of mice infected with <em>S. mansoni</em> Low shedder line. These data are presented in the manuscript entitled "No evidence for schistosome parasite fitness trade-offs in the intermediate and definitive host" (dataset # 8/11).<br> Each folder corresponds to one mouse sample and contain, along with a readme file, all the files used to quantify fibrotic area and egg counts (_TRICH.czi), to quantify granuloma area (_HE.czi), and the annotation file (.annotations) containing all the annotated granuloma areas.
description: We generated and used 11 datasets for this manuscript. - 1 dataset containing all the phenotype data collected and R scripts used for data analysis, and to generated the figures (Figures 2 to 5) present in the manuscript. - 10 larger datasets containing all the histopathology images of infected and control mice. These datasets have been uploaded separately, due to their size, and numbered 2-11/11.
DOI: 10.5281/zenodo.7504287
title: Mice infected with Low shedder S. mansoni parasites from cross B - cage 1 - Liver histopathology data.

description: The present dataset contains all the histopathology images used to quantify fibrotic areas, parasite egg counts and to quantify granuloma areas in liver of mice infected with <em>S. mansoni</em> Low shedder line. These data are presented in the manuscript entitled "No evidence for schistosome parasite fitness trade-offs in the intermediate and definitive host" (dataset # 8/11).<br> Each folder corresponds to one mouse sample and contain, along with a readme file, all the files used to quantify fibrotic area and egg counts (_TRICH.czi), to quantify granuloma area (_HE.czi), and the annotation file (.annotations) containing all the annotated granuloma areas.
description: We generated and used 11 datasets for this manuscript. - 1 dataset containing all the phenotype data collected and R scripts used for data analysis, and to generated the figures (Figures 2 to 5) present in the manuscript. - 10 larger datasets containing all the histopathology images of infected and control mice. These datasets have been uploaded separately, due to their size, and numbered 2-11/11.
DOI: 10.6084/m9.figshare.21807153
title: Geographic Information System with spatial distribution of population and forest cover in extractive reserves of the Amazon biome, Brazil, 2010

description: The Geographic Information System (GIS) presented here was elaborated with the aim of making comparisons between the spatial distribution of population and forest cover in Extractive Reserves (RESEX) in the Amazon biome. <br> Two sets of files were included: <br> 1) <strong>DANTONAetal2023_Resex_GIS.zip</strong>. Shapefiles (.shp): <br> a. <em>RESERVAS_EXTRATIVISTAS</em>: Polygons of 31 RESEX in the Amazon biome, created until 2010. <br> b. <em>POPULAÇÃO_RESEX: </em>Statistical grid from the 2010 Demographic Census, with the resident population in regular cells of approximately 1km<sup>2</sup> overlapping the 31 Resex<em>.</em> <br> c. <em>USO_COBERTURA_RESEX</em>: Land cover in all 31 Resex in 2010. <br> 2) <strong>DANTONAetal2023_Resex_GIS.xlsx</strong>: Spreadsheets in MS Excel format: <br> a. <em>Dataset</em>: table with 24 variables regarding the volume and distribution of Population; Surroundings (10km buffer); Forest Cover and Fragmentation for each of the 31 Extractive Reserves. <br> b. <em>Correlation (Pearson)</em>: correlation matrix among the 24 variables of the Dataset table. <br> c. <em>P-value (correlation)</em>: p-value of correlations in the Correlation (Pearson) table. <br> <em>Variables</em>: List with the description of the 24 variables. <br> <br>
DOI: 10.6084/m9.figshare.21807153.v2
title: Geographic Information System with spatial distribution of population and forest cover in extractive reserves of the Amazon biome, Brazil, 2010

description: The Geographic Information System (GIS) presented here was elaborated with the aim of making comparisons between the spatial distribution of population and forest cover in Extractive Reserves (RESEX) in the Amazon biome. <br> Two sets of files were included: <br> 1) <strong>DANTONAetal2023_Resex_GIS.zip</strong>. Shapefiles (.shp): <br> a. <em>RESERVAS_EXTRATIVISTAS</em>: Polygons of 31 RESEX in the Amazon biome, created until 2010. <br> b. <em>POPULAÇÃO_RESEX: </em>Statistical grid from the 2010 Demographic Census, with the resident population in regular cells of approximately 1km<sup>2</sup> overlapping the 31 Resex<em>.</em> <br> c. <em>USO_COBERTURA_RESEX</em>: Land cover in all 31 Resex in 2010. <br> 2) <strong>DANTONAetal2023_Resex_GIS.xlsx</strong>: Spreadsheets in MS Excel format: <br> a. <em>Dataset</em>: table with 24 variables regarding the volume and distribution of Population; Surroundings (10km buffer); Forest Cover and Fragmentation for each of the 31 Extractive Reserves. <br> b. <em>Correlation (Pearson)</em>: correlation matrix among the 24 variables of the Dataset table. <br> c. <em>P-value (correlation)</em>: p-value of correlations in the Correlation (Pearson) table. <br> <em>Variables</em>: List with the description of the 24 variables. <br> <br>
DOI: 10.6084/m9.figshare.21807153.v1
title: Geographic Information System with spatial distribution of population and forest cover in extractive reserves of the Amazon biome, Brazil, 2010

description: The Geographic Information System (GIS) presented here was elaborated with the aim of making comparisons between the spatial distribution of population and forest cover in Extractive Reserves (RESEX) in the Amazon biome. <br> Two sets of files were included: <br> 1) <strong>DANTONAetal2023_Resex_GIS.zip</strong>. Shapefiles (.shp): <br> a. <em>RESERVAS_EXTRATIVISTAS</em>: Polygons of 31 RESEX in the Amazon biome, created until 2010. <br> b. <em>POPULAÇÃO_RESEX: </em>Statistical grid from the 2010 Demographic Census, with the resident population in regular cells of approximately 1km<sup>2</sup> overlapping the 31 Resex<em>.</em> <br> c. <em>USO_COBERTURA_RESEX</em>: Land cover in all 31 Resex in 2010. <br> 2) <strong>DANTONAetal2023_Resex_GIS.xlsx</strong>: Spreadsheets in MS Excel format: <br> a. <em>Dataset</em>: table with 24 variables regarding the volume and distribution of Population; Surroundings (10km buffer); Forest Cover and Fragmentation for each of the 31 Extractive Reserves. <br> b. <em>Correlation (Pearson)</em>: correlation matrix among the 24 variables of the Dataset table. <br> c. <em>P-value (correlation)</em>: p-value of correlations in the Correlation (Pearson) table. <br> <em>Variables</em>: List with the description of the 24 variables. <br> <br>
DOI: 10.5281/zenodo.6842197
title: Replication package for: How the Other Half Died: Immigration and Mortality in US Cities

description: The code in this replication package replicates *How the Other Half Died: Immigration and Mortality in US Cities*, RESTUD forthcoming. All code is in R. It first builds the analysis data from several raw data sources. It then produces all the figures and tables in the paper. One master file (`master.R`) runs all of the code to generate the input data and then the figures and tables in the paper (and then the figures and tables in the appendix).
DOI: 10.5281/zenodo.7506579
title: Large-scale survey of excitatory synapses reveals sublamina-specific and asymmetric synapse disassembly patterns in a neurodegenerative circuit

description: Dataset associated with the manuscript titled "<strong>Large-scale survey of excitatory synapses reveals sublamina-specific and asymmetric synapse disassembly patterns in a neurodegenerative circuit"</strong>
DOI: 10.5281/zenodo.7506578
title: Large-scale survey of excitatory synapses reveals sublamina-specific and asymmetric synapse disassembly patterns in a neurodegenerative circuit

description: Dataset associated with the manuscript titled "<strong>Large-scale survey of excitatory synapses reveals sublamina-specific and asymmetric synapse disassembly patterns in a neurodegenerative circuit"</strong>
DOI: 10.5281/zenodo.7506206
title: Data for "From pandemic to endemic: Spatial-temporal patterns of influenza-like-illness incidence in a Swiss canton, 1918-1924"

description: Datasets underlying the analysis of the paper: "From pandemic to endemic: Spatial-temporal patterns of influenza incidence in a Swiss canton, 1918-1924" <strong>Cofactors_1918.xlsx</strong> : Ecological cofactors for each bernese municipility <strong>Data_Influenza_Bern.xlsx: </strong>Influenza data (1920-1924) <strong>Data_Spanishflu.xlsx: </strong>"Spanish flu" data (1918-1919) <strong>Data_Population.xlsx: </strong>Population for each municipality <strong>Tb_death.xlsx : </strong>Number of tuberculosis death <strong>Fabrik_Statistik_1929.xlsx : </strong>Information about the factories
description: Funding: Stiftung für Wissenschaftliche Forschung der Universität Zürich (STWF, Grant number STWF-21-011), Digital Society Initiative (DSI), Digitalisierungsinitiaitve der Zürcher Hochschulen (DIZH)
DOI: 10.5281/zenodo.7506205
title: Data for "From pandemic to endemic: Spatial-temporal patterns of influenza-like-illness incidence in a Swiss canton, 1918-1924"

description: Datasets underlying the analysis of the paper: "From pandemic to endemic: Spatial-temporal patterns of influenza incidence in a Swiss canton, 1918-1924" <strong>Cofactors_1918.xlsx</strong> : Ecological cofactors for each bernese municipility <strong>Data_Influenza_Bern.xlsx: </strong>Influenza data (1920-1924) <strong>Data_Spanishflu.xlsx: </strong>"Spanish flu" data (1918-1919) <strong>Data_Population.xlsx: </strong>Population for each municipality <strong>Tb_death.xlsx : </strong>Number of tuberculosis death <strong>Fabrik_Statistik_1929.xlsx : </strong>Information about the factories
description: Funding: Stiftung für Wissenschaftliche Forschung der Universität Zürich (STWF, Grant number STWF-21-011), Digital Society Initiative (DSI), Digitalisierungsinitiaitve der Zürcher Hochschulen (DIZH)
DOI: 10.34740/kaggle/dsv/4812301
title: Mahanagar Yatayat Dataset
DOI: 10.6084/m9.figshare.8311070.v4
title: Patience Experimental Results

description: August 2022: This version (labelled "v7-2022") is for the data for a revised version of the paper at https://arxiv.org/abs/1906.12314 <br> Files containing experimental results for our work on Patience and Solitaire games including Klondxike, Canfield and others. <br> File is a tarred and gzipped archive of many files in a number of directories, the most important being many .csv files. Each line of a csv file contains an experimental result (not necessarily conclusive) of a run of Solvitaire on a randomly generated layout of a particular game of patience. <br> Additional directories include testing of our random number generation, replications of results from code produced by Jan Wolter, and testing of our code in its most recent version against the versions used in the main csv files. <br>
DOI: 10.6084/m9.figshare.21821760.v1
title: trussart-hiC-matrices

description: Trussart data for ChromFormer
DOI: 10.6084/m9.figshare.21821760
title: trussart-hiC-matrices

description: Trussart data for ChromFormer
DOI: 10.6084/m9.figshare.21657677.v1
title: Supplimentary data for Triplophysa wudangensis and Triplophysa qingzhenensis

description: Two hypogean species of <em>Triplophysa</em> from the River Yangtze drainage, <em>T. wudangensis and T. qingzhenensis </em>were described in Journal of vertebrate biology. This is the supplimentary data for the two species.
DOI: 10.6084/m9.figshare.21821700
title: instances and code.zip

description: The file contains the information of instances and the code of the outer approximation method tested in "Multi-period scheduling problem of combination of home health care and outpatient services: based on Chinese family doctor contract services ".
DOI: 10.6084/m9.figshare.21821700.v1
title: instance and code.zip

description: The file contains the information of instances and the code of the outer approximation method tested in "Multi-period scheduling problem of combination of home health care and outpatient services: based on Chinese family doctor contract services ".
DOI: 10.6084/m9.figshare.21821802
title: trussart-data

description: Trussart data
DOI: 10.6084/m9.figshare.21821802.v1
title: trussart-data

description: Trussart data
DOI: 10.6084/m9.figshare.21572373.v3
title: Dataset for fire and plant traits

description: Daset and code used in: Consistent physiological, ecological, and evolutionary effects of fire regime on conservative leaf economics strategies in plant communities published in Ecology Letters. DOI:
DOI: 10.6084/m9.figshare.20304234
title: Midlines

description: x- and y- coordinates of points along the midline. <br> **_jnt** = joint position **_seg** = mid segment position
DOI: 10.6084/m9.figshare.20304240
title: PIV data

description: Collection of PIV data for 3 swimming trials in the thrust wake and 1 steady swimming trial: - velocity fields - fluid/body interfaces - pressure fields
DOI: 10.6084/m9.figshare.20304216
title: Kinematics

description: Summary body length, size and measured kinematic variables: frequency, phase lag, amplitude. <br> Description of columns: <br> * type A = steady swimming (reference) B = thrust wake swimming <br> * avg_freq = frequency average [Hz] * std_freq = frequency standard deviation [Hz] * BL = body length [cm] * size = size categorization * head_amp = head amplitude [%BL] * tail_amp = tail tip amplitude [%BL] * ht_phlag = head-to-tail phase lag [rad] * wavelength = wavelength in BL <br>
DOI: 10.6084/m9.figshare.20304216.v1
title: Kinematics

description: Summary body length, size and measured kinematic variables: frequency, phase lag, amplitude. <br> Description of columns: <br> * type A = steady swimming (reference) B = thrust wake swimming <br> * avg_freq = frequency average [Hz] * std_freq = frequency standard deviation [Hz] * BL = body length [cm] * size = size categorization * head_amp = head amplitude [%BL] * tail_amp = tail tip amplitude [%BL] * ht_phlag = head-to-tail phase lag [rad] * wavelength = wavelength in BL <br>
DOI: 10.5285/f15f9c60-f2aa-5f4c-e053-6c86abc01a81
title: Post-recovery ocean glider data from Seagliders deployed in the North Sea during the AlterEco project, November 2017-April 2019 (version 2).

description: As part of the Alternative Framework to Assess Marine Ecosystem Functioning in Shelf Seas (AlterECO) project (NERC grant reference NE/P013902/1), Seagliders (SG537, SG620, SG510, SG579, SG602 and SG550) were deployed in a physically dynamic region of the North Sea, known to undergo seasonal oxygen depletion, between November 2017 and April 2019. These deployments are part of an 18-month observational programme using marine autonomous vehicles. During each deployment the gliders tracked repeat North-South or East-West transects close to Dogger Bank. Each glider carried a standard CTD package measuring temperature and water conductivity (salinity) and an oxygen optode sensor with additional sensors such as downwelling PAR and optical sensors for chlorophyll fluorescence and backscatter. Nitrate and phosphate sensors were also included, and these data will be available separately. The deployments provided high resolution measurements of key hydrographical and biogeochemical parameters. In the previous version of the dataset, there were errors found in the positional record for deployment AE3 SG510. This was due to a bug in the processing routines when working with upcast only dives. This has been corrected in this version of the data where the values have been filled with the actual GPS fixes (single points per dive). During the 3-year programme and by developing and implementing a novel monitoring framework, AlterEco aims to deliver an improved spatio-temporal understanding of shelf sea function and provide measurements of critical indicators of marine ecosystem health over seasonal timescales.
DOI: 10.6084/m9.figshare.21545806
title: Two-Way Truncated Linear Regression Models with Extremely Thresholding Penalization

description: This article introduces a new type of linear regression model with regularization. Each predictor is conditionally truncated through the presence of unknown thresholds. The new model, called the two-way truncated linear regression model (TWT-LR), is not only viewed as a nonlinear generalization of a linear model but is also a much more flexible model with greatly enhanced interpretability and applicability. The TWT-LR model performs classifications through thresholds similar to the tree-based methods and conducts inferences that are the same as the classical linear model on different segments. In addition, the innovative penalization, called the extremely thresholding penalty (ETP), is applied to thresholds. The ETP is independent of the values of regression coefficients and does not require any normalizations of regressors. The TWT-LR-ETP model detects thresholds at a wide range, including the two extreme ends where data are sparse. Under suitable conditions, both the estimators for coefficients and thresholds are consistent, with the convergence rate for threshold estimators being faster than n. Furthermore, the estimators for coefficients are asymptotically normal for fixed dimension <i>p</i>. It is demonstrated in simulations and real data analyses that the TWT-LR-ETP model illustrates various threshold features and provides better estimation and prediction results than existing models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21545806.v3
title: Two-Way Truncated Linear Regression Models with Extremely Thresholding Penalization

description: This article introduces a new type of linear regression model with regularization. Each predictor is conditionally truncated through the presence of unknown thresholds. The new model, called the two-way truncated linear regression model (TWT-LR), is not only viewed as a nonlinear generalization of a linear model but is also a much more flexible model with greatly enhanced interpretability and applicability. The TWT-LR model performs classifications through thresholds similar to the tree-based methods and conducts inferences that are the same as the classical linear model on different segments. In addition, the innovative penalization, called the extremely thresholding penalty (ETP), is applied to thresholds. The ETP is independent of the values of regression coefficients and does not require any normalizations of regressors. The TWT-LR-ETP model detects thresholds at a wide range, including the two extreme ends where data are sparse. Under suitable conditions, both the estimators for coefficients and thresholds are consistent, with the convergence rate for threshold estimators being faster than n. Furthermore, the estimators for coefficients are asymptotically normal for fixed dimension <i>p</i>. It is demonstrated in simulations and real data analyses that the TWT-LR-ETP model illustrates various threshold features and provides better estimation and prediction results than existing models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21545806.v2
title: Two-way Truncated Linear Regression Models with Extremely Thresholding Penalization

description: This paper introduces a new type of linear regression model with regularization. Each predictor is conditionally truncated through the presence of unknown thresholds. The new model, called the two-way truncated linear regression model (TWT-LR), is not only viewed as a nonlinear generalization of a linear model but is also a much more flexible model with greatly enhanced interpretability and applicability. The TWT-LR model performs classifications through thresholds similar to the tree-based methods and conducts inferences that are the same as the classical linear model on different segments. In addition, the innovative penalization, called the extremely thresholding penalty (ETP), is applied to thresholds. The ETP is independent of the values of regression coefficients and does not require any normalizations of regressors. The TWT-LR-ETP model detects thresholds at a wide range, including the two extreme ends where data are sparse. Under suitable conditions, both the estimators for coefficients and thresholds are consistent, with the convergence rate for threshold estimators being faster than n. Furthermore, the estimators for coefficients are asymptotically normal for fixed dimension <i>p</i>. It is demonstrated in simulations and real data analyses that the TWT-LR-ETP model illustrates various threshold features and provides better estimation and prediction results than existing models.
DOI: 10.24339/a73353dd-9acc-4c4e-98d4-9ad86c5479ef
title: Supplementary Data File for Delapre Walk Project: Are signposted walking routes an effective intervention to increase physical activity in urban parks? – Natural experimental study

description: This data file contains processed and raw person counts within Delapre Park, St. Crispins Park, and Hunsbury Hill (Northampton, England) at baseline (1st May - 25th August 2021) and 12-month follow-up (27th August 2021 - 25th August 2022) to a signposted walking route intervention. In addition, processed intercept survey responses from walking route users is included. Notes and definitions are provided throughout the file to aid data interpretation.
DOI: 10.6084/m9.figshare.21821916
title: Data above 18

description: data for reasech: <strong>Public awareness and knowledge about the specialty of anesthesiology and the role of anesthesiologists in Al-Qassim province, Saudi Arabia</strong>
DOI: 10.6084/m9.figshare.21821916.v1
title: Data above 18

description: data for reasech: <strong>Public awareness and knowledge about the specialty of anesthesiology and the role of anesthesiologists in Al-Qassim province, Saudi Arabia</strong>
DOI: 10.6084/m9.figshare.21821901
title: Data

description: Results obtained from RHESSys, hypothetical vs base scenario. Calibration and validation comparing observed and simulated streamflow with RHESSys. R code developed to run RHESSys.