cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5802753
title: R code for simulations of infectious disease in foodweb from Assessing the direct and indirect effects of food provisioning and nutrient enrichment on wildlife infectious disease dynamics

description: Anthropogenic resource supplementation can shape wildlife disease directly by altering the traits and densities of hosts and parasites or indirectly by stimulating prey, competitor or predator species. We first assess the direct epidemiological consequences of supplementation, highlighting the similarities and differences between food provisioning and two widespread forms of nutrient input: agricultural fertilization and aquatic nutrient enrichment. We then review an aquatic disease system and a general model to assess whether predator and competitor species can enhance or overturn the direct effects of enrichment. All forms of supplementation can directly affect epidemics by increasing host population size or altering parasite production within hosts, but food provisioning is most likely to aggregate hosts and increase parasite transmission. However, if predators or competitors increase in response to supplementation, they could alter resource-fuelled outbreaks in focal hosts. We recommend identifying the traits of hosts, parasites or interacting species that best predict epidemiological responses to supplementation and evaluating the relative importance of these direct and indirect mechanisms. Theory and experiments should examine the timing of behavioural, physiological and demographic changes for realistic, variable scenarios of supplementation. A more integrative view of resource supplementation and wildlife disease could yield broadly applicable disease management strategies.This article is part of the theme issue ‘Anthropogenic resource subsidies and host–parasite dynamics in wildlife’.
DOI: 10.6084/m9.figshare.5802753.v1
title: R code for simulations of infectious disease in foodweb from Assessing the direct and indirect effects of food provisioning and nutrient enrichment on wildlife infectious disease dynamics

description: Anthropogenic resource supplementation can shape wildlife disease directly by altering the traits and densities of hosts and parasites or indirectly by stimulating prey, competitor or predator species. We first assess the direct epidemiological consequences of supplementation, highlighting the similarities and differences between food provisioning and two widespread forms of nutrient input: agricultural fertilization and aquatic nutrient enrichment. We then review an aquatic disease system and a general model to assess whether predator and competitor species can enhance or overturn the direct effects of enrichment. All forms of supplementation can directly affect epidemics by increasing host population size or altering parasite production within hosts, but food provisioning is most likely to aggregate hosts and increase parasite transmission. However, if predators or competitors increase in response to supplementation, they could alter resource-fuelled outbreaks in focal hosts. We recommend identifying the traits of hosts, parasites or interacting species that best predict epidemiological responses to supplementation and evaluating the relative importance of these direct and indirect mechanisms. Theory and experiments should examine the timing of behavioural, physiological and demographic changes for realistic, variable scenarios of supplementation. A more integrative view of resource supplementation and wildlife disease could yield broadly applicable disease management strategies.This article is part of the theme issue ‘Anthropogenic resource subsidies and host–parasite dynamics in wildlife’.
DOI: 10.6084/m9.figshare.5802720
title: Machine Learning Based Localization and Classification with Atomic Magnetometers - Shape and Position

description: Datasets created with an atomic magnetometer performing magnetic induction imaging, and later analysed with machine learning.<br>In this dataset, object's position is randomly varied in the (x,y) plane, and four different shapes are used.Shape 01: 50 mm x 50 mm x 3 mm Aluminium squareShape 02: 50 mm diameter x 3 mm Aluminium diskShape 03: 50 mm x 25 mm x 3 mm Aluminium rectangleShape 04: 50 mm base isosceles triangle, 50 mm height.Data are arranged per shape; position is indicated in the file's name, in mm.Four classes of data are collected: Radius (R), Phase (P), in-phase (X), and out-of-phase (Y). <br>Blind datasets: Information removed from filename.Details are reported in the file: Shape_Position_blind_files.pdf<br>For further details, see the paper published in Physical Review Letters: https://doi.org/10.1103/PhysRevLett.120.033204.
DOI: 10.6084/m9.figshare.5802720.v1
title: Untitled ItemMachine Learning Based Localization and Classification with Atomic Magnetometers - Shape and Position

description: Datasets created with an atomic magnetometer performing magnetic induction imaging, and later analysed with machine learning.<br>In this dataset, object's position is randomly varied in the (x,y) plane, and four different shapes are used.Shape 01: 50 mm x 50 mm x 3 mm Aluminium squareShape 02: 50 mm diameter x 3 mm Aluminium diskShape 03: 50 mm x 25 mm x 3 mm Aluminium rectangleShape 04: 50 mm base isosceles triangle, 50 mm height.Data are arranged per shape; position is indicated in the file's name, in mm.Four classes of data are collected: Radius (R), Phase (P), in-phase (X), and out-of-phase (Y). <br>Blind datasets: Information removed from filename.Details are reported in the file: Shape_Position_blind_files.pdf<br>For further details, see the paper published in Physical Review Letters: https://doi.org/10.1103/PhysRevLett.120.033204.
DOI: 10.6084/m9.figshare.5802705
title: Table S3 from Differential influences of allometry, phylogeny and environment on the rostral shape diversity of extinct South American notoungulates

description: Supporting data corresponding to Procrustes coordinates
DOI: 10.6084/m9.figshare.5802705.v1
title: Table S3 from Differential influences of allometry, phylogeny and environment on the rostral shape diversity of extinct South American notoungulates

description: Supporting data corresponding to Procrustes coordinates
DOI: 10.6084/m9.figshare.5802702
title: Table S1 from Differential influences of allometry, phylogeny and environment on the rostral shape diversity of extinct South American notoungulates

description: List of specimens and details concerning the methods of investigation.
DOI: 10.6084/m9.figshare.5802702.v1
title: Table S1 from Differential influences of allometry, phylogeny and environment on the rostral shape diversity of extinct South American notoungulates

description: List of specimens and details concerning the methods of investigation.
DOI: 10.6084/m9.figshare.5802693
title: Table S4 from Differential influences of allometry, phylogeny and environment on the rostral shape diversity of extinct South American notoungulates

description: Statistics associated with regressions.
DOI: 10.6084/m9.figshare.5802693.v1
title: Table S4 from Differential influences of allometry, phylogeny and environment on the rostral shape diversity of extinct South American notoungulates

description: Statistics associated with regressions.
DOI: 10.6084/m9.figshare.5802684
title: Table S2 from Differential influences of allometry, phylogeny and environment on the rostral shape diversity of extinct South American notoungulates

description: Stratigraphic ranges of the taxa studied.
DOI: 10.6084/m9.figshare.5802684.v1
title: Table S2 from Differential influences of allometry, phylogeny and environment on the rostral shape diversity of extinct South American notoungulates

description: Stratigraphic ranges of the taxa studied.
DOI: 10.6084/m9.figshare.5802660
title: Machine Learning Based Localization and Classification with Atomic Magnetometers - Orientation and Position

description: Datasets created with an atomic magnetometer performing magnetic induction imaging, and later analysed with machine learning.<br>In this dataset, object's position is randomly varied in the (x,y) plane, four different orientation are used, with two samples, and two scan settings.<br>Sample A: 50 mm x 25 mm x 3 mm Aluminium rectangleSample B: 40 mm x 20 mm x 3 mm Aluminium rectangle.<br>Orientation 01: +90°Orientation 02: 0°Orientation 03: +45°Orientation 04: -45°<br>Low-Res: 8 mm XY stage stepHigh-Res: 4 mm XY stage step<br>Data are arranged per target/resolution/orientation; position is indicated in the file's name, in mm.Four classes of data are collected: Radius (R), Phase (P), in-phase (X), and out-of-phase (Y). <br>Blind datasets: Information removed from filename.Details are reported in the file: Orientation_Position_blind_files.pdf and Orientation_Position_blind_files-High_Res.pdf<br>For further details, see the paper published in Physical Review Letters: https://doi.org/10.1103/PhysRevLett.120.033204.
DOI: 10.6084/m9.figshare.5802660.v1
title: Machine Learning Based Localization and Classification with Atomic Magnetometers - Orientation and Position

description: Datasets created with an atomic magnetometer performing magnetic induction imaging, and later analysed with machine learning.<br>In this dataset, object's position is randomly varied in the (x,y) plane, four different orientation are used, with two samples, and two scan settings.<br>Sample A: 50 mm x 25 mm x 3 mm Aluminium rectangleSample B: 40 mm x 20 mm x 3 mm Aluminium rectangle.<br>Orientation 01: +90°Orientation 02: 0°Orientation 03: +45°Orientation 04: -45°<br>Low-Res: 8 mm XY stage stepHigh-Res: 4 mm XY stage step<br>Data are arranged per target/resolution/orientation; position is indicated in the file's name, in mm.Four classes of data are collected: Radius (R), Phase (P), in-phase (X), and out-of-phase (Y). <br>Blind datasets: Information removed from filename.Details are reported in the file: Orientation_Position_blind_files.pdf and Orientation_Position_blind_files-High_Res.pdf<br>For further details, see the paper published in Physical Review Letters: https://doi.org/10.1103/PhysRevLett.120.033204.
DOI: 10.6084/m9.figshare.5802630
title: Machine Learning Based Localization and Classification with Atomic Magnetometers - Material and Position

description: Datasets created with an atomic magnetometer performing magnetic induction imaging, and later analysed with machine learning.<br>In this dataset, the object's position is randomly varied in the (x,y) plane, and four different materials are used.Data are arranged per material (Aluminium, Copper, Graphite, and Ti alloy); position is indicated in the file's name, in mm.Four classes of data are collected: Radius (R), Phase (P), in-phase (X), and out-of-phase (Y). <br>For further details, see the paper published in Physical Review Letters: https://doi.org/10.1103/PhysRevLett.120.033204.<br>
DOI: 10.6084/m9.figshare.5802630.v2
title: Machine Learning Based Localization and Classification with Atomic Magnetometers - Material and Position

description: Datasets created with an atomic magnetometer performing magnetic induction imaging, and later analysed with machine learning.<br>In this dataset, the object's position is randomly varied in the (x,y) plane, and four different materials are used.Data are arranged per material (Aluminium, Copper, Graphite, and Ti alloy); position is indicated in the file's name, in mm.Four classes of data are collected: Radius (R), Phase (P), in-phase (X), and out-of-phase (Y). <br>For further details, see the paper published in Physical Review Letters: https://doi.org/10.1103/PhysRevLett.120.033204.<br>
DOI: 10.6084/m9.figshare.5802630.v1
title: Material_Position.zip

description: Datasets created with an atomic magnetometer performing magnetic induction imaging, and later analysed with machine learning.<br>In this dataset, the object's position is randomly varied in the (x,y) plane, and four different materials are used.Data are arranged per material (Aluminium, Copper, Graphite, and Ti alloy); position is indicated in the file's name, in mm.Four classes of data are collected: Radius (R), Phase (P), in-phase (X), and out-of-phase (Y). <br>For further details, see the paper published in Physical Review Letters: https://doi.org/10.1103/PhysRevLett.120.033204.<br>
DOI: 10.6084/m9.figshare.5802606.v1
title: MFPA.rar

description: codeing
DOI: 10.6084/m9.figshare.5802597
title: TIMIT.zip

description: Speech Segregation Data set
DOI: 10.6084/m9.figshare.5802576
title: Supplementary Material for: Association between Dental Caries and BMI in Children: A Systematic Review and Meta-Analysis

description: Research on the association between dental caries and body mass index (BMI) in children has shown contradictory results; thus we aimed to examine the association between dental caries and the full range of BMI classes among children. We comprehensively searched PubMed, Embase, and the Cochrane Library for studies published prior to March 2017. Articles comparing dental caries among the full range of BMI classes for children below 18 years of both genders were included. Fourteen studies were eligible for this study. Basic information - i.e., first author, published year, study design, country, sample size, age, type of dental caries index and BMI, main results and conclusions, and means and standard deviations of the dental caries indexes used - was pooled. The weighted mean differences and corresponding 95% confidence intervals for dental caries between children with abnormal weight and those with normal weight were analyzed. Generally, no significant differences in caries were found between any abnormal-weight group and the normal-weight group for both primary and permanent teeth. Sensitivity analyses showed that the obese group had more caries than the normal-weight group in their primary teeth. Significantly more caries was found among the overweight and obese children in both primary and permanent teeth in high-income countries, but not in low- and middle-income countries. We recommend that further studies use suitable sample sizes, unify the criteria for BMI categorization and the dental caries index, and investigate the confounding factors that might influence dental caries and BMI.
DOI: 10.6084/m9.figshare.5802576.v1
title: Supplementary Material for: Association between Dental Caries and BMI in Children: A Systematic Review and Meta-Analysis

description: Research on the association between dental caries and body mass index (BMI) in children has shown contradictory results; thus we aimed to examine the association between dental caries and the full range of BMI classes among children. We comprehensively searched PubMed, Embase, and the Cochrane Library for studies published prior to March 2017. Articles comparing dental caries among the full range of BMI classes for children below 18 years of both genders were included. Fourteen studies were eligible for this study. Basic information - i.e., first author, published year, study design, country, sample size, age, type of dental caries index and BMI, main results and conclusions, and means and standard deviations of the dental caries indexes used - was pooled. The weighted mean differences and corresponding 95% confidence intervals for dental caries between children with abnormal weight and those with normal weight were analyzed. Generally, no significant differences in caries were found between any abnormal-weight group and the normal-weight group for both primary and permanent teeth. Sensitivity analyses showed that the obese group had more caries than the normal-weight group in their primary teeth. Significantly more caries was found among the overweight and obese children in both primary and permanent teeth in high-income countries, but not in low- and middle-income countries. We recommend that further studies use suitable sample sizes, unify the criteria for BMI categorization and the dental caries index, and investigate the confounding factors that might influence dental caries and BMI.
DOI: 10.6084/m9.figshare.5802567
title: Mangrove forests may serve as stable environments for the meiobenthic <i>Echinoderes komatsui</i> (Kinorhyncha: Cyclorhagida): distribution patterns and population dynamics in a subtropical estuary

description: <i>Echinoderes komatsui</i> (Kinorhyncha: Cyclorhagida) is a meiobenthic animal inhabiting the interstitial spaces of the sediment on a subtropical tidal flat along the Oura River, Japan. This species mostly occurred within 0–10 mm from the surface of the sediment, and the dispersion pattern was significantly patchy. The population dynamics of <i>E. komatsui</i> were investigated by monthly surveys from January 2014 to May 2016 at four sites along the river, and from April 2015 to May 2016 at one site in the mangrove forest. <i>Echinoderes komatsui</i> occurred all year round; however, the population density showed different patterns among the sampling sites in response to seasonal changes. On the tidal flat, the population density was high in winter, but very low in summer. In the mangrove forest, the density was high (or not low) throughout the year. As was shown in laboratory experiments, <i>E. komatsui</i> survived in 1–44 salinity seawater but died at 0 (salinity experiments), and died within 4.5 days at 40°C (temperature experiments). Therefore, the low population density recorded in summer on the tidal flat was likely caused by high temperature and fresh water. The shade offered by the mangrove forest suppressed the increase in temperature of the sediment in the summer, probably resulting in the higher population density at this site relative to the tidal flat population. Mangrove forests may also mitigate the disturbance of the sediment by summer typhoons and serve as stable environments that are fundamental in the sustainability of estuarine meiobenthic fauna.
DOI: 10.6084/m9.figshare.5802567.v1
title: Mangrove forests may serve as stable environments for the meiobenthic <i>Echinoderes komatsui</i> (Kinorhyncha: Cyclorhagida): distribution patterns and population dynamics in a subtropical estuary

description: <i>Echinoderes komatsui</i> (Kinorhyncha: Cyclorhagida) is a meiobenthic animal inhabiting the interstitial spaces of the sediment on a subtropical tidal flat along the Oura River, Japan. This species mostly occurred within 0–10 mm from the surface of the sediment, and the dispersion pattern was significantly patchy. The population dynamics of <i>E. komatsui</i> were investigated by monthly surveys from January 2014 to May 2016 at four sites along the river, and from April 2015 to May 2016 at one site in the mangrove forest. <i>Echinoderes komatsui</i> occurred all year round; however, the population density showed different patterns among the sampling sites in response to seasonal changes. On the tidal flat, the population density was high in winter, but very low in summer. In the mangrove forest, the density was high (or not low) throughout the year. As was shown in laboratory experiments, <i>E. komatsui</i> survived in 1–44 salinity seawater but died at 0 (salinity experiments), and died within 4.5 days at 40°C (temperature experiments). Therefore, the low population density recorded in summer on the tidal flat was likely caused by high temperature and fresh water. The shade offered by the mangrove forest suppressed the increase in temperature of the sediment in the summer, probably resulting in the higher population density at this site relative to the tidal flat population. Mangrove forests may also mitigate the disturbance of the sediment by summer typhoons and serve as stable environments that are fundamental in the sustainability of estuarine meiobenthic fauna.
DOI: 10.6084/m9.figshare.5802510
title: Raw Data

description: High-throughput sequencing raw data for 6 samples, C_1;C_2;C_3 are three samples from control group,and T_1;T_2;T_3 are three samples from treatment group.
DOI: 10.6084/m9.figshare.5802510.v1
title: Raw Data

description: High-throughput sequencing raw data for 6 samples, C_1;C_2;C_3 are three samples from control group,and T_1;T_2;T_3 are three samples from treatment group.
DOI: 10.6084/m9.figshare.5802495
title: Iterating the map for the stochastic NOR gate model from Qualitative models and experimental investigation of chaotic NOR gates and set/reset flip-flops

description: This MATLAB code iterates the map for the stochastic NOR gate model and plots the NOR and threshold outputs
DOI: 10.6084/m9.figshare.5802495.v1
title: Iterating the map for the stochastic NOR gate model from Qualitative models and experimental investigation of chaotic NOR gates and set/reset flip-flops

description: This MATLAB code iterates the map for the stochastic NOR gate model and plots the NOR and threshold outputs
DOI: 10.6084/m9.figshare.5802492
title: Iterating the map for the deterministic NOR gate model from Qualitative models and experimental investigation of chaotic NOR gates and set/reset flip-flops

description: This MATLAB code iterates the map for the deterministic NOR gate model and plots the NOR and threshold outputs
DOI: 10.6084/m9.figshare.5802492.v1
title: Iterating the map for the deterministic NOR gate model from Qualitative models and experimental investigation of chaotic NOR gates and set/reset flip-flops

description: This MATLAB code iterates the map for the deterministic NOR gate model and plots the NOR and threshold outputs
DOI: 10.6084/m9.figshare.5802486
title: Iterating the map for the stochastic set/reset flip-flop model from Qualitative models and experimental investigation of chaotic NOR gates and set/reset flip-flops

description: This MATLAB code iterates the map for the stochastic set/reset flip-flop model and plots the RSFF outputs and the capacitor voltages in phase space
DOI: 10.6084/m9.figshare.5802486.v1
title: Iterating the map for the stochastic set/reset flip-flop model from Qualitative models and experimental investigation of chaotic NOR gates and set/reset flip-flops

description: This MATLAB code iterates the map for the stochastic set/reset flip-flop model and plots the RSFF outputs and the capacitor voltages in phase space
DOI: 10.6084/m9.figshare.5802483
title: Iterating the map for the deterministic set/reset flip-flop model from Qualitative models and experimental investigation of chaotic NOR gates and set/reset flip-flops

description: This MATLAB code iterates the map for the deterministic set/reset flip-flop model and plots the RSFF outputs and the capacitor voltages in phase space
DOI: 10.6084/m9.figshare.5802483.v1
title: Iterating the map for the deterministic set/reset flip-flop model from Qualitative models and experimental investigation of chaotic NOR gates and set/reset flip-flops

description: This MATLAB code iterates the map for the deterministic set/reset flip-flop model and plots the RSFF outputs and the capacitor voltages in phase space
DOI: 10.6084/m9.figshare.5802471
title: Timelapse 6 - Maximum intensity projections

description: See accompanying word documents<br><br>
DOI: 10.6084/m9.figshare.5802471.v1
title: Timelapse 6 - Maximum intensity projections

description: See accompanying word documents<br><br>
DOI: 10.6084/m9.figshare.5802471.v3
title: Timelapse 6 - Maximum intensity projections

description: See accompanying word documents<br><br>